diff --git a/datasets/0ac98747-eb94-4c9f-aef8-56f9d3a04740.json b/datasets/0ac98747-eb94-4c9f-aef8-56f9d3a04740.json index b1eb0dbc1f..f7b84df062 100644 --- a/datasets/0ac98747-eb94-4c9f-aef8-56f9d3a04740.json +++ b/datasets/0ac98747-eb94-4c9f-aef8-56f9d3a04740.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/0ac98747-eb94-4c9f-aef8-56f9d3a04740/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/118cc853-52a2-46e2-a5be-40e1f58ab46d_1.json b/datasets/118cc853-52a2-46e2-a5be-40e1f58ab46d_1.json index 7cf43a30b4..e83a88f0d1 100644 --- a/datasets/118cc853-52a2-46e2-a5be-40e1f58ab46d_1.json +++ b/datasets/118cc853-52a2-46e2-a5be-40e1f58ab46d_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/118cc853-52a2-46e2-a5be-40e1f58ab46d_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/2940cda8-cf01-490a-a7ab-688bd54fb56a.json b/datasets/2940cda8-cf01-490a-a7ab-688bd54fb56a.json index fcbf3b9343..577e7989d1 100644 --- a/datasets/2940cda8-cf01-490a-a7ab-688bd54fb56a.json +++ b/datasets/2940cda8-cf01-490a-a7ab-688bd54fb56a.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/2940cda8-cf01-490a-a7ab-688bd54fb56a/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/2bb39206-6988-4127-89e5-85a0430e20cc.json b/datasets/2bb39206-6988-4127-89e5-85a0430e20cc.json index ac8ec2084a..e77f0cb7d1 100644 --- a/datasets/2bb39206-6988-4127-89e5-85a0430e20cc.json +++ b/datasets/2bb39206-6988-4127-89e5-85a0430e20cc.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/2bb39206-6988-4127-89e5-85a0430e20cc/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/43f81a9f-f903-43d4-8333-dcda52b2bc63.json b/datasets/43f81a9f-f903-43d4-8333-dcda52b2bc63.json index 0e8ccb335a..e6b3629ebf 100644 --- a/datasets/43f81a9f-f903-43d4-8333-dcda52b2bc63.json +++ b/datasets/43f81a9f-f903-43d4-8333-dcda52b2bc63.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/43f81a9f-f903-43d4-8333-dcda52b2bc63/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/47211801-72f3-4064-8c01-715cd2b7dc71_1.json b/datasets/47211801-72f3-4064-8c01-715cd2b7dc71_1.json index 50354924da..d6dabd0df7 100644 --- a/datasets/47211801-72f3-4064-8c01-715cd2b7dc71_1.json +++ b/datasets/47211801-72f3-4064-8c01-715cd2b7dc71_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/47211801-72f3-4064-8c01-715cd2b7dc71_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/5940d3fb-860d-4f3e-bc3a-4022639c272a_1.json b/datasets/5940d3fb-860d-4f3e-bc3a-4022639c272a_1.json index 46e8de9157..096bfd3ef8 100644 --- a/datasets/5940d3fb-860d-4f3e-bc3a-4022639c272a_1.json +++ b/datasets/5940d3fb-860d-4f3e-bc3a-4022639c272a_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/5940d3fb-860d-4f3e-bc3a-4022639c272a_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/598f86dc-01da-49e3-824e-c8b8f1089a0e_1.json b/datasets/598f86dc-01da-49e3-824e-c8b8f1089a0e_1.json index 02d3ec6f6b..32bd164740 100644 --- a/datasets/598f86dc-01da-49e3-824e-c8b8f1089a0e_1.json +++ b/datasets/598f86dc-01da-49e3-824e-c8b8f1089a0e_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/598f86dc-01da-49e3-824e-c8b8f1089a0e_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ARNd0001_103.json b/datasets/ARNd0001_103.json index 92aad7e8c5..0d9adb4286 100644 --- a/datasets/ARNd0001_103.json +++ b/datasets/ARNd0001_103.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0001_103/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ARNd0002_103.json b/datasets/ARNd0002_103.json index 9ac8cfd5ce..6d1ba61db2 100644 --- a/datasets/ARNd0002_103.json +++ b/datasets/ARNd0002_103.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0002_103/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ARNd0012_103.json b/datasets/ARNd0012_103.json index c242c0b50e..9bb79b9f78 100644 --- a/datasets/ARNd0012_103.json +++ b/datasets/ARNd0012_103.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0012_103/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ARNd0013_103.json b/datasets/ARNd0013_103.json index d076502f08..1b38f3ccf8 100644 --- a/datasets/ARNd0013_103.json +++ b/datasets/ARNd0013_103.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0013_103/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ARNd0071_103.json b/datasets/ARNd0071_103.json index 5bf9cbd1cc..eb32469b0f 100644 --- a/datasets/ARNd0071_103.json +++ b/datasets/ARNd0071_103.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0071_103/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ARNd0073_103.json b/datasets/ARNd0073_103.json index 50d7f0f01a..3227a9c834 100644 --- a/datasets/ARNd0073_103.json +++ b/datasets/ARNd0073_103.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0073_103/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ARNd0079_103.json b/datasets/ARNd0079_103.json index 4a885825ff..095a96f528 100644 --- a/datasets/ARNd0079_103.json +++ b/datasets/ARNd0079_103.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0079_103/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ARNd0082_103.json b/datasets/ARNd0082_103.json index c758a07400..341b7afceb 100644 --- a/datasets/ARNd0082_103.json +++ b/datasets/ARNd0082_103.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0082_103/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ARNd0083_103.json b/datasets/ARNd0083_103.json index 9b1ad389aa..7c147d6a16 100644 --- a/datasets/ARNd0083_103.json +++ b/datasets/ARNd0083_103.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0083_103/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ARNd0084_103.json b/datasets/ARNd0084_103.json index 65cb5ac0ba..9b23ac2506 100644 --- a/datasets/ARNd0084_103.json +++ b/datasets/ARNd0084_103.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0084_103/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ARNd0117_103.json b/datasets/ARNd0117_103.json index f8a53d1b61..740f7a12d7 100644 --- a/datasets/ARNd0117_103.json +++ b/datasets/ARNd0117_103.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0117_103/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0005_113.json b/datasets/BANd0005_113.json index 080f7dab09..e89c386db6 100644 --- a/datasets/BANd0005_113.json +++ b/datasets/BANd0005_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0005_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0009_113.json b/datasets/BANd0009_113.json index 282f8e241f..0576a2a095 100644 --- a/datasets/BANd0009_113.json +++ b/datasets/BANd0009_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0009_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0018_113.json b/datasets/BANd0018_113.json index e8ac18d1a5..e6badc0a36 100644 --- a/datasets/BANd0018_113.json +++ b/datasets/BANd0018_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0018_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0019_113.json b/datasets/BANd0019_113.json index 868502c924..55fbbb69a7 100644 --- a/datasets/BANd0019_113.json +++ b/datasets/BANd0019_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0019_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0020_113.json b/datasets/BANd0020_113.json index 51c2835687..7b2beb8323 100644 --- a/datasets/BANd0020_113.json +++ b/datasets/BANd0020_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0020_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0021_113.json b/datasets/BANd0021_113.json index 915db0a750..8744b57c7e 100644 --- a/datasets/BANd0021_113.json +++ b/datasets/BANd0021_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0021_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0023_113.json b/datasets/BANd0023_113.json index 56f361c7a6..6aaefc91d3 100644 --- a/datasets/BANd0023_113.json +++ b/datasets/BANd0023_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0023_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0024_113.json b/datasets/BANd0024_113.json index fcd525a591..76690ef0b9 100644 --- a/datasets/BANd0024_113.json +++ b/datasets/BANd0024_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0024_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0025_113.json b/datasets/BANd0025_113.json index f784946bfc..c2f4d70cd6 100644 --- a/datasets/BANd0025_113.json +++ b/datasets/BANd0025_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0025_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0038_113.json b/datasets/BANd0038_113.json index db8b6e8162..9a8c518107 100644 --- a/datasets/BANd0038_113.json +++ b/datasets/BANd0038_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0038_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0039_113.json b/datasets/BANd0039_113.json index 9bef8a0f96..6185bc837b 100644 --- a/datasets/BANd0039_113.json +++ b/datasets/BANd0039_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0039_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0041_113.json b/datasets/BANd0041_113.json index 0134666a3a..e369227333 100644 --- a/datasets/BANd0041_113.json +++ b/datasets/BANd0041_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0041_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0049_113.json b/datasets/BANd0049_113.json index d9b4d2eecd..d6de51c080 100644 --- a/datasets/BANd0049_113.json +++ b/datasets/BANd0049_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0049_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0050_113.json b/datasets/BANd0050_113.json index ad0c30ba5f..38e5b2004a 100644 --- a/datasets/BANd0050_113.json +++ b/datasets/BANd0050_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0050_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0052_113.json b/datasets/BANd0052_113.json index 074ebecdb5..1bdaac4a50 100644 --- a/datasets/BANd0052_113.json +++ b/datasets/BANd0052_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0052_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0053_113.json b/datasets/BANd0053_113.json index b8f9547292..214951acba 100644 --- a/datasets/BANd0053_113.json +++ b/datasets/BANd0053_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0053_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0054_113.json b/datasets/BANd0054_113.json index 69317c6268..495f26fa66 100644 --- a/datasets/BANd0054_113.json +++ b/datasets/BANd0054_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0054_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0056_113.json b/datasets/BANd0056_113.json index c8c859f742..f4717aa1f5 100644 --- a/datasets/BANd0056_113.json +++ b/datasets/BANd0056_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0056_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0062_113.json b/datasets/BANd0062_113.json index 787b767e4f..60c40f019a 100644 --- a/datasets/BANd0062_113.json +++ b/datasets/BANd0062_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0062_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0063_113.json b/datasets/BANd0063_113.json index d60c876d76..72084f4116 100644 --- a/datasets/BANd0063_113.json +++ b/datasets/BANd0063_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0063_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0065_113.json b/datasets/BANd0065_113.json index b5ccd4917a..9349997a7a 100644 --- a/datasets/BANd0065_113.json +++ b/datasets/BANd0065_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0065_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0067_113.json b/datasets/BANd0067_113.json index ac248a6fbc..e5d4228681 100644 --- a/datasets/BANd0067_113.json +++ b/datasets/BANd0067_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0067_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0068_113.json b/datasets/BANd0068_113.json index b4635aa355..7103afb908 100644 --- a/datasets/BANd0068_113.json +++ b/datasets/BANd0068_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0068_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0069_113.json b/datasets/BANd0069_113.json index 9e0935707f..3f84ca175a 100644 --- a/datasets/BANd0069_113.json +++ b/datasets/BANd0069_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0069_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0071_113.json b/datasets/BANd0071_113.json index 99d3a6878c..84fc6012ce 100644 --- a/datasets/BANd0071_113.json +++ b/datasets/BANd0071_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0071_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0072_113.json b/datasets/BANd0072_113.json index eae7426bf7..523be65f8e 100644 --- a/datasets/BANd0072_113.json +++ b/datasets/BANd0072_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0072_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0073_113.json b/datasets/BANd0073_113.json index 0dbd1dd06d..eca2290d52 100644 --- a/datasets/BANd0073_113.json +++ b/datasets/BANd0073_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0073_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0074_113.json b/datasets/BANd0074_113.json index 7ab4e96591..a56a76007a 100644 --- a/datasets/BANd0074_113.json +++ b/datasets/BANd0074_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0074_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0076_113.json b/datasets/BANd0076_113.json index 8a014f6546..6078636138 100644 --- a/datasets/BANd0076_113.json +++ b/datasets/BANd0076_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0076_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0077_113.json b/datasets/BANd0077_113.json index ebfd5773cc..4fa858cc3b 100644 --- a/datasets/BANd0077_113.json +++ b/datasets/BANd0077_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0077_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0078_113.json b/datasets/BANd0078_113.json index 00c52b37a4..73166e244b 100644 --- a/datasets/BANd0078_113.json +++ b/datasets/BANd0078_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0078_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0080_113.json b/datasets/BANd0080_113.json index e3200a35f9..2de3220c74 100644 --- a/datasets/BANd0080_113.json +++ b/datasets/BANd0080_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0080_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0081_113.json b/datasets/BANd0081_113.json index 8a399cd487..4237dc03ff 100644 --- a/datasets/BANd0081_113.json +++ b/datasets/BANd0081_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0081_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0084_113.json b/datasets/BANd0084_113.json index ec73f8ca21..7e043bcb2d 100644 --- a/datasets/BANd0084_113.json +++ b/datasets/BANd0084_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0084_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0085_113.json b/datasets/BANd0085_113.json index 1087f207c7..966a255848 100644 --- a/datasets/BANd0085_113.json +++ b/datasets/BANd0085_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0085_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0086_113.json b/datasets/BANd0086_113.json index 2d4a8866ec..318b9cde0b 100644 --- a/datasets/BANd0086_113.json +++ b/datasets/BANd0086_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0086_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0087_113.json b/datasets/BANd0087_113.json index 89865c8a27..11f515a15f 100644 --- a/datasets/BANd0087_113.json +++ b/datasets/BANd0087_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0087_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0088_113.json b/datasets/BANd0088_113.json index 3c23b1fa0b..f04103378e 100644 --- a/datasets/BANd0088_113.json +++ b/datasets/BANd0088_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0088_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0090_113.json b/datasets/BANd0090_113.json index a30d205895..b6a91447d9 100644 --- a/datasets/BANd0090_113.json +++ b/datasets/BANd0090_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0090_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0095_113.json b/datasets/BANd0095_113.json index 19ace5e759..aa17618c3c 100644 --- a/datasets/BANd0095_113.json +++ b/datasets/BANd0095_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0095_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0098_113.json b/datasets/BANd0098_113.json index fc1b51183c..7516e5772b 100644 --- a/datasets/BANd0098_113.json +++ b/datasets/BANd0098_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0098_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0100_113.json b/datasets/BANd0100_113.json index df205dcc8c..4fe7b10088 100644 --- a/datasets/BANd0100_113.json +++ b/datasets/BANd0100_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0100_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0101_113.json b/datasets/BANd0101_113.json index 67b56132e9..97a7341c42 100644 --- a/datasets/BANd0101_113.json +++ b/datasets/BANd0101_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0101_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0102_113.json b/datasets/BANd0102_113.json index f082aeca4e..a2b89fbfbb 100644 --- a/datasets/BANd0102_113.json +++ b/datasets/BANd0102_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0102_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0104_113.json b/datasets/BANd0104_113.json index 01561df9b3..71f9656b78 100644 --- a/datasets/BANd0104_113.json +++ b/datasets/BANd0104_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0104_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0105_113.json b/datasets/BANd0105_113.json index f27c6d6f3d..26f4073fea 100644 --- a/datasets/BANd0105_113.json +++ b/datasets/BANd0105_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0105_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0107_113.json b/datasets/BANd0107_113.json index d07b3b0349..678e94969d 100644 --- a/datasets/BANd0107_113.json +++ b/datasets/BANd0107_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0107_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0109_113.json b/datasets/BANd0109_113.json index cf64ef0591..fdd8edd496 100644 --- a/datasets/BANd0109_113.json +++ b/datasets/BANd0109_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0109_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0110_113.json b/datasets/BANd0110_113.json index 2e711f7f70..e177c761b2 100644 --- a/datasets/BANd0110_113.json +++ b/datasets/BANd0110_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0110_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0111_113.json b/datasets/BANd0111_113.json index 4f74b21355..17efdb5553 100644 --- a/datasets/BANd0111_113.json +++ b/datasets/BANd0111_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0111_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0112_113.json b/datasets/BANd0112_113.json index 97853af387..23c19c37a6 100644 --- a/datasets/BANd0112_113.json +++ b/datasets/BANd0112_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0112_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0113_113.json b/datasets/BANd0113_113.json index 1c84243dfb..b6e332402d 100644 --- a/datasets/BANd0113_113.json +++ b/datasets/BANd0113_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0113_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0114_113.json b/datasets/BANd0114_113.json index be33074e9a..08e32ff77e 100644 --- a/datasets/BANd0114_113.json +++ b/datasets/BANd0114_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0114_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0115_113.json b/datasets/BANd0115_113.json index d2f9b07bd0..dc9a0c846a 100644 --- a/datasets/BANd0115_113.json +++ b/datasets/BANd0115_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0115_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0117_113.json b/datasets/BANd0117_113.json index 5a7894427a..f251230db1 100644 --- a/datasets/BANd0117_113.json +++ b/datasets/BANd0117_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0117_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0118_113.json b/datasets/BANd0118_113.json index 3e90bd0c2a..e5cc4f2995 100644 --- a/datasets/BANd0118_113.json +++ b/datasets/BANd0118_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0118_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0119_113.json b/datasets/BANd0119_113.json index db34987113..3cc585f77a 100644 --- a/datasets/BANd0119_113.json +++ b/datasets/BANd0119_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0119_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0121_113.json b/datasets/BANd0121_113.json index 83a4379302..a02a2aae36 100644 --- a/datasets/BANd0121_113.json +++ b/datasets/BANd0121_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0121_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0125_113.json b/datasets/BANd0125_113.json index 78c2898db1..af7774e4a8 100644 --- a/datasets/BANd0125_113.json +++ b/datasets/BANd0125_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0125_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0130_113.json b/datasets/BANd0130_113.json index 0ad95aec76..1c4823747c 100644 --- a/datasets/BANd0130_113.json +++ b/datasets/BANd0130_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0130_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0131_113.json b/datasets/BANd0131_113.json index 13e02c17cf..2229a3f81b 100644 --- a/datasets/BANd0131_113.json +++ b/datasets/BANd0131_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0131_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0132_113.json b/datasets/BANd0132_113.json index bfe197fe26..341ac2d519 100644 --- a/datasets/BANd0132_113.json +++ b/datasets/BANd0132_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0132_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0133_113.json b/datasets/BANd0133_113.json index 422ba8de5d..140a51eb84 100644 --- a/datasets/BANd0133_113.json +++ b/datasets/BANd0133_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0133_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0134_113.json b/datasets/BANd0134_113.json index ab7cc0eaac..87f31a2f17 100644 --- a/datasets/BANd0134_113.json +++ b/datasets/BANd0134_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0134_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0135_113.json b/datasets/BANd0135_113.json index a47006c057..32c41379ec 100644 --- a/datasets/BANd0135_113.json +++ b/datasets/BANd0135_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0135_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0136_113.json b/datasets/BANd0136_113.json index 50efe4745d..ea44c1f7a5 100644 --- a/datasets/BANd0136_113.json +++ b/datasets/BANd0136_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0136_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0138_113.json b/datasets/BANd0138_113.json index 9fb283044f..bdef2b5942 100644 --- a/datasets/BANd0138_113.json +++ b/datasets/BANd0138_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0138_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0139_113.json b/datasets/BANd0139_113.json index a0a3c345e8..e4bbee026c 100644 --- a/datasets/BANd0139_113.json +++ b/datasets/BANd0139_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0139_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0146_113.json b/datasets/BANd0146_113.json index 0527200f0b..2fda057f2b 100644 --- a/datasets/BANd0146_113.json +++ b/datasets/BANd0146_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0146_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0147_113.json b/datasets/BANd0147_113.json index 0ce0223ce8..dc95322348 100644 --- a/datasets/BANd0147_113.json +++ b/datasets/BANd0147_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0147_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0149_113.json b/datasets/BANd0149_113.json index 6974d70ee1..a327c7f9c7 100644 --- a/datasets/BANd0149_113.json +++ b/datasets/BANd0149_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0149_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0150_113.json b/datasets/BANd0150_113.json index a0d5590427..97f324dcbc 100644 --- a/datasets/BANd0150_113.json +++ b/datasets/BANd0150_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0150_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0151_113.json b/datasets/BANd0151_113.json index e42019842b..e0e584ad54 100644 --- a/datasets/BANd0151_113.json +++ b/datasets/BANd0151_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0151_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0155_113.json b/datasets/BANd0155_113.json index 1c42a9be2c..67a5eeebaf 100644 --- a/datasets/BANd0155_113.json +++ b/datasets/BANd0155_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0155_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0157_113.json b/datasets/BANd0157_113.json index 3912562751..5cb844c9b7 100644 --- a/datasets/BANd0157_113.json +++ b/datasets/BANd0157_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0157_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0158_113.json b/datasets/BANd0158_113.json index ca8a8e755f..1bc2eed0da 100644 --- a/datasets/BANd0158_113.json +++ b/datasets/BANd0158_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0158_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0159_113.json b/datasets/BANd0159_113.json index 3dd6883800..28db553509 100644 --- a/datasets/BANd0159_113.json +++ b/datasets/BANd0159_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0159_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0160_113.json b/datasets/BANd0160_113.json index 3c9235dec2..db03349191 100644 --- a/datasets/BANd0160_113.json +++ b/datasets/BANd0160_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0160_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0161_113.json b/datasets/BANd0161_113.json index 1112eadfaa..8c6145c66b 100644 --- a/datasets/BANd0161_113.json +++ b/datasets/BANd0161_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0161_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0164_113.json b/datasets/BANd0164_113.json index 0a1fa42b70..c2b027fb16 100644 --- a/datasets/BANd0164_113.json +++ b/datasets/BANd0164_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0164_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0165_113.json b/datasets/BANd0165_113.json index b54167c14a..ea79bdce51 100644 --- a/datasets/BANd0165_113.json +++ b/datasets/BANd0165_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0165_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0166_113.json b/datasets/BANd0166_113.json index 8d313292ed..69d2c878c8 100644 --- a/datasets/BANd0166_113.json +++ b/datasets/BANd0166_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0166_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0167_113.json b/datasets/BANd0167_113.json index 62e5dd3e1c..29496adfc1 100644 --- a/datasets/BANd0167_113.json +++ b/datasets/BANd0167_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0167_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0168_113.json b/datasets/BANd0168_113.json index e769bb226b..dc7064aa48 100644 --- a/datasets/BANd0168_113.json +++ b/datasets/BANd0168_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0168_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0169_113.json b/datasets/BANd0169_113.json index 0533c0511e..e872f77ded 100644 --- a/datasets/BANd0169_113.json +++ b/datasets/BANd0169_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0169_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0170_113.json b/datasets/BANd0170_113.json index da850444ce..914202d1d5 100644 --- a/datasets/BANd0170_113.json +++ b/datasets/BANd0170_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0170_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0171_113.json b/datasets/BANd0171_113.json index 4bdd3849bb..16b7fa2a2f 100644 --- a/datasets/BANd0171_113.json +++ b/datasets/BANd0171_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0171_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0175_113.json b/datasets/BANd0175_113.json index 2f4c611f67..47cc0f8d75 100644 --- a/datasets/BANd0175_113.json +++ b/datasets/BANd0175_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0175_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0176_113.json b/datasets/BANd0176_113.json index ca1b198146..5b362fd7b1 100644 --- a/datasets/BANd0176_113.json +++ b/datasets/BANd0176_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0176_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0177_113.json b/datasets/BANd0177_113.json index b5ee092898..10c52d5f2a 100644 --- a/datasets/BANd0177_113.json +++ b/datasets/BANd0177_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0177_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0179_113.json b/datasets/BANd0179_113.json index 6cebf4f171..57c1462f7c 100644 --- a/datasets/BANd0179_113.json +++ b/datasets/BANd0179_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0179_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0181_113.json b/datasets/BANd0181_113.json index 3c7ef18856..5502b9d982 100644 --- a/datasets/BANd0181_113.json +++ b/datasets/BANd0181_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0181_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0182_113.json b/datasets/BANd0182_113.json index 5f4c5bdb61..6d0aee7f42 100644 --- a/datasets/BANd0182_113.json +++ b/datasets/BANd0182_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0182_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0183_113.json b/datasets/BANd0183_113.json index 430af8e8e3..8095796e9d 100644 --- a/datasets/BANd0183_113.json +++ b/datasets/BANd0183_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0183_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0184_113.json b/datasets/BANd0184_113.json index 1e75a84225..1969fe7f46 100644 --- a/datasets/BANd0184_113.json +++ b/datasets/BANd0184_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0184_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0193_113.json b/datasets/BANd0193_113.json index 5b7193aff1..6446f8a8aa 100644 --- a/datasets/BANd0193_113.json +++ b/datasets/BANd0193_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0193_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0194_113.json b/datasets/BANd0194_113.json index a0b30a85ec..246f66e362 100644 --- a/datasets/BANd0194_113.json +++ b/datasets/BANd0194_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0194_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0195_113.json b/datasets/BANd0195_113.json index fd8d3eacb6..3a584bd7e8 100644 --- a/datasets/BANd0195_113.json +++ b/datasets/BANd0195_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0195_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0201_113.json b/datasets/BANd0201_113.json index d44d39c8fc..7493c77013 100644 --- a/datasets/BANd0201_113.json +++ b/datasets/BANd0201_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0201_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0204_113.json b/datasets/BANd0204_113.json index 183f9c16e5..6af7b5df1d 100644 --- a/datasets/BANd0204_113.json +++ b/datasets/BANd0204_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0204_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0206_113.json b/datasets/BANd0206_113.json index 03ba737469..7540754b99 100644 --- a/datasets/BANd0206_113.json +++ b/datasets/BANd0206_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0206_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0207_113.json b/datasets/BANd0207_113.json index 2134f9e342..d15fbadb93 100644 --- a/datasets/BANd0207_113.json +++ b/datasets/BANd0207_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0207_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0209_113.json b/datasets/BANd0209_113.json index f382055bc0..89795bc888 100644 --- a/datasets/BANd0209_113.json +++ b/datasets/BANd0209_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0209_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0210_113.json b/datasets/BANd0210_113.json index 22aab8bd26..1a971ec88a 100644 --- a/datasets/BANd0210_113.json +++ b/datasets/BANd0210_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0210_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0213_113.json b/datasets/BANd0213_113.json index 00ae941598..03b851f782 100644 --- a/datasets/BANd0213_113.json +++ b/datasets/BANd0213_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0213_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0217_113.json b/datasets/BANd0217_113.json index 9931085756..17645af6c7 100644 --- a/datasets/BANd0217_113.json +++ b/datasets/BANd0217_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0217_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0218_113.json b/datasets/BANd0218_113.json index d87bd6fe51..cae2cae36f 100644 --- a/datasets/BANd0218_113.json +++ b/datasets/BANd0218_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0218_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BANd0219_113.json b/datasets/BANd0219_113.json index 173661b08b..41cbea78c4 100644 --- a/datasets/BANd0219_113.json +++ b/datasets/BANd0219_113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0219_113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BOM_HRPTCAS.json b/datasets/BOM_HRPTCAS.json index 2504b24a52..7f6a3cb3f8 100644 --- a/datasets/BOM_HRPTCAS.json +++ b/datasets/BOM_HRPTCAS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BOM_HRPTCAS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BRD_LSC002.json b/datasets/BRD_LSC002.json index af30887a80..a3b9188062 100644 --- a/datasets/BRD_LSC002.json +++ b/datasets/BRD_LSC002.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BRD_LSC002/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/BRD_LSC003_MAHA.json b/datasets/BRD_LSC003_MAHA.json index 344a67958c..33fb5d6afd 100644 --- a/datasets/BRD_LSC003_MAHA.json +++ b/datasets/BRD_LSC003_MAHA.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BRD_LSC003_MAHA/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/CAM5K30CFCLIM_003.json b/datasets/CAM5K30CFCLIM_003.json new file mode 100644 index 0000000000..22ce6bae20 --- /dev/null +++ b/datasets/CAM5K30CFCLIM_003.json @@ -0,0 +1,190 @@ +{ + "type": "Collection", + "id": "CAM5K30CFCLIM_003", + "stac_version": "1.0.0", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global coefficient climatology product (CAM5K30CFCLIM). The product is provided at 0.05 degree (~5 kilometer) resolution. The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product. They are congruent to the temporally equivalent CAM5K30EM emissivity data product. The HSR emissivity spectra for the same month each year and each unique combination of lab dataset version and number of Principal Components (PC)s are first computed independently and then combined via a weighted average. The weighted average over 2003 through 2021 (19 years) defines the weights by the number of samples from each unique combination. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD).\n\nProvided in the CAM5K30CFCLIM product are variables for PCA coefficients, the weights and sample numbers of the climatology coefficients used in the average calculation, sets of the number of PCA coefficients, laboratory version numbers, latitude, longitude, and land flag information. PCA coefficients depend on the lab PC data version and the number of PCs used.\n", + "links": [ + { + "rel": "license", + "href": "https://science.nasa.gov/earth-science/earth-science-data/data-information-policy", + "type": "text/html", + "title": "EOSDIS Data Use Policy" + }, + { + "rel": "about", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3274449168-LPCLOUD.html", + "type": "text/html", + "title": "HTML metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3274449168-LPCLOUD.native", + "type": "application/xml", + "title": "Native metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3274449168-LPCLOUD.echo10", + "type": "application/echo10+xml", + "title": "ECHO10 metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3274449168-LPCLOUD.json", + "type": "application/json", + "title": "CMR JSON metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3274449168-LPCLOUD.umm_json", + "type": "application/vnd.nasa.cmr.umm+json", + "title": "CMR UMM_JSON metadata for collection" + }, + { + "rel": "self", + "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/CAM5K30CFCLIM_003", + "type": "application/json" + }, + { + "rel": "root", + "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD", + "type": "application/json", + "title": "LPCLOUD STAC Catalog" + }, + { + "rel": "items", + "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/CAM5K30CFCLIM_003/items", + "type": "application/geo+json", + "title": "Collection Items" + } + ], + "provider": [ + { + "name": "LPCLOUD", + "roles": [ + "producer" + ] + }, + { + "name": "NASA EOSDIS", + "roles": [ + "host" + ] + } + ], + "title": "Combined ASTER and MODIS Emissivity database over Land (CAMEL) Coefficient Climatology Monthly Global 0.05Deg V003", + "extent": { + "spatial": { + "bbox": [ + [ + -180, + -90, + 180, + 90 + ] + ] + }, + "temporal": { + "interval": [ + [ + "2003-01-01T00:00:00Z", + "2022-01-01T00:00:00Z" + ] + ] + } + }, + "license": "proprietary", + "keywords": [ + "EARTH SCIENCE", + "LAND SURFACE", + "SURFACE RADIATIVE PROPERTIES", + "EMISSIVITY", + "BIOSPHERE", + "VEGETATION", + "VEGETATION INDEX", + "TERRESTRIAL HYDROSPHERE", + "SNOW/ICE", + "SNOW COVER" + ], + "summaries": { + "platform": [ + "Terra", + "Aqua" + ], + "instruments": [ + "ASTER", + "MODIS", + "MODIS" + ] + }, + "assets": { + "browse": { + "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/CAM5K30CFCLIM.003/CAM5K30CFCLIM_coef_climatology_03Month_V003/CAM5K30CFCLIM_coef_climatology_03Month_V003.jpg", + "type": "image/jpeg", + "title": "Download CAM5K30CFCLIM_coef_climatology_03Month_V003.jpg", + "roles": [ + "browse" + ] + }, + "thumbnail": { + "href": 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"title": "lp_prod_protected_CAM5K30CFCLIM_003", + "roles": [ + "data" + ] + }, + "s3_lp_prod_public_CAM5K30CFCLIM_003": { + "href": "s3://lp-prod-public/CAM5K30CFCLIM.003", + "title": "lp_prod_public_CAM5K30CFCLIM_003", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, + "metadata": { + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3274449168-LPCLOUD.xml", + "type": "application/xml", + "title": "CMR XML metadata for C3274449168-LPCLOUD", + "roles": [ + "metadata" + ] + } + } +} \ No newline at end of file diff --git a/datasets/CAM5K30COVCLIM_003.json b/datasets/CAM5K30COVCLIM_003.json new file mode 100644 index 0000000000..6656027380 --- /dev/null +++ b/datasets/CAM5K30COVCLIM_003.json @@ -0,0 +1,190 @@ +{ + "type": "Collection", + "id": "CAM5K30COVCLIM_003", + "stac_version": "1.0.0", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global covariances climatology product (CAM5K30COVCLIM). The product is provided at 0.25 degree (~25 kilometer) resolution. The CAMEL covariance product includes the mean and variance of the covariance matrixes created for each month from 2003 through 2021 (19 years) on a 0.25 x 0.25 degree grid of 416 spectral points from the V003 CAMEL Emissivity product (CAM5K30EM). Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). \n\nProvided in the CAM5K30COVCLIM product are variables for the mean and variance of the emissivity, latitude, longitude, spectral frequencies, and number of observations.\n", + "links": [ + { + "rel": "license", + "href": "https://science.nasa.gov/earth-science/earth-science-data/data-information-policy", + "type": "text/html", + "title": "EOSDIS Data Use Policy" + }, + { + "rel": "about", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3274450252-LPCLOUD.html", + "type": "text/html", + "title": "HTML metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3274450252-LPCLOUD.native", + "type": "application/xml", + "title": "Native metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3274450252-LPCLOUD.echo10", + "type": 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"roles": [ + "producer" + ] + }, + { + "name": "NASA EOSDIS", + "roles": [ + "host" + ] + } + ], + "title": "Combined ASTER and MODIS Emissivity database over Land (CAMEL) Covariances Climatology Monthly Global 0.25Deg V003", + "extent": { + "spatial": { + "bbox": [ + [ + -180, + -90, + 180, + 90 + ] + ] + }, + "temporal": { + "interval": [ + [ + "2003-01-01T00:00:00Z", + "2022-01-01T00:00:00Z" + ] + ] + } + }, + "license": "proprietary", + "keywords": [ + "EARTH SCIENCE", + "LAND SURFACE", + "SURFACE RADIATIVE PROPERTIES", + "EMISSIVITY", + "BIOSPHERE", + "VEGETATION", + "VEGETATION INDEX", + "TERRESTRIAL HYDROSPHERE", + "SNOW/ICE", + "SNOW COVER" + ], + "summaries": { + "platform": [ + "Terra", + "Aqua" + ], + "instruments": [ + "ASTER", + "MODIS", + "MODIS" + ] + }, + "assets": { + "browse": { + "href": 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This 0.05 degree (~5 kilometer) resolution product represents the mean emissivity from 2003 through 2021 (19 years). Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD).\n\nVariables provided in the CAM5K30EMCLIM product include latitude, longitude, wavelength, number of samples used to calculate climatology, CAMEL quality flag, snow fraction derived from MODIS (MOD10), and CAMEL Emissivity.", + "links": [ + { + "rel": "license", + "href": "https://science.nasa.gov/earth-science/earth-science-data/data-information-policy", + "type": "text/html", + "title": "EOSDIS Data Use Policy" + }, + { + "rel": "about", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3274448375-LPCLOUD.html", + "type": "text/html", + "title": "HTML metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3274448375-LPCLOUD.native", + "type": "application/xml", + "title": "Native metadata for collection" + }, + { + "rel": "via", + "href": 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+{ + "type": "Collection", + "id": "DSCOVR_EPIC_L2_MAIAC-DAILY_01", + "stac_version": "1.0.0", + "description": "DSOCVR EPIC_L2 MAIAC-Daily_01 contains plots of data generated from DSCOVR_EPIC_L2_MAIAC_03, the DSCOVR EPIC L2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) Version 03 data product. Data collection for this product is ongoing.\nThe datasets visualized include Aerosol Layer Height (ALH), Aerosol Optical Depth, and Single Scattering Albedo at 340nm, 388nm, 443nm, 551 nm, 680nm, and 780nm.\nLevel 2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) provides an interdisciplinary suite of products for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC). The current version 3 reports the following products:\n\na) Atmosphere: cloud mask, global aerosol optical depth at 443nm and 551nm, fine mode fraction (over the ocean), aerosol layer height (ALH) globally, and spectral aerosol absorption for detected biomass burning or mineral dust aerosols. The absorption information includes single scattering albedo at 340-780nm range, imaginary refractive index at 680nm (k0), and Spectral Absorption Exponent (SAE) characterizing spectral increase of imaginary refractive index from Red towards UV wavelengths. The aerosol optical properties {AOD, ALH, k0, SAE} are retrieved simultaneously by matching EPIC measurements in the UV-NIR range, including atmospheric oxygen A- and B-bands.\n\nb) Land: atmospherically corrected spectral bidirectional reflectance factors (BRF) along with Lambertian surface reflectance and bidirectional reflectance distribution function (BRDF) for the backscattering view geometries of EPIC. The BRDF is represented by three parameters of the Ross-Thick Li-Sparse model.\n\nc) Ocean: Water leaving reflectance (non-dimensional) at Ultraviolet-Visible (UV-Vis) bands.\n\nThe parameters are provided at 10 km resolution on a zonal sinusoidal grid with a 1\u2014to 2-hour temporal frequency. MAIAC version 03 also provides gap-filled global composite products for the Normalized Difference Vegetation Index (NDVI) over land and water, leaving reflectance in 5 UV-Vis bands over the global ocean. The composite products represent a weighted running average where the weight of the latest observation is maximized towards the local noon and low aerosol conditions.", + "links": [ + { + "rel": "license", + "href": "https://science.nasa.gov/earth-science/earth-science-data/data-information-policy", + "type": "text/html", + "title": "EOSDIS Data Use Policy" + }, + { + "rel": "about", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3264309299-LARC_ASDC.html", + "type": "text/html", + "title": "HTML metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3264309299-LARC_ASDC.native", + "type": "application/xml", + "title": "Native metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3264309299-LARC_ASDC.echo10", + "type": "application/echo10+xml", + "title": "ECHO10 metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3264309299-LARC_ASDC.json", + "type": "application/json", + "title": "CMR JSON metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3264309299-LARC_ASDC.umm_json", + "type": "application/vnd.nasa.cmr.umm+json", + "title": "CMR UMM_JSON metadata for collection" + }, + { + "rel": "self", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC_ASDC/collections/DSCOVR_EPIC_L2_MAIAC-DAILY_01", + "type": "application/json" + }, + { + "rel": "root", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC_ASDC", + "type": "application/json", + "title": "LARC_ASDC STAC Catalog" + }, + { + "rel": "items", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC_ASDC/collections/DSCOVR_EPIC_L2_MAIAC-DAILY_01/items", + "type": "application/geo+json", + "title": "Collection Items" + } + ], + "provider": [ + { + "name": "LARC_ASDC", + "roles": [ + "producer" + ] + }, + { + "name": "NASA EOSDIS", + "roles": [ + "host" + ] + } + ], + "title": "MAIAC Daily V01", + "extent": { + "spatial": { + "bbox": [ + [ + 180, + -90, + -180, + 90 + ] + ] + }, + "temporal": { + "interval": [ + [ + "2015-06-13T00:00:00Z", + null + ] + ] + } + }, + "license": "proprietary", + "keywords": [ + "EARTH SCIENCE", + "ATMOSPHERIC RADIATION", + "ATMOSPHERE", + "ALBEDO", + "AEROSOLS", + "AEROSOL OPTICAL DEPTH/THICKNESS", + "AEROSOL PARTICLE PROPERTIES", + "AEROSOL ABSORPTION" + ], + "summaries": { + "platform": [ + "DSCOVR" + ], + "instruments": [ + "EPIC" + ] + }, + "assets": { + "browse": { + "href": "https://asdc.larc.nasa.gov/static/images/project_logos/dscovr.png", + "type": "image/jpeg", + "title": "Download dscovr.png", + "roles": [ + "browse" + ] + }, + "thumbnail": { + "href": "https://asdc.larc.nasa.gov/static/images/project_logos/dscovr.png", + "title": "Thumbnail", + "description": "Mission Logo", + "roles": [ + "thumbnail" + ] + }, + "nasa": { + "href": "https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_MAIAC-DAILY/", + "title": "Direct Download [1]", + "description": "ASDC Direct Data Download for DSCOVR_EPIC_L2_MAIAC-DAILY_01", + "roles": [ + "data" + ] + }, + "provider_metadata": { + "href": "https://doi.org/10.5067/EPIC/DSCOVR/MAIAC-DAILY_L2.001", + "title": "Provider Metadata", + "description": "DOI data set landing page for DSCOVR_EPIC_L2_MAIAC-DAILY_01", + "roles": [ + "metadata" + ] + }, + "metadata": { + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3264309299-LARC_ASDC.xml", + "type": "application/xml", + "title": "CMR XML metadata for C3264309299-LARC_ASDC", + "roles": [ + "metadata" + ] + } + } +} \ No newline at end of file diff --git a/datasets/EARTH_CRUST_AUS_BMR_Min_Loc_DB.json b/datasets/EARTH_CRUST_AUS_BMR_Min_Loc_DB.json index 56fa5962db..fc29a675f5 100644 --- a/datasets/EARTH_CRUST_AUS_BMR_Min_Loc_DB.json +++ b/datasets/EARTH_CRUST_AUS_BMR_Min_Loc_DB.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_AUS_BMR_Min_Loc_DB/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_CRUST_USGS_COAL_NCRDS_DB.json b/datasets/EARTH_CRUST_USGS_COAL_NCRDS_DB.json index 4bf95aabe2..b0322469a3 100644 --- a/datasets/EARTH_CRUST_USGS_COAL_NCRDS_DB.json +++ b/datasets/EARTH_CRUST_USGS_COAL_NCRDS_DB.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_USGS_COAL_NCRDS_DB/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_CRUST_USGS_GeoNames.json b/datasets/EARTH_CRUST_USGS_GeoNames.json index 2a5d4d2866..c7d4d0fff1 100644 --- a/datasets/EARTH_CRUST_USGS_GeoNames.json +++ b/datasets/EARTH_CRUST_USGS_GeoNames.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_USGS_GeoNames/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_CRUST_USGS_NPRA_GEOCHEM1.json b/datasets/EARTH_CRUST_USGS_NPRA_GEOCHEM1.json index a318790b71..294c114e07 100644 --- a/datasets/EARTH_CRUST_USGS_NPRA_GEOCHEM1.json +++ b/datasets/EARTH_CRUST_USGS_NPRA_GEOCHEM1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_USGS_NPRA_GEOCHEM1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_CRUST_USGS_NPRA_GEO_RPTS1.json b/datasets/EARTH_CRUST_USGS_NPRA_GEO_RPTS1.json index 50b37cf7c9..535de777a6 100644 --- a/datasets/EARTH_CRUST_USGS_NPRA_GEO_RPTS1.json +++ b/datasets/EARTH_CRUST_USGS_NPRA_GEO_RPTS1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_USGS_NPRA_GEO_RPTS1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_CRUST_USGS_NPRA_SEISMIC1.json b/datasets/EARTH_CRUST_USGS_NPRA_SEISMIC1.json index 9b9dff698c..97cc23ea09 100644 --- a/datasets/EARTH_CRUST_USGS_NPRA_SEISMIC1.json +++ b/datasets/EARTH_CRUST_USGS_NPRA_SEISMIC1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_USGS_NPRA_SEISMIC1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_CRUST_USGS_NPRA_WELL_LOGS.json b/datasets/EARTH_CRUST_USGS_NPRA_WELL_LOGS.json index 6fd857117b..d22d84f8e0 100644 --- a/datasets/EARTH_CRUST_USGS_NPRA_WELL_LOGS.json +++ b/datasets/EARTH_CRUST_USGS_NPRA_WELL_LOGS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_USGS_NPRA_WELL_LOGS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_INT_USGS_NPRA_GAMMA_MAG1.json b/datasets/EARTH_INT_USGS_NPRA_GAMMA_MAG1.json index 9010e83051..a20a0039f2 100644 --- a/datasets/EARTH_INT_USGS_NPRA_GAMMA_MAG1.json +++ b/datasets/EARTH_INT_USGS_NPRA_GAMMA_MAG1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_INT_USGS_NPRA_GAMMA_MAG1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_LAND_NBS_GLACIER_TERMINUS.json b/datasets/EARTH_LAND_NBS_GLACIER_TERMINUS.json index 86ea47bb98..beed79026a 100644 --- a/datasets/EARTH_LAND_NBS_GLACIER_TERMINUS.json +++ b/datasets/EARTH_LAND_NBS_GLACIER_TERMINUS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_NBS_GLACIER_TERMINUS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_LAND_USGS_AK_Iditarod1.json b/datasets/EARTH_LAND_USGS_AK_Iditarod1.json index fe5ca41cc1..8da37f57aa 100644 --- a/datasets/EARTH_LAND_USGS_AK_Iditarod1.json +++ b/datasets/EARTH_LAND_USGS_AK_Iditarod1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_AK_Iditarod1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_LAND_USGS_AK_Innoko1.json b/datasets/EARTH_LAND_USGS_AK_Innoko1.json index 2ed6ed4639..7f1b9d358f 100644 --- a/datasets/EARTH_LAND_USGS_AK_Innoko1.json +++ b/datasets/EARTH_LAND_USGS_AK_Innoko1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_AK_Innoko1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_LAND_USGS_AK_Koyukuk1.json b/datasets/EARTH_LAND_USGS_AK_Koyukuk1.json index 6d91be67eb..79ce0a6699 100644 --- a/datasets/EARTH_LAND_USGS_AK_Koyukuk1.json +++ b/datasets/EARTH_LAND_USGS_AK_Koyukuk1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_AK_Koyukuk1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_LAND_USGS_AK_NOAA_AVHRR.json b/datasets/EARTH_LAND_USGS_AK_NOAA_AVHRR.json index 95131f9f11..6bdcce1269 100644 --- a/datasets/EARTH_LAND_USGS_AK_NOAA_AVHRR.json +++ b/datasets/EARTH_LAND_USGS_AK_NOAA_AVHRR.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_AK_NOAA_AVHRR/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_LAND_USGS_AK_NPRA_veg1.json b/datasets/EARTH_LAND_USGS_AK_NPRA_veg1.json index c5f8b09164..43d42be398 100644 --- a/datasets/EARTH_LAND_USGS_AK_NPRA_veg1.json +++ b/datasets/EARTH_LAND_USGS_AK_NPRA_veg1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_AK_NPRA_veg1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_LAND_USGS_DEM_AK1.json b/datasets/EARTH_LAND_USGS_DEM_AK1.json index e169b32989..010868d592 100644 --- a/datasets/EARTH_LAND_USGS_DEM_AK1.json +++ b/datasets/EARTH_LAND_USGS_DEM_AK1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_DEM_AK1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EARTH_LAND_USGS_EDC_AK_Landsat.json b/datasets/EARTH_LAND_USGS_EDC_AK_Landsat.json index 9e68131892..dd3ada7f9d 100644 --- a/datasets/EARTH_LAND_USGS_EDC_AK_Landsat.json +++ b/datasets/EARTH_LAND_USGS_EDC_AK_Landsat.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_EDC_AK_Landsat/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ECMWF_OPERATIONAL_WAVE.json b/datasets/ECMWF_OPERATIONAL_WAVE.json index c199d3ba94..9e3093303d 100644 --- a/datasets/ECMWF_OPERATIONAL_WAVE.json +++ b/datasets/ECMWF_OPERATIONAL_WAVE.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ECMWF_OPERATIONAL_WAVE/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ECMWF_OPER_EPS.json b/datasets/ECMWF_OPER_EPS.json index 0bf6ceb7c5..ce7c0ee3f5 100644 --- a/datasets/ECMWF_OPER_EPS.json +++ b/datasets/ECMWF_OPER_EPS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ECMWF_OPER_EPS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ECMWF_WCRP_TOGA.json b/datasets/ECMWF_WCRP_TOGA.json index d6f74cb0f3..3917ea8035 100644 --- a/datasets/ECMWF_WCRP_TOGA.json +++ b/datasets/ECMWF_WCRP_TOGA.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ECMWF_WCRP_TOGA/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/EPA0175.json b/datasets/EPA0175.json index ba5236d87f..ff842bac20 100644 --- a/datasets/EPA0175.json +++ b/datasets/EPA0175.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EPA0175/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/FAO_AGL.json b/datasets/FAO_AGL.json index f93ef6fc2d..a2351c2c97 100644 --- a/datasets/FAO_AGL.json +++ b/datasets/FAO_AGL.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/FAO_AGL/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/FAO_FIGIS.json b/datasets/FAO_FIGIS.json index e801aa5fa2..0c8fe52f07 100644 --- a/datasets/FAO_FIGIS.json +++ b/datasets/FAO_FIGIS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/FAO_FIGIS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/FAOd0008_148.json b/datasets/FAOd0008_148.json index 7cf27c3ab8..6794f0c955 100644 --- a/datasets/FAOd0008_148.json +++ b/datasets/FAOd0008_148.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/FAOd0008_148/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/FAOd0018_148.json b/datasets/FAOd0018_148.json index ae1c2a1c8f..75dfe42e63 100644 --- a/datasets/FAOd0018_148.json +++ b/datasets/FAOd0018_148.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/FAOd0018_148/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/FAOd0019_148.json b/datasets/FAOd0019_148.json index 2758788d40..3aab9f4977 100644 --- a/datasets/FAOd0019_148.json +++ b/datasets/FAOd0019_148.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/FAOd0019_148/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/FAOd0020_148.json b/datasets/FAOd0020_148.json index ae61421d4e..cbc0767874 100644 --- a/datasets/FAOd0020_148.json +++ b/datasets/FAOd0020_148.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/FAOd0020_148/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/GCIP-GREDS.json b/datasets/GCIP-GREDS.json index e8438c12c7..e6963a7eba 100644 --- a/datasets/GCIP-GREDS.json +++ b/datasets/GCIP-GREDS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GCIP-GREDS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/GCRP-DDS-10.json b/datasets/GCRP-DDS-10.json index b4aa67559e..f16a3d65f8 100644 --- a/datasets/GCRP-DDS-10.json +++ b/datasets/GCRP-DDS-10.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GCRP-DDS-10/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/GFOI_Boreno_Island.json b/datasets/GFOI_Boreno_Island.json index 159a4ce0b2..ab282f8b71 100644 --- a/datasets/GFOI_Boreno_Island.json +++ b/datasets/GFOI_Boreno_Island.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GFOI_Boreno_Island/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/GLFC_FishHabitatDatabase.json b/datasets/GLFC_FishHabitatDatabase.json index 5f5fbb7990..587b088e6a 100644 --- a/datasets/GLFC_FishHabitatDatabase.json +++ b/datasets/GLFC_FishHabitatDatabase.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GLFC_FishHabitatDatabase/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/GLOBIO_barents.json b/datasets/GLOBIO_barents.json index e639626ffb..d3df1ecb7f 100644 --- a/datasets/GLOBIO_barents.json +++ b/datasets/GLOBIO_barents.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GLOBIO_barents/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/GRAVCD-npra.json b/datasets/GRAVCD-npra.json index 2b4a3cc348..1f2d6f2835 100644 --- a/datasets/GRAVCD-npra.json +++ b/datasets/GRAVCD-npra.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GRAVCD-npra/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/GRID-INPE.json b/datasets/GRID-INPE.json index cbb98efe5a..270b4acf0f 100644 --- a/datasets/GRID-INPE.json +++ b/datasets/GRID-INPE.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GRID-INPE/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/HUC250k.json b/datasets/HUC250k.json index cae024a6ea..7925861ff6 100644 --- a/datasets/HUC250k.json +++ b/datasets/HUC250k.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/HUC250k/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ICId0001_202.json b/datasets/ICId0001_202.json index e3540b9d32..34a01abb0e 100644 --- a/datasets/ICId0001_202.json +++ b/datasets/ICId0001_202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0001_202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ICId0005_202.json b/datasets/ICId0005_202.json index 6a004c9ffa..339bb32d44 100644 --- a/datasets/ICId0005_202.json +++ b/datasets/ICId0005_202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0005_202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ICId0012_202.json b/datasets/ICId0012_202.json index 4a5fa74c03..0b3ffd71d6 100644 --- a/datasets/ICId0012_202.json +++ b/datasets/ICId0012_202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0012_202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ICId0015_202.json b/datasets/ICId0015_202.json index 6023423974..e0153dde81 100644 --- a/datasets/ICId0015_202.json +++ b/datasets/ICId0015_202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0015_202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ICId0016_202.json b/datasets/ICId0016_202.json index 69da852595..8d338d314b 100644 --- a/datasets/ICId0016_202.json +++ b/datasets/ICId0016_202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0016_202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ICId0017_202.json b/datasets/ICId0017_202.json index 1f9cb3206c..faf89cfe98 100644 --- a/datasets/ICId0017_202.json +++ b/datasets/ICId0017_202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0017_202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ICId0018_202.json b/datasets/ICId0018_202.json index 1192fc0303..59be8414df 100644 --- a/datasets/ICId0018_202.json +++ b/datasets/ICId0018_202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0018_202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ICId0019_202.json b/datasets/ICId0019_202.json index 9c1d80dca4..f89fb6213c 100644 --- a/datasets/ICId0019_202.json +++ b/datasets/ICId0019_202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0019_202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ICId0020_202.json b/datasets/ICId0020_202.json index f6b90898af..36f748ac79 100644 --- a/datasets/ICId0020_202.json +++ b/datasets/ICId0020_202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0020_202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ICId0021_202.json b/datasets/ICId0021_202.json index 96e0514a7c..159277ce18 100644 --- a/datasets/ICId0021_202.json +++ b/datasets/ICId0021_202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0021_202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ICId0023_202.json b/datasets/ICId0023_202.json index 4ca056d289..50ab3e3c0e 100644 --- a/datasets/ICId0023_202.json +++ b/datasets/ICId0023_202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0023_202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/ICId0028_202.json b/datasets/ICId0028_202.json index 45183e0ff4..1195ecc5aa 100644 --- a/datasets/ICId0028_202.json +++ b/datasets/ICId0028_202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0028_202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/IES.json b/datasets/IES.json index 394f42359f..6eead5d69e 100644 --- a/datasets/IES.json +++ b/datasets/IES.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/IES/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_CBERS2B_HRC.json b/datasets/INPE_CBERS2B_HRC.json index 0ba9ad6fc8..04fc62d8a6 100644 --- a/datasets/INPE_CBERS2B_HRC.json +++ b/datasets/INPE_CBERS2B_HRC.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CBERS2B_HRC/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_CBERS2_IRM.json b/datasets/INPE_CBERS2_IRM.json index 4baaf5f357..cadf773fcc 100644 --- a/datasets/INPE_CBERS2_IRM.json +++ b/datasets/INPE_CBERS2_IRM.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CBERS2_IRM/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_CBERS4_MUX_1.json b/datasets/INPE_CBERS4_MUX_1.json index 0ceda572d3..a2763372aa 100644 --- a/datasets/INPE_CBERS4_MUX_1.json +++ b/datasets/INPE_CBERS4_MUX_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CBERS4_MUX_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_CPTEC_CLIMATE_BRAZIL.json b/datasets/INPE_CPTEC_CLIMATE_BRAZIL.json index f0d618fad4..274ee8590f 100644 --- a/datasets/INPE_CPTEC_CLIMATE_BRAZIL.json +++ b/datasets/INPE_CPTEC_CLIMATE_BRAZIL.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CPTEC_CLIMATE_BRAZIL/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_CPTEC_CLIMATE_GLOBAL.json b/datasets/INPE_CPTEC_CLIMATE_GLOBAL.json index 8378726c34..f2f19a9d6f 100644 --- a/datasets/INPE_CPTEC_CLIMATE_GLOBAL.json +++ b/datasets/INPE_CPTEC_CLIMATE_GLOBAL.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CPTEC_CLIMATE_GLOBAL/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_CPTEC_GLOBAL_METEOGRAM.json b/datasets/INPE_CPTEC_GLOBAL_METEOGRAM.json index b802ce10c3..326b8be1d8 100644 --- a/datasets/INPE_CPTEC_GLOBAL_METEOGRAM.json +++ b/datasets/INPE_CPTEC_GLOBAL_METEOGRAM.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CPTEC_GLOBAL_METEOGRAM/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_CPTEC_IR_SAT.json b/datasets/INPE_CPTEC_IR_SAT.json index e358941068..213369a23e 100644 --- a/datasets/INPE_CPTEC_IR_SAT.json +++ b/datasets/INPE_CPTEC_IR_SAT.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CPTEC_IR_SAT/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_ER_SAR.json b/datasets/INPE_ER_SAR.json index 5b98d1b65d..1ec59a01c9 100644 --- a/datasets/INPE_ER_SAR.json +++ b/datasets/INPE_ER_SAR.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_ER_SAR/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_IRS_AWIFS.json b/datasets/INPE_IRS_AWIFS.json index ff071046f6..330bf39ddb 100644 --- a/datasets/INPE_IRS_AWIFS.json +++ b/datasets/INPE_IRS_AWIFS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_IRS_AWIFS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_IRS_LISS3.json b/datasets/INPE_IRS_LISS3.json index 0471da1612..70637f3d43 100644 --- a/datasets/INPE_IRS_LISS3.json +++ b/datasets/INPE_IRS_LISS3.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_IRS_LISS3/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_LANDSAT1_MSS.json b/datasets/INPE_LANDSAT1_MSS.json index 8166dab867..629f81b46a 100644 --- a/datasets/INPE_LANDSAT1_MSS.json +++ b/datasets/INPE_LANDSAT1_MSS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LANDSAT1_MSS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_LANDSAT2_MSS.json b/datasets/INPE_LANDSAT2_MSS.json index 00e8ef632b..0e61ff1aa4 100644 --- a/datasets/INPE_LANDSAT2_MSS.json +++ b/datasets/INPE_LANDSAT2_MSS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LANDSAT2_MSS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_LANDSAT3_MSS.json b/datasets/INPE_LANDSAT3_MSS.json index 4a40a1cd6d..831ed0ef09 100644 --- a/datasets/INPE_LANDSAT3_MSS.json +++ b/datasets/INPE_LANDSAT3_MSS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LANDSAT3_MSS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_LANDSAT5_TM.json b/datasets/INPE_LANDSAT5_TM.json index 4b8268f5f1..439d1b29dc 100644 --- a/datasets/INPE_LANDSAT5_TM.json +++ b/datasets/INPE_LANDSAT5_TM.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LANDSAT5_TM/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_LANDSAT7_ETM.json b/datasets/INPE_LANDSAT7_ETM.json index 805956581c..9a994b6ec4 100644 --- a/datasets/INPE_LANDSAT7_ETM.json +++ b/datasets/INPE_LANDSAT7_ETM.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LANDSAT7_ETM/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_LS_MSS.json b/datasets/INPE_LS_MSS.json index 82dfaae82e..b5b6668fe1 100644 --- a/datasets/INPE_LS_MSS.json +++ b/datasets/INPE_LS_MSS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LS_MSS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_LS_RBV.json b/datasets/INPE_LS_RBV.json index f4572c5aca..efd1ef23ac 100644 --- a/datasets/INPE_LS_RBV.json +++ b/datasets/INPE_LS_RBV.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LS_RBV/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/INPE_LS_TM.json b/datasets/INPE_LS_TM.json index c86afdb763..23a5fc14c4 100644 --- a/datasets/INPE_LS_TM.json +++ b/datasets/INPE_LS_TM.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LS_TM/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/IZIKO_Fish.json b/datasets/IZIKO_Fish.json index b4678c6939..e84a7c3e47 100644 --- a/datasets/IZIKO_Fish.json +++ b/datasets/IZIKO_Fish.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/IZIKO_Fish/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/IZIKO_Marine_Mammals.json b/datasets/IZIKO_Marine_Mammals.json index 32d1fd8375..5c1cc02596 100644 --- a/datasets/IZIKO_Marine_Mammals.json +++ b/datasets/IZIKO_Marine_Mammals.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/IZIKO_Marine_Mammals/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/KOMPSAT-2.json b/datasets/KOMPSAT-2.json index 0fd6566fc9..2e985876fd 100644 --- a/datasets/KOMPSAT-2.json +++ b/datasets/KOMPSAT-2.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/KOMPSAT-2/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/KV1_MSS_0.1.json b/datasets/KV1_MSS_0.1.json index 8a821b69d6..8e112264f3 100644 --- a/datasets/KV1_MSS_0.1.json +++ b/datasets/KV1_MSS_0.1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/KV1_MSS_0.1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/KYOTO_GREENHOUSEGASES.json b/datasets/KYOTO_GREENHOUSEGASES.json index c522062d2e..7bfab5d6bb 100644 --- a/datasets/KYOTO_GREENHOUSEGASES.json +++ b/datasets/KYOTO_GREENHOUSEGASES.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/KYOTO_GREENHOUSEGASES/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/LACHYSIS.json b/datasets/LACHYSIS.json index 24f7d34a65..e6b1e12690 100644 --- a/datasets/LACHYSIS.json +++ b/datasets/LACHYSIS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LACHYSIS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/LANDFIRE.json b/datasets/LANDFIRE.json index 7eff47376a..02878efba1 100644 --- a/datasets/LANDFIRE.json +++ b/datasets/LANDFIRE.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LANDFIRE/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/LIDA.json b/datasets/LIDA.json index f77b08cf70..5a4cf518d4 100644 --- a/datasets/LIDA.json +++ b/datasets/LIDA.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LIDA/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/LIFE_ECO_NBS_SIER_VEG_FUEL1.json b/datasets/LIFE_ECO_NBS_SIER_VEG_FUEL1.json index 4f4bc71f67..9b202bc383 100644 --- a/datasets/LIFE_ECO_NBS_SIER_VEG_FUEL1.json +++ b/datasets/LIFE_ECO_NBS_SIER_VEG_FUEL1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LIFE_ECO_NBS_SIER_VEG_FUEL1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/LSATUSERV.json b/datasets/LSATUSERV.json index 141501d337..d7e454ea9f 100644 --- a/datasets/LSATUSERV.json +++ b/datasets/LSATUSERV.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LSATUSERV/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/LSC_Flavobacteriumpsychrophilum.json b/datasets/LSC_Flavobacteriumpsychrophilum.json index beb51358c0..1ac92f85b4 100644 --- a/datasets/LSC_Flavobacteriumpsychrophilum.json +++ b/datasets/LSC_Flavobacteriumpsychrophilum.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LSC_Flavobacteriumpsychrophilum/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/LSC_biomarkers.json b/datasets/LSC_biomarkers.json index 244eaf91db..15e58f768f 100644 --- a/datasets/LSC_biomarkers.json +++ b/datasets/LSC_biomarkers.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LSC_biomarkers/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/LSC_immunereprohistologic.json b/datasets/LSC_immunereprohistologic.json index f6264f7c4c..f78e760d61 100644 --- a/datasets/LSC_immunereprohistologic.json +++ b/datasets/LSC_immunereprohistologic.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LSC_immunereprohistologic/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/LS_TM_ARC.json b/datasets/LS_TM_ARC.json index 43a84e370f..63f17bb29a 100644 --- a/datasets/LS_TM_ARC.json +++ b/datasets/LS_TM_ARC.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LS_TM_ARC/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/MassBay_LongTerm.json b/datasets/MassBay_LongTerm.json index 7becce102c..6a09df3671 100644 --- a/datasets/MassBay_LongTerm.json +++ b/datasets/MassBay_LongTerm.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/MassBay_LongTerm/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NAWQA.json b/datasets/NAWQA.json index 13d2d5bbc9..242f1c0c24 100644 --- a/datasets/NAWQA.json +++ b/datasets/NAWQA.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NAWQA/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NAWQAHIS.json b/datasets/NAWQAHIS.json index a85f4cb657..0df663c788 100644 --- a/datasets/NAWQAHIS.json +++ b/datasets/NAWQAHIS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NAWQAHIS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0019_101.json b/datasets/NBId0019_101.json index 46eab6ea4e..e1d180e9ac 100644 --- a/datasets/NBId0019_101.json +++ b/datasets/NBId0019_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0019_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0041_101.json b/datasets/NBId0041_101.json index 39999ecac4..f6ae669879 100644 --- a/datasets/NBId0041_101.json +++ b/datasets/NBId0041_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0041_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0042_101.json b/datasets/NBId0042_101.json index 9d84d44244..1418728985 100644 --- a/datasets/NBId0042_101.json +++ b/datasets/NBId0042_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0042_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0079_101.json b/datasets/NBId0079_101.json index 04bba0cbd4..4d1efda86e 100644 --- a/datasets/NBId0079_101.json +++ b/datasets/NBId0079_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0079_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0083_101.json b/datasets/NBId0083_101.json index 51669687c2..59dbf930f0 100644 --- a/datasets/NBId0083_101.json +++ b/datasets/NBId0083_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0083_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0089_101.json b/datasets/NBId0089_101.json index fb0c4f8e04..d7a7235c7a 100644 --- a/datasets/NBId0089_101.json +++ b/datasets/NBId0089_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0089_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0093_101.json b/datasets/NBId0093_101.json index 3606ce412e..04198fdc26 100644 --- a/datasets/NBId0093_101.json +++ b/datasets/NBId0093_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0093_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0098_101.json b/datasets/NBId0098_101.json index d7a23d0f5f..67f47afa73 100644 --- a/datasets/NBId0098_101.json +++ b/datasets/NBId0098_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0098_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0106_101.json b/datasets/NBId0106_101.json index c62e6e7352..ef4bd070a3 100644 --- a/datasets/NBId0106_101.json +++ b/datasets/NBId0106_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0106_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0110_101.json b/datasets/NBId0110_101.json index 62a9674cb6..507539abf6 100644 --- a/datasets/NBId0110_101.json +++ b/datasets/NBId0110_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0110_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0115_101.json b/datasets/NBId0115_101.json index 0255a56e54..240eb719ed 100644 --- a/datasets/NBId0115_101.json +++ b/datasets/NBId0115_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0115_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0127_101.json b/datasets/NBId0127_101.json index e853bd818b..686a4eb0fa 100644 --- a/datasets/NBId0127_101.json +++ b/datasets/NBId0127_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0127_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0128_101.json b/datasets/NBId0128_101.json index 249a624dfc..95b4ac863c 100644 --- a/datasets/NBId0128_101.json +++ b/datasets/NBId0128_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0128_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0129_101.json b/datasets/NBId0129_101.json index 6da3a5e537..9e6c2ff794 100644 --- a/datasets/NBId0129_101.json +++ b/datasets/NBId0129_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0129_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0131_101.json b/datasets/NBId0131_101.json index 0c9e5fc78d..85d4e7edb6 100644 --- a/datasets/NBId0131_101.json +++ b/datasets/NBId0131_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0131_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0136_101.json b/datasets/NBId0136_101.json index 0263a13211..fc3341cf14 100644 --- a/datasets/NBId0136_101.json +++ b/datasets/NBId0136_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0136_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0177_101.json b/datasets/NBId0177_101.json index dde2dc9c24..7ee4804abf 100644 --- a/datasets/NBId0177_101.json +++ b/datasets/NBId0177_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0177_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NBId0207_101.json b/datasets/NBId0207_101.json index bdeb3d03dd..5cfd036a72 100644 --- a/datasets/NBId0207_101.json +++ b/datasets/NBId0207_101.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0207_101/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NHS.json b/datasets/NHS.json index 73373a62ff..6b01e3b22c 100644 --- a/datasets/NHS.json +++ b/datasets/NHS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NHS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NOAA_CDR_NDVI.json b/datasets/NOAA_CDR_NDVI.json index cb0306d6ed..c618204db7 100644 --- a/datasets/NOAA_CDR_NDVI.json +++ b/datasets/NOAA_CDR_NDVI.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NOAA_CDR_NDVI/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NPWRC_effectsoffireonbirdpops.json b/datasets/NPWRC_effectsoffireonbirdpops.json index 6be4a9eb8e..a617e6d590 100644 --- a/datasets/NPWRC_effectsoffireonbirdpops.json +++ b/datasets/NPWRC_effectsoffireonbirdpops.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NPWRC_effectsoffireonbirdpops/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NURE_SEDIMENT_CHEM.json b/datasets/NURE_SEDIMENT_CHEM.json index e1d0ed06f9..7c1ff3fced 100644 --- a/datasets/NURE_SEDIMENT_CHEM.json +++ b/datasets/NURE_SEDIMENT_CHEM.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NURE_SEDIMENT_CHEM/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/NatalMuseum.json b/datasets/NatalMuseum.json index f2efafe557..89e421e310 100644 --- a/datasets/NatalMuseum.json +++ b/datasets/NatalMuseum.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NatalMuseum/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/OFR_95-78_1.json b/datasets/OFR_95-78_1.json index 4141e7143b..5e1b84bdb2 100644 --- a/datasets/OFR_95-78_1.json +++ b/datasets/OFR_95-78_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/OFR_95-78_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/OMPS_N21_LP_L2_AER_DAILY_1.0.json b/datasets/OMPS_N21_LP_L2_AER_DAILY_1.0.json index 737728a4af..033d0e0f41 100644 --- a/datasets/OMPS_N21_LP_L2_AER_DAILY_1.0.json +++ b/datasets/OMPS_N21_LP_L2_AER_DAILY_1.0.json @@ -148,6 +148,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_NOAA21_OMPS_Level2_OMPS_N21_LP_L2_AER_DAILY_1_0_": { + "href": "s3://gesdisc-cumulus-prod-protected/NOAA21_OMPS_Level2/OMPS_N21_LP_L2_AER_DAILY.1.0/", + "title": "gesdisc_cumulus_prod_protected_NOAA21_OMPS_Level2_OMPS_N21_LP_L2_AER_DAILY_1_0_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3262950749-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/PCD_INPE_web.json b/datasets/PCD_INPE_web.json index d11c9d2da8..07c2cb2fcc 100644 --- a/datasets/PCD_INPE_web.json +++ b/datasets/PCD_INPE_web.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/PCD_INPE_web/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/RDK1_GTNL1_0.1.json b/datasets/RDK1_GTNL1_0.1.json index 1da674126e..dba69f1974 100644 --- a/datasets/RDK1_GTNL1_0.1.json +++ b/datasets/RDK1_GTNL1_0.1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/RDK1_GTNL1_0.1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/RP1_GSA_0.1.json b/datasets/RP1_GSA_0.1.json index b76bf915d3..2d56e8d3a5 100644 --- a/datasets/RP1_GSA_0.1.json +++ b/datasets/RP1_GSA_0.1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/RP1_GSA_0.1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/RP1_GTNL1_0.1.json b/datasets/RP1_GTNL1_0.1.json index 44a1aeadcd..b7e4125e4c 100644 --- a/datasets/RP1_GTNL1_0.1.json +++ b/datasets/RP1_GTNL1_0.1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/RP1_GTNL1_0.1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/RP2_GSA_0.1.json b/datasets/RP2_GSA_0.1.json index d06de89958..d47b1bbfa3 100644 --- a/datasets/RP2_GSA_0.1.json +++ b/datasets/RP2_GSA_0.1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/RP2_GSA_0.1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/RP2_GTNL1_0.1.json b/datasets/RP2_GTNL1_0.1.json index 555caf961d..f5e7ce0c40 100644 --- a/datasets/RP2_GTNL1_0.1.json +++ b/datasets/RP2_GTNL1_0.1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/RP2_GTNL1_0.1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/SSDP_HAZARD_EARTHQUAKE.json b/datasets/SSDP_HAZARD_EARTHQUAKE.json index 3b20511366..876ea62b2d 100644 --- a/datasets/SSDP_HAZARD_EARTHQUAKE.json +++ b/datasets/SSDP_HAZARD_EARTHQUAKE.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/SSDP_HAZARD_EARTHQUAKE/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/Stream_GIS_USGS.json b/datasets/Stream_GIS_USGS.json index cbfdedc0a5..aa3f8a1038 100644 --- a/datasets/Stream_GIS_USGS.json +++ b/datasets/Stream_GIS_USGS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/Stream_GIS_USGS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/THIRN7L1BCLT_001.json b/datasets/THIRN7L1BCLT_001.json index 1007df01d6..3afcc37d72 100644 --- a/datasets/THIRN7L1BCLT_001.json +++ b/datasets/THIRN7L1BCLT_001.json @@ -149,6 +149,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_Nimbus7_THIR_Level1_THIRN7L1BCLT_001_": { + "href": "s3://gesdisc-cumulus-prod-protected/Nimbus7_THIR_Level1/THIRN7L1BCLT.001/", + "title": "gesdisc_cumulus_prod_protected_Nimbus7_THIR_Level1_THIRN7L1BCLT_001_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1415263464-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/THIRN7L1CLDT_001.json b/datasets/THIRN7L1CLDT_001.json index 331983cb68..f7d300c8a0 100644 --- a/datasets/THIRN7L1CLDT_001.json +++ b/datasets/THIRN7L1CLDT_001.json @@ -148,6 +148,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_Nimbus7_THIR_Level1_THIRN7L1CLDT_001_": { + "href": "s3://gesdisc-cumulus-prod-protected/Nimbus7_THIR_Level1/THIRN7L1CLDT.001/", + "title": "gesdisc_cumulus_prod_protected_Nimbus7_THIR_Level1_THIRN7L1CLDT_001_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1273652204-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/TIROS2L1FMRT_001.json b/datasets/TIROS2L1FMRT_001.json index 6670cfcfe2..7c2a4a17a8 100644 --- a/datasets/TIROS2L1FMRT_001.json +++ b/datasets/TIROS2L1FMRT_001.json @@ -141,6 +141,27 @@ "data" ] }, + "s3_gesdisc_cumulus_prod_protected_TIROS_TIROS2L1FMRT_001_": { + "href": "s3://gesdisc-cumulus-prod-protected/TIROS/TIROS2L1FMRT.001/", + "title": "gesdisc_cumulus_prod_protected_TIROS_TIROS2L1FMRT_001_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2046458866-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/UNEP_GRID_SF_GLOBAL.json b/datasets/UNEP_GRID_SF_GLOBAL.json index a15da97f21..7d6984822a 100644 --- a/datasets/UNEP_GRID_SF_GLOBAL.json +++ b/datasets/UNEP_GRID_SF_GLOBAL.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UNEP_GRID_SF_GLOBAL/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/UNEP_GRID_SF_LATINAMERICA_1.0.json b/datasets/UNEP_GRID_SF_LATINAMERICA_1.0.json index 58c20588e6..5d035d0aff 100644 --- a/datasets/UNEP_GRID_SF_LATINAMERICA_1.0.json +++ b/datasets/UNEP_GRID_SF_LATINAMERICA_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UNEP_GRID_SF_LATINAMERICA_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USDA0113.json b/datasets/USDA0113.json index 73b28aac04..5152eb5e0f 100644 --- a/datasets/USDA0113.json +++ b/datasets/USDA0113.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USDA0113/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USDA0114.json b/datasets/USDA0114.json index fce31824c1..4c2c3e4361 100644 --- a/datasets/USDA0114.json +++ b/datasets/USDA0114.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USDA0114/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USDA0115.json b/datasets/USDA0115.json index 08ea7e1823..f5de818eae 100644 --- a/datasets/USDA0115.json +++ b/datasets/USDA0115.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USDA0115/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS-DDS-058_1.0.json b/datasets/USGS-DDS-058_1.0.json index 10ca04fcff..3fcad5f5a4 100644 --- a/datasets/USGS-DDS-058_1.0.json +++ b/datasets/USGS-DDS-058_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-058_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS-DDS-067.json b/datasets/USGS-DDS-067.json index 76f54761e5..2065e233c3 100644 --- a/datasets/USGS-DDS-067.json +++ b/datasets/USGS-DDS-067.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-067/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS-DDS-11.json b/datasets/USGS-DDS-11.json index 45396e1537..16e8edd88c 100644 --- a/datasets/USGS-DDS-11.json +++ b/datasets/USGS-DDS-11.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-11/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS-DDS-18-A_1.0.json b/datasets/USGS-DDS-18-A_1.0.json index c7b71def38..01a95e3a59 100644 --- a/datasets/USGS-DDS-18-A_1.0.json +++ b/datasets/USGS-DDS-18-A_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-18-A_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS-DDS-19.json b/datasets/USGS-DDS-19.json index c367e53de5..5830f24242 100644 --- a/datasets/USGS-DDS-19.json +++ b/datasets/USGS-DDS-19.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-19/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS-DDS-27_1.json b/datasets/USGS-DDS-27_1.json index b78e83d139..21ea3b890d 100644 --- a/datasets/USGS-DDS-27_1.json +++ b/datasets/USGS-DDS-27_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-27_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS-DDS-74_2.0.json b/datasets/USGS-DDS-74_2.0.json index 59d2d52b6e..16ed45bbd8 100644 --- a/datasets/USGS-DDS-74_2.0.json +++ b/datasets/USGS-DDS-74_2.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-74_2.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS-OFR-92-299_1.0.json b/datasets/USGS-OFR-92-299_1.0.json index 1527152c0d..cadd7a1a7c 100644 --- a/datasets/USGS-OFR-92-299_1.0.json +++ b/datasets/USGS-OFR-92-299_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-OFR-92-299_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS-PRISM-PACIFIC-OSTRACODES.json b/datasets/USGS-PRISM-PACIFIC-OSTRACODES.json index c43ecd36d5..b72608e7f5 100644 --- a/datasets/USGS-PRISM-PACIFIC-OSTRACODES.json +++ b/datasets/USGS-PRISM-PACIFIC-OSTRACODES.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-PRISM-PACIFIC-OSTRACODES/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ASC_MarineEcoregionsLayer_1.0.json b/datasets/USGS_ASC_MarineEcoregionsLayer_1.0.json index c2d53716ef..28ad211b9a 100644 --- a/datasets/USGS_ASC_MarineEcoregionsLayer_1.0.json +++ b/datasets/USGS_ASC_MarineEcoregionsLayer_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ASC_MarineEcoregionsLayer_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ASTER_HydrothermalAlterationMaps.json b/datasets/USGS_ASTER_HydrothermalAlterationMaps.json index 68cf0c9736..be80501ea5 100644 --- a/datasets/USGS_ASTER_HydrothermalAlterationMaps.json +++ b/datasets/USGS_ASTER_HydrothermalAlterationMaps.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ASTER_HydrothermalAlterationMaps/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_BIO_KATRINA.json b/datasets/USGS_BIO_KATRINA.json index b028faf5d4..58c1c8dcad 100644 --- a/datasets/USGS_BIO_KATRINA.json +++ b/datasets/USGS_BIO_KATRINA.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_BIO_KATRINA/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_BRD_SageSTEP.json b/datasets/USGS_BRD_SageSTEP.json index 7eb71d84cc..88279a61c4 100644 --- a/datasets/USGS_BRD_SageSTEP.json +++ b/datasets/USGS_BRD_SageSTEP.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_BRD_SageSTEP/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Bulletin_2064-A_1.0.json b/datasets/USGS_Bulletin_2064-A_1.0.json index 1ec62cc3cd..8106ed3a1b 100644 --- a/datasets/USGS_Bulletin_2064-A_1.0.json +++ b/datasets/USGS_Bulletin_2064-A_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Bulletin_2064-A_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Bulletin_2064-C_1.0.json b/datasets/USGS_Bulletin_2064-C_1.0.json index dce5bcf464..80c4a8824f 100644 --- a/datasets/USGS_Bulletin_2064-C_1.0.json +++ b/datasets/USGS_Bulletin_2064-C_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Bulletin_2064-C_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_CT_NATTEN.json b/datasets/USGS_CT_NATTEN.json index 1421d093ad..7c86054768 100644 --- a/datasets/USGS_CT_NATTEN.json +++ b/datasets/USGS_CT_NATTEN.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_CT_NATTEN/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_CascadeRange_HydrothermalMonitoring.json b/datasets/USGS_CascadeRange_HydrothermalMonitoring.json index e7f7c9d69b..95d3787d25 100644 --- a/datasets/USGS_CascadeRange_HydrothermalMonitoring.json +++ b/datasets/USGS_CascadeRange_HydrothermalMonitoring.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_CascadeRange_HydrothermalMonitoring/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DDS-27_1.json b/datasets/USGS_DDS-27_1.json index 4b3ced1ada..967716bba1 100644 --- a/datasets/USGS_DDS-27_1.json +++ b/datasets/USGS_DDS-27_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS-27_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DDS-46.json b/datasets/USGS_DDS-46.json index 48d8df0b92..15a60e9983 100644 --- a/datasets/USGS_DDS-46.json +++ b/datasets/USGS_DDS-46.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS-46/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DDS-55_EF_1.0.json b/datasets/USGS_DDS-55_EF_1.0.json index 92b3ed3e84..da30d9ba56 100644 --- a/datasets/USGS_DDS-55_EF_1.0.json +++ b/datasets/USGS_DDS-55_EF_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS-55_EF_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DDS-55_WF.json b/datasets/USGS_DDS-55_WF.json index 2c7fa28f6d..955480f872 100644 --- a/datasets/USGS_DDS-55_WF.json +++ b/datasets/USGS_DDS-55_WF.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS-55_WF/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DDS_10_1.json b/datasets/USGS_DDS_10_1.json index 43f494693a..7a0b92d58e 100644 --- a/datasets/USGS_DDS_10_1.json +++ b/datasets/USGS_DDS_10_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS_10_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DS_2006_171.json b/datasets/USGS_DS_2006_171.json index a70140f79b..9930f4b2d7 100644 --- a/datasets/USGS_DS_2006_171.json +++ b/datasets/USGS_DS_2006_171.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2006_171/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DS_2006_199_1.0.json b/datasets/USGS_DS_2006_199_1.0.json index 6dda0cfbde..c8295ebe1d 100644 --- a/datasets/USGS_DS_2006_199_1.0.json +++ b/datasets/USGS_DS_2006_199_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2006_199_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DS_2006_220.json b/datasets/USGS_DS_2006_220.json index 3304a1f208..87afd3835d 100644 --- a/datasets/USGS_DS_2006_220.json +++ b/datasets/USGS_DS_2006_220.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2006_220/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DS_2006_221.json b/datasets/USGS_DS_2006_221.json index 24fe53c169..20e2e6d0e9 100644 --- a/datasets/USGS_DS_2006_221.json +++ b/datasets/USGS_DS_2006_221.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2006_221/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DS_2006_234_1.0.json b/datasets/USGS_DS_2006_234_1.0.json index 0270e8fa9d..268e5628a7 100644 --- a/datasets/USGS_DS_2006_234_1.0.json +++ b/datasets/USGS_DS_2006_234_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2006_234_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DS_2007_244.json b/datasets/USGS_DS_2007_244.json index 591e324052..b9fdac33ba 100644 --- a/datasets/USGS_DS_2007_244.json +++ b/datasets/USGS_DS_2007_244.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2007_244/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DS_2007_246_1.0.json b/datasets/USGS_DS_2007_246_1.0.json index cdbec5ccc9..a0b76d7961 100644 --- a/datasets/USGS_DS_2007_246_1.0.json +++ b/datasets/USGS_DS_2007_246_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2007_246_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_DS_2007_250.json b/datasets/USGS_DS_2007_250.json index c3a52bbfb8..0593046aaa 100644 --- a/datasets/USGS_DS_2007_250.json +++ b/datasets/USGS_DS_2007_250.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2007_250/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_FORT_Mesa_Verda_NP_veg.json b/datasets/USGS_FORT_Mesa_Verda_NP_veg.json index e4ff39488a..6e8d5d57e1 100644 --- a/datasets/USGS_FORT_Mesa_Verda_NP_veg.json +++ b/datasets/USGS_FORT_Mesa_Verda_NP_veg.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_FORT_Mesa_Verda_NP_veg/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_FORT_WY_WindTurbines2012.json b/datasets/USGS_FORT_WY_WindTurbines2012.json index 3e33df7dd6..9090f1f894 100644 --- a/datasets/USGS_FORT_WY_WindTurbines2012.json +++ b/datasets/USGS_FORT_WY_WindTurbines2012.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_FORT_WY_WindTurbines2012/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_GLOBAL_CRUST.json b/datasets/USGS_GLOBAL_CRUST.json index 4c4f5d49b9..25c32bd3b8 100644 --- a/datasets/USGS_GLOBAL_CRUST.json +++ b/datasets/USGS_GLOBAL_CRUST.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_GLOBAL_CRUST/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_GLSC_GreatLakesCopepods.json b/datasets/USGS_GLSC_GreatLakesCopepods.json index 107b24b7cc..78def8c45c 100644 --- a/datasets/USGS_GLSC_GreatLakesCopepods.json +++ b/datasets/USGS_GLSC_GreatLakesCopepods.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_GLSC_GreatLakesCopepods/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_KATRINA_COASTAL_IMPACT_LIDAR.json b/datasets/USGS_KATRINA_COASTAL_IMPACT_LIDAR.json index 064a72c38f..6092329672 100644 --- a/datasets/USGS_KATRINA_COASTAL_IMPACT_LIDAR.json +++ b/datasets/USGS_KATRINA_COASTAL_IMPACT_LIDAR.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_KATRINA_COASTAL_IMPACT_LIDAR/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Katrina_Coastal_Impact.json b/datasets/USGS_Katrina_Coastal_Impact.json index 76e585c010..12b7812eb0 100644 --- a/datasets/USGS_Katrina_Coastal_Impact.json +++ b/datasets/USGS_Katrina_Coastal_Impact.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Katrina_Coastal_Impact/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_MAP_MF-2336_1.0.json b/datasets/USGS_MAP_MF-2336_1.0.json index 695cf7cd2b..ad0eeab7c9 100644 --- a/datasets/USGS_MAP_MF-2336_1.0.json +++ b/datasets/USGS_MAP_MF-2336_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF-2336_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_MAP_MF-2349_1.0.json b/datasets/USGS_MAP_MF-2349_1.0.json index e975960423..1a6e3e5c92 100644 --- a/datasets/USGS_MAP_MF-2349_1.0.json +++ b/datasets/USGS_MAP_MF-2349_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF-2349_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_MAP_MF-2352_Version 1.0.json b/datasets/USGS_MAP_MF-2352_Version 1.0.json index 572a914038..631d55790e 100644 --- a/datasets/USGS_MAP_MF-2352_Version 1.0.json +++ b/datasets/USGS_MAP_MF-2352_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF-2352_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_MAP_MF-2354_Version 1.0.json b/datasets/USGS_MAP_MF-2354_Version 1.0.json index 9f2abc467b..04e0f39384 100644 --- a/datasets/USGS_MAP_MF-2354_Version 1.0.json +++ b/datasets/USGS_MAP_MF-2354_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF-2354_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_MAP_MF-2356_1.0.json b/datasets/USGS_MAP_MF-2356_1.0.json index d16a8a173b..85b25931e7 100644 --- a/datasets/USGS_MAP_MF-2356_1.0.json +++ b/datasets/USGS_MAP_MF-2356_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF-2356_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_MAP_MF-2359_1.0.json b/datasets/USGS_MAP_MF-2359_1.0.json index e0652362a9..7deea34d69 100644 --- a/datasets/USGS_MAP_MF-2359_1.0.json +++ b/datasets/USGS_MAP_MF-2359_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF-2359_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_MAP_MF2329_1.0.json b/datasets/USGS_MAP_MF2329_1.0.json index 708fc16fb5..c3160c43b6 100644 --- a/datasets/USGS_MAP_MF2329_1.0.json +++ b/datasets/USGS_MAP_MF2329_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF2329_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_MASSBAY.json b/datasets/USGS_MASSBAY.json index 9b4a36d99f..021957091a 100644 --- a/datasets/USGS_MASSBAY.json +++ b/datasets/USGS_MASSBAY.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MASSBAY/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_MF-2323_1.0.json b/datasets/USGS_MF-2323_1.0.json index 858fb9768a..3a369cedb4 100644 --- a/datasets/USGS_MF-2323_1.0.json +++ b/datasets/USGS_MF-2323_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MF-2323_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map-MF-2377_1.0.json b/datasets/USGS_Map-MF-2377_1.0.json index 2f62bff373..26ddff96d8 100644 --- a/datasets/USGS_Map-MF-2377_1.0.json +++ b/datasets/USGS_Map-MF-2377_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map-MF-2377_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map-MF-2387_1.0.json b/datasets/USGS_Map-MF-2387_1.0.json index af87b1d799..0005203582 100644 --- a/datasets/USGS_Map-MF-2387_1.0.json +++ b/datasets/USGS_Map-MF-2387_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map-MF-2387_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map-MF-2388_1.0.json b/datasets/USGS_Map-MF-2388_1.0.json index 538bb252ba..6b0b8e0dd5 100644 --- a/datasets/USGS_Map-MF-2388_1.0.json +++ b/datasets/USGS_Map-MF-2388_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map-MF-2388_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2326_1.0.json b/datasets/USGS_Map_MF-2326_1.0.json index 319ec174a8..e82d406515 100644 --- a/datasets/USGS_Map_MF-2326_1.0.json +++ b/datasets/USGS_Map_MF-2326_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2326_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2331_1.0.json b/datasets/USGS_Map_MF-2331_1.0.json index 471170e218..4a895e6b5a 100644 --- a/datasets/USGS_Map_MF-2331_1.0.json +++ b/datasets/USGS_Map_MF-2331_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2331_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2337_1.0.json b/datasets/USGS_Map_MF-2337_1.0.json index 46bb423ccd..4d05d49257 100644 --- a/datasets/USGS_Map_MF-2337_1.0.json +++ b/datasets/USGS_Map_MF-2337_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2337_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2341_1.0.json b/datasets/USGS_Map_MF-2341_1.0.json index b412685637..b988577acd 100644 --- a/datasets/USGS_Map_MF-2341_1.0.json +++ b/datasets/USGS_Map_MF-2341_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2341_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2342_1.0.json b/datasets/USGS_Map_MF-2342_1.0.json index 541e6524d8..e64d6840b4 100644 --- a/datasets/USGS_Map_MF-2342_1.0.json +++ b/datasets/USGS_Map_MF-2342_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2342_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2343_1.0.json b/datasets/USGS_Map_MF-2343_1.0.json index 31a3b58fcd..0cc0cf8d4a 100644 --- a/datasets/USGS_Map_MF-2343_1.0.json +++ b/datasets/USGS_Map_MF-2343_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2343_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2347_1.0.json b/datasets/USGS_Map_MF-2347_1.0.json index 45c722c266..01e78fee23 100644 --- a/datasets/USGS_Map_MF-2347_1.0.json +++ b/datasets/USGS_Map_MF-2347_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2347_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2361_1.0.json b/datasets/USGS_Map_MF-2361_1.0.json index 2da4cc4f77..9951b2f849 100644 --- a/datasets/USGS_Map_MF-2361_1.0.json +++ b/datasets/USGS_Map_MF-2361_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2361_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2363_1.0.json b/datasets/USGS_Map_MF-2363_1.0.json index 9ce5ca2c91..5289d30475 100644 --- a/datasets/USGS_Map_MF-2363_1.0.json +++ b/datasets/USGS_Map_MF-2363_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2363_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2364_1.0.json b/datasets/USGS_Map_MF-2364_1.0.json index 7eb38823bc..ed83e7d248 100644 --- a/datasets/USGS_Map_MF-2364_1.0.json +++ b/datasets/USGS_Map_MF-2364_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2364_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2366_1.0.json b/datasets/USGS_Map_MF-2366_1.0.json index b7b1a04f13..a7205ba79c 100644 --- a/datasets/USGS_Map_MF-2366_1.0.json +++ b/datasets/USGS_Map_MF-2366_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2366_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2367_1.0.json b/datasets/USGS_Map_MF-2367_1.0.json index 66d15618c4..224bf734fd 100644 --- a/datasets/USGS_Map_MF-2367_1.0.json +++ b/datasets/USGS_Map_MF-2367_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2367_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2368_1.0.json b/datasets/USGS_Map_MF-2368_1.0.json index 91a7bab4cc..2d4383bd88 100644 --- a/datasets/USGS_Map_MF-2368_1.0.json +++ b/datasets/USGS_Map_MF-2368_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2368_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2369_1.0.json b/datasets/USGS_Map_MF-2369_1.0.json index 5dfa4e606b..83b7152835 100644 --- a/datasets/USGS_Map_MF-2369_1.0.json +++ b/datasets/USGS_Map_MF-2369_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2369_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2371.json b/datasets/USGS_Map_MF-2371.json index 2429309174..8613890cca 100644 --- a/datasets/USGS_Map_MF-2371.json +++ b/datasets/USGS_Map_MF-2371.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2371/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2372_1.0.json b/datasets/USGS_Map_MF-2372_1.0.json index 0522646015..f933e86d50 100644 --- a/datasets/USGS_Map_MF-2372_1.0.json +++ b/datasets/USGS_Map_MF-2372_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2372_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2373_1.0.json b/datasets/USGS_Map_MF-2373_1.0.json index a3f64e15ea..16a13a7711 100644 --- a/datasets/USGS_Map_MF-2373_1.0.json +++ b/datasets/USGS_Map_MF-2373_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2373_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2381-A_1.0.json b/datasets/USGS_Map_MF-2381-A_1.0.json index eab56e90a5..a94c3b725c 100644 --- a/datasets/USGS_Map_MF-2381-A_1.0.json +++ b/datasets/USGS_Map_MF-2381-A_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2381-A_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2381-C_1.0.json b/datasets/USGS_Map_MF-2381-C_1.0.json index e186c3353e..b7915df832 100644 --- a/datasets/USGS_Map_MF-2381-C_1.0.json +++ b/datasets/USGS_Map_MF-2381-C_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2381-C_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2381-E_1.0.json b/datasets/USGS_Map_MF-2381-E_1.0.json index d7d8b383fa..c6594fe834 100644 --- a/datasets/USGS_Map_MF-2381-E_1.0.json +++ b/datasets/USGS_Map_MF-2381-E_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2381-E_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Map_MF-2385_1.0.json b/datasets/USGS_Map_MF-2385_1.0.json index 548fc907f8..820af0fdac 100644 --- a/datasets/USGS_Map_MF-2385_1.0.json +++ b/datasets/USGS_Map_MF-2385_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2385_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_NHD_CATCH.json b/datasets/USGS_NHD_CATCH.json index 14c47ff287..85f2541ee7 100644 --- a/datasets/USGS_NHD_CATCH.json +++ b/datasets/USGS_NHD_CATCH.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_NHD_CATCH/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_NSHMP.json b/datasets/USGS_NSHMP.json index 2c381d2732..9c3af9b3a9 100644 --- a/datasets/USGS_NSHMP.json +++ b/datasets/USGS_NSHMP.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_NSHMP/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_NWRC_LA_LandChange_1932-2010.json b/datasets/USGS_NWRC_LA_LandChange_1932-2010.json index 755d20dd14..79075eb7e1 100644 --- a/datasets/USGS_NWRC_LA_LandChange_1932-2010.json +++ b/datasets/USGS_NWRC_LA_LandChange_1932-2010.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_NWRC_LA_LandChange_1932-2010/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OF99-535_1.0.json b/datasets/USGS_OF99-535_1.0.json index ec1678fe7f..864df0850d 100644 --- a/datasets/USGS_OF99-535_1.0.json +++ b/datasets/USGS_OF99-535_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OF99-535_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR00-494.json b/datasets/USGS_OFR00-494.json index 06268b4982..bd16c63f7c 100644 --- a/datasets/USGS_OFR00-494.json +++ b/datasets/USGS_OFR00-494.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR00-494/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR00-495_1.0.json b/datasets/USGS_OFR00-495_1.0.json index 96a785f5b6..f2b3d6298e 100644 --- a/datasets/USGS_OFR00-495_1.0.json +++ b/datasets/USGS_OFR00-495_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR00-495_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR00503dredgesite.json b/datasets/USGS_OFR00503dredgesite.json index 2514f1db57..6dc19ca98d 100644 --- a/datasets/USGS_OFR00503dredgesite.json +++ b/datasets/USGS_OFR00503dredgesite.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR00503dredgesite/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR01-131_Version 1.0.json b/datasets/USGS_OFR01-131_Version 1.0.json index 46ccbfefed..36be0ee516 100644 --- a/datasets/USGS_OFR01-131_Version 1.0.json +++ b/datasets/USGS_OFR01-131_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-131_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR01-132_Version 1.0.json b/datasets/USGS_OFR01-132_Version 1.0.json index fad7341fce..2477eead4c 100644 --- a/datasets/USGS_OFR01-132_Version 1.0.json +++ b/datasets/USGS_OFR01-132_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-132_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR01-153_Version 1.0, February 13, 2001.json b/datasets/USGS_OFR01-153_Version 1.0, February 13, 2001.json index 06cd912d38..42aa94d40c 100644 --- a/datasets/USGS_OFR01-153_Version 1.0, February 13, 2001.json +++ b/datasets/USGS_OFR01-153_Version 1.0, February 13, 2001.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-153_Version%201.0%2C%20February%2013%2C%202001/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR01-173_Version 1.0.json b/datasets/USGS_OFR01-173_Version 1.0.json index ba60a814fe..2bba3dbf02 100644 --- a/datasets/USGS_OFR01-173_Version 1.0.json +++ b/datasets/USGS_OFR01-173_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-173_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR01-227_1.0.json b/datasets/USGS_OFR01-227_1.0.json index 61105ac2ec..6f34d1050d 100644 --- a/datasets/USGS_OFR01-227_1.0.json +++ b/datasets/USGS_OFR01-227_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-227_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR01-290_1.0.json b/datasets/USGS_OFR01-290_1.0.json index f0c03b5b09..6dee32ab36 100644 --- a/datasets/USGS_OFR01-290_1.0.json +++ b/datasets/USGS_OFR01-290_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-290_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR01-293_Version 1.0.json b/datasets/USGS_OFR01-293_Version 1.0.json index 255cf8abe9..11ba4bed5e 100644 --- a/datasets/USGS_OFR01-293_Version 1.0.json +++ b/datasets/USGS_OFR01-293_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-293_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR01-308_Version 1.0.json b/datasets/USGS_OFR01-308_Version 1.0.json index a21f11202f..56e4460c3f 100644 --- a/datasets/USGS_OFR01-308_Version 1.0.json +++ b/datasets/USGS_OFR01-308_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-308_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR01-30_1.0.json b/datasets/USGS_OFR01-30_1.0.json index 7ee1f801bb..99b52df09d 100644 --- a/datasets/USGS_OFR01-30_1.0.json +++ b/datasets/USGS_OFR01-30_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-30_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR01-311_Version 1.0.json b/datasets/USGS_OFR01-311_Version 1.0.json index 4827996232..2c934b4176 100644 --- a/datasets/USGS_OFR01-311_Version 1.0.json +++ b/datasets/USGS_OFR01-311_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-311_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR01-31_Version 1.0.json b/datasets/USGS_OFR01-31_Version 1.0.json index 7e0c427691..0b4510aa59 100644 --- a/datasets/USGS_OFR01-31_Version 1.0.json +++ b/datasets/USGS_OFR01-31_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-31_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR01139_Version 1.0.json b/datasets/USGS_OFR01139_Version 1.0.json index 3e28abb0c8..3877364e47 100644 --- a/datasets/USGS_OFR01139_Version 1.0.json +++ b/datasets/USGS_OFR01139_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01139_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00-376_1.0.json b/datasets/USGS_OFR_00-376_1.0.json index f72bbcbffa..bed5cbe46c 100644 --- a/datasets/USGS_OFR_00-376_1.0.json +++ b/datasets/USGS_OFR_00-376_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00-376_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00135_Version 1.0.json b/datasets/USGS_OFR_00135_Version 1.0.json index d2beac9a55..9b165fafa8 100644 --- a/datasets/USGS_OFR_00135_Version 1.0.json +++ b/datasets/USGS_OFR_00135_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00135_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00145_Version 1.0.json b/datasets/USGS_OFR_00145_Version 1.0.json index 5b928b3b55..fe9843e7fc 100644 --- a/datasets/USGS_OFR_00145_Version 1.0.json +++ b/datasets/USGS_OFR_00145_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00145_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_0014_version 1.0.json b/datasets/USGS_OFR_0014_version 1.0.json index 81b22db3d0..826a813022 100644 --- a/datasets/USGS_OFR_0014_version 1.0.json +++ b/datasets/USGS_OFR_0014_version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_0014_version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00175_Version 1.0.json b/datasets/USGS_OFR_00175_Version 1.0.json index 93cd68655b..38e72127b8 100644 --- a/datasets/USGS_OFR_00175_Version 1.0.json +++ b/datasets/USGS_OFR_00175_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00175_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00192_Version 1.0.json b/datasets/USGS_OFR_00192_Version 1.0.json index c9179a697d..8a02792dc4 100644 --- a/datasets/USGS_OFR_00192_Version 1.0.json +++ b/datasets/USGS_OFR_00192_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00192_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00222_1.0.json b/datasets/USGS_OFR_00222_1.0.json index d9ecdc835e..16d8ae2ae0 100644 --- a/datasets/USGS_OFR_00222_1.0.json +++ b/datasets/USGS_OFR_00222_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00222_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00304.json b/datasets/USGS_OFR_00304.json index a33fa71a9e..86a0625d86 100644 --- a/datasets/USGS_OFR_00304.json +++ b/datasets/USGS_OFR_00304.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00304_BUZAS.json b/datasets/USGS_OFR_00304_BUZAS.json index 3cdbfa8c53..2d9796d6a1 100644 --- a/datasets/USGS_OFR_00304_BUZAS.json +++ b/datasets/USGS_OFR_00304_BUZAS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_BUZAS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00304_CPERFLOC.json b/datasets/USGS_OFR_00304_CPERFLOC.json index d9d53839e6..bb360f0295 100644 --- a/datasets/USGS_OFR_00304_CPERFLOC.json +++ b/datasets/USGS_OFR_00304_CPERFLOC.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_CPERFLOC/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00304_CST27.json b/datasets/USGS_OFR_00304_CST27.json index 7ba122be23..67ec4b9547 100644 --- a/datasets/USGS_OFR_00304_CST27.json +++ b/datasets/USGS_OFR_00304_CST27.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_CST27/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00304_GRAVITY.json b/datasets/USGS_OFR_00304_GRAVITY.json index c24c616ce1..3e3e0d83c1 100644 --- a/datasets/USGS_OFR_00304_GRAVITY.json +++ b/datasets/USGS_OFR_00304_GRAVITY.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_GRAVITY/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00304_LISGRABS.json b/datasets/USGS_OFR_00304_LISGRABS.json index 128b1171f0..b6ec09d5a9 100644 --- a/datasets/USGS_OFR_00304_LISGRABS.json +++ b/datasets/USGS_OFR_00304_LISGRABS.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_LISGRABS/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00304_LISTEX.json b/datasets/USGS_OFR_00304_LISTEX.json index 0764ec276d..3ab12a0ae8 100644 --- a/datasets/USGS_OFR_00304_LISTEX.json +++ b/datasets/USGS_OFR_00304_LISTEX.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_LISTEX/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00304_LISTOC.json b/datasets/USGS_OFR_00304_LISTOC.json index 35bbb10ad2..5ec05cabff 100644 --- a/datasets/USGS_OFR_00304_LISTOC.json +++ b/datasets/USGS_OFR_00304_LISTOC.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_LISTOC/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00304_MOSAREA.json b/datasets/USGS_OFR_00304_MOSAREA.json index 165796f19e..da7bdb9390 100644 --- a/datasets/USGS_OFR_00304_MOSAREA.json +++ b/datasets/USGS_OFR_00304_MOSAREA.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_MOSAREA/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00304_NLMOSINT.json b/datasets/USGS_OFR_00304_NLMOSINT.json index 28a67b9924..68ea17b678 100644 --- a/datasets/USGS_OFR_00304_NLMOSINT.json +++ b/datasets/USGS_OFR_00304_NLMOSINT.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_NLMOSINT/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00304_PARKER.json b/datasets/USGS_OFR_00304_PARKER.json index ba5f292ea7..11cac62da7 100644 --- a/datasets/USGS_OFR_00304_PARKER.json +++ b/datasets/USGS_OFR_00304_PARKER.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_PARKER/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00304_TOCPNT1.json b/datasets/USGS_OFR_00304_TOCPNT1.json index f2021c4822..346144db73 100644 --- a/datasets/USGS_OFR_00304_TOCPNT1.json +++ b/datasets/USGS_OFR_00304_TOCPNT1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_TOCPNT1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00351_1.0.json b/datasets/USGS_OFR_00351_1.0.json index 6c1af6c683..aba4d95034 100644 --- a/datasets/USGS_OFR_00351_1.0.json +++ b/datasets/USGS_OFR_00351_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00351_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00356_Version 1.0.json b/datasets/USGS_OFR_00356_Version 1.0.json index 2af14bcb43..3ea26576ce 100644 --- a/datasets/USGS_OFR_00356_Version 1.0.json +++ b/datasets/USGS_OFR_00356_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00356_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00359_Version 1.0.json b/datasets/USGS_OFR_00359_Version 1.0.json index 4e92a33df5..208567c7e6 100644 --- a/datasets/USGS_OFR_00359_Version 1.0.json +++ b/datasets/USGS_OFR_00359_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00359_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_00409_Digital Version 1.0.json b/datasets/USGS_OFR_00409_Digital Version 1.0.json index 64f8cd0334..fac43029e7 100644 --- a/datasets/USGS_OFR_00409_Digital Version 1.0.json +++ b/datasets/USGS_OFR_00409_Digital Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00409_Digital%20Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_02-266.json b/datasets/USGS_OFR_02-266.json index dc2f094f42..2f07f38205 100644 --- a/datasets/USGS_OFR_02-266.json +++ b/datasets/USGS_OFR_02-266.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_02-266/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_02005.json b/datasets/USGS_OFR_02005.json index f7773768ff..2e3adaa4dd 100644 --- a/datasets/USGS_OFR_02005.json +++ b/datasets/USGS_OFR_02005.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_02005/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_02006.json b/datasets/USGS_OFR_02006.json index 09d9accf8d..b1c5514f5e 100644 --- a/datasets/USGS_OFR_02006.json +++ b/datasets/USGS_OFR_02006.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_02006/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_0205.json b/datasets/USGS_OFR_0205.json index 73b41aa4a8..1ce001b140 100644 --- a/datasets/USGS_OFR_0205.json +++ b/datasets/USGS_OFR_0205.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_0205/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_0206.json b/datasets/USGS_OFR_0206.json index 21ce6069aa..aa7feb7e30 100644 --- a/datasets/USGS_OFR_0206.json +++ b/datasets/USGS_OFR_0206.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_0206/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_02110_1.0.json b/datasets/USGS_OFR_02110_1.0.json index 5db84642d1..f8bc88d293 100644 --- a/datasets/USGS_OFR_02110_1.0.json +++ b/datasets/USGS_OFR_02110_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_02110_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_0221_Version 1.0.json b/datasets/USGS_OFR_0221_Version 1.0.json index dfb8b4f460..ef3b68c300 100644 --- a/datasets/USGS_OFR_0221_Version 1.0.json +++ b/datasets/USGS_OFR_0221_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_0221_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_0222_Version 1.0.json b/datasets/USGS_OFR_0222_Version 1.0.json index 87184f96b0..3b6322e8a7 100644 --- a/datasets/USGS_OFR_0222_Version 1.0.json +++ b/datasets/USGS_OFR_0222_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_0222_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2001_164.json b/datasets/USGS_OFR_2001_164.json index 683d6f15ca..0f78e7bbb7 100644 --- a/datasets/USGS_OFR_2001_164.json +++ b/datasets/USGS_OFR_2001_164.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2001_164/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2002_002.json b/datasets/USGS_OFR_2002_002.json index 45690dba15..efed1fa36f 100644 --- a/datasets/USGS_OFR_2002_002.json +++ b/datasets/USGS_OFR_2002_002.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2002_002/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2002_206.json b/datasets/USGS_OFR_2002_206.json index 9d8f78e404..e821e74469 100644 --- a/datasets/USGS_OFR_2002_206.json +++ b/datasets/USGS_OFR_2002_206.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2002_206/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2003_080.json b/datasets/USGS_OFR_2003_080.json index ae6d05ac61..f4c5c613f9 100644 --- a/datasets/USGS_OFR_2003_080.json +++ b/datasets/USGS_OFR_2003_080.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_080/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2003_095_1.1.json b/datasets/USGS_OFR_2003_095_1.1.json index fbc3df1711..6196b915b5 100644 --- a/datasets/USGS_OFR_2003_095_1.1.json +++ b/datasets/USGS_OFR_2003_095_1.1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_095_1.1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2003_096_1.0.json b/datasets/USGS_OFR_2003_096_1.0.json index 746aa9870c..f7849f144d 100644 --- a/datasets/USGS_OFR_2003_096_1.0.json +++ b/datasets/USGS_OFR_2003_096_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_096_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2003_102_1.0.json b/datasets/USGS_OFR_2003_102_1.0.json index 43138d7f78..7b63d7c4aa 100644 --- a/datasets/USGS_OFR_2003_102_1.0.json +++ b/datasets/USGS_OFR_2003_102_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_102_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2003_103_1.0.json b/datasets/USGS_OFR_2003_103_1.0.json index 76ff39602b..9384724b22 100644 --- a/datasets/USGS_OFR_2003_103_1.0.json +++ b/datasets/USGS_OFR_2003_103_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_103_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2003_135.json b/datasets/USGS_OFR_2003_135.json index 8dc6ed38a6..3faf28e8b7 100644 --- a/datasets/USGS_OFR_2003_135.json +++ b/datasets/USGS_OFR_2003_135.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_135/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2003_150.json b/datasets/USGS_OFR_2003_150.json index d52067528f..9d318fe148 100644 --- a/datasets/USGS_OFR_2003_150.json +++ b/datasets/USGS_OFR_2003_150.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_150/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2003_225_1.0.json b/datasets/USGS_OFR_2003_225_1.0.json index 1280e8c97f..44b9243300 100644 --- a/datasets/USGS_OFR_2003_225_1.0.json +++ b/datasets/USGS_OFR_2003_225_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_225_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2003_235.json b/datasets/USGS_OFR_2003_235.json index b40b6d4876..9452888b2b 100644 --- a/datasets/USGS_OFR_2003_235.json +++ b/datasets/USGS_OFR_2003_235.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_235/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2003_236_1.0.json b/datasets/USGS_OFR_2003_236_1.0.json index 994b88c134..3fd16831e0 100644 --- a/datasets/USGS_OFR_2003_236_1.0.json +++ b/datasets/USGS_OFR_2003_236_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_236_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2003_265.json b/datasets/USGS_OFR_2003_265.json index be5c7756b8..5718c29b0e 100644 --- a/datasets/USGS_OFR_2003_265.json +++ b/datasets/USGS_OFR_2003_265.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_265/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2003_85_1.0.json b/datasets/USGS_OFR_2003_85_1.0.json index cd0f0c1d82..9ebe7de087 100644 --- a/datasets/USGS_OFR_2003_85_1.0.json +++ b/datasets/USGS_OFR_2003_85_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_85_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2004_1008_1.0.json b/datasets/USGS_OFR_2004_1008_1.0.json index 1a89cbf019..040dbb0704 100644 --- a/datasets/USGS_OFR_2004_1008_1.0.json +++ b/datasets/USGS_OFR_2004_1008_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1008_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2004_1009.json b/datasets/USGS_OFR_2004_1009.json index 28f345433d..7217fd9cb2 100644 --- a/datasets/USGS_OFR_2004_1009.json +++ b/datasets/USGS_OFR_2004_1009.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1009/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2004_1010_1.0.json b/datasets/USGS_OFR_2004_1010_1.0.json index 6d0d256942..dc9312b980 100644 --- a/datasets/USGS_OFR_2004_1010_1.0.json +++ b/datasets/USGS_OFR_2004_1010_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1010_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2004_1011_1.0.json b/datasets/USGS_OFR_2004_1011_1.0.json index fef3ac5063..a929b82044 100644 --- a/datasets/USGS_OFR_2004_1011_1.0.json +++ b/datasets/USGS_OFR_2004_1011_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1011_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2004_1013_1.0.json b/datasets/USGS_OFR_2004_1013_1.0.json index e4064a192e..70f448c008 100644 --- a/datasets/USGS_OFR_2004_1013_1.0.json +++ b/datasets/USGS_OFR_2004_1013_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1013_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2004_1014.json b/datasets/USGS_OFR_2004_1014.json index 0ff1676979..6c25a2b605 100644 --- a/datasets/USGS_OFR_2004_1014.json +++ b/datasets/USGS_OFR_2004_1014.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1014/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2004_1038.json b/datasets/USGS_OFR_2004_1038.json index c659bbb8c2..0f3bf591bc 100644 --- a/datasets/USGS_OFR_2004_1038.json +++ b/datasets/USGS_OFR_2004_1038.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1038/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2004_1039.json b/datasets/USGS_OFR_2004_1039.json index 40a1482494..5aa81e173d 100644 --- a/datasets/USGS_OFR_2004_1039.json +++ b/datasets/USGS_OFR_2004_1039.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1039/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2004_1049_1.0.json b/datasets/USGS_OFR_2004_1049_1.0.json index 9c1ef86228..85f0233c32 100644 --- a/datasets/USGS_OFR_2004_1049_1.0.json +++ b/datasets/USGS_OFR_2004_1049_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1049_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2004_1074.json b/datasets/USGS_OFR_2004_1074.json index 022fe3920c..a45087f304 100644 --- a/datasets/USGS_OFR_2004_1074.json +++ b/datasets/USGS_OFR_2004_1074.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1074/items", - 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"type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1038_1.0.json b/datasets/USGS_OFR_2005_1038_1.0.json index f107caad40..41e6c6e712 100644 --- a/datasets/USGS_OFR_2005_1038_1.0.json +++ b/datasets/USGS_OFR_2005_1038_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1038_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1063.json b/datasets/USGS_OFR_2005_1063.json index 67e09d8ce1..c1665facbf 100644 --- a/datasets/USGS_OFR_2005_1063.json +++ b/datasets/USGS_OFR_2005_1063.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1063/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1067_1.0.json b/datasets/USGS_OFR_2005_1067_1.0.json index 08ada49393..99e1491ca9 100644 --- a/datasets/USGS_OFR_2005_1067_1.0.json +++ b/datasets/USGS_OFR_2005_1067_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1067_1.0/items", - 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"type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1201.json b/datasets/USGS_OFR_2005_1201.json index 726d1b49ef..a05e8701bc 100644 --- a/datasets/USGS_OFR_2005_1201.json +++ b/datasets/USGS_OFR_2005_1201.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1201/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1203_1.0.json b/datasets/USGS_OFR_2005_1203_1.0.json index 5ccdfeab0c..18469e5a65 100644 --- a/datasets/USGS_OFR_2005_1203_1.0.json +++ b/datasets/USGS_OFR_2005_1203_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1203_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1253_1.0.json b/datasets/USGS_OFR_2005_1253_1.0.json index b9472fa64c..037c469105 100644 --- a/datasets/USGS_OFR_2005_1253_1.0.json +++ b/datasets/USGS_OFR_2005_1253_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1253_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1315.json b/datasets/USGS_OFR_2005_1315.json index 8ff9837c52..cbd420c57a 100644 --- a/datasets/USGS_OFR_2005_1315.json +++ b/datasets/USGS_OFR_2005_1315.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1315/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1329.json b/datasets/USGS_OFR_2005_1329.json index 1b6acb6dba..5a7ea9776b 100644 --- a/datasets/USGS_OFR_2005_1329.json +++ b/datasets/USGS_OFR_2005_1329.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1329/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1333.json b/datasets/USGS_OFR_2005_1333.json index b2156c7174..39b871b06b 100644 --- a/datasets/USGS_OFR_2005_1333.json +++ b/datasets/USGS_OFR_2005_1333.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1333/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1339_1.0.json b/datasets/USGS_OFR_2005_1339_1.0.json index a09278538a..0eb94c45cb 100644 --- a/datasets/USGS_OFR_2005_1339_1.0.json +++ b/datasets/USGS_OFR_2005_1339_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1339_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1402.json b/datasets/USGS_OFR_2005_1402.json index 506af95b18..ba9473aac9 100644 --- a/datasets/USGS_OFR_2005_1402.json +++ b/datasets/USGS_OFR_2005_1402.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1402/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1405.json b/datasets/USGS_OFR_2005_1405.json index b7e305d7e1..7cadcc9823 100644 --- a/datasets/USGS_OFR_2005_1405.json +++ b/datasets/USGS_OFR_2005_1405.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1405/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1407.json b/datasets/USGS_OFR_2005_1407.json index 51521a722e..c90f79c8ae 100644 --- a/datasets/USGS_OFR_2005_1407.json +++ b/datasets/USGS_OFR_2005_1407.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1407/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2005_1450.json b/datasets/USGS_OFR_2005_1450.json index df0ab04483..fbeace4a8b 100644 --- a/datasets/USGS_OFR_2005_1450.json +++ b/datasets/USGS_OFR_2005_1450.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1450/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2006_1032.json b/datasets/USGS_OFR_2006_1032.json index 9840925436..eeb13f2b10 100644 --- a/datasets/USGS_OFR_2006_1032.json +++ b/datasets/USGS_OFR_2006_1032.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1032/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2006_1038.json b/datasets/USGS_OFR_2006_1038.json index ddb33580f0..6e66b623ad 100644 --- a/datasets/USGS_OFR_2006_1038.json +++ b/datasets/USGS_OFR_2006_1038.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1038/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2006_1042.json b/datasets/USGS_OFR_2006_1042.json index 8a5fcfc75e..bdd0e04c73 100644 --- a/datasets/USGS_OFR_2006_1042.json +++ b/datasets/USGS_OFR_2006_1042.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1042/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2006_1051.json b/datasets/USGS_OFR_2006_1051.json index 09b198ea22..39c7d0a68f 100644 --- a/datasets/USGS_OFR_2006_1051.json +++ b/datasets/USGS_OFR_2006_1051.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1051/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2006_1070.json b/datasets/USGS_OFR_2006_1070.json index a8d60f4788..0daf269ed8 100644 --- a/datasets/USGS_OFR_2006_1070.json +++ b/datasets/USGS_OFR_2006_1070.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1070/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2006_1081.json b/datasets/USGS_OFR_2006_1081.json index 5712ef8b05..463268b451 100644 --- a/datasets/USGS_OFR_2006_1081.json +++ b/datasets/USGS_OFR_2006_1081.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1081/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2006_1110.json b/datasets/USGS_OFR_2006_1110.json index a8d4623dec..0af8f7121a 100644 --- a/datasets/USGS_OFR_2006_1110.json +++ b/datasets/USGS_OFR_2006_1110.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1110/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2006_1247.json b/datasets/USGS_OFR_2006_1247.json index 05023dad0e..39d97ddc28 100644 --- a/datasets/USGS_OFR_2006_1247.json +++ b/datasets/USGS_OFR_2006_1247.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1247/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2006_1274.json b/datasets/USGS_OFR_2006_1274.json index 3f3f56a4b9..e765b36484 100644 --- a/datasets/USGS_OFR_2006_1274.json +++ b/datasets/USGS_OFR_2006_1274.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1274/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2006_1280.json b/datasets/USGS_OFR_2006_1280.json index 5882c4852d..1d67c09835 100644 --- a/datasets/USGS_OFR_2006_1280.json +++ b/datasets/USGS_OFR_2006_1280.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1280/items", - 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"type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1006.json b/datasets/USGS_OFR_2007_1006.json index 103db8dff7..ae2d2b2d1b 100644 --- a/datasets/USGS_OFR_2007_1006.json +++ b/datasets/USGS_OFR_2007_1006.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1006/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1029_1.0.json b/datasets/USGS_OFR_2007_1029_1.0.json index 61bba3817e..7487e24db9 100644 --- a/datasets/USGS_OFR_2007_1029_1.0.json +++ b/datasets/USGS_OFR_2007_1029_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1029_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1055.json b/datasets/USGS_OFR_2007_1055.json index 0ee1f64848..1b728e96f5 100644 --- a/datasets/USGS_OFR_2007_1055.json +++ b/datasets/USGS_OFR_2007_1055.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1055/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1073_1.0.json b/datasets/USGS_OFR_2007_1073_1.0.json index 5023b753e3..87dbbee7f1 100644 --- a/datasets/USGS_OFR_2007_1073_1.0.json +++ b/datasets/USGS_OFR_2007_1073_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1073_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1084.json b/datasets/USGS_OFR_2007_1084.json index 96da854d7c..5d89d82c3d 100644 --- a/datasets/USGS_OFR_2007_1084.json +++ b/datasets/USGS_OFR_2007_1084.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1084/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1115_1.0.json b/datasets/USGS_OFR_2007_1115_1.0.json index bcacc2be68..655c2e47b4 100644 --- a/datasets/USGS_OFR_2007_1115_1.0.json +++ b/datasets/USGS_OFR_2007_1115_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1115_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1122.json b/datasets/USGS_OFR_2007_1122.json index 4e3307ab43..9e0a4cc1c6 100644 --- a/datasets/USGS_OFR_2007_1122.json +++ b/datasets/USGS_OFR_2007_1122.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1122/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1133.json b/datasets/USGS_OFR_2007_1133.json index 0b48d7d9a8..d126d1d5ae 100644 --- a/datasets/USGS_OFR_2007_1133.json +++ b/datasets/USGS_OFR_2007_1133.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1133/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1146.json b/datasets/USGS_OFR_2007_1146.json index 07c3f710cc..34a3e59908 100644 --- a/datasets/USGS_OFR_2007_1146.json +++ b/datasets/USGS_OFR_2007_1146.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1146/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1152.json b/datasets/USGS_OFR_2007_1152.json index e437ca5670..3a971b531f 100644 --- a/datasets/USGS_OFR_2007_1152.json +++ b/datasets/USGS_OFR_2007_1152.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1152/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1159_2007-1159.json b/datasets/USGS_OFR_2007_1159_2007-1159.json index 728e039328..880a720585 100644 --- a/datasets/USGS_OFR_2007_1159_2007-1159.json +++ b/datasets/USGS_OFR_2007_1159_2007-1159.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1159_2007-1159/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1161.json b/datasets/USGS_OFR_2007_1161.json index eecc5c4e81..ce355f48f4 100644 --- a/datasets/USGS_OFR_2007_1161.json +++ b/datasets/USGS_OFR_2007_1161.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1161/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1190.json b/datasets/USGS_OFR_2007_1190.json index a55e6c7a34..806b42939d 100644 --- a/datasets/USGS_OFR_2007_1190.json +++ b/datasets/USGS_OFR_2007_1190.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1190/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1202.json b/datasets/USGS_OFR_2007_1202.json index c001c0d1df..8918f6dc2c 100644 --- a/datasets/USGS_OFR_2007_1202.json +++ b/datasets/USGS_OFR_2007_1202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1208.json b/datasets/USGS_OFR_2007_1208.json index f07928bd97..a6e565d5bb 100644 --- a/datasets/USGS_OFR_2007_1208.json +++ b/datasets/USGS_OFR_2007_1208.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1208/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1238.json b/datasets/USGS_OFR_2007_1238.json index 14936dd665..ed06c07d50 100644 --- a/datasets/USGS_OFR_2007_1238.json +++ b/datasets/USGS_OFR_2007_1238.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1238/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1264.json b/datasets/USGS_OFR_2007_1264.json index edc91b7a5e..eb1dc99481 100644 --- a/datasets/USGS_OFR_2007_1264.json +++ b/datasets/USGS_OFR_2007_1264.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1264/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1269.json b/datasets/USGS_OFR_2007_1269.json index 165d7b4bd4..e2bbb41c58 100644 --- a/datasets/USGS_OFR_2007_1269.json +++ b/datasets/USGS_OFR_2007_1269.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1269/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1270.json b/datasets/USGS_OFR_2007_1270.json index 46464b16c2..6a86a56d68 100644 --- a/datasets/USGS_OFR_2007_1270.json +++ b/datasets/USGS_OFR_2007_1270.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1270/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1306_1.0.json b/datasets/USGS_OFR_2007_1306_1.0.json index 7288a285c9..70d3293112 100644 --- a/datasets/USGS_OFR_2007_1306_1.0.json +++ b/datasets/USGS_OFR_2007_1306_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1306_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1392.json b/datasets/USGS_OFR_2007_1392.json index 6726e066b4..4b0e6d3f24 100644 --- a/datasets/USGS_OFR_2007_1392.json +++ b/datasets/USGS_OFR_2007_1392.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1392/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2007_1405.json b/datasets/USGS_OFR_2007_1405.json index 9871dd3bea..12cb003f94 100644 --- a/datasets/USGS_OFR_2007_1405.json +++ b/datasets/USGS_OFR_2007_1405.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1405/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2008_1005.json b/datasets/USGS_OFR_2008_1005.json index bef5b94d3f..8cbc08a092 100644 --- a/datasets/USGS_OFR_2008_1005.json +++ b/datasets/USGS_OFR_2008_1005.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1005/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2008_1086.json b/datasets/USGS_OFR_2008_1086.json index a336b18e16..d8461d142f 100644 --- a/datasets/USGS_OFR_2008_1086.json +++ b/datasets/USGS_OFR_2008_1086.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1086/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2008_1088.json b/datasets/USGS_OFR_2008_1088.json index 4318ce611e..2ca983b761 100644 --- a/datasets/USGS_OFR_2008_1088.json +++ b/datasets/USGS_OFR_2008_1088.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1088/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2008_1100.json b/datasets/USGS_OFR_2008_1100.json index f1c1e2c65f..f83400e35a 100644 --- a/datasets/USGS_OFR_2008_1100.json +++ b/datasets/USGS_OFR_2008_1100.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1100/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2008_1119.json b/datasets/USGS_OFR_2008_1119.json index 544a615583..dae5c2ccba 100644 --- a/datasets/USGS_OFR_2008_1119.json +++ b/datasets/USGS_OFR_2008_1119.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1119/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2008_1121_1.0.json b/datasets/USGS_OFR_2008_1121_1.0.json index a78f580be1..4f7e100c9b 100644 --- a/datasets/USGS_OFR_2008_1121_1.0.json +++ b/datasets/USGS_OFR_2008_1121_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1121_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2008_1132.json b/datasets/USGS_OFR_2008_1132.json index 975a947914..e63c330610 100644 --- a/datasets/USGS_OFR_2008_1132.json +++ b/datasets/USGS_OFR_2008_1132.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1132/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2008_1169_1.0.json b/datasets/USGS_OFR_2008_1169_1.0.json index cfb902c627..8b7f57df59 100644 --- a/datasets/USGS_OFR_2008_1169_1.0.json +++ b/datasets/USGS_OFR_2008_1169_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1169_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2008_1270_1.0.json b/datasets/USGS_OFR_2008_1270_1.0.json index fa394f9ade..635f6df8c9 100644 --- a/datasets/USGS_OFR_2008_1270_1.0.json +++ b/datasets/USGS_OFR_2008_1270_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1270_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2008_1299.json b/datasets/USGS_OFR_2008_1299.json index 38fcec93d0..275049725a 100644 --- a/datasets/USGS_OFR_2008_1299.json +++ b/datasets/USGS_OFR_2008_1299.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1299/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2008_1306_1.0.json b/datasets/USGS_OFR_2008_1306_1.0.json index 0c7ad23367..7ef9ce9323 100644 --- a/datasets/USGS_OFR_2008_1306_1.0.json +++ b/datasets/USGS_OFR_2008_1306_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1306_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2010_1190_1.0.json b/datasets/USGS_OFR_2010_1190_1.0.json index 83a4179e24..b671643a02 100644 --- a/datasets/USGS_OFR_2010_1190_1.0.json +++ b/datasets/USGS_OFR_2010_1190_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2010_1190_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2010_1198.json b/datasets/USGS_OFR_2010_1198.json index 40a900724a..ccdce915f7 100644 --- a/datasets/USGS_OFR_2010_1198.json +++ b/datasets/USGS_OFR_2010_1198.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2010_1198/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2010_1223.json b/datasets/USGS_OFR_2010_1223.json index 7691274dc9..306adce4a0 100644 --- a/datasets/USGS_OFR_2010_1223.json +++ b/datasets/USGS_OFR_2010_1223.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2010_1223/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2010_1330.json b/datasets/USGS_OFR_2010_1330.json index cc03549fb5..7525169bd0 100644 --- a/datasets/USGS_OFR_2010_1330.json +++ b/datasets/USGS_OFR_2010_1330.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2010_1330/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_2013_1305_1.0.json b/datasets/USGS_OFR_2013_1305_1.0.json index 16135e28b6..d56305d79d 100644 --- a/datasets/USGS_OFR_2013_1305_1.0.json +++ b/datasets/USGS_OFR_2013_1305_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2013_1305_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_94-710.json b/datasets/USGS_OFR_94-710.json index 209684f583..bd72e7b089 100644 --- a/datasets/USGS_OFR_94-710.json +++ b/datasets/USGS_OFR_94-710.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_94-710/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_97_745_E.json b/datasets/USGS_OFR_97_745_E.json index 6562797e2f..7e435e814f 100644 --- a/datasets/USGS_OFR_97_745_E.json +++ b/datasets/USGS_OFR_97_745_E.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_97_745_E/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_99-422_1.0.json b/datasets/USGS_OFR_99-422_1.0.json index d5e4c75c7e..b2f0fdc810 100644 --- a/datasets/USGS_OFR_99-422_1.0.json +++ b/datasets/USGS_OFR_99-422_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_99-422_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_99_414_1.0.json b/datasets/USGS_OFR_99_414_1.0.json index de6aed6761..1d9bf5f11f 100644 --- a/datasets/USGS_OFR_99_414_1.0.json +++ b/datasets/USGS_OFR_99_414_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_99_414_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_OFR_99_438_1.0.json b/datasets/USGS_OFR_99_438_1.0.json index f46963cfc5..c129e9902a 100644 --- a/datasets/USGS_OFR_99_438_1.0.json +++ b/datasets/USGS_OFR_99_438_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_99_438_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_PA_DIGIT_1.0.json b/datasets/USGS_PA_DIGIT_1.0.json index 09cccd7349..7832c1ddae 100644 --- a/datasets/USGS_PA_DIGIT_1.0.json +++ b/datasets/USGS_PA_DIGIT_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_PA_DIGIT_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_PONTCHARTRAIN.json b/datasets/USGS_PONTCHARTRAIN.json index a3f7268b28..9747b7898e 100644 --- a/datasets/USGS_PONTCHARTRAIN.json +++ b/datasets/USGS_PONTCHARTRAIN.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_PONTCHARTRAIN/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_RITA_COASTAL_IMPACT.json b/datasets/USGS_RITA_COASTAL_IMPACT.json index 3405f9e8a2..b278ba9c5a 100644 --- a/datasets/USGS_RITA_COASTAL_IMPACT.json +++ b/datasets/USGS_RITA_COASTAL_IMPACT.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_RITA_COASTAL_IMPACT/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Report_MF-2332_1.0.json b/datasets/USGS_Report_MF-2332_1.0.json index beb019df35..e7f3a59e05 100644 --- a/datasets/USGS_Report_MF-2332_1.0.json +++ b/datasets/USGS_Report_MF-2332_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Report_MF-2332_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SESC_ExtinctFish.json b/datasets/USGS_SESC_ExtinctFish.json index d06b89b6f8..b3f17032c9 100644 --- a/datasets/USGS_SESC_ExtinctFish.json +++ b/datasets/USGS_SESC_ExtinctFish.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SESC_ExtinctFish/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SESC_ImperiledFreshwaterOrganisms.json b/datasets/USGS_SESC_ImperiledFreshwaterOrganisms.json index 9802b92bf6..c15927c218 100644 --- a/datasets/USGS_SESC_ImperiledFreshwaterOrganisms.json +++ b/datasets/USGS_SESC_ImperiledFreshwaterOrganisms.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SESC_ImperiledFreshwaterOrganisms/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_75_29_hydro_data.json b/datasets/USGS_SOFIA_75_29_hydro_data.json index f861ae686c..d5b54cd6cc 100644 --- a/datasets/USGS_SOFIA_75_29_hydro_data.json +++ b/datasets/USGS_SOFIA_75_29_hydro_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_75_29_hydro_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_Caloos_Franklin_Locks_flow.json b/datasets/USGS_SOFIA_Caloos_Franklin_Locks_flow.json index 1d65bb847a..25ee208494 100644 --- a/datasets/USGS_SOFIA_Caloos_Franklin_Locks_flow.json +++ b/datasets/USGS_SOFIA_Caloos_Franklin_Locks_flow.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Caloos_Franklin_Locks_flow/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_Caloosahatchee_water_quality.json b/datasets/USGS_SOFIA_Caloosahatchee_water_quality.json index 9781b78e51..1facb91d84 100644 --- a/datasets/USGS_SOFIA_Caloosahatchee_water_quality.json +++ b/datasets/USGS_SOFIA_Caloosahatchee_water_quality.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Caloosahatchee_water_quality/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_Ding_Darling_baseline.json b/datasets/USGS_SOFIA_Ding_Darling_baseline.json index 96a6634ed1..991c2977a8 100644 --- a/datasets/USGS_SOFIA_Ding_Darling_baseline.json +++ b/datasets/USGS_SOFIA_Ding_Darling_baseline.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Ding_Darling_baseline/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_EDEN_grid_shapefile_v02.json b/datasets/USGS_SOFIA_EDEN_grid_shapefile_v02.json index 6775338d49..dd17661f73 100644 --- a/datasets/USGS_SOFIA_EDEN_grid_shapefile_v02.json +++ b/datasets/USGS_SOFIA_EDEN_grid_shapefile_v02.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_EDEN_grid_shapefile_v02/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_EDEN_proj.json b/datasets/USGS_SOFIA_EDEN_proj.json index cde687c3ce..7d8305db1b 100644 --- a/datasets/USGS_SOFIA_EDEN_proj.json +++ b/datasets/USGS_SOFIA_EDEN_proj.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_EDEN_proj/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_Ever_hydr_FB_dynam.json b/datasets/USGS_SOFIA_Ever_hydr_FB_dynam.json index 490e2392ab..e991d3c03f 100644 --- a/datasets/USGS_SOFIA_Ever_hydr_FB_dynam.json +++ b/datasets/USGS_SOFIA_Ever_hydr_FB_dynam.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Ever_hydr_FB_dynam/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_Fbbslmap.json b/datasets/USGS_SOFIA_Fbbslmap.json index 48fbf0335f..2145480131 100644 --- a/datasets/USGS_SOFIA_Fbbslmap.json +++ b/datasets/USGS_SOFIA_Fbbslmap.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Fbbslmap/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_Fbbtypes.json b/datasets/USGS_SOFIA_Fbbtypes.json index d46c0655a4..f481658424 100644 --- a/datasets/USGS_SOFIA_Fbbtypes.json +++ b/datasets/USGS_SOFIA_Fbbtypes.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Fbbtypes/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_Fbsaldat.json b/datasets/USGS_SOFIA_Fbsaldat.json index 2cc9247712..b8c41e730d 100644 --- a/datasets/USGS_SOFIA_Fbsaldat.json +++ b/datasets/USGS_SOFIA_Fbsaldat.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Fbsaldat/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_FireHydroSoils.json b/datasets/USGS_SOFIA_FireHydroSoils.json index 0116d462a2..06690bd07e 100644 --- a/datasets/USGS_SOFIA_FireHydroSoils.json +++ b/datasets/USGS_SOFIA_FireHydroSoils.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_FireHydroSoils/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_HAED_WCA_Everglades.json b/datasets/USGS_SOFIA_HAED_WCA_Everglades.json index 6ae7c41fe1..e87c04f83b 100644 --- a/datasets/USGS_SOFIA_HAED_WCA_Everglades.json +++ b/datasets/USGS_SOFIA_HAED_WCA_Everglades.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_HAED_WCA_Everglades/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_HAED_okee.json b/datasets/USGS_SOFIA_HAED_okee.json index 521a5e4cf3..6f7cc91b53 100644 --- a/datasets/USGS_SOFIA_HAED_okee.json +++ b/datasets/USGS_SOFIA_HAED_okee.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_HAED_okee/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_HAED_truck.json b/datasets/USGS_SOFIA_HAED_truck.json index b045ec01ae..cc49b93a07 100644 --- a/datasets/USGS_SOFIA_HAED_truck.json +++ b/datasets/USGS_SOFIA_HAED_truck.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_HAED_truck/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_Hg_DOC_fy04.json b/datasets/USGS_SOFIA_Hg_DOC_fy04.json index e78a5d5f19..c5bbcf9b74 100644 --- a/datasets/USGS_SOFIA_Hg_DOC_fy04.json +++ b/datasets/USGS_SOFIA_Hg_DOC_fy04.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Hg_DOC_fy04/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_Hi_res_bathy_FB.json b/datasets/USGS_SOFIA_Hi_res_bathy_FB.json index 81f1372faa..47c7018270 100644 --- a/datasets/USGS_SOFIA_Hi_res_bathy_FB.json +++ b/datasets/USGS_SOFIA_Hi_res_bathy_FB.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Hi_res_bathy_FB/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_IMMAGE.json b/datasets/USGS_SOFIA_IMMAGE.json index 8e9fac1f30..96165c96df 100644 --- a/datasets/USGS_SOFIA_IMMAGE.json +++ b/datasets/USGS_SOFIA_IMMAGE.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_IMMAGE/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_L-31NSeep_Pilot.json b/datasets/USGS_SOFIA_L-31NSeep_Pilot.json index cddc57c420..66183f5878 100644 --- a/datasets/USGS_SOFIA_L-31NSeep_Pilot.json +++ b/datasets/USGS_SOFIA_L-31NSeep_Pilot.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_L-31NSeep_Pilot/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_L-31N_wells_data.json b/datasets/USGS_SOFIA_L-31N_wells_data.json index 43972150be..63fde4282a 100644 --- a/datasets/USGS_SOFIA_L-31N_wells_data.json +++ b/datasets/USGS_SOFIA_L-31N_wells_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_L-31N_wells_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_LOX_NWR_data.json b/datasets/USGS_SOFIA_LOX_NWR_data.json index de5965b84a..e795f87927 100644 --- a/datasets/USGS_SOFIA_LOX_NWR_data.json +++ b/datasets/USGS_SOFIA_LOX_NWR_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_LOX_NWR_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_LinkingLandAirManagement.json b/datasets/USGS_SOFIA_LinkingLandAirManagement.json index 6646e1f7f5..1727329680 100644 --- a/datasets/USGS_SOFIA_LinkingLandAirManagement.json +++ b/datasets/USGS_SOFIA_LinkingLandAirManagement.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_LinkingLandAirManagement/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_LinkingLandAirManagement_Task1.json b/datasets/USGS_SOFIA_LinkingLandAirManagement_Task1.json index bbab7ca7cc..fd97a85117 100644 --- a/datasets/USGS_SOFIA_LinkingLandAirManagement_Task1.json +++ b/datasets/USGS_SOFIA_LinkingLandAirManagement_Task1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_LinkingLandAirManagement_Task1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_LinkingLandAirManagement_Task2.json b/datasets/USGS_SOFIA_LinkingLandAirManagement_Task2.json index 4ba960dcdc..cf9a91b13e 100644 --- a/datasets/USGS_SOFIA_LinkingLandAirManagement_Task2.json +++ b/datasets/USGS_SOFIA_LinkingLandAirManagement_Task2.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_LinkingLandAirManagement_Task2/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_LinkingLandAirManagement_Task3.json b/datasets/USGS_SOFIA_LinkingLandAirManagement_Task3.json index 803e47665c..a59f78e8a0 100644 --- a/datasets/USGS_SOFIA_LinkingLandAirManagement_Task3.json +++ b/datasets/USGS_SOFIA_LinkingLandAirManagement_Task3.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_LinkingLandAirManagement_Task3/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_MeHg_degrad_rates.json b/datasets/USGS_SOFIA_MeHg_degrad_rates.json index 0db569dae0..712b59fbde 100644 --- a/datasets/USGS_SOFIA_MeHg_degrad_rates.json +++ b/datasets/USGS_SOFIA_MeHg_degrad_rates.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_MeHg_degrad_rates/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_bcunits_pts_point.json b/datasets/USGS_SOFIA_bcunits_pts_point.json index 537997c1f1..25b8003ec0 100644 --- a/datasets/USGS_SOFIA_bcunits_pts_point.json +++ b/datasets/USGS_SOFIA_bcunits_pts_point.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_bcunits_pts_point/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_brwd_config_base_biscayne_arc.json b/datasets/USGS_SOFIA_brwd_config_base_biscayne_arc.json index 71e0cb5649..8754a46748 100644 --- a/datasets/USGS_SOFIA_brwd_config_base_biscayne_arc.json +++ b/datasets/USGS_SOFIA_brwd_config_base_biscayne_arc.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_brwd_config_base_biscayne_arc/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_brwd_config_base_surficial_arc.json b/datasets/USGS_SOFIA_brwd_config_base_surficial_arc.json index be8ac3029c..02abcee0b4 100644 --- a/datasets/USGS_SOFIA_brwd_config_base_surficial_arc.json +++ b/datasets/USGS_SOFIA_brwd_config_base_surficial_arc.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_brwd_config_base_surficial_arc/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_brwd_glime_altbase_arc.json b/datasets/USGS_SOFIA_brwd_glime_altbase_arc.json index 1c106f058f..4dfca8bd8a 100644 --- a/datasets/USGS_SOFIA_brwd_glime_altbase_arc.json +++ b/datasets/USGS_SOFIA_brwd_glime_altbase_arc.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_brwd_glime_altbase_arc/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_brwd_glime_alttop_arc.json b/datasets/USGS_SOFIA_brwd_glime_alttop_arc.json index 151f309bb6..38c473bcc2 100644 --- a/datasets/USGS_SOFIA_brwd_glime_alttop_arc.json +++ b/datasets/USGS_SOFIA_brwd_glime_alttop_arc.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_brwd_glime_alttop_arc/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_dawmet.json b/datasets/USGS_SOFIA_dawmet.json index 4135590c6a..b0266d79de 100644 --- a/datasets/USGS_SOFIA_dawmet.json +++ b/datasets/USGS_SOFIA_dawmet.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_dawmet/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_discharge_tamiami_canal.json b/datasets/USGS_SOFIA_discharge_tamiami_canal.json index 67a7ab5535..82d9c77f2b 100644 --- a/datasets/USGS_SOFIA_discharge_tamiami_canal.json +++ b/datasets/USGS_SOFIA_discharge_tamiami_canal.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_discharge_tamiami_canal/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_dk_merc_cycl_bio.json b/datasets/USGS_SOFIA_dk_merc_cycl_bio.json index 3ba460cbcc..d44e72420e 100644 --- a/datasets/USGS_SOFIA_dk_merc_cycl_bio.json +++ b/datasets/USGS_SOFIA_dk_merc_cycl_bio.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_dk_merc_cycl_bio/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_eco_assess_risk_toxics.json b/datasets/USGS_SOFIA_eco_assess_risk_toxics.json index 009a25090d..344ad90f81 100644 --- a/datasets/USGS_SOFIA_eco_assess_risk_toxics.json +++ b/datasets/USGS_SOFIA_eco_assess_risk_toxics.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eco_assess_risk_toxics/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_eco_hist_db_version 3.json b/datasets/USGS_SOFIA_eco_hist_db_version 3.json index f99b9bcc3b..cddde18adb 100644 --- a/datasets/USGS_SOFIA_eco_hist_db_version 3.json +++ b/datasets/USGS_SOFIA_eco_hist_db_version 3.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eco_hist_db_version%203/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_eco_hist_swcoast_srs_04.json b/datasets/USGS_SOFIA_eco_hist_swcoast_srs_04.json index 916d5e9fe2..dfbc0d61d6 100644 --- a/datasets/USGS_SOFIA_eco_hist_swcoast_srs_04.json +++ b/datasets/USGS_SOFIA_eco_hist_swcoast_srs_04.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eco_hist_swcoast_srs_04/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_eden_dem_cm_nov07_nc.json b/datasets/USGS_SOFIA_eden_dem_cm_nov07_nc.json index a10725de06..0aea3896be 100644 --- a/datasets/USGS_SOFIA_eden_dem_cm_nov07_nc.json +++ b/datasets/USGS_SOFIA_eden_dem_cm_nov07_nc.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eden_dem_cm_nov07_nc/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_eden_em_oct07_400m.json b/datasets/USGS_SOFIA_eden_em_oct07_400m.json index 0f88ed37f6..a1d6c90e9a 100644 --- a/datasets/USGS_SOFIA_eden_em_oct07_400m.json +++ b/datasets/USGS_SOFIA_eden_em_oct07_400m.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eden_em_oct07_400m/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_eden_water_surfs.json b/datasets/USGS_SOFIA_eden_water_surfs.json index 2f6869aedf..846ad0e91b 100644 --- a/datasets/USGS_SOFIA_eden_water_surfs.json +++ b/datasets/USGS_SOFIA_eden_water_surfs.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eden_water_surfs/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_estero_bay_ap_data.json b/datasets/USGS_SOFIA_estero_bay_ap_data.json index 1fd0ee1ad6..cc532150df 100644 --- a/datasets/USGS_SOFIA_estero_bay_ap_data.json +++ b/datasets/USGS_SOFIA_estero_bay_ap_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_estero_bay_ap_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_ever_hydro_wq_data.json b/datasets/USGS_SOFIA_ever_hydro_wq_data.json index 0792d9859f..965a0f48b0 100644 --- a/datasets/USGS_SOFIA_ever_hydro_wq_data.json +++ b/datasets/USGS_SOFIA_ever_hydro_wq_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_ever_hydro_wq_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_ever_isotope_data.json b/datasets/USGS_SOFIA_ever_isotope_data.json index 3e249db0e9..08d048175c 100644 --- a/datasets/USGS_SOFIA_ever_isotope_data.json +++ b/datasets/USGS_SOFIA_ever_isotope_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_ever_isotope_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_fb_1890-1990_data_version 1.json b/datasets/USGS_SOFIA_fb_1890-1990_data_version 1.json index 546a9f1164..c7d35f46c8 100644 --- a/datasets/USGS_SOFIA_fb_1890-1990_data_version 1.json +++ b/datasets/USGS_SOFIA_fb_1890-1990_data_version 1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_fb_1890-1990_data_version%201/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_fb_bb_pollen_data.json b/datasets/USGS_SOFIA_fb_bb_pollen_data.json index 1d4c064b28..b38028ef8b 100644 --- a/datasets/USGS_SOFIA_fb_bb_pollen_data.json +++ b/datasets/USGS_SOFIA_fb_bb_pollen_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_fb_bb_pollen_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_field_data_bicy.json b/datasets/USGS_SOFIA_field_data_bicy.json index 74ea899955..4e14295231 100644 --- a/datasets/USGS_SOFIA_field_data_bicy.json +++ b/datasets/USGS_SOFIA_field_data_bicy.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_field_data_bicy/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_field_data_br105.json b/datasets/USGS_SOFIA_field_data_br105.json index a4e4135bc0..97384a8cf3 100644 --- a/datasets/USGS_SOFIA_field_data_br105.json +++ b/datasets/USGS_SOFIA_field_data_br105.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_field_data_br105/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_field_data_interEver.json b/datasets/USGS_SOFIA_field_data_interEver.json index 1d7d8c4d37..45b7a9f347 100644 --- a/datasets/USGS_SOFIA_field_data_interEver.json +++ b/datasets/USGS_SOFIA_field_data_interEver.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_field_data_interEver/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_fire_ecol_sfl_04.json b/datasets/USGS_SOFIA_fire_ecol_sfl_04.json index f392f16751..d84fe08ce1 100644 --- a/datasets/USGS_SOFIA_fire_ecol_sfl_04.json +++ b/datasets/USGS_SOFIA_fire_ecol_sfl_04.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_fire_ecol_sfl_04/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_fk_gw_seep.json b/datasets/USGS_SOFIA_fk_gw_seep.json index ec4541c7f7..2ae58ced98 100644 --- a/datasets/USGS_SOFIA_fk_gw_seep.json +++ b/datasets/USGS_SOFIA_fk_gw_seep.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_fk_gw_seep/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_fl_coop_map.json b/datasets/USGS_SOFIA_fl_coop_map.json index 59aa832a01..5e20ec3f04 100644 --- a/datasets/USGS_SOFIA_fl_coop_map.json +++ b/datasets/USGS_SOFIA_fl_coop_map.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_fl_coop_map/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_flow_murray_solis.json b/datasets/USGS_SOFIA_flow_murray_solis.json index a7f2b08797..01b67ba885 100644 --- a/datasets/USGS_SOFIA_flow_murray_solis.json +++ b/datasets/USGS_SOFIA_flow_murray_solis.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_flow_murray_solis/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_flow_velocity.json b/datasets/USGS_SOFIA_flow_velocity.json index 31ed41af9c..18556cd2ae 100644 --- a/datasets/USGS_SOFIA_flow_velocity.json +++ b/datasets/USGS_SOFIA_flow_velocity.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_flow_velocity/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_flow_velocity_data.json b/datasets/USGS_SOFIA_flow_velocity_data.json index 331e6746cc..b1c3fb248a 100644 --- a/datasets/USGS_SOFIA_flow_velocity_data.json +++ b/datasets/USGS_SOFIA_flow_velocity_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_flow_velocity_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_freshwater_east_coast.json b/datasets/USGS_SOFIA_freshwater_east_coast.json index 9e9d9b4405..f72ead3b7d 100644 --- a/datasets/USGS_SOFIA_freshwater_east_coast.json +++ b/datasets/USGS_SOFIA_freshwater_east_coast.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_freshwater_east_coast/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_freshwtr_flow.json b/datasets/USGS_SOFIA_freshwtr_flow.json index 88ce43f449..db9fa4988f 100644 --- a/datasets/USGS_SOFIA_freshwtr_flow.json +++ b/datasets/USGS_SOFIA_freshwtr_flow.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_freshwtr_flow/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_frnkflow.json b/datasets/USGS_SOFIA_frnkflow.json index d099fa9c52..cab10c2810 100644 --- a/datasets/USGS_SOFIA_frnkflow.json +++ b/datasets/USGS_SOFIA_frnkflow.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_frnkflow/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_gachemca.json b/datasets/USGS_SOFIA_gachemca.json index 2c4ec9a83e..56addc9f25 100644 --- a/datasets/USGS_SOFIA_gachemca.json +++ b/datasets/USGS_SOFIA_gachemca.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gachemca/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_gachmdoc.json b/datasets/USGS_SOFIA_gachmdoc.json index c8e13356e5..2dd880d409 100644 --- a/datasets/USGS_SOFIA_gachmdoc.json +++ b/datasets/USGS_SOFIA_gachmdoc.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gachmdoc/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_gaqwfp.json b/datasets/USGS_SOFIA_gaqwfp.json index 1712568b20..4edb038956 100644 --- a/datasets/USGS_SOFIA_gaqwfp.json +++ b/datasets/USGS_SOFIA_gaqwfp.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gaqwfp/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_gaqwssi.json b/datasets/USGS_SOFIA_gaqwssi.json index db6f6b9883..6649785de9 100644 --- a/datasets/USGS_SOFIA_gaqwssi.json +++ b/datasets/USGS_SOFIA_gaqwssi.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gaqwssi/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_gawlik_wading_birds.json b/datasets/USGS_SOFIA_gawlik_wading_birds.json index 278a9c3f7e..2b42dd576f 100644 --- a/datasets/USGS_SOFIA_gawlik_wading_birds.json +++ b/datasets/USGS_SOFIA_gawlik_wading_birds.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gawlik_wading_birds/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_geochem_asr_lo.json b/datasets/USGS_SOFIA_geochem_asr_lo.json index e52a8d382a..7e75df79c4 100644 --- a/datasets/USGS_SOFIA_geochem_asr_lo.json +++ b/datasets/USGS_SOFIA_geochem_asr_lo.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_geochem_asr_lo/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_geochem_mon_restore_fy04.json b/datasets/USGS_SOFIA_geochem_mon_restore_fy04.json index 4653d4bcf5..4a80b8e23d 100644 --- a/datasets/USGS_SOFIA_geochem_mon_restore_fy04.json +++ b/datasets/USGS_SOFIA_geochem_mon_restore_fy04.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_geochem_mon_restore_fy04/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_geophys_mon_fy04.json b/datasets/USGS_SOFIA_geophys_mon_fy04.json index 3549ce60a1..116ee384b7 100644 --- a/datasets/USGS_SOFIA_geophys_mon_fy04.json +++ b/datasets/USGS_SOFIA_geophys_mon_fy04.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_geophys_mon_fy04/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_german_et_04.json b/datasets/USGS_SOFIA_german_et_04.json index 9579478cf1..4215ce1695 100644 --- a/datasets/USGS_SOFIA_german_et_04.json +++ b/datasets/USGS_SOFIA_german_et_04.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_german_et_04/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_german_et_data.json b/datasets/USGS_SOFIA_german_et_data.json index 6f9d601870..f6c65065d2 100644 --- a/datasets/USGS_SOFIA_german_et_data.json +++ b/datasets/USGS_SOFIA_german_et_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_german_et_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_gfr_bay.json b/datasets/USGS_SOFIA_gfr_bay.json index 1b8eb98c8c..7eb59ef337 100644 --- a/datasets/USGS_SOFIA_gfr_bay.json +++ b/datasets/USGS_SOFIA_gfr_bay.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gfr_bay/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_gfr_ocean.json b/datasets/USGS_SOFIA_gfr_ocean.json index 987d8bab83..598c038b1c 100644 --- a/datasets/USGS_SOFIA_gfr_ocean.json +++ b/datasets/USGS_SOFIA_gfr_ocean.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gfr_ocean/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_glime_ext_aq_polygon.json b/datasets/USGS_SOFIA_glime_ext_aq_polygon.json index e95709efd5..bb00c74486 100644 --- a/datasets/USGS_SOFIA_glime_ext_aq_polygon.json +++ b/datasets/USGS_SOFIA_glime_ext_aq_polygon.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_glime_ext_aq_polygon/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_glime_lim_ucu_arc.json b/datasets/USGS_SOFIA_glime_lim_ucu_arc.json index a63aaf0cef..2d786bbe7d 100644 --- a/datasets/USGS_SOFIA_glime_lim_ucu_arc.json +++ b/datasets/USGS_SOFIA_glime_lim_ucu_arc.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_glime_lim_ucu_arc/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_glime_limit_arc.json b/datasets/USGS_SOFIA_glime_limit_arc.json index 806b6c7fa5..cf124bb705 100644 --- a/datasets/USGS_SOFIA_glime_limit_arc.json +++ b/datasets/USGS_SOFIA_glime_limit_arc.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_glime_limit_arc/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_grndwtr_seepage.json b/datasets/USGS_SOFIA_grndwtr_seepage.json index 8067572311..f9c6c1025a 100644 --- a/datasets/USGS_SOFIA_grndwtr_seepage.json +++ b/datasets/USGS_SOFIA_grndwtr_seepage.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_grndwtr_seepage/items", - 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"type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_helio_mag_data.json b/datasets/USGS_SOFIA_helio_mag_data.json index c023717c61..48df4e220e 100644 --- a/datasets/USGS_SOFIA_helio_mag_data.json +++ b/datasets/USGS_SOFIA_helio_mag_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_helio_mag_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_hi_accuracy_elev_collection_04.json b/datasets/USGS_SOFIA_hi_accuracy_elev_collection_04.json index cf40e99992..f34d639922 100644 --- a/datasets/USGS_SOFIA_hi_accuracy_elev_collection_04.json +++ b/datasets/USGS_SOFIA_hi_accuracy_elev_collection_04.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hi_accuracy_elev_collection_04/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_high_acc_elev_data.json b/datasets/USGS_SOFIA_high_acc_elev_data.json index ec4ac8d530..2584b4b778 100644 --- a/datasets/USGS_SOFIA_high_acc_elev_data.json +++ b/datasets/USGS_SOFIA_high_acc_elev_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_high_acc_elev_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_highres_bathy_sfl_est-coast_sys.json b/datasets/USGS_SOFIA_highres_bathy_sfl_est-coast_sys.json index 37c6659f7e..9c553b60a5 100644 --- a/datasets/USGS_SOFIA_highres_bathy_sfl_est-coast_sys.json +++ b/datasets/USGS_SOFIA_highres_bathy_sfl_est-coast_sys.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_highres_bathy_sfl_est-coast_sys/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_hist_salinity_wq_veg_bb_04.json b/datasets/USGS_SOFIA_hist_salinity_wq_veg_bb_04.json index b851ed196a..c84d4cdb57 100644 --- a/datasets/USGS_SOFIA_hist_salinity_wq_veg_bb_04.json +++ b/datasets/USGS_SOFIA_hist_salinity_wq_veg_bb_04.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hist_salinity_wq_veg_bb_04/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_hlms_physical_data.json b/datasets/USGS_SOFIA_hlms_physical_data.json index edfc858add..75ff17fecd 100644 --- a/datasets/USGS_SOFIA_hlms_physical_data.json +++ b/datasets/USGS_SOFIA_hlms_physical_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hlms_physical_data/items", - 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"type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_hydro_wq_ofr_00-168.json b/datasets/USGS_SOFIA_hydro_wq_ofr_00-168.json index 36d713d633..ee394fcbad 100644 --- a/datasets/USGS_SOFIA_hydro_wq_ofr_00-168.json +++ b/datasets/USGS_SOFIA_hydro_wq_ofr_00-168.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hydro_wq_ofr_00-168/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_hydro_wq_ofr_00-483.json b/datasets/USGS_SOFIA_hydro_wq_ofr_00-483.json index 1eed400025..9546239bd4 100644 --- a/datasets/USGS_SOFIA_hydro_wq_ofr_00-483.json +++ b/datasets/USGS_SOFIA_hydro_wq_ofr_00-483.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hydro_wq_ofr_00-483/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_hydrology_data_zwp.json b/datasets/USGS_SOFIA_hydrology_data_zwp.json index e2cce92fb4..3680082100 100644 --- a/datasets/USGS_SOFIA_hydrology_data_zwp.json +++ b/datasets/USGS_SOFIA_hydrology_data_zwp.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hydrology_data_zwp/items", - 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"type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_integrating_manatee.json b/datasets/USGS_SOFIA_integrating_manatee.json index 354a32ba91..ee55fd13b4 100644 --- a/datasets/USGS_SOFIA_integrating_manatee.json +++ b/datasets/USGS_SOFIA_integrating_manatee.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_integrating_manatee/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_karst_model.json b/datasets/USGS_SOFIA_karst_model.json index 1e5ac684be..597298bf62 100644 --- a/datasets/USGS_SOFIA_karst_model.json +++ b/datasets/USGS_SOFIA_karst_model.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_karst_model/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_kitchens_snail_kite.json b/datasets/USGS_SOFIA_kitchens_snail_kite.json index f684fc2cd9..624dd45825 100644 --- a/datasets/USGS_SOFIA_kitchens_snail_kite.json +++ b/datasets/USGS_SOFIA_kitchens_snail_kite.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_kitchens_snail_kite/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_lake_okee_bathy_data.json b/datasets/USGS_SOFIA_lake_okee_bathy_data.json index ac315f9ca2..92c152e348 100644 --- a/datasets/USGS_SOFIA_lake_okee_bathy_data.json +++ b/datasets/USGS_SOFIA_lake_okee_bathy_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_lake_okee_bathy_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_land_margin_ecosystems.json b/datasets/USGS_SOFIA_land_margin_ecosystems.json index fcdcc1e319..fa1b74fcc1 100644 --- a/datasets/USGS_SOFIA_land_margin_ecosystems.json +++ b/datasets/USGS_SOFIA_land_margin_ecosystems.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_land_margin_ecosystems/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_lbwfbay.json b/datasets/USGS_SOFIA_lbwfbay.json index 7ad7a8519b..ba7adb07c4 100644 --- a/datasets/USGS_SOFIA_lbwfbay.json +++ b/datasets/USGS_SOFIA_lbwfbay.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_lbwfbay/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_levesque_field_params.json b/datasets/USGS_SOFIA_levesque_field_params.json index 23940e6f2c..6ac027fe66 100644 --- a/datasets/USGS_SOFIA_levesque_field_params.json +++ b/datasets/USGS_SOFIA_levesque_field_params.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_levesque_field_params/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_levesque_flow.json b/datasets/USGS_SOFIA_levesque_flow.json index d0c11efc86..845cd08a7e 100644 --- a/datasets/USGS_SOFIA_levesque_flow.json +++ b/datasets/USGS_SOFIA_levesque_flow.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_levesque_flow/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_mangrove_modeling_04.json b/datasets/USGS_SOFIA_mangrove_modeling_04.json index d3fd96c910..8868c79249 100644 --- a/datasets/USGS_SOFIA_mangrove_modeling_04.json +++ b/datasets/USGS_SOFIA_mangrove_modeling_04.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_mangrove_modeling_04/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_mcivor_hydroimpact.json b/datasets/USGS_SOFIA_mcivor_hydroimpact.json index 104c5eb9f6..dfb0d0b9bb 100644 --- a/datasets/USGS_SOFIA_mcivor_hydroimpact.json +++ b/datasets/USGS_SOFIA_mcivor_hydroimpact.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_mcivor_hydroimpact/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_mdcsoil.json b/datasets/USGS_SOFIA_mdcsoil.json index a0abe5380f..6b3cd649e4 100644 --- a/datasets/USGS_SOFIA_mdcsoil.json +++ b/datasets/USGS_SOFIA_mdcsoil.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_mdcsoil/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_metholms.json b/datasets/USGS_SOFIA_metholms.json index 1f9c82fee0..73d6f2a57e 100644 --- a/datasets/USGS_SOFIA_metholms.json +++ b/datasets/USGS_SOFIA_metholms.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metholms/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_metish.json b/datasets/USGS_SOFIA_metish.json index adefdf89fe..53837f8f09 100644 --- a/datasets/USGS_SOFIA_metish.json +++ b/datasets/USGS_SOFIA_metish.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metish/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_metjen.json b/datasets/USGS_SOFIA_metjen.json index 72324f0472..9c8482ac0a 100644 --- a/datasets/USGS_SOFIA_metjen.json +++ b/datasets/USGS_SOFIA_metjen.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metjen/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_metkotra.json b/datasets/USGS_SOFIA_metkotra.json index 325318ab57..bb93508281 100644 --- a/datasets/USGS_SOFIA_metkotra.json +++ b/datasets/USGS_SOFIA_metkotra.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metkotra/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_metlang.json b/datasets/USGS_SOFIA_metlang.json index f3e915f278..c32d86b672 100644 --- a/datasets/USGS_SOFIA_metlang.json +++ b/datasets/USGS_SOFIA_metlang.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metlang/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_metorem.json b/datasets/USGS_SOFIA_metorem.json index 4c3927a792..39d6e194e0 100644 --- a/datasets/USGS_SOFIA_metorem.json +++ b/datasets/USGS_SOFIA_metorem.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metorem/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_metroys.json b/datasets/USGS_SOFIA_metroys.json index 8df44038be..fa5c44bd30 100644 --- a/datasets/USGS_SOFIA_metroys.json +++ b/datasets/USGS_SOFIA_metroys.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metroys/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_metweed.json b/datasets/USGS_SOFIA_metweed.json index 0e102054b1..9a5b37c107 100644 --- a/datasets/USGS_SOFIA_metweed.json +++ b/datasets/USGS_SOFIA_metweed.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metweed/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_monitor_sav_rs_fb_04.json b/datasets/USGS_SOFIA_monitor_sav_rs_fb_04.json index 18ee634f2e..b3301ae9e8 100644 --- a/datasets/USGS_SOFIA_monitor_sav_rs_fb_04.json +++ b/datasets/USGS_SOFIA_monitor_sav_rs_fb_04.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_monitor_sav_rs_fb_04/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_nuts_S_orgmat_04.json b/datasets/USGS_SOFIA_nuts_S_orgmat_04.json index 214ea6d36c..c1083e3643 100644 --- a/datasets/USGS_SOFIA_nuts_S_orgmat_04.json +++ b/datasets/USGS_SOFIA_nuts_S_orgmat_04.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_nuts_S_orgmat_04/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_orem_fb_sed_geochem.json b/datasets/USGS_SOFIA_orem_fb_sed_geochem.json index 2de05c5577..b172412b72 100644 --- a/datasets/USGS_SOFIA_orem_fb_sed_geochem.json +++ b/datasets/USGS_SOFIA_orem_fb_sed_geochem.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_orem_fb_sed_geochem/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_panther_refuge_hydro.json b/datasets/USGS_SOFIA_panther_refuge_hydro.json index 73c3fe5d61..66a2acbf48 100644 --- a/datasets/USGS_SOFIA_panther_refuge_hydro.json +++ b/datasets/USGS_SOFIA_panther_refuge_hydro.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_panther_refuge_hydro/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_robblee_fb_shrimp_04.json b/datasets/USGS_SOFIA_robblee_fb_shrimp_04.json index 67427b743a..c09040ad6f 100644 --- a/datasets/USGS_SOFIA_robblee_fb_shrimp_04.json +++ b/datasets/USGS_SOFIA_robblee_fb_shrimp_04.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_robblee_fb_shrimp_04/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_robblee_shrimp.json b/datasets/USGS_SOFIA_robblee_shrimp.json index f9e92199f3..c62ebb7522 100644 --- a/datasets/USGS_SOFIA_robblee_shrimp.json +++ b/datasets/USGS_SOFIA_robblee_shrimp.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_robblee_shrimp/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_rsl30dv.json b/datasets/USGS_SOFIA_rsl30dv.json index f6c79e434f..e0dda278c2 100644 --- a/datasets/USGS_SOFIA_rsl30dv.json +++ b/datasets/USGS_SOFIA_rsl30dv.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_rsl30dv/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_rsl30uv.json b/datasets/USGS_SOFIA_rsl30uv.json index 9c4f0ef577..8c7184b26d 100644 --- a/datasets/USGS_SOFIA_rsl30uv.json +++ b/datasets/USGS_SOFIA_rsl30uv.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_rsl30uv/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_solomet.json b/datasets/USGS_SOFIA_solomet.json index bcd8086374..630745b6e1 100644 --- a/datasets/USGS_SOFIA_solomet.json +++ b/datasets/USGS_SOFIA_solomet.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_solomet/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_sus_parts.json b/datasets/USGS_SOFIA_sus_parts.json index b95661280b..e4e5253853 100644 --- a/datasets/USGS_SOFIA_sus_parts.json +++ b/datasets/USGS_SOFIA_sus_parts.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_sus_parts/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_sw-pore_water_DOC_SUVA.json b/datasets/USGS_SOFIA_sw-pore_water_DOC_SUVA.json index aba0ee14f7..f8ae6ec9c3 100644 --- a/datasets/USGS_SOFIA_sw-pore_water_DOC_SUVA.json +++ b/datasets/USGS_SOFIA_sw-pore_water_DOC_SUVA.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_sw-pore_water_DOC_SUVA/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIA_terrapin_mark-recap_data.json b/datasets/USGS_SOFIA_terrapin_mark-recap_data.json index 8f8ee660a6..18b4203c6d 100644 --- a/datasets/USGS_SOFIA_terrapin_mark-recap_data.json +++ b/datasets/USGS_SOFIA_terrapin_mark-recap_data.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_terrapin_mark-recap_data/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_SOFIF_Fbbtypes.json b/datasets/USGS_SOFIF_Fbbtypes.json index 8fe7ffa8b2..381b6d3733 100644 --- a/datasets/USGS_SOFIF_Fbbtypes.json +++ b/datasets/USGS_SOFIF_Fbbtypes.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIF_Fbbtypes/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_Sherman_QUAD_1.0.json b/datasets/USGS_Sherman_QUAD_1.0.json index 5fb5d4c571..d7cb008699 100644 --- a/datasets/USGS_Sherman_QUAD_1.0.json +++ b/datasets/USGS_Sherman_QUAD_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Sherman_QUAD_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_TamiamiFlowMonitoring_2007-2010.json b/datasets/USGS_TamiamiFlowMonitoring_2007-2010.json index 6fc02711a5..670fed90a4 100644 --- a/datasets/USGS_TamiamiFlowMonitoring_2007-2010.json +++ b/datasets/USGS_TamiamiFlowMonitoring_2007-2010.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_TamiamiFlowMonitoring_2007-2010/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_VOLCANO.json b/datasets/USGS_VOLCANO.json index 8f9b966cfb..a1e67660c5 100644 --- a/datasets/USGS_VOLCANO.json +++ b/datasets/USGS_VOLCANO.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_VOLCANO/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_WHFC_SUPERDIF3.json b/datasets/USGS_WHFC_SUPERDIF3.json index 3477a048bd..aed8574074 100644 --- a/datasets/USGS_WHFC_SUPERDIF3.json +++ b/datasets/USGS_WHFC_SUPERDIF3.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WHFC_SUPERDIF3/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_WHFC_SUPERDIF4.json b/datasets/USGS_WHFC_SUPERDIF4.json index 822424571e..ea1c5a43f6 100644 --- a/datasets/USGS_WHFC_SUPERDIF4.json +++ b/datasets/USGS_WHFC_SUPERDIF4.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WHFC_SUPERDIF4/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_WHFC_SUPERDIF6.json b/datasets/USGS_WHFC_SUPERDIF6.json index 010b69c656..6106bb69b5 100644 --- a/datasets/USGS_WHFC_SUPERDIF6.json +++ b/datasets/USGS_WHFC_SUPERDIF6.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WHFC_SUPERDIF6/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_WHFC_SUPERDIF8.json b/datasets/USGS_WHFC_SUPERDIF8.json index 58f19a072c..ed515e26f1 100644 --- a/datasets/USGS_WHFC_SUPERDIF8.json +++ b/datasets/USGS_WHFC_SUPERDIF8.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WHFC_SUPERDIF8/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_WHSC_MassBay_89-06_3.0.json b/datasets/USGS_WHSC_MassBay_89-06_3.0.json index 005c4b7ad0..b1389e8209 100644 --- a/datasets/USGS_WHSC_MassBay_89-06_3.0.json +++ b/datasets/USGS_WHSC_MassBay_89-06_3.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WHSC_MassBay_89-06_3.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_WILMA_COASTAL_IMPACT.json b/datasets/USGS_WILMA_COASTAL_IMPACT.json index 1adbbe72c6..3eb80a2190 100644 --- a/datasets/USGS_WILMA_COASTAL_IMPACT.json +++ b/datasets/USGS_WILMA_COASTAL_IMPACT.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WILMA_COASTAL_IMPACT/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_WRD_NWIS-W.json b/datasets/USGS_WRD_NWIS-W.json index a04505f7de..5fbcc8fbfa 100644 --- a/datasets/USGS_WRD_NWIS-W.json +++ b/datasets/USGS_WRD_NWIS-W.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WRD_NWIS-W/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_YosemiteRockFalls.json b/datasets/USGS_YosemiteRockFalls.json index 625d3edb2e..6f3636aa14 100644 --- a/datasets/USGS_YosemiteRockFalls.json +++ b/datasets/USGS_YosemiteRockFalls.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_YosemiteRockFalls/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ag_chem_1.0.json b/datasets/USGS_ag_chem_1.0.json index d6db39172a..f28c043c4b 100644 --- a/datasets/USGS_ag_chem_1.0.json +++ b/datasets/USGS_ag_chem_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ag_chem_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ag_stock_1.0.json b/datasets/USGS_ag_stock_1.0.json index 970ef2da3f..06be9f6532 100644 --- a/datasets/USGS_ag_stock_1.0.json +++ b/datasets/USGS_ag_stock_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ag_stock_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_benchmark_1.0.json b/datasets/USGS_benchmark_1.0.json index 06c38d1879..7cae9f6b4d 100644 --- a/datasets/USGS_benchmark_1.0.json +++ b/datasets/USGS_benchmark_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_benchmark_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_erf1_Version 1.2, August 01, 1999.json b/datasets/USGS_erf1_Version 1.2, August 01, 1999.json index b6b4af9d3d..1f9a2e243a 100644 --- a/datasets/USGS_erf1_Version 1.2, August 01, 1999.json +++ b/datasets/USGS_erf1_Version 1.2, August 01, 1999.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_erf1_Version%201.2%2C%20August%2001%2C%201999/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_erfi-2_2.0, November 19, 2001.json b/datasets/USGS_erfi-2_2.0, November 19, 2001.json index 880c372bfe..7b91ea16b7 100644 --- a/datasets/USGS_erfi-2_2.0, November 19, 2001.json +++ b/datasets/USGS_erfi-2_2.0, November 19, 2001.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_erfi-2_2.0%2C%20November%2019%2C%202001/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_etsite_Version 1.0.json b/datasets/USGS_etsite_Version 1.0.json index d6f00a2cd8..49ed051440 100644 --- a/datasets/USGS_etsite_Version 1.0.json +++ b/datasets/USGS_etsite_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_etsite_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_gpwa_utm27f_met.json b/datasets/USGS_gpwa_utm27f_met.json index 12676ca213..073d0b3bf2 100644 --- a/datasets/USGS_gpwa_utm27f_met.json +++ b/datasets/USGS_gpwa_utm27f_met.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_gpwa_utm27f_met/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_herbicide2_1.0.json b/datasets/USGS_herbicide2_1.0.json index 1b81b6ec75..c4e4e52115 100644 --- a/datasets/USGS_herbicide2_1.0.json +++ b/datasets/USGS_herbicide2_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_herbicide2_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_herbicide3_1.0.json b/datasets/USGS_herbicide3_1.0.json index 77445c74ca..8fcfe6cc38 100644 --- a/datasets/USGS_herbicide3_1.0.json +++ b/datasets/USGS_herbicide3_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_herbicide3_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_herbicide4_1.0.json b/datasets/USGS_herbicide4_1.0.json index 2da6fcf3fc..5e9d4a209d 100644 --- a/datasets/USGS_herbicide4_1.0.json +++ b/datasets/USGS_herbicide4_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_herbicide4_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_herbicidel_01_1.0.json b/datasets/USGS_herbicidel_01_1.0.json index 1eb65203ca..cc271f9402 100644 --- a/datasets/USGS_herbicidel_01_1.0.json +++ b/datasets/USGS_herbicidel_01_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_herbicidel_01_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_hgmr_Version 1.json b/datasets/USGS_hgmr_Version 1.json index 3333b2b929..0b82ef17d9 100644 --- a/datasets/USGS_hgmr_Version 1.json +++ b/datasets/USGS_hgmr_Version 1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_hgmr_Version%201/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_hydmain_hum_Version 1.0, (September, 2001).json b/datasets/USGS_hydmain_hum_Version 1.0, (September, 2001).json index c59a8fec17..d6b2ce4944 100644 --- a/datasets/USGS_hydmain_hum_Version 1.0, (September, 2001).json +++ b/datasets/USGS_hydmain_hum_Version 1.0, (September, 2001).json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_hydmain_hum_Version%201.0%2C%20(September%2C%202001)/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_landfills_1.1.json b/datasets/USGS_landfills_1.1.json index 3b641928e9..5419486254 100644 --- a/datasets/USGS_landfills_1.1.json +++ b/datasets/USGS_landfills_1.1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_landfills_1.1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_landuse_1.json b/datasets/USGS_landuse_1.json index 8631d77898..a4152048c9 100644 --- a/datasets/USGS_landuse_1.json +++ b/datasets/USGS_landuse_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_landuse_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_map-2653_1.0.json b/datasets/USGS_map-2653_1.0.json index 009e827bb0..5c125b6f2c 100644 --- a/datasets/USGS_map-2653_1.0.json +++ b/datasets/USGS_map-2653_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_map-2653_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-1300_Version 1.0.json b/datasets/USGS_mapi-1300_Version 1.0.json index 1c7d725ad5..e2a87dddc6 100644 --- a/datasets/USGS_mapi-1300_Version 1.0.json +++ b/datasets/USGS_mapi-1300_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-1300_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-1509A_version 1.0.json b/datasets/USGS_mapi-1509A_version 1.0.json index c3b47492b6..f631e4dcb8 100644 --- a/datasets/USGS_mapi-1509A_version 1.0.json +++ b/datasets/USGS_mapi-1509A_version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-1509A_version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-1803_1.0.json b/datasets/USGS_mapi-1803_1.0.json index 310da26b56..be8243e3d1 100644 --- a/datasets/USGS_mapi-1803_1.0.json +++ b/datasets/USGS_mapi-1803_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-1803_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-1819_1.0.json b/datasets/USGS_mapi-1819_1.0.json index eefff4c633..dbea3beecb 100644 --- a/datasets/USGS_mapi-1819_1.0.json +++ b/datasets/USGS_mapi-1819_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-1819_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-2267.json b/datasets/USGS_mapi-2267.json index cbb2661298..bb9f02e58d 100644 --- a/datasets/USGS_mapi-2267.json +++ b/datasets/USGS_mapi-2267.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2267/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-2395_1.0.json b/datasets/USGS_mapi-2395_1.0.json index fed1a8015b..03ea3d3249 100644 --- a/datasets/USGS_mapi-2395_1.0.json +++ b/datasets/USGS_mapi-2395_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2395_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-2494_1.0.json b/datasets/USGS_mapi-2494_1.0.json index 869d8b61db..937b0d07d8 100644 --- a/datasets/USGS_mapi-2494_1.0.json +++ b/datasets/USGS_mapi-2494_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2494_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-2634_2.0.json b/datasets/USGS_mapi-2634_2.0.json index beb736fb1e..25e0b9a3a4 100644 --- a/datasets/USGS_mapi-2634_2.0.json +++ b/datasets/USGS_mapi-2634_2.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2634_2.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-2645_version 1.0.json b/datasets/USGS_mapi-2645_version 1.0.json index 01cdfbbe28..2f23235468 100644 --- a/datasets/USGS_mapi-2645_version 1.0.json +++ b/datasets/USGS_mapi-2645_version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2645_version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-2690.json b/datasets/USGS_mapi-2690.json index 6e773c9338..2e9edf9e8d 100644 --- a/datasets/USGS_mapi-2690.json +++ b/datasets/USGS_mapi-2690.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2690/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-2691_1.0.json b/datasets/USGS_mapi-2691_1.0.json index 5215f901ba..03df9078c6 100644 --- a/datasets/USGS_mapi-2691_1.0.json +++ b/datasets/USGS_mapi-2691_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2691_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-2737.json b/datasets/USGS_mapi-2737.json index bc99d0b547..3d1fe02610 100644 --- a/datasets/USGS_mapi-2737.json +++ b/datasets/USGS_mapi-2737.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2737/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-2740_1.0.json b/datasets/USGS_mapi-2740_1.0.json index 0e974c0bf7..95f3105d2f 100644 --- a/datasets/USGS_mapi-2740_1.0.json +++ b/datasets/USGS_mapi-2740_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2740_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-797Scan_Version 1.0.json b/datasets/USGS_mapi-797Scan_Version 1.0.json index 05e8ec6af5..7dcb016b36 100644 --- a/datasets/USGS_mapi-797Scan_Version 1.0.json +++ b/datasets/USGS_mapi-797Scan_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-797Scan_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-797Topo_Version 1.0.json b/datasets/USGS_mapi-797Topo_Version 1.0.json index 07164f556b..bc748fef0b 100644 --- a/datasets/USGS_mapi-797Topo_Version 1.0.json +++ b/datasets/USGS_mapi-797Topo_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-797Topo_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mapi-797_Version 1.0.json b/datasets/USGS_mapi-797_Version 1.0.json index f3b3bf2b30..879aa3fd0f 100644 --- a/datasets/USGS_mapi-797_Version 1.0.json +++ b/datasets/USGS_mapi-797_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-797_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mdnet_Version 1.3 (July 06, 2001).json b/datasets/USGS_mdnet_Version 1.3 (July 06, 2001).json index fbf25765d6..7bd2c06bcf 100644 --- a/datasets/USGS_mdnet_Version 1.3 (July 06, 2001).json +++ b/datasets/USGS_mdnet_Version 1.3 (July 06, 2001).json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mdnet_Version%201.3%20(July%2006%2C%202001)/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_mdwu_98_Version 1.3, July 06, 2001.json b/datasets/USGS_mdwu_98_Version 1.3, July 06, 2001.json index 00dbced9d4..8a9d6fec0b 100644 --- a/datasets/USGS_mdwu_98_Version 1.3, July 06, 2001.json +++ b/datasets/USGS_mdwu_98_Version 1.3, July 06, 2001.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mdwu_98_Version%201.3%2C%20July%2006%2C%202001/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_msavi_92_Version 1.0.json b/datasets/USGS_msavi_92_Version 1.0.json index 191b9f002e..1653d56e18 100644 --- a/datasets/USGS_msavi_92_Version 1.0.json +++ b/datasets/USGS_msavi_92_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_msavi_92_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_msavi_Version 1.0.json b/datasets/USGS_msavi_Version 1.0.json index edc0374359..115d2a0b78 100644 --- a/datasets/USGS_msavi_Version 1.0.json +++ b/datasets/USGS_msavi_Version 1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_msavi_Version%201.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_nit85_1.0.json b/datasets/USGS_nit85_1.0.json index 881931f8a8..e4e0f14295 100644 --- a/datasets/USGS_nit85_1.0.json +++ b/datasets/USGS_nit85_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_nit85_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr00-96_wlc80_97_1.0.json b/datasets/USGS_ofr00-96_wlc80_97_1.0.json index 19e4a48b41..47ff27bac8 100644 --- a/datasets/USGS_ofr00-96_wlc80_97_1.0.json +++ b/datasets/USGS_ofr00-96_wlc80_97_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00-96_wlc80_97_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr00471_ddwdscon_Version 1.0, March 27, 2000.json b/datasets/USGS_ofr00471_ddwdscon_Version 1.0, March 27, 2000.json index 10488128a3..e986cd1e82 100644 --- a/datasets/USGS_ofr00471_ddwdscon_Version 1.0, March 27, 2000.json +++ b/datasets/USGS_ofr00471_ddwdscon_Version 1.0, March 27, 2000.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00471_ddwdscon_Version%201.0%2C%20March%2027%2C%202000/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr00471_hydrogeo_Version 1.0, April 27, 2000.json b/datasets/USGS_ofr00471_hydrogeo_Version 1.0, April 27, 2000.json index fa5a755eb0..e07dd6a4a0 100644 --- a/datasets/USGS_ofr00471_hydrogeo_Version 1.0, April 27, 2000.json +++ b/datasets/USGS_ofr00471_hydrogeo_Version 1.0, April 27, 2000.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00471_hydrogeo_Version%201.0%2C%20April%2027%2C%202000/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr00471_inkrscon_Version 1.0, March 17, 2000.json b/datasets/USGS_ofr00471_inkrscon_Version 1.0, March 17, 2000.json index f32eedc89f..c2631744a0 100644 --- a/datasets/USGS_ofr00471_inkrscon_Version 1.0, March 17, 2000.json +++ b/datasets/USGS_ofr00471_inkrscon_Version 1.0, March 17, 2000.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00471_inkrscon_Version%201.0%2C%20March%2017%2C%202000/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr00471_mnktscon_Version 1.0, March 21, 2000.json b/datasets/USGS_ofr00471_mnktscon_Version 1.0, March 21, 2000.json index a56d9a6a13..4e3dc4090e 100644 --- a/datasets/USGS_ofr00471_mnktscon_Version 1.0, March 21, 2000.json +++ b/datasets/USGS_ofr00471_mnktscon_Version 1.0, March 21, 2000.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00471_mnktscon_Version%201.0%2C%20March%2021%2C%202000/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr00471_mnlsscon_Version 1.0, March 21, 2000.json b/datasets/USGS_ofr00471_mnlsscon_Version 1.0, March 21, 2000.json index 7e564a98ba..3698b74b4a 100644 --- a/datasets/USGS_ofr00471_mnlsscon_Version 1.0, March 21, 2000.json +++ b/datasets/USGS_ofr00471_mnlsscon_Version 1.0, March 21, 2000.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00471_mnlsscon_Version%201.0%2C%20March%2021%2C%202000/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr00471_mnlssurf_Version 1.0, March 21, 2000.json b/datasets/USGS_ofr00471_mnlssurf_Version 1.0, March 21, 2000.json index 0bbf5e4843..365feec082 100644 --- a/datasets/USGS_ofr00471_mnlssurf_Version 1.0, March 21, 2000.json +++ b/datasets/USGS_ofr00471_mnlssurf_Version 1.0, March 21, 2000.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00471_mnlssurf_Version%201.0%2C%20March%2021%2C%202000/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr02-007_lithogeo_1.0, February, 2002.json b/datasets/USGS_ofr02-007_lithogeo_1.0, February, 2002.json index 14edb811e5..b2ed2e18c4 100644 --- a/datasets/USGS_ofr02-007_lithogeo_1.0, February, 2002.json +++ b/datasets/USGS_ofr02-007_lithogeo_1.0, February, 2002.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr02-007_lithogeo_1.0%2C%20February%2C%202002/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr96-443_cond_1.0.json b/datasets/USGS_ofr96-443_cond_1.0.json index 3c960aa903..a1f0be9c20 100644 --- a/datasets/USGS_ofr96-443_cond_1.0.json +++ b/datasets/USGS_ofr96-443_cond_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-443_cond_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr96-444_cond_1.0.json b/datasets/USGS_ofr96-444_cond_1.0.json index cd11f57ad5..3528c9cce7 100644 --- a/datasets/USGS_ofr96-444_cond_1.0.json +++ b/datasets/USGS_ofr96-444_cond_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-444_cond_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr96-444_wlelev_1.0.json b/datasets/USGS_ofr96-444_wlelev_1.0.json index 60f35ae784..77d7eb79ae 100644 --- a/datasets/USGS_ofr96-444_wlelev_1.0.json +++ b/datasets/USGS_ofr96-444_wlelev_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-444_wlelev_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr96-445_cond_1.0.json b/datasets/USGS_ofr96-445_cond_1.0.json index d070e3a34d..8350bc4450 100644 --- a/datasets/USGS_ofr96-445_cond_1.0.json +++ b/datasets/USGS_ofr96-445_cond_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-445_cond_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr96-445_wlelev_1.0.json b/datasets/USGS_ofr96-445_wlelev_1.0.json index 2146b04317..94ad85208d 100644 --- a/datasets/USGS_ofr96-445_wlelev_1.0.json +++ b/datasets/USGS_ofr96-445_wlelev_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-445_wlelev_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr96-446_cond_1.0.json b/datasets/USGS_ofr96-446_cond_1.0.json index 210df7ef1b..d3e1c34fb3 100644 --- a/datasets/USGS_ofr96-446_cond_1.0.json +++ b/datasets/USGS_ofr96-446_cond_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-446_cond_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofr96-446_recharg_1.0.json b/datasets/USGS_ofr96-446_recharg_1.0.json index 5711f90e07..87e7c6e2ec 100644 --- a/datasets/USGS_ofr96-446_recharg_1.0.json +++ b/datasets/USGS_ofr96-446_recharg_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-446_recharg_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/USGS_ofroo-300_SATTK9697_1.0.json b/datasets/USGS_ofroo-300_SATTK9697_1.0.json index 0097111dd7..611f1143d7 100644 --- a/datasets/USGS_ofroo-300_SATTK9697_1.0.json +++ b/datasets/USGS_ofroo-300_SATTK9697_1.0.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofroo-300_SATTK9697_1.0/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/US_FOREST_FRAGMENTATION.json b/datasets/US_FOREST_FRAGMENTATION.json index 7c19f1caf5..328e8b0ddb 100644 --- a/datasets/US_FOREST_FRAGMENTATION.json +++ b/datasets/US_FOREST_FRAGMENTATION.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/US_FOREST_FRAGMENTATION/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/UTC_TNgeologicmaps.json b/datasets/UTC_TNgeologicmaps.json index bd3bde1e8d..7920b0cbd0 100644 --- a/datasets/UTC_TNgeologicmaps.json +++ b/datasets/UTC_TNgeologicmaps.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UTC_TNgeologicmaps/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/UTC_TRIfacilities.json b/datasets/UTC_TRIfacilities.json index 596f007f1a..58b31de518 100644 --- a/datasets/UTC_TRIfacilities.json +++ b/datasets/UTC_TRIfacilities.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UTC_TRIfacilities/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/UTC_USdams.json b/datasets/UTC_USdams.json index e6cd785191..21849ab667 100644 --- a/datasets/UTC_USdams.json +++ b/datasets/UTC_USdams.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UTC_USdams/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/UTC_hydrography.json b/datasets/UTC_hydrography.json index b390cc76d1..b97db98e03 100644 --- a/datasets/UTC_hydrography.json +++ b/datasets/UTC_hydrography.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UTC_hydrography/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/UTC_landpolygonfeatures.json b/datasets/UTC_landpolygonfeatures.json index ef2ebcb634..abb660ec4a 100644 --- a/datasets/UTC_landpolygonfeatures.json +++ b/datasets/UTC_landpolygonfeatures.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UTC_landpolygonfeatures/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/VATECH_VAdust.json b/datasets/VATECH_VAdust.json index aa8d1f19ce..bdfd82962d 100644 --- a/datasets/VATECH_VAdust.json +++ b/datasets/VATECH_VAdust.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/VATECH_VAdust/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/WARd0004_108.json b/datasets/WARd0004_108.json index 9783035a3e..4f103894cf 100644 --- a/datasets/WARd0004_108.json +++ b/datasets/WARd0004_108.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WARd0004_108/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/WARd0005_108.json b/datasets/WARd0005_108.json index f8b41e8bca..f87bd3d7c3 100644 --- a/datasets/WARd0005_108.json +++ b/datasets/WARd0005_108.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WARd0005_108/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/WARd0006_108.json b/datasets/WARd0006_108.json index 9523593e9e..30a55569db 100644 --- a/datasets/WARd0006_108.json +++ b/datasets/WARd0006_108.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WARd0006_108/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/WARd0011_108.json b/datasets/WARd0011_108.json index 4ef394b868..e198679ac1 100644 --- a/datasets/WARd0011_108.json +++ b/datasets/WARd0011_108.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WARd0011_108/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/WARd0012_108.json b/datasets/WARd0012_108.json index 1b9a4db9f7..0ec8c6f7e5 100644 --- a/datasets/WARd0012_108.json +++ b/datasets/WARd0012_108.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WARd0012_108/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/WCMC_157.json b/datasets/WCMC_157.json index d6ffb86979..f8de2433f0 100644 --- a/datasets/WCMC_157.json +++ b/datasets/WCMC_157.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WCMC_157/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/WCMC_158.json b/datasets/WCMC_158.json index a5ce61d859..bd1b0ef660 100644 --- a/datasets/WCMC_158.json +++ b/datasets/WCMC_158.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WCMC_158/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/WIR_98_4105.json b/datasets/WIR_98_4105.json index a10a279a27..b3561f5ff3 100644 --- a/datasets/WIR_98_4105.json +++ b/datasets/WIR_98_4105.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WIR_98_4105/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/WRIR_97_4268.json b/datasets/WRIR_97_4268.json index b29d502693..57c564e03b 100644 --- a/datasets/WRIR_97_4268.json +++ b/datasets/WRIR_97_4268.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WRIR_97_4268/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/WRIR_99_4196.json b/datasets/WRIR_99_4196.json index 2fd080821c..2b37cbdbcc 100644 --- a/datasets/WRIR_99_4196.json +++ b/datasets/WRIR_99_4196.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WRIR_99_4196/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/a78f0eb5-a146-4129-9066-519378e22fd8_1.json b/datasets/a78f0eb5-a146-4129-9066-519378e22fd8_1.json index 5eaa250de1..f4d685a8c5 100644 --- a/datasets/a78f0eb5-a146-4129-9066-519378e22fd8_1.json +++ b/datasets/a78f0eb5-a146-4129-9066-519378e22fd8_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/a78f0eb5-a146-4129-9066-519378e22fd8_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/b480d7c8-3694-4772-8294-941f3d3ede9f_1.json b/datasets/b480d7c8-3694-4772-8294-941f3d3ede9f_1.json index 69c05b20ae..f3a4edb5fe 100644 --- a/datasets/b480d7c8-3694-4772-8294-941f3d3ede9f_1.json +++ b/datasets/b480d7c8-3694-4772-8294-941f3d3ede9f_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/b480d7c8-3694-4772-8294-941f3d3ede9f_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/brdglsc0001.json b/datasets/brdglsc0001.json index 270d9d22e7..427c45e94b 100644 --- a/datasets/brdglsc0001.json +++ b/datasets/brdglsc0001.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/brdglsc0001/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/brdlsc0007.json b/datasets/brdlsc0007.json index ba8927e68b..7980225615 100644 --- a/datasets/brdlsc0007.json +++ b/datasets/brdlsc0007.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/brdlsc0007/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/c241e665-5175-4c26-b0cd-f0dfee32afdb.json b/datasets/c241e665-5175-4c26-b0cd-f0dfee32afdb.json index 9964a002ae..0b8d5ab7f8 100644 --- a/datasets/c241e665-5175-4c26-b0cd-f0dfee32afdb.json +++ b/datasets/c241e665-5175-4c26-b0cd-f0dfee32afdb.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/c241e665-5175-4c26-b0cd-f0dfee32afdb/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/c5064da0-ce61-47fc-b17f-c837bd2847be.json b/datasets/c5064da0-ce61-47fc-b17f-c837bd2847be.json index 9c4aa5e6d0..b131d684b2 100644 --- a/datasets/c5064da0-ce61-47fc-b17f-c837bd2847be.json +++ b/datasets/c5064da0-ce61-47fc-b17f-c837bd2847be.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/c5064da0-ce61-47fc-b17f-c837bd2847be/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/cc4d85ee-6c72-4249-8775-a96e359457ad_1.json b/datasets/cc4d85ee-6c72-4249-8775-a96e359457ad_1.json index 7bc2809863..8b0836aa37 100644 --- a/datasets/cc4d85ee-6c72-4249-8775-a96e359457ad_1.json +++ b/datasets/cc4d85ee-6c72-4249-8775-a96e359457ad_1.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/cc4d85ee-6c72-4249-8775-a96e359457ad_1/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0028.json b/datasets/geodata_0028.json index 51ca2e543e..7afeaeb8e5 100644 --- a/datasets/geodata_0028.json +++ b/datasets/geodata_0028.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0028/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0032.json b/datasets/geodata_0032.json index 441c9420d6..825a3fd4fe 100644 --- a/datasets/geodata_0032.json +++ b/datasets/geodata_0032.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0032/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0048.json b/datasets/geodata_0048.json index 9f0f2e40c7..37251c51c6 100644 --- a/datasets/geodata_0048.json +++ b/datasets/geodata_0048.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0048/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0049.json b/datasets/geodata_0049.json index 7592a697ea..f6acf5099b 100644 --- a/datasets/geodata_0049.json +++ b/datasets/geodata_0049.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0049/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0052.json b/datasets/geodata_0052.json index 74433ee3f8..1f91d30253 100644 --- a/datasets/geodata_0052.json +++ b/datasets/geodata_0052.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0052/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0058.json b/datasets/geodata_0058.json index d9459c6e9f..2ce36964d3 100644 --- a/datasets/geodata_0058.json +++ b/datasets/geodata_0058.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0058/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0059.json b/datasets/geodata_0059.json index db2fa67f49..6988cb0d93 100644 --- a/datasets/geodata_0059.json +++ b/datasets/geodata_0059.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0059/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0060.json b/datasets/geodata_0060.json index 472cc02311..3d3dfa6db6 100644 --- a/datasets/geodata_0060.json +++ b/datasets/geodata_0060.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0060/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0063.json b/datasets/geodata_0063.json index c50a1b6de5..434fc3ea76 100644 --- a/datasets/geodata_0063.json +++ b/datasets/geodata_0063.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0063/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0065.json b/datasets/geodata_0065.json index caa37bb11c..45e4f93eaf 100644 --- a/datasets/geodata_0065.json +++ b/datasets/geodata_0065.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0065/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0066.json b/datasets/geodata_0066.json index ea26a53945..dfbdd59413 100644 --- a/datasets/geodata_0066.json +++ b/datasets/geodata_0066.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0066/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0165.json b/datasets/geodata_0165.json index e7c636ae8c..85357d2fdc 100644 --- a/datasets/geodata_0165.json +++ b/datasets/geodata_0165.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0165/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0179.json b/datasets/geodata_0179.json index 4e103b817a..8ff756bccb 100644 --- a/datasets/geodata_0179.json +++ b/datasets/geodata_0179.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0179/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0180.json b/datasets/geodata_0180.json index 43ac223d8c..ce42895041 100644 --- a/datasets/geodata_0180.json +++ b/datasets/geodata_0180.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0180/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0181.json b/datasets/geodata_0181.json index c0aee17f3a..08846f2eb4 100644 --- a/datasets/geodata_0181.json +++ b/datasets/geodata_0181.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0181/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0199.json b/datasets/geodata_0199.json index 5d8df9647a..3f1b896d2d 100644 --- a/datasets/geodata_0199.json +++ b/datasets/geodata_0199.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0199/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0200.json b/datasets/geodata_0200.json index fab2a54f03..a400841f34 100644 --- a/datasets/geodata_0200.json +++ b/datasets/geodata_0200.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0200/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0201.json b/datasets/geodata_0201.json index 484f96c16f..5fc1354d46 100644 --- a/datasets/geodata_0201.json +++ b/datasets/geodata_0201.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0201/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0227.json b/datasets/geodata_0227.json index 6f57afb948..d5819efc49 100644 --- a/datasets/geodata_0227.json +++ b/datasets/geodata_0227.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0227/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0231.json b/datasets/geodata_0231.json index f800a787ec..707b131a65 100644 --- a/datasets/geodata_0231.json +++ b/datasets/geodata_0231.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0231/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0237.json b/datasets/geodata_0237.json index 3ba8271c3f..f0c63cbadd 100644 --- a/datasets/geodata_0237.json +++ b/datasets/geodata_0237.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0237/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0261.json b/datasets/geodata_0261.json index 1388254556..b7a411e507 100644 --- a/datasets/geodata_0261.json +++ b/datasets/geodata_0261.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0261/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0271.json b/datasets/geodata_0271.json index 7b86fff9e3..6ffc54159f 100644 --- a/datasets/geodata_0271.json +++ b/datasets/geodata_0271.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0271/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0278.json b/datasets/geodata_0278.json index 41829afa5c..3cefac75eb 100644 --- a/datasets/geodata_0278.json +++ b/datasets/geodata_0278.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0278/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0295.json b/datasets/geodata_0295.json index a91cd79c1a..a0ef614e87 100644 --- a/datasets/geodata_0295.json +++ b/datasets/geodata_0295.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0295/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0335.json b/datasets/geodata_0335.json index 0101d5a378..a388301e1e 100644 --- a/datasets/geodata_0335.json +++ b/datasets/geodata_0335.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0335/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0337.json b/datasets/geodata_0337.json index 3db7b8bde4..219e28e40d 100644 --- a/datasets/geodata_0337.json +++ b/datasets/geodata_0337.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0337/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0344.json b/datasets/geodata_0344.json index c31f62f662..2aa348cb13 100644 --- a/datasets/geodata_0344.json +++ b/datasets/geodata_0344.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0344/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0365.json b/datasets/geodata_0365.json index cc30785722..fc719fb031 100644 --- a/datasets/geodata_0365.json +++ b/datasets/geodata_0365.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0365/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0368.json b/datasets/geodata_0368.json index ec4f4af46b..a5adc4bf61 100644 --- a/datasets/geodata_0368.json +++ b/datasets/geodata_0368.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0368/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0418.json b/datasets/geodata_0418.json index ced0d59bea..18b044ac5d 100644 --- a/datasets/geodata_0418.json +++ b/datasets/geodata_0418.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0418/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0436.json b/datasets/geodata_0436.json index d91947d7d0..793c8860ea 100644 --- a/datasets/geodata_0436.json +++ b/datasets/geodata_0436.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0436/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0438.json b/datasets/geodata_0438.json index 7bd49ab7ae..27b8324c05 100644 --- a/datasets/geodata_0438.json +++ b/datasets/geodata_0438.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0438/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0458.json b/datasets/geodata_0458.json index 11d233aa86..39080e4eca 100644 --- a/datasets/geodata_0458.json +++ b/datasets/geodata_0458.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0458/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0465.json b/datasets/geodata_0465.json index 02294bfdbf..7f846ee486 100644 --- a/datasets/geodata_0465.json +++ b/datasets/geodata_0465.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0465/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0469.json b/datasets/geodata_0469.json index 17c0e1d1a2..7b67a19fa0 100644 --- a/datasets/geodata_0469.json +++ b/datasets/geodata_0469.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0469/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0470.json b/datasets/geodata_0470.json index 475cffea23..4843848b61 100644 --- a/datasets/geodata_0470.json +++ b/datasets/geodata_0470.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0470/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0473.json b/datasets/geodata_0473.json index ed6e6dcc68..7c04a0e4d3 100644 --- a/datasets/geodata_0473.json +++ b/datasets/geodata_0473.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0473/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0476.json b/datasets/geodata_0476.json index 89486b6a04..52594367dd 100644 --- a/datasets/geodata_0476.json +++ b/datasets/geodata_0476.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0476/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0480.json b/datasets/geodata_0480.json index 27d1bb340e..409938705a 100644 --- a/datasets/geodata_0480.json +++ b/datasets/geodata_0480.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0480/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0543.json b/datasets/geodata_0543.json index af02892385..2a63a861fd 100644 --- a/datasets/geodata_0543.json +++ b/datasets/geodata_0543.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0543/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0588.json b/datasets/geodata_0588.json index b577bc41eb..0b1d88a1cf 100644 --- a/datasets/geodata_0588.json +++ b/datasets/geodata_0588.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0588/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0633.json b/datasets/geodata_0633.json index cc69184000..19f5fa95aa 100644 --- a/datasets/geodata_0633.json +++ b/datasets/geodata_0633.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0633/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0758.json b/datasets/geodata_0758.json index 8400eccafd..ceff11e89e 100644 --- a/datasets/geodata_0758.json +++ b/datasets/geodata_0758.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0758/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0776.json b/datasets/geodata_0776.json index be478d858a..378c1b1363 100644 --- a/datasets/geodata_0776.json +++ b/datasets/geodata_0776.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0776/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0879.json b/datasets/geodata_0879.json index 997335615f..1a7e7882b0 100644 --- a/datasets/geodata_0879.json +++ b/datasets/geodata_0879.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0879/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0885.json b/datasets/geodata_0885.json index 5e938d6df3..c88da22df7 100644 --- a/datasets/geodata_0885.json +++ b/datasets/geodata_0885.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0885/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0927.json b/datasets/geodata_0927.json index e490238231..b457714020 100644 --- a/datasets/geodata_0927.json +++ b/datasets/geodata_0927.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0927/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0930.json b/datasets/geodata_0930.json index efe716b592..e792764eca 100644 --- a/datasets/geodata_0930.json +++ b/datasets/geodata_0930.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0930/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0938.json b/datasets/geodata_0938.json index 0b61a7b95b..cfff7b12c2 100644 --- a/datasets/geodata_0938.json +++ b/datasets/geodata_0938.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0938/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0940.json b/datasets/geodata_0940.json index 3755349a54..6e8f0344b0 100644 --- a/datasets/geodata_0940.json +++ b/datasets/geodata_0940.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0940/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0960.json b/datasets/geodata_0960.json index adf4f04020..5fdb3c4d7c 100644 --- a/datasets/geodata_0960.json +++ b/datasets/geodata_0960.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0960/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_0992.json b/datasets/geodata_0992.json index 86302cd953..5996875168 100644 --- a/datasets/geodata_0992.json +++ b/datasets/geodata_0992.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0992/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1029.json b/datasets/geodata_1029.json index a35b4f46f2..6def410532 100644 --- a/datasets/geodata_1029.json +++ b/datasets/geodata_1029.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1029/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1034.json b/datasets/geodata_1034.json index bcea519927..a1ef2a39d4 100644 --- a/datasets/geodata_1034.json +++ b/datasets/geodata_1034.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1034/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1088.json b/datasets/geodata_1088.json index 9fa4aae5f3..a5ec71c314 100644 --- a/datasets/geodata_1088.json +++ b/datasets/geodata_1088.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1088/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1147.json b/datasets/geodata_1147.json index 4963514433..9be569d8fa 100644 --- a/datasets/geodata_1147.json +++ b/datasets/geodata_1147.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1147/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1150.json b/datasets/geodata_1150.json index 8a7358f0d0..d55ba3fe5d 100644 --- a/datasets/geodata_1150.json +++ b/datasets/geodata_1150.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1150/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1153.json b/datasets/geodata_1153.json index 109c62ff2d..749eb83612 100644 --- a/datasets/geodata_1153.json +++ b/datasets/geodata_1153.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1153/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1156.json b/datasets/geodata_1156.json index 36a2e7d7a9..e40110e4e4 100644 --- a/datasets/geodata_1156.json +++ b/datasets/geodata_1156.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1156/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1162.json b/datasets/geodata_1162.json index 561ddee11f..cc49cbf1eb 100644 --- a/datasets/geodata_1162.json +++ b/datasets/geodata_1162.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1162/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1165.json b/datasets/geodata_1165.json index 2575a4b7df..76abcb61f9 100644 --- a/datasets/geodata_1165.json +++ b/datasets/geodata_1165.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1165/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1198.json b/datasets/geodata_1198.json index eadb2a54a7..ab06b4a3cb 100644 --- a/datasets/geodata_1198.json +++ b/datasets/geodata_1198.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1198/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1204.json b/datasets/geodata_1204.json index e686cec2e1..374016eb06 100644 --- a/datasets/geodata_1204.json +++ b/datasets/geodata_1204.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1204/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1210.json b/datasets/geodata_1210.json index c181666285..4a521366a0 100644 --- a/datasets/geodata_1210.json +++ b/datasets/geodata_1210.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1210/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1213.json b/datasets/geodata_1213.json index 1d9d1eeb3d..7f785a8dde 100644 --- a/datasets/geodata_1213.json +++ b/datasets/geodata_1213.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1213/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1216.json b/datasets/geodata_1216.json index 50dc706e26..7be45d7d25 100644 --- a/datasets/geodata_1216.json +++ b/datasets/geodata_1216.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1216/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1219.json b/datasets/geodata_1219.json index 97073c8e3f..c36de85702 100644 --- a/datasets/geodata_1219.json +++ b/datasets/geodata_1219.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1219/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1222.json b/datasets/geodata_1222.json index 72f23b8e01..a601f7962c 100644 --- a/datasets/geodata_1222.json +++ b/datasets/geodata_1222.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1222/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1253.json b/datasets/geodata_1253.json index e1c87d9fa9..99d207ef64 100644 --- a/datasets/geodata_1253.json +++ b/datasets/geodata_1253.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1253/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1256.json b/datasets/geodata_1256.json index fb2ef58cce..545fcf0a21 100644 --- a/datasets/geodata_1256.json +++ b/datasets/geodata_1256.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1256/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1261.json b/datasets/geodata_1261.json index 058c9abd47..85eb636600 100644 --- a/datasets/geodata_1261.json +++ b/datasets/geodata_1261.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1261/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1262.json b/datasets/geodata_1262.json index a872448d63..c76885628d 100644 --- a/datasets/geodata_1262.json +++ b/datasets/geodata_1262.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1262/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1264.json b/datasets/geodata_1264.json index 32c6f5c53c..50c789d4d8 100644 --- a/datasets/geodata_1264.json +++ b/datasets/geodata_1264.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1264/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1265.json b/datasets/geodata_1265.json index 4031b85840..160c728afb 100644 --- a/datasets/geodata_1265.json +++ b/datasets/geodata_1265.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1265/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1266.json b/datasets/geodata_1266.json index 64438116f9..fba5f15212 100644 --- a/datasets/geodata_1266.json +++ b/datasets/geodata_1266.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1266/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1267.json b/datasets/geodata_1267.json index 51def899da..07f726b0f9 100644 --- a/datasets/geodata_1267.json +++ b/datasets/geodata_1267.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1267/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1268.json b/datasets/geodata_1268.json index 6e2038b4b5..48639ca0cb 100644 --- a/datasets/geodata_1268.json +++ b/datasets/geodata_1268.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1268/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1269.json b/datasets/geodata_1269.json index 8c7e423370..7d93efd296 100644 --- a/datasets/geodata_1269.json +++ b/datasets/geodata_1269.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1269/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1270.json b/datasets/geodata_1270.json index 1e35bd0d58..fb2e835578 100644 --- a/datasets/geodata_1270.json +++ b/datasets/geodata_1270.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1270/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1271.json b/datasets/geodata_1271.json index 295899fcb9..824c10053c 100644 --- a/datasets/geodata_1271.json +++ b/datasets/geodata_1271.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1271/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1272.json b/datasets/geodata_1272.json index 21c68a7738..810962edaf 100644 --- a/datasets/geodata_1272.json +++ b/datasets/geodata_1272.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1272/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1273.json b/datasets/geodata_1273.json index 30b835e2e3..13b3f1288f 100644 --- a/datasets/geodata_1273.json +++ b/datasets/geodata_1273.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1273/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1274.json b/datasets/geodata_1274.json index cc2cf8e85d..f1027289b5 100644 --- a/datasets/geodata_1274.json +++ b/datasets/geodata_1274.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1274/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1275.json b/datasets/geodata_1275.json index 997f1aae13..d12ca2e6cd 100644 --- a/datasets/geodata_1275.json +++ b/datasets/geodata_1275.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1275/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1276.json b/datasets/geodata_1276.json index 4d1a79c39e..65d7922021 100644 --- a/datasets/geodata_1276.json +++ b/datasets/geodata_1276.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1276/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1277.json b/datasets/geodata_1277.json index f546ff1629..a1dfe9f8f3 100644 --- a/datasets/geodata_1277.json +++ b/datasets/geodata_1277.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1277/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1278.json b/datasets/geodata_1278.json index 05e65b5b85..1a60bdab08 100644 --- a/datasets/geodata_1278.json +++ b/datasets/geodata_1278.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1278/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1279.json b/datasets/geodata_1279.json index c543bb88c7..d0862d908a 100644 --- a/datasets/geodata_1279.json +++ b/datasets/geodata_1279.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1279/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1280.json b/datasets/geodata_1280.json index 103d6675ae..45613743d7 100644 --- a/datasets/geodata_1280.json +++ b/datasets/geodata_1280.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1280/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1281.json b/datasets/geodata_1281.json index 8682be793e..aed7cdf7c7 100644 --- a/datasets/geodata_1281.json +++ b/datasets/geodata_1281.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1281/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1282.json b/datasets/geodata_1282.json index 1fb162b3ee..e16209c442 100644 --- a/datasets/geodata_1282.json +++ b/datasets/geodata_1282.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1282/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1283.json b/datasets/geodata_1283.json index c274b8e34c..197203e984 100644 --- a/datasets/geodata_1283.json +++ b/datasets/geodata_1283.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1283/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1284.json b/datasets/geodata_1284.json index 885d632b6a..c69219cbc0 100644 --- a/datasets/geodata_1284.json +++ b/datasets/geodata_1284.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1284/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1285.json b/datasets/geodata_1285.json index be43df6992..e410a3e899 100644 --- a/datasets/geodata_1285.json +++ b/datasets/geodata_1285.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1285/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1286.json b/datasets/geodata_1286.json index 44aabab9ac..c1f2750291 100644 --- a/datasets/geodata_1286.json +++ b/datasets/geodata_1286.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1286/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1315.json b/datasets/geodata_1315.json index c7fc9e604f..18eb92597b 100644 --- a/datasets/geodata_1315.json +++ b/datasets/geodata_1315.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1315/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1351.json b/datasets/geodata_1351.json index 4d4eed7ea1..96a2d77388 100644 --- a/datasets/geodata_1351.json +++ b/datasets/geodata_1351.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1351/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1352.json b/datasets/geodata_1352.json index 7b555670fe..25be49ab04 100644 --- a/datasets/geodata_1352.json +++ b/datasets/geodata_1352.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1352/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1353.json b/datasets/geodata_1353.json index 3f82db59d8..a2d15a27b4 100644 --- a/datasets/geodata_1353.json +++ b/datasets/geodata_1353.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1353/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1354.json b/datasets/geodata_1354.json index 058b5f1e04..e99e898c9d 100644 --- a/datasets/geodata_1354.json +++ b/datasets/geodata_1354.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1354/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1355.json b/datasets/geodata_1355.json index 4bc46256ae..b119396025 100644 --- a/datasets/geodata_1355.json +++ b/datasets/geodata_1355.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1355/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1356.json b/datasets/geodata_1356.json index 3d2f9b8682..0b5f07ec55 100644 --- a/datasets/geodata_1356.json +++ b/datasets/geodata_1356.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1356/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1358.json b/datasets/geodata_1358.json index 80ed5a4d0e..78f26a32f4 100644 --- a/datasets/geodata_1358.json +++ b/datasets/geodata_1358.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1358/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1359.json b/datasets/geodata_1359.json index fe5fa2579a..7d2053d3ef 100644 --- a/datasets/geodata_1359.json +++ b/datasets/geodata_1359.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1359/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1360.json b/datasets/geodata_1360.json index 3d99f8d033..9c2bcc08b1 100644 --- a/datasets/geodata_1360.json +++ b/datasets/geodata_1360.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1360/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1361.json b/datasets/geodata_1361.json index 4c0682765f..77276293d6 100644 --- a/datasets/geodata_1361.json +++ b/datasets/geodata_1361.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1361/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1362.json b/datasets/geodata_1362.json index a484e3d61e..b5878867e9 100644 --- a/datasets/geodata_1362.json +++ b/datasets/geodata_1362.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1362/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1363.json b/datasets/geodata_1363.json index ff10087b07..9320cd3959 100644 --- a/datasets/geodata_1363.json +++ b/datasets/geodata_1363.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1363/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1364.json b/datasets/geodata_1364.json index ebe2669eff..592ebeda5b 100644 --- a/datasets/geodata_1364.json +++ b/datasets/geodata_1364.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1364/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1365.json b/datasets/geodata_1365.json index e2c90728ae..a635c6ae17 100644 --- a/datasets/geodata_1365.json +++ b/datasets/geodata_1365.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1365/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1366.json b/datasets/geodata_1366.json index 6a41625b4b..5925f9aa6f 100644 --- a/datasets/geodata_1366.json +++ b/datasets/geodata_1366.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1366/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1367.json b/datasets/geodata_1367.json index 8853370a79..e8145a7750 100644 --- a/datasets/geodata_1367.json +++ b/datasets/geodata_1367.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1367/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1368.json b/datasets/geodata_1368.json index 8d13f684ec..8d354a3142 100644 --- a/datasets/geodata_1368.json +++ b/datasets/geodata_1368.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1368/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1369.json b/datasets/geodata_1369.json index d436ec7e42..8c7a562625 100644 --- a/datasets/geodata_1369.json +++ b/datasets/geodata_1369.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1369/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1370.json b/datasets/geodata_1370.json index 7e8f089087..e322ee08c3 100644 --- a/datasets/geodata_1370.json +++ b/datasets/geodata_1370.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1370/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1371.json b/datasets/geodata_1371.json index b32198ec5e..fa65a649da 100644 --- a/datasets/geodata_1371.json +++ b/datasets/geodata_1371.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1371/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1372.json b/datasets/geodata_1372.json index fc337d58a1..3f132166aa 100644 --- a/datasets/geodata_1372.json +++ b/datasets/geodata_1372.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1372/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1373.json b/datasets/geodata_1373.json index 57d0d8072d..abaf4462f4 100644 --- a/datasets/geodata_1373.json +++ b/datasets/geodata_1373.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1373/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1374.json b/datasets/geodata_1374.json index 56ae005fad..2beea97adb 100644 --- a/datasets/geodata_1374.json +++ b/datasets/geodata_1374.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1374/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1375.json b/datasets/geodata_1375.json index a02c39dcaf..260d6d1146 100644 --- a/datasets/geodata_1375.json +++ b/datasets/geodata_1375.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1375/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1376.json b/datasets/geodata_1376.json index ccb7bf52af..ac82256bae 100644 --- a/datasets/geodata_1376.json +++ b/datasets/geodata_1376.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1376/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1395.json b/datasets/geodata_1395.json index 48c862eb1d..360064fc24 100644 --- a/datasets/geodata_1395.json +++ b/datasets/geodata_1395.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1395/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1398.json b/datasets/geodata_1398.json index 9a12c5f568..30b2b9c4eb 100644 --- a/datasets/geodata_1398.json +++ b/datasets/geodata_1398.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1398/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1399.json b/datasets/geodata_1399.json index 9db8753d75..c9c36009b2 100644 --- a/datasets/geodata_1399.json +++ b/datasets/geodata_1399.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1399/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1425.json b/datasets/geodata_1425.json index 359c79bf12..257cea747c 100644 --- a/datasets/geodata_1425.json +++ b/datasets/geodata_1425.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1425/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1458.json b/datasets/geodata_1458.json index e201262277..55422b7a3b 100644 --- a/datasets/geodata_1458.json +++ b/datasets/geodata_1458.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1458/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1459.json b/datasets/geodata_1459.json index f7539c6894..750ece9754 100644 --- a/datasets/geodata_1459.json +++ b/datasets/geodata_1459.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1459/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1474.json b/datasets/geodata_1474.json index 1ba19a00f4..92f3ba0be9 100644 --- a/datasets/geodata_1474.json +++ b/datasets/geodata_1474.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1474/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1480.json b/datasets/geodata_1480.json index 9ccc5c15aa..9569a94edb 100644 --- a/datasets/geodata_1480.json +++ b/datasets/geodata_1480.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1480/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1498.json b/datasets/geodata_1498.json index c220cb3076..d090f3af3f 100644 --- a/datasets/geodata_1498.json +++ b/datasets/geodata_1498.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1498/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1501.json b/datasets/geodata_1501.json index c19febe88d..cee4fe8b82 100644 --- a/datasets/geodata_1501.json +++ b/datasets/geodata_1501.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1501/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1525.json b/datasets/geodata_1525.json index a2e0d84c88..49dad08f26 100644 --- a/datasets/geodata_1525.json +++ b/datasets/geodata_1525.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1525/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1540.json b/datasets/geodata_1540.json index 7551da0cb3..a120da0a6a 100644 --- a/datasets/geodata_1540.json +++ b/datasets/geodata_1540.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1540/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1624.json b/datasets/geodata_1624.json index 3cf29e039e..177f44d337 100644 --- a/datasets/geodata_1624.json +++ b/datasets/geodata_1624.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1624/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1627.json b/datasets/geodata_1627.json index 6266f5c95a..20f2e20ca0 100644 --- a/datasets/geodata_1627.json +++ b/datasets/geodata_1627.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1627/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1646.json b/datasets/geodata_1646.json index 6c4ac981c1..d0fca227a9 100644 --- a/datasets/geodata_1646.json +++ b/datasets/geodata_1646.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1646/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1647.json b/datasets/geodata_1647.json index 1a355985be..c6e1112e56 100644 --- a/datasets/geodata_1647.json +++ b/datasets/geodata_1647.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1647/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1648.json b/datasets/geodata_1648.json index cd9f6f4890..1e2f63fadf 100644 --- a/datasets/geodata_1648.json +++ b/datasets/geodata_1648.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1648/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1649.json b/datasets/geodata_1649.json index 8c70946ddb..3ceec0ea13 100644 --- a/datasets/geodata_1649.json +++ b/datasets/geodata_1649.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1649/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1650.json b/datasets/geodata_1650.json index b04b13d017..1c775a9095 100644 --- a/datasets/geodata_1650.json +++ b/datasets/geodata_1650.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1650/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1651.json b/datasets/geodata_1651.json index 81bbc18526..25753fec63 100644 --- a/datasets/geodata_1651.json +++ b/datasets/geodata_1651.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1651/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1652.json b/datasets/geodata_1652.json index 1251536c9b..600bd994ca 100644 --- a/datasets/geodata_1652.json +++ b/datasets/geodata_1652.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1652/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1685.json b/datasets/geodata_1685.json index 2e9d236a8d..614741721e 100644 --- a/datasets/geodata_1685.json +++ b/datasets/geodata_1685.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1685/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1717.json b/datasets/geodata_1717.json index 72d3f63e3a..7ac68555f1 100644 --- a/datasets/geodata_1717.json +++ b/datasets/geodata_1717.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1717/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1720.json b/datasets/geodata_1720.json index 6df81d974c..808818256e 100644 --- a/datasets/geodata_1720.json +++ b/datasets/geodata_1720.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1720/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1723.json b/datasets/geodata_1723.json index c36d2d5660..ae6c909305 100644 --- a/datasets/geodata_1723.json +++ b/datasets/geodata_1723.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1723/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1726.json b/datasets/geodata_1726.json index 207471904a..69c1d0e13f 100644 --- a/datasets/geodata_1726.json +++ b/datasets/geodata_1726.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1726/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1730.json b/datasets/geodata_1730.json index dde7cf0fae..00f4216ca3 100644 --- a/datasets/geodata_1730.json +++ b/datasets/geodata_1730.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1730/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1741.json b/datasets/geodata_1741.json index da01cd0324..0c5858b2c6 100644 --- a/datasets/geodata_1741.json +++ b/datasets/geodata_1741.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1741/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1744.json b/datasets/geodata_1744.json index 4cbbc15446..54e3490e1a 100644 --- a/datasets/geodata_1744.json +++ b/datasets/geodata_1744.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1744/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1745.json b/datasets/geodata_1745.json index 62bdf69efb..095443729f 100644 --- a/datasets/geodata_1745.json +++ b/datasets/geodata_1745.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1745/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1761.json b/datasets/geodata_1761.json index a54ddf09b6..31a1f708fd 100644 --- a/datasets/geodata_1761.json +++ b/datasets/geodata_1761.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1761/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1786.json b/datasets/geodata_1786.json index 87bb6143d3..fbb82b53d0 100644 --- a/datasets/geodata_1786.json +++ b/datasets/geodata_1786.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1786/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1787.json b/datasets/geodata_1787.json index fe2f5df0b6..2283ff0572 100644 --- a/datasets/geodata_1787.json +++ b/datasets/geodata_1787.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1787/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1788.json b/datasets/geodata_1788.json index f4d484e0ed..4ad9e2883e 100644 --- a/datasets/geodata_1788.json +++ b/datasets/geodata_1788.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1788/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1835.json b/datasets/geodata_1835.json index a27e291017..4ff24bbdfb 100644 --- a/datasets/geodata_1835.json +++ b/datasets/geodata_1835.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1835/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1840.json b/datasets/geodata_1840.json index d75f3916c3..6429e2ab7c 100644 --- a/datasets/geodata_1840.json +++ b/datasets/geodata_1840.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1840/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1843.json b/datasets/geodata_1843.json index 5d738b8ee0..c5ebcf3d6d 100644 --- a/datasets/geodata_1843.json +++ b/datasets/geodata_1843.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1843/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1846.json b/datasets/geodata_1846.json index e9b3b2549a..ca3ff2d7d8 100644 --- a/datasets/geodata_1846.json +++ b/datasets/geodata_1846.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1846/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1848.json b/datasets/geodata_1848.json index 6bee1b32c0..f68c32e53a 100644 --- a/datasets/geodata_1848.json +++ b/datasets/geodata_1848.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1848/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1890.json b/datasets/geodata_1890.json index 5f75c730ef..907b336b19 100644 --- a/datasets/geodata_1890.json +++ b/datasets/geodata_1890.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1890/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1896.json b/datasets/geodata_1896.json index 58bf34531b..513b1b1739 100644 --- a/datasets/geodata_1896.json +++ b/datasets/geodata_1896.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1896/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1897.json b/datasets/geodata_1897.json index a63f84feb1..841f1eb8c3 100644 --- a/datasets/geodata_1897.json +++ b/datasets/geodata_1897.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1897/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1933.json b/datasets/geodata_1933.json index 28a44c3a0c..190bdd8bb9 100644 --- a/datasets/geodata_1933.json +++ b/datasets/geodata_1933.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1933/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1965.json b/datasets/geodata_1965.json index b48513c78f..fc77e09c4a 100644 --- a/datasets/geodata_1965.json +++ b/datasets/geodata_1965.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1965/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1966.json b/datasets/geodata_1966.json index 2d6af84dac..f3ad3a7082 100644 --- a/datasets/geodata_1966.json +++ b/datasets/geodata_1966.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1966/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1967.json b/datasets/geodata_1967.json index 52b72aa1c0..792e6087dd 100644 --- a/datasets/geodata_1967.json +++ b/datasets/geodata_1967.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1967/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1977.json b/datasets/geodata_1977.json index bd2d54c30f..0a9fdb984b 100644 --- a/datasets/geodata_1977.json +++ b/datasets/geodata_1977.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1977/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1980.json b/datasets/geodata_1980.json index 51c7e1e066..32b4f9e2aa 100644 --- a/datasets/geodata_1980.json +++ b/datasets/geodata_1980.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1980/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1982.json b/datasets/geodata_1982.json index febc3668bd..5be279c568 100644 --- a/datasets/geodata_1982.json +++ b/datasets/geodata_1982.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1982/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1986.json b/datasets/geodata_1986.json index 9f4fb504c9..11117248a2 100644 --- a/datasets/geodata_1986.json +++ b/datasets/geodata_1986.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1986/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1988.json b/datasets/geodata_1988.json index 6fbb070cc3..d30ca7e6ff 100644 --- a/datasets/geodata_1988.json +++ b/datasets/geodata_1988.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1988/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1993.json b/datasets/geodata_1993.json index 7d170faf87..ffb22d44c3 100644 --- a/datasets/geodata_1993.json +++ b/datasets/geodata_1993.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1993/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1995.json b/datasets/geodata_1995.json index 4544690210..9e1f45ff03 100644 --- a/datasets/geodata_1995.json +++ b/datasets/geodata_1995.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1995/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_1998.json b/datasets/geodata_1998.json index 655f53db99..b43b813bb6 100644 --- a/datasets/geodata_1998.json +++ b/datasets/geodata_1998.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1998/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2001.json b/datasets/geodata_2001.json index 160398102b..1baebaa704 100644 --- a/datasets/geodata_2001.json +++ b/datasets/geodata_2001.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2001/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2004.json b/datasets/geodata_2004.json index 51e9823fd2..d2916ca7fe 100644 --- a/datasets/geodata_2004.json +++ b/datasets/geodata_2004.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2004/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2018.json b/datasets/geodata_2018.json index 0485b23247..251f26d597 100644 --- a/datasets/geodata_2018.json +++ b/datasets/geodata_2018.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2018/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2021.json b/datasets/geodata_2021.json index a747de686d..98ccf89180 100644 --- a/datasets/geodata_2021.json +++ b/datasets/geodata_2021.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2021/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2026.json b/datasets/geodata_2026.json index fdd18a2fad..2d37c47b8f 100644 --- a/datasets/geodata_2026.json +++ b/datasets/geodata_2026.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2026/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2027.json b/datasets/geodata_2027.json index f09703a3b2..07a7057af4 100644 --- a/datasets/geodata_2027.json +++ b/datasets/geodata_2027.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2027/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2031.json b/datasets/geodata_2031.json index 08c7c64cd9..58d6227ef9 100644 --- a/datasets/geodata_2031.json +++ b/datasets/geodata_2031.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2031/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2032.json b/datasets/geodata_2032.json index 9b1eee2461..b3c37c3c63 100644 --- a/datasets/geodata_2032.json +++ b/datasets/geodata_2032.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2032/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2039.json b/datasets/geodata_2039.json index b8ba73af26..0bd0def8e4 100644 --- a/datasets/geodata_2039.json +++ b/datasets/geodata_2039.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2039/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2045.json b/datasets/geodata_2045.json index d88aeade5b..4c35c97cd6 100644 --- a/datasets/geodata_2045.json +++ b/datasets/geodata_2045.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2045/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2048.json b/datasets/geodata_2048.json index 2dfafe0df7..dfc269f46f 100644 --- a/datasets/geodata_2048.json +++ b/datasets/geodata_2048.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2048/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2126.json b/datasets/geodata_2126.json index 5dd9e01778..42ce16bb81 100644 --- a/datasets/geodata_2126.json +++ b/datasets/geodata_2126.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2126/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2129.json b/datasets/geodata_2129.json index 750d3c06f8..a744946cc3 100644 --- a/datasets/geodata_2129.json +++ b/datasets/geodata_2129.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2129/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2130.json b/datasets/geodata_2130.json index ca1f72c69c..3685575d03 100644 --- a/datasets/geodata_2130.json +++ b/datasets/geodata_2130.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2130/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2131.json b/datasets/geodata_2131.json index fe52a05227..1f9e6c2ea4 100644 --- a/datasets/geodata_2131.json +++ b/datasets/geodata_2131.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2131/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2136.json b/datasets/geodata_2136.json index 1ca4c8a947..229f154e07 100644 --- a/datasets/geodata_2136.json +++ b/datasets/geodata_2136.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2136/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2173.json b/datasets/geodata_2173.json index 67cd0b9735..f42f4561cf 100644 --- a/datasets/geodata_2173.json +++ b/datasets/geodata_2173.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2173/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2195.json b/datasets/geodata_2195.json index bcb382f4e2..d5a2a55e90 100644 --- a/datasets/geodata_2195.json +++ b/datasets/geodata_2195.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2195/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2197.json b/datasets/geodata_2197.json index 2a5b84337a..e700eb1e8f 100644 --- a/datasets/geodata_2197.json +++ b/datasets/geodata_2197.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2197/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2200.json b/datasets/geodata_2200.json index 24d115deb8..54c290e439 100644 --- a/datasets/geodata_2200.json +++ b/datasets/geodata_2200.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2200/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2201.json b/datasets/geodata_2201.json index 51fc2a4b00..76c1453968 100644 --- a/datasets/geodata_2201.json +++ b/datasets/geodata_2201.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2201/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2202.json b/datasets/geodata_2202.json index d2226efa12..ca7a99c937 100644 --- a/datasets/geodata_2202.json +++ b/datasets/geodata_2202.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2202/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2203.json b/datasets/geodata_2203.json index 271d3ab230..b14ffd96cd 100644 --- a/datasets/geodata_2203.json +++ b/datasets/geodata_2203.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2203/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2206.json b/datasets/geodata_2206.json index ca069dc25b..be83236fee 100644 --- a/datasets/geodata_2206.json +++ b/datasets/geodata_2206.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2206/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2207.json b/datasets/geodata_2207.json index 016fcee025..782cb2d069 100644 --- a/datasets/geodata_2207.json +++ b/datasets/geodata_2207.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2207/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2215.json b/datasets/geodata_2215.json index 2ef8b3604d..c716a5cdd0 100644 --- a/datasets/geodata_2215.json +++ b/datasets/geodata_2215.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2215/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2216.json b/datasets/geodata_2216.json index fdc20750af..cdfa4ba9a9 100644 --- a/datasets/geodata_2216.json +++ b/datasets/geodata_2216.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2216/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2223.json b/datasets/geodata_2223.json index 23c159cd09..991cae82be 100644 --- a/datasets/geodata_2223.json +++ b/datasets/geodata_2223.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2223/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2224.json b/datasets/geodata_2224.json index 3c0eeb4b52..d9a8de8890 100644 --- a/datasets/geodata_2224.json +++ b/datasets/geodata_2224.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2224/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2225.json b/datasets/geodata_2225.json index adfb5472ba..b50dc3f698 100644 --- a/datasets/geodata_2225.json +++ b/datasets/geodata_2225.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2225/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2226.json b/datasets/geodata_2226.json index 81e513a30c..005abd7aab 100644 --- a/datasets/geodata_2226.json +++ b/datasets/geodata_2226.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2226/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2227.json b/datasets/geodata_2227.json index 50c014e00a..a6ab50d2f9 100644 --- a/datasets/geodata_2227.json +++ b/datasets/geodata_2227.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2227/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2228.json b/datasets/geodata_2228.json index 83ce325e3e..7d52bf547e 100644 --- a/datasets/geodata_2228.json +++ b/datasets/geodata_2228.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2228/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2229.json b/datasets/geodata_2229.json index 5fefbbfd22..f40207f4d4 100644 --- a/datasets/geodata_2229.json +++ b/datasets/geodata_2229.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2229/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2230.json b/datasets/geodata_2230.json index 639325ee5c..b75f046e34 100644 --- a/datasets/geodata_2230.json +++ b/datasets/geodata_2230.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2230/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2231.json b/datasets/geodata_2231.json index 848a286012..66a2811ad9 100644 --- a/datasets/geodata_2231.json +++ b/datasets/geodata_2231.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2231/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2232.json b/datasets/geodata_2232.json index 307ca662cd..93765ad7ab 100644 --- a/datasets/geodata_2232.json +++ b/datasets/geodata_2232.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2232/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2237.json b/datasets/geodata_2237.json index f18a483f0f..09f13fda88 100644 --- a/datasets/geodata_2237.json +++ b/datasets/geodata_2237.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2237/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2244.json b/datasets/geodata_2244.json index 4e1547f057..77d20ef9ac 100644 --- a/datasets/geodata_2244.json +++ b/datasets/geodata_2244.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2244/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2245.json b/datasets/geodata_2245.json index c279ca2865..e163783609 100644 --- a/datasets/geodata_2245.json +++ b/datasets/geodata_2245.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2245/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2246.json b/datasets/geodata_2246.json index 8f5897f364..f741436e3c 100644 --- a/datasets/geodata_2246.json +++ b/datasets/geodata_2246.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2246/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2247.json b/datasets/geodata_2247.json index 127b7b8526..b9580620d8 100644 --- a/datasets/geodata_2247.json +++ b/datasets/geodata_2247.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2247/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2251.json b/datasets/geodata_2251.json index 17fa2c5d0e..6f5884dfc4 100644 --- a/datasets/geodata_2251.json +++ b/datasets/geodata_2251.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2251/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/geodata_2253.json b/datasets/geodata_2253.json index cf36786536..171bfd5314 100644 --- a/datasets/geodata_2253.json +++ b/datasets/geodata_2253.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2253/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/inpe_CPTEC_GLOBAl_FORECAST.json b/datasets/inpe_CPTEC_GLOBAl_FORECAST.json index 458e9c1fc1..22382d0891 100644 --- a/datasets/inpe_CPTEC_GLOBAl_FORECAST.json +++ b/datasets/inpe_CPTEC_GLOBAl_FORECAST.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/inpe_CPTEC_GLOBAl_FORECAST/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/instm_trawl.json b/datasets/instm_trawl.json index dd94832342..5e9c8e4099 100644 --- a/datasets/instm_trawl.json +++ b/datasets/instm_trawl.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/instm_trawl/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/iziko_Crustaceans.json b/datasets/iziko_Crustaceans.json index 47cf4e9310..566bbfba7a 100644 --- a/datasets/iziko_Crustaceans.json +++ b/datasets/iziko_Crustaceans.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/iziko_Crustaceans/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/iziko_molluscs.json b/datasets/iziko_molluscs.json index 80864b9883..8a57734368 100644 --- a/datasets/iziko_molluscs.json +++ b/datasets/iziko_molluscs.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/iziko_molluscs/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/iziko_sharks.json b/datasets/iziko_sharks.json index a7a2352708..71a38e2a27 100644 --- a/datasets/iziko_sharks.json +++ b/datasets/iziko_sharks.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/iziko_sharks/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/kenya_marine.json b/datasets/kenya_marine.json index 02156b89dd..82e09e6aa1 100644 --- a/datasets/kenya_marine.json +++ b/datasets/kenya_marine.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/kenya_marine/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/madagascar_diatoms.json b/datasets/madagascar_diatoms.json index 7401ed9782..fe179ce2f5 100644 --- a/datasets/madagascar_diatoms.json +++ b/datasets/madagascar_diatoms.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/madagascar_diatoms/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/madagascar_dinoflagelles.json b/datasets/madagascar_dinoflagelles.json index 28e9f86b94..b111bf54f4 100644 --- a/datasets/madagascar_dinoflagelles.json +++ b/datasets/madagascar_dinoflagelles.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/madagascar_dinoflagelles/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/madagascar_fish.json b/datasets/madagascar_fish.json index e8045275b4..e9b8cb11c4 100644 --- a/datasets/madagascar_fish.json +++ b/datasets/madagascar_fish.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/madagascar_fish/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/madagascar_invertebrates.json b/datasets/madagascar_invertebrates.json index 3c31b3bca3..f085679c53 100644 --- a/datasets/madagascar_invertebrates.json +++ b/datasets/madagascar_invertebrates.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/madagascar_invertebrates/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/mcm_seals.json b/datasets/mcm_seals.json index 96f10137fc..f356997932 100644 --- a/datasets/mcm_seals.json +++ b/datasets/mcm_seals.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/mcm_seals/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/nigeria_marine.json b/datasets/nigeria_marine.json index 7d81c81ee5..45b14ab3cb 100644 --- a/datasets/nigeria_marine.json +++ b/datasets/nigeria_marine.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/nigeria_marine/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/sediments_gom.json b/datasets/sediments_gom.json index a037e35407..159830bfe6 100644 --- a/datasets/sediments_gom.json +++ b/datasets/sediments_gom.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/sediments_gom/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/unep_marineturtle.json b/datasets/unep_marineturtle.json index c80e30ff7e..23463df1c2 100644 --- a/datasets/unep_marineturtle.json +++ b/datasets/unep_marineturtle.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/unep_marineturtle/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_nps_d_microbialcontam.json b/datasets/usgs_nps_d_microbialcontam.json index 52d51327c0..1b07aefba0 100644 --- a/datasets/usgs_nps_d_microbialcontam.json +++ b/datasets/usgs_nps_d_microbialcontam.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_d_microbialcontam/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_nps_fortlaramie.json b/datasets/usgs_nps_fortlaramie.json index b987d0de27..723ee58604 100644 --- a/datasets/usgs_nps_fortlaramie.json +++ b/datasets/usgs_nps_fortlaramie.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_fortlaramie/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_nps_fortlaramiespatial.json b/datasets/usgs_nps_fortlaramiespatial.json index fcd8303c4f..8983398199 100644 --- a/datasets/usgs_nps_fortlaramiespatial.json +++ b/datasets/usgs_nps_fortlaramiespatial.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_fortlaramiespatial/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_nps_isleroyale.json b/datasets/usgs_nps_isleroyale.json index 5cca9d7cca..7030c56a5c 100644 --- a/datasets/usgs_nps_isleroyale.json +++ b/datasets/usgs_nps_isleroyale.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_isleroyale/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_nps_isleroyalespatial.json b/datasets/usgs_nps_isleroyalespatial.json index f3543ccd90..b0372610ea 100644 --- a/datasets/usgs_nps_isleroyalespatial.json +++ b/datasets/usgs_nps_isleroyalespatial.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_isleroyalespatial/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_nps_jewelcave.json b/datasets/usgs_nps_jewelcave.json index 926f34bc96..04c2ce4d63 100644 --- a/datasets/usgs_nps_jewelcave.json +++ b/datasets/usgs_nps_jewelcave.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_jewelcave/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_nps_jewelcavespatial.json b/datasets/usgs_nps_jewelcavespatial.json index 67d2d87e73..6efd0d1118 100644 --- a/datasets/usgs_nps_jewelcavespatial.json +++ b/datasets/usgs_nps_jewelcavespatial.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_jewelcavespatial/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_nps_mountrushmore.json b/datasets/usgs_nps_mountrushmore.json index 22a2a1f29d..2eb811027a 100644 --- a/datasets/usgs_nps_mountrushmore.json +++ b/datasets/usgs_nps_mountrushmore.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_mountrushmore/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_npwrc_canvasbacks_Version 13NOV2001.json b/datasets/usgs_npwrc_canvasbacks_Version 13NOV2001.json index d08266a354..68c50f5767 100644 --- a/datasets/usgs_npwrc_canvasbacks_Version 13NOV2001.json +++ b/datasets/usgs_npwrc_canvasbacks_Version 13NOV2001.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_npwrc_canvasbacks_Version%2013NOV2001/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_npwrc_incidentalmarinecatc_Version 11APR2001.json b/datasets/usgs_npwrc_incidentalmarinecatc_Version 11APR2001.json index 9cff7d28e2..3234025cf7 100644 --- a/datasets/usgs_npwrc_incidentalmarinecatc_Version 11APR2001.json +++ b/datasets/usgs_npwrc_incidentalmarinecatc_Version 11APR2001.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_npwrc_incidentalmarinecatc_Version%2011APR2001/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_npwrc_muskoxen_Version 31MAY2000.json b/datasets/usgs_npwrc_muskoxen_Version 31MAY2000.json index 92ffcd3187..b164c166b9 100644 --- a/datasets/usgs_npwrc_muskoxen_Version 31MAY2000.json +++ b/datasets/usgs_npwrc_muskoxen_Version 31MAY2000.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_npwrc_muskoxen_Version%2031MAY2000/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_npwrc_nestingsuccess_Version 26MAR2001.json b/datasets/usgs_npwrc_nestingsuccess_Version 26MAR2001.json index 002902062c..fa6ede3225 100644 --- a/datasets/usgs_npwrc_nestingsuccess_Version 26MAR2001.json +++ b/datasets/usgs_npwrc_nestingsuccess_Version 26MAR2001.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_npwrc_nestingsuccess_Version%2026MAR2001/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgs_npwrc_saltmam.json b/datasets/usgs_npwrc_saltmam.json index 4e01ce9c88..19408529fe 100644 --- a/datasets/usgs_npwrc_saltmam.json +++ b/datasets/usgs_npwrc_saltmam.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_npwrc_saltmam/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgsbrdfcsc_d_seagrass.json b/datasets/usgsbrdfcsc_d_seagrass.json index 9f408fcc0d..929d38872d 100644 --- a/datasets/usgsbrdfcsc_d_seagrass.json +++ b/datasets/usgsbrdfcsc_d_seagrass.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgsbrdfcsc_d_seagrass/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgsbrdfcsc_d_vieques.json b/datasets/usgsbrdfcsc_d_vieques.json index b998b645a4..3f67ffb446 100644 --- a/datasets/usgsbrdfcsc_d_vieques.json +++ b/datasets/usgsbrdfcsc_d_vieques.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgsbrdfcsc_d_vieques/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgsbrdnpwrc_d_ndfleas_Version 16JUL97.json b/datasets/usgsbrdnpwrc_d_ndfleas_Version 16JUL97.json index a0f2041d9f..e78056b697 100644 --- a/datasets/usgsbrdnpwrc_d_ndfleas_Version 16JUL97.json +++ b/datasets/usgsbrdnpwrc_d_ndfleas_Version 16JUL97.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgsbrdnpwrc_d_ndfleas_Version%2016JUL97/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgsbrdnpwrcd00000002_Version 02MAR98.json b/datasets/usgsbrdnpwrcd00000002_Version 02MAR98.json index 0b87330e99..fa41f8b39d 100644 --- a/datasets/usgsbrdnpwrcd00000002_Version 02MAR98.json +++ b/datasets/usgsbrdnpwrcd00000002_Version 02MAR98.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgsbrdnpwrcd00000002_Version%2002MAR98/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/usgsbrdnpwrcd0000003_Version 16JUL97.json b/datasets/usgsbrdnpwrcd0000003_Version 16JUL97.json index 7c910ba3f9..e4bc6fcda7 100644 --- a/datasets/usgsbrdnpwrcd0000003_Version 16JUL97.json +++ b/datasets/usgsbrdnpwrcd0000003_Version 16JUL97.json @@ -54,7 +54,8 @@ { "rel": "items", "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgsbrdnpwrcd0000003_Version%2016JUL97/items", - "type": "application/json" + "type": "application/geo+json", + "title": "Collection Items" } ], "provider": [ diff --git a/datasets/wwllnmth_1.json b/datasets/wwllnmth_1.json index a472026ec7..b96d5ff23c 100644 --- a/datasets/wwllnmth_1.json +++ b/datasets/wwllnmth_1.json @@ -88,7 +88,7 @@ "interval": [ [ "2013-01-01T00:00:00Z", - "2023-12-31T00:00:00Z" + "2023-12-31T23:59:59Z" ] ] } diff --git a/nasa_cmr_catalog.json b/nasa_cmr_catalog.json index 6aab9b06f3..28dbb165bd 100644 --- a/nasa_cmr_catalog.json +++ b/nasa_cmr_catalog.json @@ -116,6 +116,19 @@ "description": "We completed a field season in Antarctica in 2010-11 with a 5-person field party. Ten sampling sites along the Transantarctic Mountains from the Convoy Range to Hatcher Bluffs were visited by helicopter or fixed-wing aircraft, where rock samples were collected. All samples were returned to the University of Minnesota-Duluth, where they were prepared for laboratory study. Laboratory work includes examination of polished thin sections by optical microscope and scanning electron microscope to determine textures, mineral assemblages, and mineral compositions. Samples of igneous and metamorphic rock clasts were crushed in order to isolate the mineral zircon; zircon from these samples was analyzed by U-Pb, O and Hf isotopic analysis in order to determine their ages and isotopic character. Monazite was identified in selected samples for U-Pb age dating in polished thin section. A suite of Ross Orogen granitoids was also prepared for zircon separation and for whole-rock geochemical analysis. Petrographic study is complete for over 300 samples of igneous and metamorphic rock clasts collected from glacial moraines on the \u2018backside\u2019 of the Transantarctic Mountains, mainly between the inlets to the Byrd through Shackleton Glaciers. We U-Pb, O and Hf analyses of zircon and monazite in igneous and metamorphic clasts, and in samples of TAM granitoids.", "license": "proprietary" }, + { + "id": "0ac98747-eb94-4c9f-aef8-56f9d3a04740", + "title": "Earthquake Risk-Annual Average Losses", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2012-01-01", + "end_date": "2013-01-01", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848506-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848506-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/0ac98747-eb94-4c9f-aef8-56f9d3a04740", + "description": "The map (risk map) presents the results of earthquake annual average losses (AAL) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (AAL-absolute value) and millar (AAL/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL.", + "license": "proprietary" + }, { "id": "0b23b3c771db4fff8958196432d978cb_NA", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Margin from ERS-2 for winter 1995-1996, v1.1 (June 2016 release)", @@ -2157,6 +2170,19 @@ "description": "- Spectrograms or pictures of gape, tongue, eye-ring and bill of each adult that was caught on the tower in Middleton island. - Colour data obtained from those spectrograms and pictures.", "license": "proprietary" }, + { + "id": "118cc853-52a2-46e2-a5be-40e1f58ab46d_1", + "title": "HUMAN POPULATION AND ADMINISTRATIVE BOUNDARIES DATABASE FOR THE RUSSIAN FED.", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-36, 41.19658, 180, 81.85193", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847474-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847474-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/118cc853-52a2-46e2-a5be-40e1f58ab46d_1", + "description": "The Russian administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. The database was prepared at UNEP/GRID-Geneva in collaboration with Denis Eckert (Centre National de la Recherche Scientifique / France) and the Centre Universitaire d'Ecologie Humaine/Universit\u00e9 de Gen\u00e8ve. BOUNDARY AND POPULATION DATA 2486 third-level administrative units (1863 raions, 325 gorods, 298 gorsoviets) digitized by D. Eckert have been matched to the national boundaries of the widely used Digital Chart of the World. 649 cities have been digitized from various sources and adjusted to the administrative map. Population figures of the administrative units refer to the 1993 official figures whereas urban data was compiled from heterogeneous sources and projected to the year 1995. INTERPOLATION The interpolation of the vectorial data to a population density raster grid at a resolution of 2.5 arc-minutes was carried out according to a model developed by Uwe Deichman for a previous work on Asia (UNEP/GRID-Geneva). The basic assumption upon which the model is based is that population densities are strongly correlated with accessibility. A complex transport network (more than 170'000 arcs) was used to distribute populations and densities to a raster grid. OUTPUTS - a vector dataset containing the 2846 administrative units fitted to the Digital Chart of the World used as template. Population figures of the 1993 official figures and subsequent projections to year 1995 are stored in the polygon attribute file. - a raster dataset of the interpolated and distributed population totals at a resolution of 2.5 arc-minutes - a raster dataset of the interpolated and distributed population densities at a resolution of 2.5 arc-minutes GIF images for display on the Web ", + "license": "proprietary" + }, { "id": "11c5f6df1abc41968d0b28fe36393c9d_NA", "title": "ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 3 aerosol products from MERIS (ALAMO algorithm), Version 2.2", @@ -4887,6 +4913,19 @@ "description": "This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for December.", "license": "proprietary" }, + { + "id": "2940cda8-cf01-490a-a7ab-688bd54fb56a", + "title": "Earthquake Risk-Probable Maximum Losses", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2012-01-01", + "end_date": "2013-01-01", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848573-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848573-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/2940cda8-cf01-490a-a7ab-688bd54fb56a", + "description": "The map (risk map) presents the results of earthquake probable maximum loss (PML) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (PML-absolute value) and percentage (PML/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL.", + "license": "proprietary" + }, { "id": "294b4075ddbc4464bb06742816813bdc_NA", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the BESD algorithm (CO2_SCI_BESD), v02.01.02", @@ -4913,6 +4952,19 @@ "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-day maps.", "license": "proprietary" }, + { + "id": "2bb39206-6988-4127-89e5-85a0430e20cc", + "title": "Earthquakes frequency for MMI categories higher than 9 1973-2007", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1973-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -58, 180, 85.03594", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848457-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848457-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/2bb39206-6988-4127-89e5-85a0430e20cc", + "description": "This dataset includes an estimate of earthquake frequency of MMI categories higher than 9 over the period 1973-2007. It is based on Modified Mercalli Intensity map available in the Shakemap Atlas from USGS. Unit is expected average number of events per 1000 years. This product was compiled by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing Shakemap Atlas from USGS, compilation and global hazard distribution UNEP/GRID-Europe.", + "license": "proprietary" + }, { "id": "2dimpacts_1", "title": "Two-Dimensional Video Disdrometer (2DVD) IMPACTS V1", @@ -7097,6 +7149,19 @@ "description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "license": "proprietary" }, + { + "id": "43f81a9f-f903-43d4-8333-dcda52b2bc63", + "title": "Global estimated risk index for flood hazard", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 84", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847571-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847571-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/43f81a9f-f903-43d4-8333-dcda52b2bc63", + "description": "This dataset includes an estimate of the global risk induced by flood hazard. Unit is estimated risk index from 1 (low) to 5 (extreme). This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: UNEP/GRID-Europe.", + "license": "proprietary" + }, { "id": "466b48b8-78c4-4009-97a2-c8d70f9075bf_NA", "title": "MERIS - Water Parameters - Baltic Sea, 10-Day", @@ -7123,6 +7188,19 @@ "description": "The CH4_GOS_SRFP dataset is comprised of level 2, column-averaged mole fractiona (mixing ratioa) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra onboard the Japanese Greenhouse gases Observing Satellite (GOSAT) using the SRFP (RemoTec) algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the dataset is v2.3.8 and forms part of the Climate Research Data Package 4.The RemoTeC SRFP baseline algorithm is a Full Physics algorithm. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key products with and without bias correction. Information relevant for the use of the data is also included in the data file, such as the vertical layering and averaging kernels. Additionally, the parameters retrieved simultaneously with XCH4 are included (e.g. surface albedo), as well as retrieval diagnostics like retrieval errors and the quality of the fit. For further information on the product, including the RemoTeC Full Physics algorithm and the TANSO-FTS instrument please see the Product User Guide (PUG) or the Algorithm Theoretical Basis Document.", "license": "proprietary" }, + { + "id": "47211801-72f3-4064-8c01-715cd2b7dc71_1", + "title": "Matthews' global vegetation DB for climate studies", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1984-05-01", + "end_date": "1984-05-01", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847392-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847392-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/47211801-72f3-4064-8c01-715cd2b7dc71_1", + "description": "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes. Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice. The proper reference to these data sets is \"Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487.\" The Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes.", + "license": "proprietary" + }, { "id": "48fc3d1e8ada405c8486ada522dae9e8_NA", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0", @@ -7370,6 +7448,19 @@ "description": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. These MODIS Fire_cci v5.1 pixel products are distributed as 6 continental tiles and are based upon data from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001-2020. This product supersedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has later been periodically extended to include 2018 to 2020. The Fire_cci v5.1 Pixel product described here includes maps at 0.00224573-degrees (approx. 250m) resolution. Burned area(BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Land_Cover_cci v2.0.7 product.Files are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carr\u00c3\u00a9e projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation.", "license": "proprietary" }, + { + "id": "5940d3fb-860d-4f3e-bc3a-4022639c272a_1", + "title": "Matthews' global cultivation intensity (land use)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1984-05-01", + "end_date": "1984-05-01", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848577-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848577-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/5940d3fb-860d-4f3e-bc3a-4022639c272a_1", + "description": "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes. Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice. The proper reference to these data sets is \"Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487.\" The Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes. ", + "license": "proprietary" + }, { "id": "5970b33c92ef444793fb6d7e54d1230e_NA", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by TU Dresden, v1.3", @@ -7383,6 +7474,19 @@ "description": "This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by TU Dresden. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to August 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.3) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. This GMB product has been produced by TU Dresden for comparison with the existing GMB product derived by DTU Space.Please cite the dataset as follows: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065", "license": "proprietary" }, + { + "id": "598f86dc-01da-49e3-824e-c8b8f1089a0e_1", + "title": "Matthews' seasonal integrated surface albedo", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1984-05-01", + "end_date": "1984-05-01", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848146-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848146-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/598f86dc-01da-49e3-824e-c8b8f1089a0e_1", + "description": "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes. Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice. The proper reference to these data sets is \"Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487.\" The Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes. ", + "license": "proprietary" + }, { "id": "5990f1bd-9fc6-4a22-bbb5-c269312fec06_NA", "title": "IRS-1D - Wide Field Sensor Images (WiFS) - Europe", @@ -25297,6 +25401,32 @@ "description": "The Amazonian Region Micrometeorological Experiment (ARME) data contain micrometeorological data (climate, interception of precipitation, mircometeorology and soil moisture) on the elements of the energy balance and evapotranspiration for the Amazonian forest. ASCII text data files for each of the four data types have been zipped toghether. One of the many scientific findings of this experiment was that tropical forest does not experience water stress due to the lack of precipitation, during periods when evapotranspiration is at the potential rate (Shuttleworth, 1988). ARME data types include climate (meteorological), interception of precipitation, micrometeorology, and soil moisture. These data are described in the Data Description section below. ", "license": "proprietary" }, + { + "id": "ARNd0001_103", + "title": "Global CO2 Emission", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232997868-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232997868-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0001_103", + "description": "Based on fossil fuel statistics from OECD, UNEP and the World bank. (SAF, Centre for Applied Res., Norw. School of Economy & Buisiness Administration, Oslo.)SAF Working Paper no. 59/89 included Members informations: Attached Vector(s): MemberID: 1 Vector Name: CO2 Emission database - 2 floppy disks Vector CO2 Emission database on 2 floppy disks in Lotus 2.01 format. SAF Working Paper no. 59/89.", + "license": "proprietary" + }, + { + "id": "ARNd0002_103", + "title": "Global vegetation regions and climate change effects - The biome model", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "3.88, 56.69, 32.56, 81.95", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847832-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847832-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0002_103", + "description": "Global vegetation change due to climate change modelled by using climate change models from Global Fluid Dynamics Laboratory (GFD, Princeton), Goddard Inst. for Space Studies (GISS, NASA) and Oregon State University combined with the by the Biomes classification system (Cramer & Prentice 1990). Vegetation regions are defined by parameters. University of Trondheim, Dep. of Geography, Norway Programme toidrisi to convert from tabular data. Programme found in /global/themes/cl_clima/inform Attached Raster(s): Member_ID: 1 Raster Name: GFDL Coldest month in degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 2 Raster Name: GIS Coldest month in degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 3 Raster Name: GIS Warmest moth in degrees celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 4 Raster Name: GFDL Warmest month in degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 5 Raster Name: GFD Actual evotranspiration/potenial evotranspiration Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 6 Raster Name: GISS Actual evotranspiration/potenial evotranspiration Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 7 Raster Name: GIS Growing degree days baseline 0 degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 8 Raster Name: GIS Growing degree days baseline 5 degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 9 Raster Name: GFDL Growing degree days baseline 5 degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 10 Raster Name: GFDL Growing degree days baseline 0 degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 11 Raster Name: Global DTM from Cramer 30 mm lat/long grid from ETOPO5 Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 12 Raster Name: Global 3\" lat/long grid, % sunshine hours of possible Raster 12 images from January through to December. Global 30 min lat/lon raster model; digital terrain model derived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 13 Raster Name: Global 30\" lat/long grid, modelled precipitation in mm Raster 12 images from January through to December. Global 30 min lat/lon raster model; digital terrain model derived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 14 Raster Name: Global 3\" lat/long grid, modelled normal monthly temperature Raster 12 images from January through to December. Global 30 min lat/lon raster model; digital terrain model derived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 15 Raster Name: Biome 1.1 - normal climate (reclassified) Raster Categories include: ice, tundra, boreal forest, temp. deciduous, temp. evergreen, steppe, savannah, semi-desert, desert, trop. seasonal, trop. evergreen, cool desert. Attached Raster(s): Member_ID: 16 Raster Name: Biome 1.1 - OSU climate (reclassified) Raster Categories include: ice, tundra, boreal forest, temp. deciduous, temp. evergreen, steppe, savannah, semi-desert, desert, trop. seasonal, trop. evergreen, cool desert. Attached Raster(s): Member_ID: 17 Raster Name: Shift of the Boreal Zone - OSU climate Raster Categories include: decreasing, stable, available. Attached Raster(s): Member_ID: 18 Raster Name: Shift of the Boreal Zone - OSU climate - larger grid Raster Categories include: decreasing, stable, available. Attached Raster(s): Member_ID: 19 Raster Name: Shift of the Boreal Zone in Europe - OSU climate Raster Categories include: decreasing, stable, available. Attached Raster(s): Member_ID: 20 Raster Name: Percent change in temperature sum > 5 degrees Celsius (OSU) Raster Catergories include: 0-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80-90, 90-100, 100-150, 150-200, >200. Attached Raster(s): Member_ID: 21 Raster Name: Absolute change of the vegetative period >5 degrees Celsius (OSU) Raster Categories include: no change, <7 days, 7-14, 14-21, 21-28, 28-35, 35-42, 42-49, 49-56, 56-63, 63-70, >70. Members informations: Attached Vector(s): MemberID: 22 Vector Name: Original data files Vector Original data files for global vegetation regions and climate change effects. Files include: bic30gfd.dta, bic30gis.dta, bic30nor.dta, bic30osu.dta, bic30ukm.dta, clo30.gdd, prc30.gdd, tmp30.gdd.", + "license": "proprietary" + }, { "id": "ARNd0003_103", "title": "Cramer global climate database", @@ -25310,6 +25440,32 @@ "description": "Climate model based on climate stations and parameters from national weather monitoring stations, and digital terrain model. Interpolation method used: Partial thin-plate spline smoothing (Huthcinson). Attached Raster(s): Member_ID: 1 Raster Name: Modeled percent sunshine hours of possible .Global 30 min latitude/longitude Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 2 Raster Name: Modeled precipitation in mm. Global 30 min latitude/longitude Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 3 Raster Name: Modeled monthly temperature in degrrees Celsius Global 30 min latitude/longitude Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre.", "license": "proprietary" }, + { + "id": "ARNd0012_103", + "title": "Digital terrain model Norway", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "3.88, 56.69, 32.56, 81.95", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847831-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847831-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0012_103", + "description": "Contourlines with ekv. 300 meters in the scale of 1:1mill. Gridded data extracted from the same source are also available. Generated on basis of data from Norwegian Mapping Authorities, H?nefoss, Norway", + "license": "proprietary" + }, + { + "id": "ARNd0013_103", + "title": "Digital terrain model continental shelf Norway", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "3.88, 56.69, 32.56, 81.95", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847974-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847974-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0013_103", + "description": "Extract from the ETOPO5 for the continental shelf of Norway. Raster 0.5 * 0.5 degree**2. Depthlines from (NSKV) Mapping authority of Norway are also available. Digital terrain model continental shelf Norway", + "license": "proprietary" + }, { "id": "ARNd0015_103", "title": "Arctic Base map", @@ -25362,6 +25518,32 @@ "description": "Various coverages representing bedrock geology in Norway. One aml for producing eps files. Members informations: Attached Vector(s): MemberID: 1 Vector Name: Coverage showing bedrock geology in Norway Vector Description to be added Members informations: Attached Vector(s): MemberID: 2 Vector Name: Line coverage displaying geological faults in Norway Vector Description to be added Members informations: Attached Vector(s): MemberID: 3 Vector Name: Coverage displaying trust/reversed faults Vector Description to be added", "license": "proprietary" }, + { + "id": "ARNd0071_103", + "title": "Latvian Drainage network", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "20.76, 55.32, 28.76, 58.44", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848019-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848019-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0071_103", + "description": "Digitised from paper maps by Lativan Environment Data Centre. Converted from DXF files to ARC/INFO coverage (GRID-Arendal). Details start-end date ask Sindre To get precise coordinates use ARC/INFO command \"describe\" on member 1 Members informations: Attached Vector(s): MemberID: 1 Vector Name: Rivers of Latvia Projection: UTM Projection_desc: Zone 35 Projection_meas: Metres Feature_type: lline Vector Source map is Russian topographic map, scale supposed to be 1:500 000.", + "license": "proprietary" + }, + { + "id": "ARNd0073_103", + "title": "Ice classes - Barents Sea", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -89.24, 180, 89.24", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846647-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846647-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0073_103", + "description": "The polar regions hold large masses of water in the form of ice, and this ice has a modifying effect on temperature variations. An increase in the mean temperature of the Arctic, as has been predicted, may result in an intensified melting of the sea ice. While a compact ice cover absorbs 15-50% of the incoming solar radiation, an ice free ocean absorbs about 90%. The absorbed radiation causes warming and evaporation. A change in the ice cover may therefore drastically effect the heat budget of the sea surface. This illustrates an important self-magnifying effect of the increased heat absorption causing the acceleration of ice melting. An increased greenhouse effect due to changes in the gas composition of the atmosphere could therefore be monitored by studying the changes in the total mass of sea ice in the Arctic. Ice charts of the Barents Sea , with the ice separated in ten ice classes, weekly, from 1966 to 1989. The classes are from open water to dense pack ice. Attached Raster(s): Member_ID: 1 Raster Name: *.BPIX files - internal format of Norwegian Polar Institute Raster Raster files received from the Norwegian Polar Institute in their own internal format. Attached Raster(s): Member_ID: 2 Raster Name: ArcInfo grids representing sea ice in the barents region Raster ArcInfo grids representing the barents sea ice cover from 1966 to 1989. Attached Raster(s): Member_ID: 3 Raster Name: ArcInfo grids for january 1989. Raster Five ArcInfo grids representing ice coverage in January 1989. Attached Raster(s): Member_ID: 4 Raster Name: Arcinfo grid of sea ice for February 1989. Raster ArcInfo grid representing sea ice cover for February 1989. Attached Raster(s): Member_ID: 5 Raster Name: ArcInfo grid of sea ice for January - year unknown. Raster ArcInfo grid representing sea ice cover in January - year unknown. Attached Raster(s): Member_ID: 6 Raster Name: Arcinfo grid of sea ice Raster ArcInfo grid representing sea ice - date unknown. Need better documentation for this grid - need to know the coverage date. Attached Raster(s): Member_ID: 7 Raster Name: ArcInfo grid of sea ice for July and August Raster ArcInfo grid representing sea ice cover for July and August, year unknown. Attached Raster(s): Member_ID: 8 Raster Name: ArcInfo grid of minimum sea ice extent for 1989. Raster Arcinfo grid representing minimum sea ice extent for 1989. Members informations: Attached Vector(s): MemberID: 9 Vector Name: Vector coverages of ice extent Feature_type: polygons Vector Vector coverages representing ice extent for the years 1966 through to 1989. Members informations: Attached Vector(s): MemberID: 10 Vector Name: Vector ArcInfo coverages of sea ice extent Feature_type: polygons Vector Vector coverages representing sea ice extent for the years 1966 through to 1989. Members informations: Attached Vector(s): MemberID: 11 Vector Name: Clipping area for the barents sea region Feature_type: polygon Vector ArcInfo clipping vector coverage of the Barents sea region. Members informations: Attached Vector(s): MemberID: 12 Vector Name: ArcInfo coverage of July-August ice extent Feature_type: polygon Vector ArcInfo vector coverage representing the ice extent for July and August. Year unknown", + "license": "proprietary" + }, { "id": "ARNd0075_103", "title": "Antarctic coastline", @@ -25401,6 +25583,58 @@ "description": "Coastline of Bear Island. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo coverage of the coastline of Bear Island Vector ArcInfo coverage of Bear Island as dervied from data provided by the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo coverage of the coastline of Bear Island in UTM coordinates Vector ArcInfo coverage of the coastline of Bear Island as derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 3 Vector Name: ArcInfo generate file of the coastline of Bear Island Vector ArcInfo generate file representing the coastline of Bear Island derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 4 Vector Name: Norwegian Polar Institute internal format file of the coastline of Bear Island Vector Norwegian Polar Institute internal format file representing the coastline of Bear Island as received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 5 Vector Name: Line-koordinate file of the coastline of Bear Island Vector Line-koordinate file representing the coastline of Bear Island derived from data received from the Norwegian Polar Institute.", "license": "proprietary" }, + { + "id": "ARNd0079_103", + "title": "Franz Josef Land basemap", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "25, 23.21, -175, 71", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847672-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847672-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0079_103", + "description": "Franz Josef Land coastline information. Members informations: Attached Vector(s): MemberID: 1 Vector Name: Six ArcInfo coverages of the coastline of Franz Josef Land Projection: geographic Projection_desc: lat/long Projection_meas: decimal degrees Feature_type: arcs Vector Six ArcInfo coverages representing the coastline of Franz Josef land. FRAJO1.....FRAJO5 represent only parts of the whole coverage FRAJO. These coverages are derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo coverage of the coastline fo Franz Josef Land in UTM coordinates Projection: UTM Projection_desc: zone 33 Projection_meas: metres Feature_type: arcs/polys Vector ArcInfo coverage of the coastline of Franz Josef Land as derived from data received from the Norwegian Polar Institute. Attached Raster(s): Member_ID: 3 Raster Name: Projection file, geographic to utm and utm to geographic. Raster Projection files used to project coverages in geographic coordinates to UTM coordinates, zone 33 and vise versa. Members informations: Attached Vector(s): MemberID: 4 Vector Name: ArcInfo generate files of the coastline of Franz Josef Land Vector Five ArcInfo generate files representing the coastline of Franz Josef Land derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 5 Vector Name: Norwegian Polar Institute internal format files of the coast of Franz Josef Land Vector Five Norwegian Polar Institute internal format files of the coastline of Franz Josef Land as received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 6 Vector Name: Line-koordinate files (.lik) of the coastline of Franz Josef Land Vector Five line-koordinate files (.lik) representing the coastline of Franz Josef Land derived from data received from the Norwegian Polar Institute.", + "license": "proprietary" + }, + { + "id": "ARNd0082_103", + "title": "Jan Mayen Island basemap", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "3.88, 56.69, 32.56, 81.95", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849393-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849393-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0082_103", + "description": "Jan Mayen Island coastline. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate file of the coastline of Jan Mayen Island Feature_type: arcs Vector ArcInfo generate file representing the coastline of Jan Mayen Island derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal format file of the Jan Mayen coastline Vector Norwegian Polar institute file representing the coastline of Jan Mayen island as received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 3 Vector Name: Several ArcInfo coverages representing basemap information for Jan Mayen Island Source Map Name: DCW Source Map Scale: 1000000 Projection: geographic Projection_desc: lat/long Projection_meas: metres Feature_type: polyarcpt Vector Several ArcInfo coverages representing basemap information for Jan Mayen Island extract from the Digital Chart fo the World CD-ROM. Layers include cultural point features (clpoint), drainage network (dnnet), data quality layer (dqnet), supplemental drainage points (dspoint), hyposography supplemental lines (hsline), hypsography supplemental points (hspoint), hypsography network (hynet), hypsography points (hypoint), political and oceanic boundaries (ponet). Members informations: Attached Vector(s): MemberID: 4 Vector Name: Several ArcInfo coverages representing basemap info for Jan Mayen Island in UTM Source Map Name: DCW Source Map Scale: 1000000 Projection: UTM Projection_desc: zone 29 Projection_meas: metres Feature_type: polyarcpt Vector Several ArcInfo coverages representing basemap information for Jan Mayen Island extract from the Digital Chart fo the World CD-ROM. Layers include drainage network (dnnet), hyposography supplemental lines (hsline), hypsography network (hynet), political and oceanic boundaries (ponet). Associated projection file used is included (geo-utm).", + "license": "proprietary" + }, + { + "id": "ARNd0083_103", + "title": "Iceland basemap", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-24.55, 62.81, -12.79, 67.01", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849025-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849025-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0083_103", + "description": "Basemap of Iceland Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate file of Iceland's coastline Vector ArcInfo generate file representing the coastline of Iceland derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal fromat files of the Icelandic coastline Vector Norwegian Polar Institute internal format files of the Icelandic coastline as received from the Norwegian Polar institute.", + "license": "proprietary" + }, + { + "id": "ARNd0084_103", + "title": "Greenland basemap", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-75.34, 56.78, -9.36, 86.6", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847703-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847703-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0084_103", + "description": "Basemap information of Greenland Incorrect bounding box Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate files of Greenlands coastline Vector Thirteen ArcInfo generate files representing the coastline of Greenland derived from data received from the Norwegian Polar Insitute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal format files of the Greenland coastline Vector Norwegian Polar Institute internal format files representing the coastline of Greenland as received from the norwegian Polar Institute.", + "license": "proprietary" + }, { "id": "ARNd0086_103", "title": "Alaska basemap", @@ -25440,6 +25674,19 @@ "description": "Climatic zones in Norway at different times of the year. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo coverages of climatic zones of Norway at different times of the year Feature_type: polys/arcs Vector ArcInfo coverages of climatic zones of Norway at different times of the year. Coverages include: kl623, klima613, klima622, klima623, klima626, klima626b, klima632, klima632b, klima653, klima653b, klima656, klima656b. Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo coverage of temperature isolines for January. Feature_type: polyarcpt Vector ArcInfo coverage of temperature isolines for January, including Svalbard. Members informations: Attached Vector(s): MemberID: 3 Vector Name: ArcInfo coverage of temperature isolines for July. Feature_type: polyarcpt Vector ArcInfo coverage of temperature isolines for July, including Svalbard.", "license": "proprietary" }, + { + "id": "ARNd0117_103", + "title": "Economic regions of Europe", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-15, 35, 45, 70", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849218-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849218-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ARNd0117_103", + "description": "Economic boundaries within Europe. This shows which countries belong to the E?S, the agreement between countries belonging to EFTA (European Trade Agreement) and the EU. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo coverage showing countries belonging to the E?S Source Map Name: WDBII Projection: geographic Projection_meas: decimal degrees Feature_type: polyarcpt Vector ArcInfo coverage showing countries belonging to the E?S, the agreement between countries belonging to EFTA (European Trade Agreement) and the EU. This coverage was taken from the World Databank II data set. Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo coverage showing countries belonging to the E?S in a polar projection Projection: polar Projection_desc: long 10 0 0/lat 60 0 0 Projection_meas: metres Feature_type: polyarcpt Vector ArcInfo coverage showing countries belonging to the E?S (in a polar projection), the agreement between countries belonging to EFTA (European Trade Agreement) and the EU. This coverage was taken from the World Databank II data set.", + "license": "proprietary" + }, { "id": "ARNd0132_103", "title": "Conservation of Arctic Flora & Fauna (CAFF)", @@ -31241,52 +31488,52 @@ { "id": "ATL02_006", "title": "ATLAS/ICESat-2 L1B Converted Telemetry Data V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2547589158-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2547589158-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL02_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL02_006", "description": "This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations.", "license": "proprietary" }, { "id": "ATL02_006", "title": "ATLAS/ICESat-2 L1B Converted Telemetry Data V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL02_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2547589158-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2547589158-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL02_006", "description": "This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations.", "license": "proprietary" }, { "id": "ATL03_006", "title": "ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL03_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL03_006", "description": "This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03.", "license": "proprietary" }, { "id": "ATL03_006", "title": "ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL03_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL03_006", "description": "This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03.", "license": "proprietary" }, @@ -31449,26 +31696,26 @@ { "id": "ATL09_006", "title": "ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL09_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL09_006", "description": "This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "license": "proprietary" }, { "id": "ATL09_006", "title": "ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL09_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL09_006", "description": "This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "license": "proprietary" }, @@ -31514,52 +31761,52 @@ { "id": "ATL11_006", "title": "ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2019-03-29", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL11_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL11_006", "description": "This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations.", "license": "proprietary" }, { "id": "ATL11_006", "title": "ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2019-03-29", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL11_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL11_006", "description": "This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations.", "license": "proprietary" }, { "id": "ATL12_006", "title": "ATLAS/ICESat-2 L3A Ocean Surface Height V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL12_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL12_006", "description": "This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "license": "proprietary" }, { "id": "ATL12_006", "title": "ATLAS/ICESat-2 L3A Ocean Surface Height V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL12_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL12_006", "description": "This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "license": "proprietary" }, @@ -31657,26 +31904,26 @@ { "id": "ATL15_003", "title": "ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2019-03-29", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776464171-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776464171-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL15_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776895930-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776895930-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL15_003", "description": "ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change.", "license": "proprietary" }, { "id": "ATL15_003", "title": "ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2019-03-29", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776895930-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776895930-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL15_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776464171-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776464171-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL15_003", "description": "ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change.", "license": "proprietary" }, @@ -31709,104 +31956,104 @@ { "id": "ATL16_005", "title": "ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL16_005", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL16_005", "description": "This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.", "license": "proprietary" }, { "id": "ATL16_005", "title": "ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL16_005", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL16_005", "description": "This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.", "license": "proprietary" }, { "id": "ATL17_005", "title": "ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL17_005", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL17_005", "description": "This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.", "license": "proprietary" }, { "id": "ATL17_005", "title": "ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL17_005", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL17_005", "description": "This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.", "license": "proprietary" }, { "id": "ATL19_003", "title": "ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -88, 180, 88", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL19_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL19_003", "description": "This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography.", "license": "proprietary" }, { "id": "ATL19_003", "title": "ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -88, 180, 88", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL19_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL19_003", "description": "This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography.", "license": "proprietary" }, { "id": "ATL20_004", "title": "ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-14", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL20_004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL20_004", "description": "ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.", "license": "proprietary" }, { "id": "ATL20_004", "title": "ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-14", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL20_004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL20_004", "description": "ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.", "license": "proprietary" }, @@ -36802,6 +37049,32 @@ "description": "The British Australian (and) New Zealand Antarctic Research Expedition (BANZARE) was a research expedition into Antarctica between 1929 and 1931, involving two voyages over consecutive Austral summers. This document describes the ship's log and station list taken from Biological Organisation and Station List by T. Harvey Johnston, BANZARE Reports, Series B, Vol I, Part 1, pages 1-48 Data are stored in an Access database. The 5 tables are banzare_noon_log_1929_1930 and banzare_noon_log_1930_1931 noon positions from page 46-47 - assumed log_date is local noon, latitude and longitude in decimals. banzare_stations_1929_1930 and banzare_stations_1930_1931 odate is station date (no time is given) depth is echo depth (metres) latg and long is refined positions using Google Earth and Kerguelen map on page 14 full_speed_nets_1930_1931 log of full sped nets - see pages 40-44; time is possibly UTC distance is travel of ship when net is deployed depth is possible depth of net in fathoms tow_speed is ship speed in knots", "license": "proprietary" }, + { + "id": "BANd0005_113", + "title": "NOAA Weekly Global Vegetation Index (GVI) - Asia - 1982 to 1989", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "20, -10, -170, 79", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848172-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848172-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0005_113", + "description": "NOAA Weekly Global Vegetation Index (GVI maxima) dataset for Asia. The dataset represents the period from April 1982 to December 1989", + "license": "proprietary" + }, + { + "id": "BANd0009_113", + "title": "NOAA AVHRR GAC Images of South East Asia - November 8-9, 1989", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "90, 4, 112, 30", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848446-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848446-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0009_113", + "description": "Global Area Coverage (GAC) 5 band data for South East Asia derived from the NOAA Satellite AVHRR sensor Global Area Coverage (GAC)", + "license": "proprietary" + }, { "id": "BANd0016_113", "title": "AVHRR False Color Composition, Kuwait region, January 21-March 1, 1991", @@ -36815,6 +37088,162 @@ "description": "NOAA-11 AVHRR 3 band False Color Composites of the Kuwait region from January 21, 1991 to March 1, 1991. Dataset includes 17 false color composites from imageries acquired over this period", "license": "proprietary" }, + { + "id": "BANd0018_113", + "title": "District Boundaries of India dataset", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "66.79, 6.58, 99.01, 36.96", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848622-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848622-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0018_113", + "description": "District boundaries of India dataset prepared for FAO by Department of Energy and natural resources, University of Illinois. 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Scene ID 8122003033500, Path 137, Row 55.", + "license": "proprietary" + }, + { + "id": "BANd0171_113", + "title": "Landsat MSS Image of Songkhla region, Thailand (27 June 1991)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "96.83, 4.8, 106.42, 21.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848448-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848448-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0171_113", + "description": "Landsat MSS 5 band Image of Songkhla region, Thailand on 27 June 1991 obtained from the MSS Historical Archive at EROS Data Center, USA. Scene ID unknown, Path 138, Row 55.", + "license": "proprietary" + }, { "id": "BANd0173_113", "title": "Classified Landsat MSS Image (30 March 1976) of Lake Songkhla", @@ -36984,6 +38362,110 @@ "description": "Land classification of Songkhla lake region using Landsat TM Image of 20 September 1991 used in the ONEB-ILEC-GRID project on the study of Lake Songkhla region.", "license": "proprietary" }, + { + "id": "BANd0175_113", + "title": "Forest Classification Map of Thailand", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "96.83, 4.8, 106.42, 21.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847856-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847856-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0175_113", + "description": "Classified Digital Forest Map of Thailand together with National and Provincial boundaries.", + "license": "proprietary" + }, + { + "id": "BANd0176_113", + "title": "National & Provincial Boundaries map of Thailand from WBD II", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "96.83, 4.8, 106.42, 21.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846669-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846669-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0176_113", + "description": "Digital map of National and Provincial boundaries of Thailand compiled from the World Boudary Database II (WBD II). Updated to 76 provinces from Royal Thai Survey Department Map.", + "license": "proprietary" + }, + { + "id": "BANd0177_113", + "title": "Hydrology (Rivers and Lakes) map of Thailand from WBD II", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "96.83, 4.8, 106.42, 21.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847541-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847541-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0177_113", + "description": "Digital map of Hydrology consisting Rivers and Lakes of Thailand compiled from the World Boundary Database II (WBD II).", + "license": "proprietary" + }, + { + "id": "BANd0179_113", + "title": "Elevation map of Thailand from Global Elevation dataset ETOPO5", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "96.83, 4.8, 106.42, 21.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848758-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848758-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0179_113", + "description": "Digital Elevation Contour map of Thailand at 100 meters contour interval produced from the Global Elevation ETOPO5 dataset.", + "license": "proprietary" + }, + { + "id": "BANd0181_113", + "title": "Landsat-5 TM Image of Suratthani, Thailand (4 March 1990)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "96.83, 4.8, 106.42, 21.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847717-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847717-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0181_113", + "description": "Landsat-5 TM 7 band Image of Suratthani region, Thailand on 4 March 1990. Scene ID 52194 - 030019, Path 129, Row 54.", + "license": "proprietary" + }, + { + "id": "BANd0182_113", + "title": "Landsat-5 TM Image of Thailand-Cambodia border (27 March 92)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "96.83, 4.8, 106.42, 21.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846608-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846608-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0182_113", + "description": "Landsat-5 TM 7 band Image of Thailand-Cambodia border on 27 March 1992. Scene ID 52948-025018, Path 127, Row 52.", + "license": "proprietary" + }, + { + "id": "BANd0183_113", + "title": "Forest-Nonforest of Thailand 1991", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "96.83, 4.8, 106.42, 21.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847520-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847520-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0183_113", + "description": "Forest Area of Thailand 1991", + "license": "proprietary" + }, + { + "id": "BANd0184_113", + "title": "Landuse Map of Thailand", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "96.83, 4.8, 106.42, 21.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848628-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848628-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0184_113", + "description": "Landuse Map of Thailand 197 classes", + "license": "proprietary" + }, { "id": "BANd0185_113", "title": "Asia Biomass Dataset 1980 (5 files)", @@ -37023,6 +38505,45 @@ "description": "Classified image of the Northern Highland of Thailand, Myanmar and Laos (Golden Triangle). Under the contract of TREES project with JRC (Ispra) Italy. Path 131 Row 46", "license": "proprietary" }, + { + "id": "BANd0193_113", + "title": "Landsat-5 TM Image of Huay Kha Kang, Thailand (17 Mar '98)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "96.83, 4.8, 106.42, 21.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847522-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847522-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0193_113", + "description": "Landsat-5 TM 7 bands Image of Thailand on 17 Mar 1998. Path 130, Row 49 Quad 9.", + "license": "proprietary" + }, + { + "id": "BANd0194_113", + "title": "Landsat-5 TM Image of Huay Kha Kang, Thailand (6 Mar '94)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "96.83, 4.8, 106.42, 21.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848643-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848643-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0194_113", + "description": "Landsat-5 TM 7 bands Image of Thailand on 6 Mar 1994. Path 130, Row 50 Quad 6", + "license": "proprietary" + }, + { + "id": "BANd0195_113", + "title": "Landsat-5 TM Image of Huay Kha Kang, Thailand (9 Mar '95)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "96.83, 4.8, 106.42, 21.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848479-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848479-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0195_113", + "description": "Landsat-5 TM 7 bands Image of Thailand on 9 Mar 1995. Path 130, Row 50 Quad 6", + "license": "proprietary" + }, { "id": "BANd0198_113", "title": "Classified Landsat TM image (25 Jan '92) of East Cambodia", @@ -37049,6 +38570,19 @@ "description": "Classified image of the Western part of Cambodia. Under the contract of TREES project with JRC (Ispra) Italy. Path 127 Row 52", "license": "proprietary" }, + { + "id": "BANd0201_113", + "title": "Nature Reserves in the Coastal Zone of Cambodia", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "102.28, 10.07, 107.98, 14.86", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847530-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847530-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0201_113", + "description": "Nature Reserves in the Coastal Zone of Cambodia with names", + "license": "proprietary" + }, { "id": "BANd0202_113", "title": "Depleted Mangrove Forest in the Coastal Zone of Cambodia", @@ -37075,6 +38609,84 @@ "description": "Coral Reef of Cambodia classified into 4 classes", "license": "proprietary" }, + { + "id": "BANd0204_113", + "title": "Fishing Ground in the Coastal Zone of Cambodia", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "102.28, 10.07, 107.98, 14.86", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848945-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848945-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0204_113", + "description": "It shows the marine fish exploitation caught by the villagers of the coastal provinces and actual yield in 1994 - 95", + "license": "proprietary" + }, + { + "id": "BANd0206_113", + "title": "Landsat-5 TM Image of Nepal (25 Feb '97)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "79.9, 26, 88.84, 30.88", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848574-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848574-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0206_113", + "description": "Landsat-5 TM 7 bands Image of Nepal on 25 Feb 1997. Path 139, Row 42", + "license": "proprietary" + }, + { + "id": "BANd0207_113", + "title": "NOAA AVHRR LAC Data for the Philippines (11 Feb '85)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "116.68, 4.85, 127.23, 19.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848442-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848442-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0207_113", + "description": "NOAA AVHRR LAC Data (11 Feb '85) for the Philippines supplied by STAR program of AIT.", + "license": "proprietary" + }, + { + "id": "BANd0209_113", + "title": "Elevation map of Philippines from Global Elevation data ETOPO5", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "116.68, 4.85, 127.23, 19.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847211-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847211-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0209_113", + "description": "Digital Elevation Contour map of Philippines at 100 meters contour interval produced from the global Elevation ETOPO5 dataset", + "license": "proprietary" + }, + { + "id": "BANd0210_113", + "title": "Hydrology (Rivers and Lakes) map of Philippines from WBDII", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "116.68, 4.85, 127.23, 19.22", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846656-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846656-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0210_113", + "description": "Digital map of Hydrology consisting Rivers and Lakes of Philippines compiled from the World Boundary Database II (WBDII)", + "license": "proprietary" + }, + { + "id": "BANd0213_113", + "title": "Nature Reserves in the Coastal Zone of China", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "70.83, 15.06, 137.97, 56.58", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847866-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847866-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0213_113", + "description": "Nature Reserves in the Coastal Zone of China with general sites information", + "license": "proprietary" + }, { "id": "BANd0214_113", "title": "Coral Reef of China", @@ -37101,6 +38713,45 @@ "description": "District boundaries of Vietnam", "license": "proprietary" }, + { + "id": "BANd0217_113", + "title": "Geological map of Vietnam", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "101.43, 7.75, 110.25, 24.05", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848441-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848441-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0217_113", + "description": "Geological complex of Vietnam", + "license": "proprietary" + }, + { + "id": "BANd0218_113", + "title": "Main rivers of Vietnam", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "101.43, 7.75, 110.25, 24.05", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847445-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847445-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0218_113", + "description": "Main rivers of Vietnam", + "license": "proprietary" + }, + { + "id": "BANd0219_113", + "title": "Main roads of Vietnam", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "101.43, 7.75, 110.25, 24.05", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847235-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847235-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BANd0219_113", + "description": "Main roads of Vietnam", + "license": "proprietary" + }, { "id": "BANd0221_113", "title": "Asia Pacific Mosaic Poster 1993", @@ -37907,6 +39558,32 @@ "description": "Measurements made in the Amazon River outflow region of the Atlantic Ocean off the coast of Brazil in 2002.", "license": "proprietary" }, + { + "id": "BRD_LSC002", + "title": "Index of Biotic Integrity for Fish Communities in the DE River Basin", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-06-01", + "end_date": "1996-08-01", + "bbox": "-76, 41, -75, 42", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411633-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411633-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BRD_LSC002", + "description": "Two years of field work have been completed in the development of an index of biotic integrity (IBI) for fish communities in the middle-to-upper Delaware River basin (Delaware Water Gap to Callicoon, New York). Fish were collected in riffles and pools on 200 m segments of eight tributaries. Collections were made concurrently in the Delaware River mainstem within 0.5 mile (downstream) of the same tributary mouths, in three habitat types: riffles, deep pools, and inshore submerged vegetation zones. Quality control on species identity, as well as length/weight/disease determination, has been completed on all specimens. A total of 15,673 fish were collected (7,655 in tributaries and 8,018 in mainstem habitats) representing 44 species (36 in tributaries and 36 in mainstem habitats). Fish community data (species richness, trophic composition, and population/health data) will be related to both water quality and land use data to develop IBIs. Water quality data, including a dozen or more physical, chemical, and biological parameters taken during the same seasons/years by Delaware River Basin Commission personnel, are currently being indexed for use in the models. Land use data in four categories (22 subcategories), obtained from the Anderson Level 2 database using GIS techniques, have been summarized for use in the models. Current work involves examination of the variance associated with traditional fish metrics and the identification of alternative metrics that may better explain the covariance with water quality and land use. The Research and Development Laboratory-Wellsboro (RDL-W) is located on 55 acres near Wellsboro, Pennsylvania (Tioga County). Laboratory facilities include 3 modern buildings, 8x200-foot concrete raceways, 3 production wells, and support equipment. The RDL-W conducts research for restoration of depleted fisheries and other aquatic biological resources. A diversified research program in ecology, conservation technology, genetics, and physiology emphases the integration of laboratory and field studies to develop scientifically sound approaches to the management of aquatic ecosystems. Research is directed primarily towards development of information and technology to increase understanding of aquatic ecosystems in the northeastern United States and to assist client agencies to better manage these ecosystems and their biota. Technical assistance is provided to clients throughout the nation.", + "license": "proprietary" + }, + { + "id": "BRD_LSC003_MAHA", + "title": "MAHA Stream Order Fish Community Study", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1993-10-01", + "end_date": "1997-09-01", + "bbox": "-81, 40, -75, 42", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548545-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548545-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/BRD_LSC003_MAHA", + "description": "The research establishes a national repository for fishieries data and management practices to improve technical understanding of the status, trends, causes, and effects of changes in native fish populations and their habitats and 2) increase investigations of fish contaminant impacts, habitat losses, and control of exotics to restore depleted or endangered fishes.", + "license": "proprietary" + }, { "id": "BRD_LSC_AMERSHAD001", "title": "American Shad Riverine Habitat Requirements", @@ -40273,6 +41950,19 @@ "description": "CAL_WFC_L1_IIR-ValStage1-V3-02 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera Level 1B 1 km Registered Science data. Version 3.02 represents a transition of the Lidar, IIR, and WFC processing and browse code to a new cluster computing system. No algorithm changes were introduced and very minor changes were observed between V3.01 and V3.02 as a result of the compiler and computer architecture differences. The primary Wide Field Camera Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During the normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The Wide Field Camera Level 1B 1 IIR Registered Science grid provides WFC data on the identical grid as the CALIPSO IIR data and is produced to facilitate the use of the WFC data in the IIR retrievals. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES.", "license": "proprietary" }, + { + "id": "CAM5K30CFCLIM_003", + "title": "Combined ASTER and MODIS Emissivity database over Land (CAMEL) Coefficient Climatology Monthly Global 0.05Deg V003", + "catalog": "LPCLOUD STAC Catalog", + "state_date": "2003-01-01", + "end_date": "2022-01-01", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3274449168-LPCLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3274449168-LPCLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/CAM5K30CFCLIM_003", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global coefficient climatology product (CAM5K30CFCLIM). The product is provided at 0.05 degree (~5 kilometer) resolution. The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product. They are congruent to the temporally equivalent CAM5K30EM emissivity data product. The HSR emissivity spectra for the same month each year and each unique combination of lab dataset version and number of Principal Components (PC)s are first computed independently and then combined via a weighted average. The weighted average over 2003 through 2021 (19 years) defines the weights by the number of samples from each unique combination. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Provided in the CAM5K30CFCLIM product are variables for PCA coefficients, the weights and sample numbers of the climatology coefficients used in the average calculation, sets of the number of PCA coefficients, laboratory version numbers, latitude, longitude, and land flag information. PCA coefficients depend on the lab PC data version and the number of PCs used. ", + "license": "proprietary" + }, { "id": "CAM5K30CF_002", "title": "Combined ASTER and MODIS Emissivity database over Land (CAMEL) Coefficient Monthly Global 0.05Deg V002", @@ -40299,6 +41989,32 @@ "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly coefficients at 0.05 degree (~5 kilometer) resolution (CAM5K30CF). The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product and are congruent to the temporally equivalent CAM5K30EM (https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) emissivity data product. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Provided in the CAM5K30CF product are layers for PCA coefficients, number of PCA coefficients, laboratory version, snow fraction derived from MODIS Snow Cover data (MOD10), latitude, longitude, and the CAMEL quality information. PCA coefficients are dependent on the version of lab Principal Component (PC) data and the number of PCs used. ", "license": "proprietary" }, + { + "id": "CAM5K30COVCLIM_003", + "title": "Combined ASTER and MODIS Emissivity database over Land (CAMEL) Covariances Climatology Monthly Global 0.25Deg V003", + "catalog": "LPCLOUD STAC Catalog", + "state_date": "2003-01-01", + "end_date": "2022-01-01", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3274450252-LPCLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3274450252-LPCLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/CAM5K30COVCLIM_003", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global covariances climatology product (CAM5K30COVCLIM). The product is provided at 0.25 degree (~25 kilometer) resolution. The CAMEL covariance product includes the mean and variance of the covariance matrixes created for each month from 2003 through 2021 (19 years) on a 0.25 x 0.25 degree grid of 416 spectral points from the V003 CAMEL Emissivity product (CAM5K30EM). Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Provided in the CAM5K30COVCLIM product are variables for the mean and variance of the emissivity, latitude, longitude, spectral frequencies, and number of observations. ", + "license": "proprietary" + }, + { + "id": "CAM5K30EMCLIM_003", + "title": "Combined ASTER and MODIS Emissivity database over Land (CAMEL) Emissivity Climatology Monthly Global 0.05Deg V003", + "catalog": "LPCLOUD STAC Catalog", + "state_date": "2003-01-01", + "end_date": "2022-01-01", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3274448375-LPCLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3274448375-LPCLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/CAM5K30EMCLIM_003", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global emissivity climatology product (CAM5K30EMCLIM). This 0.05 degree (~5 kilometer) resolution product represents the mean emissivity from 2003 through 2021 (19 years). Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Variables provided in the CAM5K30EMCLIM product include latitude, longitude, wavelength, number of samples used to calculate climatology, CAMEL quality flag, snow fraction derived from MODIS (MOD10), and CAMEL Emissivity.", + "license": "proprietary" + }, { "id": "CAM5K30EM_002", "title": "Combined ASTER and MODIS Emissivity database over Land (CAMEL) Emissivity Monthly Global 0.05Deg V002", @@ -40325,6 +42041,19 @@ "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) emissivity database and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the UWBF 5 kilometer resolution, merging of the five ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and the UW Baseline Fit (UWBF) Emissivity quality information. ", "license": "proprietary" }, + { + "id": "CAM5K30UCCLIM_003", + "title": "Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Climatology Monthly Global 0.05Deg V003", + "catalog": "LPCLOUD STAC Catalog", + "state_date": "2003-01-01", + "end_date": "2022-01-01", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3274446180-LPCLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3274446180-LPCLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/CAM5K30UCCLIM_003", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global uncertainty climatology product (CAM5K30UCCLIM). The product is provided at 0.05 degree (~5 kilometer) resolution. The 13 hinge-point uncertainty climatology is computed by taking an average over each available month from 2003 through 2021 (19 years) and includes three independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty climatology is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Corresponding emissivity values can be found in the CAM5K30EMCLIM data product. Provided in the CAM5K30UCCLIM product are variables for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, and total uncertainty quality flag information.", + "license": "proprietary" + }, { "id": "CAM5K30UC_002", "title": "Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Monthly Global 0.05Deg V002", @@ -54469,6 +56198,71 @@ "description": "Images provided by an All Sky Camera installed at the SAS Juan Carlos I on Livingston Island since 2023", "license": "proprietary" }, + { + "id": "CNES_http__cnes.fr_ark_68059_0b222c7948a7ffa8035b3053b4b3ad30_IDN_1.4", + "title": "JASON 3 experiment: Satellite information", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2016-01-17", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2226555601-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2226555601-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/CNES_http__cnes.fr_ark_68059_0b222c7948a7ffa8035b3053b4b3ad30_IDN_1.4", + "description": "By succeeding Topex/Poseidon, Jason-1 and Jason-2, Jason-3 extends the high-precision ocean altimetry data record to support climate monitoring, operational oceanography and seasonal forecasting. Jason-3 is the result of a joint effort by Cnes, NASA, EUMETSAT and NOAA, pursuing a heritage that has been keeping the oceans under close watch for more than 20 years. Partnership is as for Jason-2, but the operational agencies (NOAA and EUMETSAT) take the lead; Cnes serves as the system coordinator and all partners -including NASA- support science team activities. By continuing long-term operational oceanography observations, Jason-3 is a key element of the constellation of altimetry satellites in the years ahead. Succeeding Jason-2, it boasts a number of enhancements to its systems and in processing of the data it delivers. From June 2016, Jason-3 is the reference altimetry mission.The satellite is built around a PROTEUS bus carrying a typical suite of altimetry mission instruments that acquires highly accurate measurements of ocean surface height to extend the data record compiled by Topex/Poseidon, Jason-1 and Jason-2. The orbit is the traditional T/P-Jason orbit -non-sun-synchronous, 1336 km, 66° inclination on a Proteus platform. By succeeding Topex/Poseidon, Jason-1 and Jason-2, Jason-3 extends the high-precision ocean altimetry data record to support climate monitoring, operational oceanography and seasonal forecasting. Jason-3 is the result of a joint effort by CNES, NASA, EUMETSAT and NOAA, pursuing a heritage that has been keeping the oceans under close watch for more than 20 years. Partnership is as for Jason-2, but the operational agencies (NOAA and EUMETSAT) take the lead; CNES serves as the system coordinator and all partners -including NASA- support science team activities. By continuing long-term operational oceanography observations, Jason-3 is a key element of the constellation of altimetry satellites in the years ahead. From June 2016, Jason-3 is the reference mission for the altimetry constellation. Succeeding Jason-2, it boasts a number of enhancements to its systems and in processing of the data it will deliver. The satellite is built around a PROTEUS bus carrying a typical suite of altimetry mission instruments that acquires highly accurate measurements of ocean surface height to extend the data record compiled by Topex/Poseidon, Jason-1 and Jason-2. The orbit is the traditional T/P-Jason orbit -non-sun-synchronous, 1336 km, 66° inclination on a Proteus platform. [http://www.aviso.altimetry.fr/en/missions/future-missions/jason-3.html] [http://www.aviso.altimetry.fr/en/missions/future-missions/jason-3.html]", + "license": "proprietary" + }, + { + "id": "CNES_http__cnes.fr_ark_68059_0b7a761c3e62fd4332cd4f66eff0c845_IDN_1.2", + "title": "HYDROWEB experiment: RIVER PRODUCT", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2226555501-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2226555501-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/CNES_http__cnes.fr_ark_68059_0b7a761c3e62fd4332cd4f66eff0c845_IDN_1.2", + "description": "The products offered by the Hydroweb project consist of continuous, long-duration time-series of the levels of large lakes with surface areas over 100 km2, reservoirs and the 20 biggest rivers in the world.The operational measurement series are updated no later than 1.5 days after a new altimetry measurement becomes available. They cover about 80 large lakes and 300 measurement points along about 20 major rivers.The research measurement series are updated at regular intervals according to the progress made with processing by the LEGOS laboratory. They cover about 150 large lakes and 1,000 measurement points along about 20 major rivers.Continental waters account for only 0.65% of the total amount of water on Earth, 97% being stored in the oceans and 2.15% in the cryosphere. However, these waters have a significant impact on life on Earth and household needs. They also play a major role in climate variability. Water on Earth is continually recycled through precipitation, evaporation and run-off towards the sea. The increasingly accurate characterisation of the water cycle on land surfaces enables more accurate forecasting of the climate and more careful control of global water resources (human consumption and activities such as agriculture, urbanisation and the production of hydroelectric power, for example). Radar echoes over land surfaces are hampered by interfering reflections due to water, vegetation and topography. As a consequence, waveforms (e.g., the power distribution of the radar echo within the range window) may not have the simple broad peaked shape seen over ocean surfaces, but can be complex, multi-peaked, preventing from precise determination of the altimetric height. If the surface is flat, problems may arise from interference between the vegetation canopy and water from wetlands, floodplains, tributaries and main river. In other cases, elevated topography sometimes prevents the altimeter to lock on the water surface, leading to less valid data than over flat areas. The time series available in Hydroweb are constructed using Jason-2 and Saral GDRs. The basic data used for rivers are the 20 or 40Hz data(\u201chigh rate\u201d data).To construct river water level time series, we need to define virtual stations corresponding to the intersection of the satellite track with the river. For that purpose, we select for each cycle a rectangular \u201cwindow\u201d taking into account all available along track high rate altimetry data over the river area. The coordinate of the virtual station is defined as the barycenter of the selected data within the \u201cwindow\u201d. After rigourous data editing, all available high rate data of a given cycle are geographically averaged. At least two high rate data are needed for averaging otherwise no mean height is provided. Scattering of high rate data with respect to the mean height defines the uncertainty associated with the mean height.The water level and volume time series is operationally updated less than 1.5 working days after the availability of the input altimetry data, for some virtual stations on rivers. Other virtual stations are monitored on a research mode basis. [https://www.theia-land.fr/fr/projets/hydroweb]", + "license": "proprietary" + }, + { + "id": "CNES_http__cnes.fr_ark_68059_3bf5d54adb12f57809057d19c2ea4f25_IDN_1.2", + "title": "HYDROWEB experiment: LAKE PRODUCT", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2226555523-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2226555523-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/CNES_http__cnes.fr_ark_68059_3bf5d54adb12f57809057d19c2ea4f25_IDN_1.2", + "description": "The products offered by the Hydroweb project consist of continuous, long-duration time-series of the levels of large lakes with surface areas over 100 km2, reservoirs and the 20 biggest rivers in the world.The operational measurement series are updated no later than 1.5 days after a new altimetry measurement becomes available. They cover about 80 large lakes and 300 measurement points along about 20 major rivers.The research measurement series are updated at regular intervals according to the progress made with processing by the LEGOS laboratory. They cover about 150 large lakes and 1,000 measurement points along about 20 major rivers.Continental waters account for only 0.65% of the total amount of water on Earth, 97% being stored in the oceans and 2.15% in the cryosphere. However, these waters have a significant impact on life on Earth and household needs. They also play a major role in climate variability. Water on Earth is continually recycled through precipitation, evaporation and run-off towards the sea. The increasingly accurate characterisation of the water cycle on land surfaces enables more accurate forecasting of the climate and more careful control of global water resources (human consumption and activities such as agriculture, urbanisation and the production of hydroelectric power, for example). Altimetry missions used are repetitive, i.e. the satellite overflow the same point at a given time interval (10, 17 or 35 days depending on the satellite). The satellite does not deviate from more than +/-1 km across its track. A given lake can be overflown by several satellites, with potentially several passes. The water level and volume time series is operationally updated less than 1.5 working days after the availability of the input altimetry data, for some lakes. Other lakes are also monitored on a research mode basis. [https://www.theia-land.fr/fr/projets/hydroweb]", + "license": "proprietary" + }, + { + "id": "CNES_http__cnes.fr_ark_68059_467a7851f76cf868d9568f8d85bd664a_IDN_1.6", + "title": "JASON 2 experiment: Geophysical products", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2008-06-20", + "end_date": "", + "bbox": "-180, -66, 180, 66", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2226555596-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2226555596-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/CNES_http__cnes.fr_ark_68059_467a7851f76cf868d9568f8d85bd664a_IDN_1.6", + "description": "The JASON-2 project is a response to the international demand for programs to study and observe oceans and the climate, through a worldwide ocean observation system. It is a continuation to the TOPEX/POSEIDON and JASON-1 altimetry missions developed by CNES and NASA. Altimetry, i.e. the precise measurement of ocean surface topography, has indeed become since 1992 (launch of TOPEX/POSEIDON) an essential tool for the study of oceans on a global scale.JASON-2 is part of cooperation between CNES, EUMETSAT, NASA and NOAA. Space and ground segments of the JASON-2 mission strongly inherit from the JASON-1 mission.Onboard the JASON-2 satellite, which uses a PROTEUS platform, the payload is composed of a Poseidon-3 radar altimeter supplied by CNES, an Advanced Microwave Radiometer (AMR) supplied by NASA/JPL, and a triple system for precise orbit determination: the DORIS instrument (CNES), GPS receiver and a Laser Retroflector Array (LRA) (NASA). Three further onboard instruments (T2L2, LPT, CARMEN-2) will also be included.In order to ensure continuity and optimal inter-calibration of observations over the long term, JASON-2 flies the same orbit as JASON-1 and TOPEX/POSEIDON. Moreover, data processing is integrated into the CNES ground segment \"SALP\" (altimetry and precise positioning system), which already operates the altimetry missions TOPEX/POSEIDON, JASON-1, ENVISAT, GFO, whose data is distributed on the AVISO website.The level 2 data stored at CNES are those addressed in this description. The JASON-2 project is a response to the international demand for programs to study and observe oceans and the climate, through a worldwide ocean observation system. It is a continuation to the TOPEX/POSEIDON and JASON-1 altimetry missions developed by CNES and NASA. Altimetry, i.e. the precise measurement of ocean surface topography, has indeed become since 1992 (launch of TOPEX/POSEIDON) an essential tool for the study of oceans on a global scale.JASON-2 is part of cooperation between CNES, EUMETSAT, NASA and NOAA. Space and ground segments of the JASON-2 mission strongly inherit from the JASON-1 mission.Onboard the JASON-2 satellite, which uses a PROTEUS platform, the payload is composed of a Poseidon-3 radar altimeter supplied by CNES, an Advanced Microwave Radiometer (AMR) supplied by NASA/JPL, and a triple system for precise orbit determination: the DORIS instrument (CNES), GPS receiver and a Laser Retroflector Array (LRA) (NASA). Three further onboard instruments (T2L2, LPT, CARMEN-2) will also be included.In order to ensure continuity and optimal inter-calibration of observations over the long term, JASON-2 will fly the same orbit as JASON-1 and TOPEX/POSEIDON. Moreover, data processing will be integrated into the CNES ground segment \"SALP\" (altimetry and precise positioning system), which already operates the altimetry missions TOPEX/POSEIDON, JASON-1, ENVISAT, GFO, whose data is distributed on the AVISO website.The level 2 data stored at CNES are those addressed in this description. The data described here are part of the European Directive INSPIRE. [http://smsc.cnes.fr/JASON2/] [http://smsc.cnes.fr/JASON2/]", + "license": "proprietary" + }, + { + "id": "CNES_http__cnes.fr_ark_68059_54736f5916e9134386cb9725dbbe67ae_IDN_1.5", + "title": "JASON 1 experiment: Geophysical products", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-12-07", + "end_date": "2013-07-03", + "bbox": "-180, -66, 180, 66", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2226555543-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2226555543-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/CNES_http__cnes.fr_ark_68059_54736f5916e9134386cb9725dbbe67ae_IDN_1.5", + "description": "JASON-1 is the follow-on to Topex/Poseidon, whose main features it has inherited (orbit, instruments, measurement accuracy, etc.). JASON-1 is the result of close international cooperation between space agencies (CNES and NASA), industry and data users working to accomplish a benchmark mission in terms of data quality and science and economic return. JASON-1 flies on the same orbit as TOPEX/POSEIDON to ensure a continuity and an optimal inter comparison for long term observations. The data processing is integrated to the CNES \"SALP\" (Systeme d'Altimetrie et de Localisation Precise) Ground Segment, which operates many other missions (TOPEX/POSEIDON, ENVISAT, GFO altimetry missions, JASON-2, SARAL...) whose data are distributed on AVISO web site. The level 2 data stored at CNES are those addressed in this description. JASON-1 is the follow-on to Topex/Poseidon, whose main features it has inherited (orbit, instruments, measurement accuracy, etc.). JASON-1 is the result of close international cooperation between space agencies (CNES and NASA), industry and data users working to accomplish a benchmark mission in terms of data quality and science and economic return. JASON-1 flies on the same orbit as TOPEX/POSEIDON to ensure a continuity and an optimal inter comparison for long term observations. The data processing is integrated to the CNES \"SALP\" (Systeme d'Altimetrie et de Localisation Precise) Ground Segment, which operates many other missions (TOPEX/POSEIDON, ENVISAT, GFO altimetry missions, JASON-2, SARAL...) whose data are distributed on AVISO web site.The level 2 data stored at CNES are those addressed in this description. [http://smsc.cnes.fr/JASON/index.htm] [http://smsc.cnes.fr/JASON/index.htm]", + "license": "proprietary" + }, { "id": "CNNADC_1999_ARCTIC_MAP", "title": "1:5000000 map of Arctic Ocean area", @@ -58538,6 +60332,19 @@ "description": "DSCOVR_EPIC_L2_GLINT_01 is Version 1 of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 2 glint data product. This product indicates the presence of glint caused by the single scattering specular reflection of sunlight either from horizontally oriented ice crystals floating in clouds or from smooth, highly reflective water surfaces. Such glints can prevent accurate retrievals of atmospheric and surface properties using existing algorithms but can also be used to learn more about the glint-causing objects. The glint detection algorithm relies on EPIC taking images at different wavelengths at slightly different times. For example, red images are taken about 4 minutes after blue images. During these few minutes, the Earth's rotation changes the scene's orientation by one degree, affecting whether EPIC observations at a specific wavelength will capture or miss the narrowly focused specular reflection from ice clouds or smooth water surfaces. As a result, sharp brightness differences between EPIC images taken a few minutes apart can identify glint signals. The glint product includes three parameters for each pixel in the part of EPIC images where the alignment of solar and viewing directions is suitable for sun glint observations: (1) The surface type flag shows whether the area of a pixel is covered mainly by water, desert, or non-desert land; (2) The glint angle\u2014the angle between the actual EPIC view direction and the direction of looking straight into the specular reflection from a perfectly horizontal surface\u2014tells how favorable the EPIC view direction is for glint detection and can help in estimating the distribution of ice crystal orientation; (3) The glint mask indicates whether or not glint has been detected.", "license": "proprietary" }, + { + "id": "DSCOVR_EPIC_L2_MAIAC-DAILY_01", + "title": "MAIAC Daily V01", + "catalog": "LARC_ASDC STAC Catalog", + "state_date": "2015-06-13", + "end_date": "", + "bbox": "180, -90, -180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3264309299-LARC_ASDC.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3264309299-LARC_ASDC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC_ASDC/collections/DSCOVR_EPIC_L2_MAIAC-DAILY_01", + "description": "DSOCVR EPIC_L2 MAIAC-Daily_01 contains plots of data generated from DSCOVR_EPIC_L2_MAIAC_03, the DSCOVR EPIC L2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) Version 03 data product. Data collection for this product is ongoing. The datasets visualized include Aerosol Layer Height (ALH), Aerosol Optical Depth, and Single Scattering Albedo at 340nm, 388nm, 443nm, 551 nm, 680nm, and 780nm. Level 2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) provides an interdisciplinary suite of products for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC). The current version 3 reports the following products: a) Atmosphere: cloud mask, global aerosol optical depth at 443nm and 551nm, fine mode fraction (over the ocean), aerosol layer height (ALH) globally, and spectral aerosol absorption for detected biomass burning or mineral dust aerosols. The absorption information includes single scattering albedo at 340-780nm range, imaginary refractive index at 680nm (k0), and Spectral Absorption Exponent (SAE) characterizing spectral increase of imaginary refractive index from Red towards UV wavelengths. The aerosol optical properties {AOD, ALH, k0, SAE} are retrieved simultaneously by matching EPIC measurements in the UV-NIR range, including atmospheric oxygen A- and B-bands. b) Land: atmospherically corrected spectral bidirectional reflectance factors (BRF) along with Lambertian surface reflectance and bidirectional reflectance distribution function (BRDF) for the backscattering view geometries of EPIC. The BRDF is represented by three parameters of the Ross-Thick Li-Sparse model. c) Ocean: Water leaving reflectance (non-dimensional) at Ultraviolet-Visible (UV-Vis) bands. The parameters are provided at 10 km resolution on a zonal sinusoidal grid with a 1\u2014to 2-hour temporal frequency. MAIAC version 03 also provides gap-filled global composite products for the Normalized Difference Vegetation Index (NDVI) over land and water, leaving reflectance in 5 UV-Vis bands over the global ocean. The composite products represent a weighted running average where the weight of the latest observation is maximized towards the local noon and low aerosol conditions.", + "license": "proprietary" + }, { "id": "DSCOVR_EPIC_L2_MAIAC_02", "title": "DSCOVR EPIC L2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) Version 02", @@ -60098,6 +61905,19 @@ "description": "A collection of petrographic thin sections made from rock samples collected by USGS field geologists in Alaska. Many of the sections have corresponding descriptions cards; thin sections number 30,000. Written requests or appointment only. Access to materials restricted to on-site use for non-USGS employees.", "license": "proprietary" }, + { + "id": "EARTH_CRUST_AUS_BMR_Min_Loc_DB", + "title": "Mineral Occurrence Location Data Base; BMR, Australia", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1989-08-01", + "end_date": "", + "bbox": "110, -45, 155, -10", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231957528-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231957528-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_AUS_BMR_Min_Loc_DB", + "description": "The Australian Mineral Occurrence Location Database provides information on mineral occurrence and deposit locations in Australia. Currently, data covers 52% of Australia by area. The data base contains the name of mineral occurrences with the geographical coordinates of each occurrence. The spatial resolution: varies from 10m to 10 km, mainly about 500m. Sources of the data are named, such as maps, bibliographies, or correspondence. Commodities associated with the occurrence are delineated. The data is about 32 Megabytes and is rectified to standard coordinates. There are charges for the data. Order form and information about the data set are available from B. Elliott, Project Manager, Mineral Databases (telephone 06-2499502) or BMR Publication Sales (fax 06-2499982).", + "license": "proprietary" + }, { "id": "EARTH_CRUST_USGS_AK_NOTEBOOKS1", "title": "Alaskan Geologic Field Notebooks; USGS, Anchorage", @@ -60111,6 +61931,84 @@ "description": "These notebooks are the original field records of geologic observations made by USGS geologists working in Alaska. They contain field stations, sample numbers, rock types and descriptions, terrain conditions and outcrop sketches. Some also report topographic measurements, daily weather, and camp life. A few personal diaries of the early explorers are preserved. The notebooks may be viewed by written requests or appointment only. Access to certain materials may be restricted for non-government employees. Microfilm for these records is stored in Menlo Park, California. The data consists of 3,600 notebooks.", "license": "proprietary" }, + { + "id": "EARTH_CRUST_USGS_COAL_NCRDS_DB", + "title": "National Coal Resources Data System; USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1966-01-01", + "end_date": "", + "bbox": "-125, 25, -66, 50", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554417-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554417-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_USGS_COAL_NCRDS_DB", + "description": "The basic National Coal Resources Data System (NCRDS) allows users to interactively retrieve information on coal quantity and quality and to build new resource data from ongoing research on the geology of coal by the U.S. Geological Survey and state agencies. The NCRDS is an automated system. Data bases accessed contain some proprietary data. Access to non-U.S. Geological Survey users is limited to nonproprietary data. NCRDS is a user-oriented computerized storage, retrieval, and display system devised by the U.S. Geological Survey to assess the quantity and quality of national coal resources. The U.S. Geological Survey has initiated a 5- to 10-year program to provide point-source coverage for the coal-bearing rocks in the U.S. Cooperative projects with many state geologic agencies have been funded to supplement U.S. Geological Survey work and to amass the volume of data required to assess U.S. coal resources. Currently, files containing summary areal coal tonnage estimates and proximate/ultimate chemical analyses and point-located major, minor, and trace-element analyses are available. The point-source files are used to calculate resource estimates and to depict trends in the occurrence and chemical characteristics of coal. Primary data for measurements, coal-bed outcrop patterns, burned, channeled, and mined-out areas, and geochemical analyses. System software can calculate coal resource estimates, generate overburden or interburden distribution, and delineate areas of coal with selected quality parameters (for example, >1 percent sulfur, <50 ppm Zinc) within specified boundaries, (for example, county, quadrangle, or lease tract). Data may be displayed descriptively by preformated tables, user-determined listings, and selected statistics or graphically by two-and three-dimensional diagrams, trend surfaces, isoline maps, and stratigraphic sections. The system runs on a SUN Network of servers and workstations located in Reston, VA. and Denver, CO.", + "license": "proprietary" + }, + { + "id": "EARTH_CRUST_USGS_GeoNames", + "title": "Geologic Names of the U.S., Territories and Possessions, USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1800-01-01", + "end_date": "", + "bbox": "-125, 25, -66, 50", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552995-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552995-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_USGS_GeoNames", + "description": "The data base contains an annotated index lexicon of formal geologic nomenclature of the United States, territories, and possessions, with data on location, geologic age, U.S. Geological Survey usage, lithology, geologic province, thickness at type section, location of type section, and naming reference for each geologic unit.", + "license": "proprietary" + }, + { + "id": "EARTH_CRUST_USGS_NPRA_GEOCHEM1", + "title": "National Petroleum Reserve-Alaska Geochemical Data; USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1977-01-01", + "end_date": "1978-12-31", + "bbox": "-162, 60, -152, 70", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554279-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554279-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_USGS_NPRA_GEOCHEM1", + "description": "The National Petroleum Reserve in Alaska (NPRA) is located in the primitive wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began some geological surveying in this area in the early 1900's, and the U.S. Navy began geological and geophysical surveys and drilling in 1945 to appraise the petroleum potential of the Reserve. GEOCHEMICAL DATA Includes microfilm reels of Phase I, II, and III geochemical analyses of well cores. See also the NPPRA legacy data archive:http://energy.cr.usgs.gov/", + "license": "proprietary" + }, + { + "id": "EARTH_CRUST_USGS_NPRA_GEO_RPTS1", + "title": "National Petroleum Reserve-Alaska Geophysical & Geological Data Reports; USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1977-01-01", + "end_date": "", + "bbox": "-162, 60, -152, 70", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553277-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553277-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_USGS_NPRA_GEO_RPTS1", + "description": "The National Petroleum Reserve in Alaska (NPRA) is located in the primitive wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began some geological surveying in this area in the early 1900's, and the U.S. Navy began geological and geophysical surveys and drilling in 1945 to appraise the petroleum potential of the Reserve. GEOPHYSICAL AND GEOLOGICAL REPORTS A variety of reports are available from the USGS summarizing and interpreting geophysical and geological data about the NPRA. See the NPRA Legacy Data Archive: http://energy.cr.usgs.gov/", + "license": "proprietary" + }, + { + "id": "EARTH_CRUST_USGS_NPRA_SEISMIC1", + "title": "National Petroleum Reserve (NPR) Alaska Seismic Reflection Data; USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1972-01-01", + "end_date": "1981-12-31", + "bbox": "-162, 60, -152, 70", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555099-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555099-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_USGS_NPRA_SEISMIC1", + "description": "The National Petroleum Reserve in Alaska (NPRA) is located in the primitive wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began some geological surveying in this area in the early 1900's, and the U.S. Navy began geological and geophysical surveys and drilling in 1945 to appraise the petroleum potential of the Reserve. Information on surveys prior to 1955 may be obtained from the Branch of Alaskan Geology at: Alaska Technical Data Unit Mail Stop 48 U.S. Geological Survey 345 Middlefield Road Menlo Park, CA 94025. SEISMIC DATA: Common Depth Point (CDP) seismic reflection data and documentation covering about 13,000 miles for 1972-81 are available from USGS. Full-scale (5 in./sec) sections are available for most of the lines, which were shot at either 6-, 12-, or 24-fold multiplicity. Data sets include index maps, shot-point location maps, seismic sections and velocity analyses. MAJOR DATA SETS: CDP seismic reflection data Reprocessed seismic sections Seismic reflection field tapes Processed field tapes Miscellaneous Geophysical data tapes Barrow data Stacking velocities Anciliary Data for the Seismic Data: TIME, VELOCITY, DEPTH DATA This file contains time, velocity and depth (TVD) information for up to 25 identified seismic horizons. Position information includes line number, shot- point number, latitude and longitude for most of 1972-1981. These data were generated by Petroleum Information for 1981. The data are preliminary and are from Terra Tech (contractor). SHOT-POINT LOCATION DATA This file contains position information for shot locations during 1972-1981. The file was created by National Geophysical Data Center (NGDC) from TVD data and other shot-point tapes. ELEVATION DATA Elevation data for the National Petroleum Reserve in Alaska includes elevation, northing and easting information for 1972-1979. This file was created by Tetra Tech (contractor for USGS) and contains position information, including line number, shot point, latitude and longitude.", + "license": "proprietary" + }, + { + "id": "EARTH_CRUST_USGS_NPRA_WELL_LOGS", + "title": "National Petroleum Reserve-Alaska Well Log Data for 1946 to 1981; USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1946-01-01", + "end_date": "1981-12-31", + "bbox": "-162, 60, -152, 70", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550414-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550414-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_CRUST_USGS_NPRA_WELL_LOGS", + "description": "The National Petroleum Reserve in Alaska (NPRA) is located in the primitive wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began some geological surveying in this area in the early 1900's, and the U.S. Navy began geological and geophysical surveys and drilling in 1945 to appraise the petroleum potential of the Reserve. Information on surveys prior to 1955 may be obtained from the Branch of Alaskan Geology, Alaska Technical Data Unit, Mail Stop 48, U.S. Geological Survey, 345 Middlefield Road, Menlo Park, CA 94025. WELL LOG DATA Well logs and associated information are available from USGS. These data deal primarily with NPRA exploration and development since the drilling of the South Barrow No. 5 Well in 1955. Well log formats include Schlumberger LISLOG, DRESSER, and DATOUT. Included with some well log data sets are auxiliary information such as drilling history of the wells and velocity check-shot surveys. Well core analyses include porosity, permeability and fluid saturation measurements. MAJOR DATA SETS: Well logs; Digitized well logs; Well core analyses; Seismic velocity surveys; Synthetic seismograms; Palynology/Micropaleontology reports.", + "license": "proprietary" + }, { "id": "EARTH_INT_AUS_BMR_AIR_MAG_GAMMA", "title": "Digital Airborne Magnetic and Gamma Ray Geophysical Data; BMR, Australia", @@ -60163,6 +62061,19 @@ "description": "The Australian National Gravity Database contains point located observed gravity and height values over Australia and its continental shelf. Here, observed gravity is the acceleration due to gravity measured at the location measured on an 11 km grid or finer. Ground elevation is the elevation of the observation point on the Australian Height Datum measured on an 11 km grid or finer. The whole data base is about 100 Megabytes. Some of the data have been provided by State Government Departments, universities and private exploration companies. Copies of the database on magnetic tapes are available for $7500. Copies of the database on 1:1M sheet on disc are available for $350. A Bouguer anomaly map dyeline is $25. Order with a written request to BMR or fax (06)2488420 for data and fax (06)2472728 for maps.", "license": "proprietary" }, + { + "id": "EARTH_INT_USGS_NPRA_GAMMA_MAG1", + "title": "National Petroleum Reserve Alaska: Aerial Gamma Ray and Magnetic Survey data; USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1977-01-01", + "end_date": "1978-12-31", + "bbox": "-162, 60, -152, 70", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550010-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550010-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_INT_USGS_NPRA_GAMMA_MAG1", + "description": "The National Petroleum Reserve in Alaska (NPRA) is located in the primitive wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began some geological surveying in this area in the eary 1900's, and the U.S. Navy began geological and geophysical surveys and drilling in 1945 to appraise the petroleum potential of the Reserve. Information on surveys prior to 1955 may be obtained from the Branch of Alaskan Geology, Alaska Technical Data Unit, Mail Stop 48, U.S. Geological Survey, 345 Middlefield Road, Menlo Park, CA 94025. AERIAL GAMMA RAY AND MAGNETIC DATA Radiometric and magnetic profiles from 1977 are available from USGS. Aerial data were recorded at 1-sec intgervals from a helicopter about 800 feet above the terrain with average ground speed of 100m/hr. Included with the data set are 5 index maps, 2 record location maps, 2 residual total magnetic-field profile maps, and an interpreted depth-to-basement map. These files are available as Open-File Report 95-835. \"http://pubs.usgs.gov/of/1995/ofr-95-0835/\"", + "license": "proprietary" + }, { "id": "EARTH_INT_USGS_S_AK_EARTHQUAKES", "title": "Catalog of Earthquake Hypocenters at Alaskan Volcanoes: January 1,1994 through December 31, 1999; USGS", @@ -60176,6 +62087,19 @@ "description": "Between 1994 and 1999, the Alaska Volcano Observatory (AVO) seismic monitoring program underwent significant changes with networks added at new volcanoes during each summer from 1995 through 1999. The existing network at Katmai ?Valley of Ten Thousand Smokes (VTTS) was repaired in 1995, and new networks were installed at Makushin (1996), Akutan (1996), Pavlof (1996), Katmai - south (1996), Aniakchak (1997), Shishaldin (1997), Katmai - north (1998), Westdahl, (1998), Great Sitkin (1999) and Kanaga (1999). These networks added to AVO's existing seismograph networks in the Cook Inlet area and increased the number of AVO seismograph stations from 46 sites and 57 components in 1994 to 121 sites and 155 components in 1999. The 1995?1999 seismic network expansion increased the number of volcanoes monitored in real-time from 4 to 22, including Mount Spurr, Redoubt Volcano, Iliamna Volcano, Augustine Volcano, Mount Snowy, Mount Griggs, Mount Katmai, Novarupta, Trident Volcano, Mount Mageik, Mount Martin, Aniakchak Crater, Pavlof Volcano, Mount Dutton, Isanotski volcano, Shisaldin Volcano, Fisher Caldera, Westdahl volcano, Akutan volcano, Makushin Volcano, Great Sitkin volcano, and Kanaga Volcano (see Figures 1-15). The network expansion also increased the number of earthquakes located from about 600 per year in 1994 and 1995 to about 3000 per year between 1997 and 1999. Highlights of the catalog period include: 1) a large volcanogenic seismic swarm at Akutan volcano in March and April 1996 (Lu and others, 2000); 2) an eruption at Pavlof Volcano in fall 1996 (Garces and others, 2000; McNutt and others, 2000); 3) an earthquake swarm at Iliamna volcano between September and December 1996; 4) an earthquake swarm at Mount Mageik in October 1996 (Jolly and McNutt, 1999); 5) an earthquake swarm located at shallow depth near Strandline Lake; 6) a strong swarm of earthquakes near Becharof Lake; 7) precursory seismicity and an eruption at Shishaldin Volcano in April 1999 that included a 5.2 ML earthquake and aftershock sequence (Moran and others, in press; Thompson and others, in press). The 1996 calendar year is also notable as the seismicity rate was very high, especially in the fall when 3 separate areas (Strandline Lake, Iliamna Volcano, and several of the Katmai volcanoes) experienced high rates of located earthquakes. This catalog covers the period from January 1, 1994, through December 31,1999, and includes: 1) earthquake origin times, hypocenters, and magnitudes with summary statistics describing the earthquake location quality; 2) a description of instruments deployed in the field and their locations and magnifications; 3) a description of earthquake detection, recording, analysis, and data archival; 4) velocity models used for earthquake locations; 5) phase arrival times recorded at individual stations; and 6) a summary of daily station usage from throughout the report period. We have made calculated hypocenters, station locations, system magnifications, velocity models, and phase arrival information available for download via computer network as a compressed Unix tar file.", "license": "proprietary" }, + { + "id": "EARTH_LAND_NBS_GLACIER_TERMINUS", + "title": "Glacier National Park Glacier Terminus Positions Data from the National Biological Service (NBS)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1887-01-01", + "end_date": "", + "bbox": "-115, 48, -113, 50", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554369-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554369-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_NBS_GLACIER_TERMINUS", + "description": "Glacier terminus positions data are provided by the project on glacier dynamics in relation to climate. The terminus positions are surveyed by field crews or derived from aerial and oblique photos of glaciers from 1887 to the present in Glacier National Park, Montana, USA.", + "license": "proprietary" + }, { "id": "EARTH_LAND_UAK_GI_Permafrost1", "title": "Alaska Permafrost Drillhole Temperature Logs (PTDAK); U. Alaska Geophysical Institute", @@ -60254,6 +62178,71 @@ "description": "[From GeoData Center Home Page descriptions, \"http://www.gi.alaska.edu/alaska-satellite-facility/geodata-center\"] The GeoData Center is the browse facility for the state copy of the AHAP collection, which covers approximately 95% of the State of Alaska in 1:60,000 color infrared (CIR) and 1:120,000 black and white (B&W) photography. The data reside in 10\" film format. Approximately 70,000 frames of photography were acquired between 1978 and 1986.", "license": "proprietary" }, + { + "id": "EARTH_LAND_USGS_AK_Iditarod1", + "title": "Iditarod/George Resource Management Area; USGS, Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1980-01-01", + "end_date": "2000-02-01", + "bbox": "-161, 61, -156, 63", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548832-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548832-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_AK_Iditarod1", + "description": "Since the early 1980's the EROS Alaska Field Office (AFO) has been involved in the acquisition, classification, and analysis of digital land cover data over the State of Alaska, and to a limited extent, northwestern Canada and Wrangle Island, Russia. The digital data currently covers approximately 77% of the land and water within the boundaries of the State of Alaska. These data are currently being made available via the AFO web site to land managers and researchers who may be interested in land cover conditions over various portions of Alaska. The land cover maps as a result of digital analysis of Landsat multispectral scanner, Landsat thematic mapper, and SPOT multispectral scanner satellite data. Some data however, are missing from the database due to data degradation on storage media or loss of data tapes, although a limited number of hard copy map products may be in existence, for example, U.S. Forest Service land cover data for southeast Alaska. The land cover data are stored online in a series of directories and are available via the web. Each directory name is indicative of the area for which the land cover data exists. Some directories connote a U.S. Geological Survey 1:250,000 quadrangle (for example, anchorage_int), while others indicate a particular project area (for example, anwr represents Arctic National Wildlife Refuge) or the agency responsible for producing the data. For example, nowitna.fws indicates that the data were produced for the U.S. Fish and Wildlife Service. Directories with an '_int' suffix denote that the data correspond to the Interim Landcover Mapping project. Each directory contains five files with the following extensions: .bil, .blw, .hdr, .prj, and .stx. As a group, one or more of the files can be read by most image analysis and GIS software packages. The .bil file contains the binary raster land cover data and the others are ASCII files. The .bil file does not contain any header or footer records, and can be easily imported into image processing software by using the information contained in the .blw and .hdr files. The .blw file provides pixel size information as well as the upper left corner coordinate. The .hdr contains number of rows and columns of the data set, as well as band format (band interleave). The .prj file gives projection information, and the .stx file gives information on minimum/maximum/ mean values of the data. Projection parameters for the data sets are either in Universal Transverse Projection (UTM) or Alaska Albers Equal Area Conic.", + "license": "proprietary" + }, + { + "id": "EARTH_LAND_USGS_AK_Innoko1", + "title": "Innoko National Wildlife Refuge Landcover and Topography; USGS, Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1986-12-31", + "end_date": "1986-12-31", + "bbox": "-160, 62, -155, 64", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550865-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550865-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_AK_Innoko1", + "description": "The Innoko National Wildlife Refuge digital data sets contain land cover classifications derived from Landsat MSS data, and elevation, slope and aspect data derived from DEM data. Data can be keyed on a U.S. Geological Survey 1:250,000 quadrangle basis. Spatial referencing is from 50 meter grid cells and data source is Landsat MSS data and Digital Elevation Model (DEM) data. This data set contains 113 records. The amount of storage required varies by storage medium and selected area. The file structure is sequential. Data are available online. The data is organized by 7 1/2 ' or 15 ' quads. General area covered: 62 to 64 north to 155 to 45' to 160 to 15' west.", + "license": "proprietary" + }, + { + "id": "EARTH_LAND_USGS_AK_Koyukuk1", + "title": "Koyukuk National Wildlife Refuge Landcover, Topography, Etc.; USGS, Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1956-01-01", + "end_date": "1985-12-31", + "bbox": "-157, 63, -152, 65", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548673-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548673-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_AK_Koyukuk1", + "description": "Digital data is given on the Koyukuk National Wildlife Refuge in west-central Alaska. The data set contains information on land status, lake and streams digitized at 1:63,360, and wildlife ditribution digitized at 1:250,000. Also included are digitized land cover, slope, a scale of elevation, and aspect from EROS Data Center; data are digitized from U.S. Geological Survey quadrangle maps and updated periodically. The amount of storage required is unknown. Data are available on 9-track, 800 bpi, 1600 bpi, unlabeled, AMS variable block, or DLG 3 fixed block in binary or ASCII, variable record length tape, 5 1/4 inch floppy disk, and cassette. Subsets and custom formats are available. The data dictionary has been completed for this record. The data is organized by 7 1/2 ' or 15 ' quads.", + "license": "proprietary" + }, + { + "id": "EARTH_LAND_USGS_AK_NOAA_AVHRR", + "title": "NOAA Digital AVHRR Satellite Data; USGS, Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1984-01-01", + "end_date": "", + "bbox": "170, 52, -130, 72", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553312-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553312-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_AK_NOAA_AVHRR", + "description": "This digital data set contains selected NOAA 6, 7, 8 and 9 Advanced Very High Resolution Radiometer (AVHRR) imagery of Alaska; AVHRR is carried on NOAA's polar orbiting satellites. Spatial referencing is 1.1 km at nadir. Data source is National Oceanic and Atmospheric Administration (NOAA). The data set includes 47 records with estimated growth rate of 100 records per year. Storage required varies by storage medium and selected scene. The file structure is sequential. Data are available on 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, BCD, fixed record length tape. Subsets and customs formats are available. Limited documentation is available. Data is organized by 7 1/2 ' or 15 ' quads. Uses include fuel mapping, vegetation monitoring, large area mosaic, and monitoring of ice/snow dynamics.", + "license": "proprietary" + }, + { + "id": "EARTH_LAND_USGS_AK_NPRA_veg1", + "title": "National Petroleum Reserve in Alaska (NPRA) - Vegetation Map, USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1975-01-01", + "end_date": "1977-12-31", + "bbox": "-180, 53, -132, 75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553282-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553282-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_AK_NPRA_veg1", + "description": "A vegetation/land cover raster digital data set for the entire National Petroleum Reserve in Alaska (NPR-A) was generated from Landsat multispectral data sets. Included are eleven categories of vegetation and land cover which are derived from all or portions of 10 Landsat MSS scenes. The data set covers all or part of thirteen 1:250,000-scale topographic quadrangles. Data are stored in 50 meter pixels and registered to a UTM base. A full NPR-A mosaic as well as the 1:250,000 topographic series. Data are available in two forms: a digital mosaic of (1) the entire NPR-A coverage, split into two pieces each and registered to a separate UTM zone, or (2) for each 1:250,000-scale topo quadrangle area within the NPR-A. This file is too large to remain online. It is stored on magnetic tape at Moffett Field, CA.", + "license": "proprietary" + }, { "id": "EARTH_LAND_USGS_AK_Wildlif_Ref1", "title": "Arctic National Wildlife Refuge Data; USGS, Alaska", @@ -60306,6 +62295,32 @@ "description": "The aerial photography inventoried by the Pilot Land Data System (PLDS) at NASA AMES Research Center has been transferred to the USGS EROS Data Center. The photos were obtained from cameras mounted on high and medium altitude aircraft based at the NASA Ames Research Center. Several cameras with varying focal lengths, lenses and film formats are used, but the Wild RC-10 camera with a focal length of 152 millimeters and a 9 by 9 inch film format is most common. The positive transparencies are typically used for ancillary ground checks in conjunctions with digital processing for the same sites. The aircraft flights, specifically requested by scientists performing approved research, often simultaneously collect data using other sensors on board (e.g. Thematic Mapper Simulators (TMS) and Thermal Infrared Multispectral Scanners). High altitude color infrared photography is used regularly by government agencies for such applications as crop yield forecasting, timber inventory and defoliation assessment, water resource management, land use surveys, water pollution monitoring, and natural disaster assessment. To order, specify the latitude and longitude of interest. You will then be given a list of photos available for that location. In some cases, \"flight books\" are available at EDC that describe the nature of the mission during which the photos were taken and other attribute information. The customer service personnel have access to these books for those photo sets for which the books exist.", "license": "proprietary" }, + { + "id": "EARTH_LAND_USGS_DEM_AK1", + "title": "Digital Terrain Data Sets for Alaska, USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1982-01-01", + "end_date": "", + "bbox": "-180, 54, -135, 72", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550167-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550167-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_DEM_AK1", + "description": "This data set contains up to nine types of digital elevation data: 1-1 degree blocks, 2-1 degree x 3 degree mosaic of elevation (latitude/longitude coordinate system), 3-1 degree x 3 degree mosaic of slope, 4-1 degree x 3 degree mosaic of aspect (latitude/longitude coordinate system), 5-1 degree x 3 degree mosaic of filtered elevation (5 x 5 filter), 6-1 degree x 3 degree mosaic of elevation (UTM registered), 7-1 degree x 3 degree mosaic of slope (UTM registered), 8-1 degree x 3 degree mosaic of aspect (UTM registered), 9-1 degree x 3 degree mosaic of shaded relief (latitude/longitude coordinate system). Data coverage is from 1982 to present with work ongoing. Data source is 1:250,000 scale Defense Mapping Agency Digital Terrain Series. The data set currently contains 966 records with estimated growth of 5-15 records per year. Storage required varies by selection on area size. Data are available on: 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, or BCD tape. Subsets on the main file and custom formats as well as limited documentation is available. Data is organized by 7 1/2 ' or 15 ' quads. This data is intended to be used for land cover analysis, wildlife refuge studies, drainage analysis, and land use planning.", + "license": "proprietary" + }, + { + "id": "EARTH_LAND_USGS_EDC_AK_Landsat", + "title": "Landsat 1-5 dataset from Alaska Field Office's Dbase; USGS, Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1972-01-01", + "end_date": "", + "bbox": "170, 52, -130, 72", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551714-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551714-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EARTH_LAND_USGS_EDC_AK_Landsat", + "description": "This data set contains raw unregistered Landsat digital data covering most of Alaska. Data obtained from EROS Data Center in Sioux Falls, South Dakota. Data acquired from 1980 and is ongoing. Some Landsat scenes date back to 1972. The data set currently has 585 records with a growth chart at 5-10 records per year. The amount of storage required varies by medium used or full scene or subscene selection; the file structure is sequential. Spatial referencing of data is by 57 x 59 meter grid cell size-MSS data. Data are available on 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, BCD, fixed record length tape. Subsets and custom formats are available. Limited documentation is available. The data is organized in 7 1/2 ' or 15 ' quads. Data is used for false color composites, land cover analysis, geologic analysis, hydrogeologic analysis, land use planning, basis for update of topographic maps, production of image maps.", + "license": "proprietary" + }, { "id": "EARTH_LAND_USGS_Water_PKFIL", "title": "Annual Peak Discharge and Stage of US Surface Water; USGS", @@ -61593,6 +63608,45 @@ "description": "[Excerpt from: Palmer, T.N., A. Alessandri, U. Andersen, P. Cantelaube, M. Davey, P. Délécluse, M. Déqué, E. Díez, F. J. Doblas-Reyes, H. Feddersen, R. Graham, S. Gualdi, J.-F. Guérémy, R. Hagedorn, M. Hoshen, N. Keenlyside, M. Latif, A. Lazar, E. Maisonnave, V. Marletto, A. P. Morse, B. Orfila, P. Rogel, J.-M. Terres and M. C. Thomson, Development of a European Multi-Model Ensemble System for Seasonal to Inter-Annual Prediction (DEMETER), ECMWF Technical Memorandum 434, 2004 ] The DEMETER project (Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction) was conceived, and funded under the European Union Vth Framework Environment Programme. The principal aim of DEMETER was to advance the concept of multi- model ensemble prediction by installing a number of state-of-the-art global coupled ocean-atmosphere models on a single supercomputer, and to produce a series of six-month multi-model ensemble hindcasts with common archiving and common diagnostic software. Such a strategy posed substantial technical problems, as well as more mundane but nevertheless important issues (e.g. on agreeing units in which model variables were archived).", "license": "proprietary" }, + { + "id": "ECMWF_OPERATIONAL_WAVE", + "title": "European Centre for Medium-Range Weather Forecasts (ECMWF) Operational Wave Data Sets", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2230056995-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2230056995-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ECMWF_OPERATIONAL_WAVE", + "description": "These data sets contain data at the resolution of the data assimilation and forecast system in operational use at ECMWF. Since the resolution and internal representation of the archive may vary according to changes in ECMWF's operational practice, data services associated with these data sets include the provision of interpolation to requested resolutions and representation forms. Four data sets are separately supported: Analysis - Global wave analysis - Mediterranean wave analysis Forecast - Global wave forecast - Mediterranean wave forecast Access the ECMWF Wave Data Sets: http://apps.ecmwf.int/archive-catalogue/?class=od", + "license": "proprietary" + }, + { + "id": "ECMWF_OPER_EPS", + "title": "European Centre fro Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) Data Sets", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-05-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2230056988-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2230056988-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ECMWF_OPER_EPS", + "description": "These data sets contain data at the resolution of the ensemble prediction forecast system in operational use at ECMWF. Since the resolution and internal representation of the archive may vary according to changes in ECMWF's operational practice, data services associated with these data sets include the provision of interpolation to requested resolutions and representation forms. Five data sets are separately supported: - Control Forecast + Surface ensemble + Pressure level ensemble + Model level ensemble + Wave model ensemble - Perturbed Forecast + Surface ensemble perturbed forecasts + Pressure level ensemble perturbed forecasts + Wave model ensemble [Summary Extracted from the ECMWF home page]", + "license": "proprietary" + }, + { + "id": "ECMWF_WCRP_TOGA", + "title": "European Centre for Medium-Range Weather Forecasts (ECMWF)/World Climate Research Program (WCRP) level III-A Global Atmospheric (TOGA) Data Sets", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1985-01-01", + "end_date": "", + "bbox": "-180, -20, 180, 20", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2230056982-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2230056982-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ECMWF_WCRP_TOGA", + "description": "ECMWF created and maintains an archive of level III-A atmospheric data in support of projects associated with the World Climate Research Program (WCRP). This archive is directly interpolated from the ECMWF operational, full resolution, surface and pressure level data. It is accommodated the 10 year period beginning 1 January 1985, fulfilling ECMWF's role as a Tropical Ocean and Global Atmosphere (TOGA) Level III Atmospheric Data Centre. The Level III-A archive is subdivided into three classes of data sets: * Basic Level III * Supplementary Fields * Extension The data sets are based on quantities analyzed or computed within the ECMWF data assimilation scheme or from forecasts based on these analyses. The Basic Data Set contain selected analysed values in a compact form at a resolution of 2.5 degree x 2.5degree. They are particularly suitable for users with limited data processing resources. Derived quantities (fluxes, etc.) are not included, but can in principle be calculated from the data provided in the data sets. The Supplementary Fields Data Set contains additional surface data, fluxes and net radiation data derived from short-range forecasts used as first-guess data for the analyses. Most of the fields in this data set contain values accumulated over the first 6 (or 12) hours of the forecast. The exceptions, total cloud cover fields, contain instantaneous 6 (or 12) hour forecast values. This is a subset of the operational first-guess surface data. The Extension Data Set contains additional surface data, fluxes, net radiation data and precipitation derived from 24-hour forecast values. All the fields in this data set contain values accumulated between time step 12 and time step 36 of the forecast. The archive is currently maintained using the WMO FM 92-IX Ext GRIB (grid in binary) form of data representation, with ECMWF local versions of GRIB Table 2. All fields of data are global within the archive. A full extraction service is supported, enabling users to obtain sub-areas of data and data at various resolutions on regular Gaussian or latitude/longitude grids, or as spherical harmonics with selected triangular truncation. All extracted data are delivered using the GRIB representation. [Summary Extracted from the ECMWF home page]", + "license": "proprietary" + }, { "id": "ECO1BATT_001", "title": "ECOSTRESS Attitude Daily L1B Global 70m V001", @@ -63426,6 +65480,19 @@ "description": " Annual crop data from 1972 to 1998 are now available on EOS-WEBSTER. These data are county-based acreage, production, and yield estimates published by the National Agricultural Statistics Service. We also provide county level livestock, geography, agricultural management, and soil properties derived from datasets from the early 1990s. The National Agricultural Statistics Service (NASS), the statistical arm of the U.S. Department of Agriculture, publishes U.S., state, and county level agricultural statistics for many commodities and data series. In response to our users requests, EOS-WEBSTER now provides 27 years of crop statistics, which can be subset temporally and/or spatially. All data are at the county scale, and are only for the conterminous US (48 states + DC). There are 3111 counties in the database. The list includes 43 cities that are classified as counties: Baltimore City, MD; St. Louis City, MO; and 41 cities in Virginia. In addition, a collection of livestock, geography, agricultural practices, and soil properties variables for 1992 is available through EOS-WEBSTER. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF. The US County data has been divided into seven datasets. US County Data Datasets: 1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties ", "license": "proprietary" }, + { + "id": "EPA0175", + "title": "National Water Quality Assessment Program (NAWQA) Home Page", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1991-01-01", + "end_date": "", + "bbox": "-125, 25, -67, 50", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411681-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411681-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/EPA0175", + "description": "The \"National Water Quality Assessment Program (NAWQA) Home Page\" is an Internet resource that provides information on research dealing with water quality in the United States. This home page provides links to NAWQA activities, selected publications, a bibliography, and summaries of current research projects. The NAWQA program is designed to assess historical, current, and future water-quality conditions in representative river basins and aquifers nationwide. One of the primary objectives of the program is to describe relations between natural factors, human activities, and water quality conditions and to define those factors that most affect water quality in different parts of the Nation. The linkage of water quality to environmental processes is of fundamental importance to water-resource managers, planners, and policy makers. It provides a strong and unbiased basis for better decision making by those responsible for making decisions that affect our water resources, including the United States Congress, Federal, State, and local agencies, environmental groups, and industry. Information from the NAWQA Program also will be useful for guiding research, monitoring, and regulatory activities in cost effective ways. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: National Water Quality Assessment Program Dissemination Media: Online File Format: Size: Memory Requirements: Operating System: Hardware Required: Software Required: Availability Status: On Request Documentation Available:", + "license": "proprietary" + }, { "id": "EPA_AQA", "title": "Air Quality Atlas", @@ -64778,6 +66845,84 @@ "description": "The 'Eyes on the Ground' project ([lacunafund.org](https://lacunafund.org/ag2020awards/)) is a collaboration between ACRE Africa, the International Food Policy Research Institute (IFPRI), and the Lacuna Fund, to create a large machine learning (ML) dataset of smallholder farmer's fields based upon previous work within the Picture Based Insurance framework (Ceballos, Kramer and Robles, 2019, [https://doi.org/10.1016/j.deveng.2019.100042](https://doi.org/10.1016/j.deveng.2019.100042)). This is a unique dataset of georeferenced crop images along with labels on input use, crop management, phenology, crop damage, and yields, collected across 8 counties in Kenya.The research leading to this dataset was undertaken as part of the CGIAR research program on Policies, Institutions and Markets (PIM)", "license": "proprietary" }, + { + "id": "FAO_AGL", + "title": "FAO/AGL World River Sediment Yields Database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, -90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232283741-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232283741-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/FAO_AGL", + "description": "Food and Agricultural Organization of the United Nations (FAO)/AGL World River Sediment Yields Database The World River Sediment Yields database contains data on annual sediment yields in worldwide rivers and reservoirs, searchable by river, country and continent. The database was compiled from different sources by HR Wallingford, UK, on behalf of the FAO Land and Water Development Division. It is currently in a test phase. The database allows its user to enter the name of the river, the country, or the continent for which they would like to see summary sedimentation data. From this data, you can discover explanations of the data and complete sedimentation records Data URL: \"http://www.fao.org/ag/AGL/aglw/sediment/default.asp\" Information taken from \"http://www.fao.org/ag/AGL/aglw/sediment/default.asp\"", + "license": "proprietary" + }, + { + "id": "FAO_FIGIS", + "title": "FAO Fisheries Global Information System", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232284263-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232284263-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/FAO_FIGIS", + "description": "FAO's major program on Fisheries aims to promote sustainable development of responsible fisheries and contribute to food security. To implement this major program, the Fisheries Department focuses its activities, through programs in Fishery Resources, Fishery Policy, Fishery Industries and Fishery Information on three medium-term strategic objectives, including promotion of responsible fisheries sector management at the global, regional and national levels, promotion of increased contribution of responsible fisheries and aquaculture to world food supplies and food security, and global monitoring and strategic analysis of fisheries The FAO Fisheries Global Information System is a global network of integrated fisheries information. FIGIS is a work in progress - sections are currently under development. Valuable information can be accessed on topics such as aquatic species, marine resources, marine fisheries, and fishing technology. Soon you will be able to access databases on trade and marketing, aquaculture, inland fisheries, and fisheries issues. http://www.fao.org/fishery/figis", + "license": "proprietary" + }, + { + "id": "FAOd0008_148", + "title": "FAO World Soil Resources", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232283938-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232283938-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/FAOd0008_148", + "description": "In 1990 a Map of World Soil Resources was completed at scale 1:25.000.000, generalized from the FAO/UNESCO Soil Map or the World at scale 1:5.000.000 (FAO, 1971 - 1981). The map was issued on the occasion of the 14th International Congress of Soil Science held in Kyoto, Japan in 1990. Since then new material has become available, the FAO/UNESCO Soil Map of the World has been partly updated under the SOTER Programme and the FAO legend has been replaced by the World Reference Base for Soil Resources (WRB). In 1998 the latter was adopted by the International Union of Soil Sciences as the standard for soil correlation and nomenclature. In the light of these new developments it was decided to prepare an updated version of the generalized Map of the World Soil Resources at 1:25.000.000. The updating exercise covered: - the switch from the original map projection to a Flat Polar Quartic projection - the conversion of the FAO legend into the WRB classification - the incorporation of additional soil data obtained from new or revised soil map sources - the matching, when possible of soil unit boundaries with major landforms", + "license": "proprietary" + }, + { + "id": "FAOd0018_148", + "title": "Distribution of Major Soil Types", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232283838-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232283838-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/FAOd0018_148", + "description": "This CD-ROM is released in conjunction with World Soil Resources Reports No. 94: \"Lecture Notes on the Major Soils of the World\". In addition to the complete (hyperlinked) text of the book, it contains many additional pictures, a slideshow with a virtual tour of soils and landscapes and a typical soil profile for each of the thirty reference soil groups of the World Reference Base for Soil Resources. In total more than 550 slides and pictures illustrate the lecture notes. \"http://www.fao.org/icatalog/search/dett.asp?aries_id=102985\"", + "license": "proprietary" + }, + { + "id": "FAOd0019_148", + "title": "Digital Soil Map of the World and Derived Soil Properties.", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "6.11, 36.15, 19.33, 47.71", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232283478-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232283478-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/FAOd0019_148", + "description": "This CD-ROM contains the Digital Soil Map of the World in various formats, verctor as well as raster, supported by most GIS software. The base material is the FAO/UNESCO Soil Map of the World at an original scale of 1:5 million. Programs and data files give tabular country information on soil characteristics and derived soil properties from the map are included, such as pH, organic carbon content and soil moisture storage capability. In addition programs and data files are included that display derived soil properties. The revision included the adding of a number of user-friendly ArcView files allowing the display of dominant soils by continent and the inclusion of the update of the image of the WRB World Soil Resources Map. \"http://www.fao.org/icatalog/search/dett.asp?aries_id=103540\"", + "license": "proprietary" + }, + { + "id": "FAOd0020_148", + "title": "Hydrological Basins of Africa", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-17.3, -34.6, 51.1, 38.2", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232283043-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232283043-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/FAOd0020_148", + "description": "Hydrological Basins of Africa, with major basins and sub basins, automatically derived from USGS topographic data with some manual corrections in flat areas. Current version completed March 2000.", + "license": "proprietary" + }, { "id": "FAUNA_PENGUIN_COLONY_1", "title": "A census of penguin colony counts (provided to OBIS) from the year 1900 to 1996 in the Antarctic Region", @@ -68652,6 +70797,19 @@ "description": "The data provided are annual land cover projections for years 2005 through 2095 generated by the Global Change Assessment Model (GCAM) Version 3.1. For the conterminous USA, the GCAM global gridded results were downscaled to ~5.6 km (0.05 degree) resolution. For each 5.6 x 5.6 km area, the annual land cover percentage comprised by each of the nineteen different land cover classes/plant functional types (PFTs) of the Community Land Model (CLM) (Table 1) are provided.Results are reported for GCAM runs of three scenarios of future human efforts towards climate mitigation as related to global carbon emissions, radiative forcing, and land cover change. Specific scenario conditions were 1) a reference scenario with no explicit climate mitigation efforts that reaches a radiative forcing level of over 7 W/m2 in 2100, 2) the 2.6 mitigation pathway (MP) scenario which is a very low emission scenario with a mid-century peak in radiative forcing at ~3 W/m2, declining to 2.6 W/m2 in 2100, and 3) the 4.5 MP scenario which stabilizes radiative forcing at 4.5 W/m2 (~ 650 ppm CO2-equivalent) before 2100.These downscaled land cover projections can be used to derive spatially explicit estimates of potential shifts in croplands, grasslands, shrub lands, and forest lands in each future climate scenario.Data are presented as three NetCDF v4 files (.nc4), one for each future climate scenario -- 2.6 MP, 4.5 MP, and GCAM reference). ", "license": "proprietary" }, + { + "id": "GCIP-GREDS", + "title": "GCIP Reference Data Set (GREDS), U.S. Geological Survey Open-File Report 94-388", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1951-01-01", + "end_date": "1995-04-01", + "bbox": "-125, 24, -66, 52", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554529-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554529-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GCIP-GREDS", + "description": "The data sets on this compact disc are a compilation of several geographic reference data sets of interest to the global-change research community. The data sets were chosen with input from the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) Data Committee and the GCIP Hydrometeorology and Atmospheric Subpanels. The data sets include: locations and periods of record for stream gages, reservoir gages, and meteorological stations; a 500-meter-resolution digital elevation model; grid-node locations for the Eta numerical weather-prediction model; and digital map data sets of geology, land use, streams, large reservoirs, average annual runoff, average annual precipitation, average annual temperature, average annual heating and cooling degree days, hydrologic units, and state and county boundaries. Also included are digital index maps for LANDSAT scenes, and for the U.S. Geological Survey 1:250,000, 1:100,000, and 1:24,000-scale map series. Most of the data sets cover the conterminous United States; the digital elevation model also includes part of southern Canada. The stream and reservoir gage and meteorological station files cover all states having area within the Mississippi River Basin plus that part of the Mississippi River Basin lying within Canada. Several data-base retrievals were processed by state, therefore many sites outside the Mississippi River Basin are included. See: \"http://nsdi.usgs.gov\" for a complete desciption of metadata and browse images.", + "license": "proprietary" + }, { "id": "GCOM-C_SGLI_L1A_SWI_and_TIR_1km_NA", "title": "GCOM-C/SGLI L1A Shortwave Infrared Thermal Infrared (1km)", @@ -76634,6 +78792,19 @@ "description": "GCOM-W/AMSR2 L3 Integrated Water Vapor (1-Month, 0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Integrated Water Vapor (WV), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). WV is amount of vertically accumulated water vapor (H2O in gaseous state) in the atmosphere, and defined as amount of water per unit area. Coverage of the product is over the ocean only, and unit is [kg/m2]. Total precipitable water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and cloud liquid water. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "license": "proprietary" }, + { + "id": "GCRP-DDS-10", + "title": "Modern Average Global Sea-Surface Temperature, GCRP Subset", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1981-10-01", + "end_date": "1989-12-31", + "bbox": "-180, -66, 180, 72", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550138-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550138-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GCRP-DDS-10", + "description": "Information about the Modern Average Global Sea-Surface Temperature (USGS data series DDS-10) Data Set archived at the USGS Global Change Research Program is available via FTP: \"ftp://geochange.er.usgs.gov in /pub/magsst\" or via World Wide Web: \"http://pubs.usgs.gov/dds/dds10/magsst.html\" Directions on how to obtain the CD-ROM or access it on-line are made available on the WWW site. The following information about the data set was provided by the data center contact: The purpose of this data set is to provide Paleoclimate researchers with a tool for estimating the average seasonal variation in sea- surface temperature (SST) through out the modern world ocean and for estimating the modern monthly and weekly sea-surface temperature at any given oceanic location. It is expected that these data will be compared with temperature estimates derived from geological proxy measures such as faunal census analyses and stable isotopic analyses. The results can then be used to constrain general circulation models of climate change. The data contained in this data set are derived from the NOAA Advanced Very High Resolution Radiometer Multichannel Sea Surface Temperature data (AVHRR MCSST), which are obtainable from the Distributed Active Archive Center at the Jet Propulsion Laboratory (JPL) in Pasadena, Calif. The JPL tapes contain weekly images of SST from October 1981 through December 1990 in nine regions of the world ocean: North Atlantic, Eastern North Atlantic, South Atlantic, Agulhas, Indian, Southeast Pacific, Southwest Pacific, Northeast Pacific, and Northwest Pacific. This data set represents the results of calculations carried out on the NOAA data and also contains the source code of the programs that made the calculations. The objective was to derive the average sea- surface temperature of each month and week throughout the whole 10-year series, meaning, for example, that data from January of each year would be averaged together. The result is 12 monthly and 52 weekly images for each of the oceanic regions. Averaging the images in this way tends to reduce the number of grid cells that lack valid data and to suppress interannual variability. As ancillary data, the ETOPO5 global gridded elevation and bathymetry data (Edwards, 1989) were interpolated to the resolution of the SST data; the interpolated topographic data are included. The images are provided in three formats: a modified form of run-length encoding (MRLE), Graphics Interchange Format (GIF), and Macintosh PICT format. Also included in the data set are programs that can retrieve seasonal temperature profiles at user-specified locations and that can decompress the data files. These nongraphical SST retrieval programs are provided in versions for UNIX, MS-DOS, and Macintosh computers. Graphical browse utilities are included for users of UNIX with the X Window System, 80386-based PC's, and Macintosh computers. ", + "license": "proprietary" + }, { "id": "GCRW_DEM_2016_1793_1", "title": "Digital Elevation Models for the Global Change Research Wetland, Maryland, USA, 2016", @@ -77466,6 +79637,19 @@ "description": "This is a global inventory of methane emissions from fuel exploitation (GFEI) created for the NASA Carbon Monitoring System (CMS). The emission sources represented in this dataset include fugitive emission sources from oil, gas, and coal exploitation following IPCC 2006 definitions and are estimated using bottom-up methods. The inventory emissions are based on individual country reports submitted in accordance with the United Nations Framework Convention on Climate Change (UNFCCC). For those countries that do not report, the emissions are estimated following IPCC 2006 methods. Emissions are allocated to infrastructure locations including mines, wells, pipelines, compressor stations, storage facilities, processing plants, and refineries. The purpose of the inventory is to be used as a prior estimate of fuel exploitation emissions in inverse modeling of atmospheric methane observations. GFEI only includes fugitive methane emissions from oil, gas, and coal exploitation activities and does not include any combustion emissions as defined in IPCC 2006 category 1A. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.", "license": "proprietary" }, + { + "id": "GFOI_Boreno_Island", + "title": "GFOI_Borneo_Island", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1972-01-01", + "end_date": "", + "bbox": "114, 1, 114, 1", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555055-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555055-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GFOI_Boreno_Island", + "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:foster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC). ", + "license": "proprietary" + }, { "id": "GFSAD1KCD_001", "title": "Global Food Security Support Analysis Data (GFSAD) Crop Dominance 2010 Global 1 km V001", @@ -78025,19 +80209,6 @@ "description": "Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product.", "license": "proprietary" }, - { - "id": "GLAH02_033", - "title": "GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033", - "catalog": "NSIDC_CPRD STAC Catalog", - "state_date": "2003-02-20", - "end_date": "2009-10-11", - "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH02_033", - "description": "GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product.", - "license": "proprietary" - }, { "id": "GLAH02_033", "title": "GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033", @@ -78052,16 +80223,16 @@ "license": "proprietary" }, { - "id": "GLAH03_033", - "title": "GLAS/ICESat L1A Global Engineering Data (HDF5) V033", + "id": "GLAH02_033", + "title": "GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033", "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH03_033", - "description": "Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02.", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH02_033", + "description": "GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product.", "license": "proprietary" }, { @@ -78078,16 +80249,16 @@ "license": "proprietary" }, { - "id": "GLAH04_033", - "title": "GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033", + "id": "GLAH03_033", + "title": "GLAS/ICESat L1A Global Engineering Data (HDF5) V033", "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH04_033", - "description": "Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH03_033", + "description": "Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02.", "license": "proprietary" }, { @@ -78104,16 +80275,16 @@ "license": "proprietary" }, { - "id": "GLAH05_034", - "title": "GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034", - "catalog": "NSIDC_ECS STAC Catalog", + "id": "GLAH04_033", + "title": "GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH05_034", - "description": "GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product.", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH04_033", + "description": "Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.", "license": "proprietary" }, { @@ -78130,16 +80301,16 @@ "license": "proprietary" }, { - "id": "GLAH06_034", - "title": "GLAS/ICESat L1B Global Elevation Data (HDF5) V034", - "catalog": "NSIDC_CPRD STAC Catalog", + "id": "GLAH05_034", + "title": "GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2033638023-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2033638023-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH06_034", - "description": "GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product.", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH05_034", + "description": "GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product.", "license": "proprietary" }, { @@ -78156,16 +80327,16 @@ "license": "proprietary" }, { - "id": "GLAH07_033", - "title": "GLAS/ICESat L1B Global Backscatter Data (HDF5) V033", + "id": "GLAH06_034", + "title": "GLAS/ICESat L1B Global Elevation Data (HDF5) V034", "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH07_033", - "description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product.", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2033638023-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2033638023-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH06_034", + "description": "GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product.", "license": "proprietary" }, { @@ -78181,6 +80352,19 @@ "description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product.", "license": "proprietary" }, + { + "id": "GLAH07_033", + "title": "GLAS/ICESat L1B Global Backscatter Data (HDF5) V033", + "catalog": "NSIDC_CPRD STAC Catalog", + "state_date": "2003-02-20", + "end_date": "2009-10-11", + "bbox": "-180, -86, 180, 86", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH07_033", + "description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product.", + "license": "proprietary" + }, { "id": "GLAH08_033", "title": "GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033", @@ -78262,52 +80446,52 @@ { "id": "GLAH11_033", "title": "GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549738-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549738-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH11_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH11_033", "description": "GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product.", "license": "proprietary" }, { "id": "GLAH11_033", "title": "GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH11_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549738-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549738-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH11_033", "description": "GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product.", "license": "proprietary" }, { "id": "GLAH12_034", "title": "GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH12_034", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH12_034", "description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.", "license": "proprietary" }, { "id": "GLAH12_034", "title": "GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH12_034", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH12_034", "description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.", "license": "proprietary" }, @@ -78844,6 +81028,19 @@ "description": "NOTE: This database has been taken offline, as it was no longer maintained. A copy of the data that was stored in the database is available for download from the provided URL. The database holds all known cosmic ray data from the worldwide network of observatories that observed Ground Level Enhancements. The Cosmic Ray program contributes to our understanding of the radiation environment in space near the Earth. Radiation constantly bombards the Earth from space and is measurable at and below the surface of the Earth. The high energy particles are detected at the Mawson cosmic ray laboratory with large detectors located on the surface and in an underground vault. This is the only system of its type in polar regions and gives a unique view of the radiation effects. Variations in the radiation are constantly monitored. The sun plays a major role in generating the changes. The radiation levels are important to spacecraft and crew and to high altitude aircraft flying on polar routes. There is also some evidence that the radiation may influence climate.", "license": "proprietary" }, + { + "id": "GLFC_FishHabitatDatabase", + "title": "Fish Habitat Database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-96, 44, -70, 49", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551991-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551991-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GLFC_FishHabitatDatabase", + "description": "The Fish Habitat Database is a synthesis of extensive information on habitat requirements and characteristics of selected Great Lakes fish species. Sponsored by the Great Lakes Fishery Commission through its Habitat Advisory Board, the Fish Habitat Database was developed in response to a need for habitat information on Great Lakes fish species. This need was identified in part in the Joint Strategic Plan for Management of the Great Lakes Fisheries. In addition, natural resource managers from environmental agencies indicated a need for fish habitat data in order to develop and implement Fish Management Plans, Lakewide Management Plans, and Remedial Action Plans that recognize fish as an integral component of the Great Lakes ecosystem. The database potentially contains habitat information for 18 selected fish species at five stages of their life and in six bodies of water. This information was obtained primarily from the U. S. Fish and Wildlife Service's Habitat Suitability Index Models and other data where available. This version of the database is not the final product. In fact, information gaps are present in this version. The database is intended to be an ongoing effort which will need maintenance as new needs are identified and new information is discovered. The Great Lakes Fishery Commission is seeking an individual or agency that will be able to take over this role on behalf of other users in the Great Lakes basin.", + "license": "proprietary" + }, { "id": "GLHYANC_001", "title": "G-LiHT Hyperspectral Ancillary V001", @@ -78922,6 +81119,19 @@ "description": "The aim is to establish a numerical model system providing a robust 3-dimensional physical environment within which ecosystem and zooplankton models of different structure and complexity will be compared and assessed. The principle aims are: to provide a hydrodynamic/ecological testbed for development and testing of models of zooplankton dynamics; to formally compare existing models of ecosystem dynamics in the testbed and evaluate performance against archived data; to identify important processes and scales of interaction for Irish Sea zooplankton populations and to determine the optimal complexity of marine hydrodynamic and ecosystem models necessary to describe zooplankton dynamics in the Irish Sea.", "license": "proprietary" }, + { + "id": "GLOBIO_barents", + "title": "Impact of human activities in the Barents region using the GLOBIO methodology", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "13.02604, 56.781113, 107.00503, 74.40539", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848150-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848150-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GLOBIO_barents", + "description": "Abstract - Impact of human activities in the Barents region using the GLOBIO methodology. Distance impact from Infrastructure and defined in the GLOBIO report Purpose - To provide policy makers with a tool to help assess the likelihood of environmental impacts in the Barents Region Format - Windows NT Version 5.0 (Build 2195) Service Pack 2; ESRI ArcInfo 8.1.0.415 Raw data are the B1000 dataset from the Barents GIT and National Mapping agency over the Barents region Grid Cell - Row Count 5192 Cell Count 6436 Map Projection - Albers Conical Equal Area", + "license": "proprietary" + }, { "id": "GLORTHO_001", "title": "G-LiHT Aerial Orthomosaic V001", @@ -83017,6 +85227,19 @@ "description": "FOR EXPERT USE ONLY. The GRACE Level 1B data provide all necessary inputs to derive monthly time variations in the Earth's gravity field. Level 1B data are also used for GRACE orbit and mean gravity field determination. It contains K-Band Ranging Data Product (KBR1B), Star Camera Data Product (SCA1B), Accelerometer Data Product (ACC1B), GPS Data Product (GPS1B), Vector Products (VGN1B, VGO1B, VGB1B, VCM1B, VKB1B, VSL1B), Quaternion Products (QSA1B, QSB1B), and Housekeeping Products (AHK1B, IHK1B, THR1B, TNK1B, MAG1B, MAS1B, TIM1B)The GRACE Level-1B RL03 data consists only of updated spacecraft attitude (SCA1B) and K-band inter-satellite ranging (KBR1B) data. All other Level-1B were not changed and it is recommended to use the RL02 products with the updated RL03 KBR1B and SCA1B products. The RL03 SCA1B data were corrected for a stellar aberration error in the onboard star tracker software and incorrect data weighting in the star tracker combination software. For the RL03 SCA1B data a new software module was developed that uses Kalman filtering, field of view error modeling, relative alignment adjustment and the inclusion of angular spacecraft body acceleration measurements from the ACC instrument. This new processing resulted in a significant reduction in high frequency noise and the elimination of jumps during transitions between dual and single star tracker operation. The KBR1B product is updated as well because the KBR antenna phase center range correction, range rate correction and range acceleration are computed using the spacecraft attitude information (SCA1B). Only these three correction values were updated in the KBR1B product. All other entries in the KBR1B remained the same.", "license": "proprietary" }, + { + "id": "GRAVCD-npra", + "title": "Gravity Data for the National Petroleum Reserve-Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1974-01-01", + "end_date": "1980-01-01", + "bbox": "-162, 60, -152, 70", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548712-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548712-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GRAVCD-npra", + "description": "This data set is part of a 2 CD-ROM set from NOAA's National Geophysical Data Center entitled Land and Marine Gravity Data - 1999 Edition. The gravity station data (53,520 records) were gathered by various governmental organizations (and academia) using a variety of methods. This data base was received in November 1980. Principal gravity parameters include Free-air Anomalies and Simple Bouguer Anomalies (no terrain correction applied). The observed gravity values are referenced to the International Gravity Standardization Net 1971 (IGSN 71). The gravity anomaly computation uses the Geodetic Reference System 1967 (GRS 67) theoretical gravity formula. The data are randomly distributed within the boundaries of the National Petroleum Reserve-Alaska (NPRA).", + "license": "proprietary" + }, { "id": "GRAVITY_LD_WL_1967_1986_CSV_1", "title": "Gravity data collected from the Australian Antarctic Territory and subantarctic between 1967 and 1986", @@ -83095,6 +85318,19 @@ "description": "This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL06.3Mv04 dataset, which can be found at https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V3. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability is provided as an ASCII table.", "license": "proprietary" }, + { + "id": "GRID-INPE", + "title": "GRID-INPE; UNEP Global Resource Information Database - INPE Cooperating Center", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1993-04-15", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849388-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849388-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/GRID-INPE", + "description": "This is a collection of data-sets held by GRID-INPE. Please contact the technical contact for further details and data-set breakdown. GRID-INPE is a cooperating center to UNEP's Global Resource Information Database. Grid is a system of cooperating centers within the United Nations Environmental Programme that is dedicated to making environmental information more readily accessible to environmental analysis as well as to international and national decision makers. Its mission is to provide timely and reliable geo-referenced environmental information. Besides acquiring and disseminating integrated, spatially-referenced environmental data, GRID provides decision-support services to environmental analysts and international and national decision makers, and fosters the use of geographic information systems (GIS) and satellite image processing (IP) as tools for environmental analysis.", + "license": "proprietary" + }, { "id": "GSI_ABSOLUT_GRAVITY_ANT", "title": "Absolute gravity measurement", @@ -86215,6 +88451,19 @@ "description": "An ultraviolet spectral Atlas of a sunspot with high spectral and spatial resolution in the wavelength region 1190 - 1730 A is presented. The sunspot was observed with the High Resolution Telescope and Spectrograph (HRTS). The HRTS instrument was built at the U.S. Naval Research Laboratory (NRL), Washington, D.C. (Bartoe and Brueckner, 1975). The instrument combines high spatial, spectral, and time resolution with an extensive wavelength and angular coverage. This makes HRTS particularly well suited for studies of fine structure and mass flows in the upper solar atmosphere. HRTS has flown six times on rockets between 1975 and 1989 and as a part of Spacelab 2 in 1985. The spectrograms used for the Atlas are from the second HRTS rocket flight, known as HRTS II, flown on 13 February 1978 aboard a Black Brant VC rocket (NASA Flight 21.042) at White Sands, New Mexico. During the rocket flight the slit was oriented radially from the solar disc center through the active region McMath 15139, including a sunspot, and across the solar limb. The Solar Pointing Aerobee Rocket Control System (SPARCS) kept the spectrograph slit positioned on the solar surface during the observing time of 4.2 minutes. The spatial resolution on this flight was 2 arcsec with a time resolution from 0.2 - 20.2 sec. The HRTS spectra were recorded on Eastman Kodak 101-01 photographic film. Microphotometry of the spectrograms has been carried out at the Institute of Theoretical Astrophysics in Oslo. The data reduction includes correcting the spectral images for geometrical distortion, Fourier filtering to remove high frequency noise, transformation to absolute calibrated solar intensity and calibration of the wavelength scale. The absolute intensity calibration was obtained by comparing relative intensity scans of a quiet solar region with absolute intensities from the Skylab S082B calibration rocket, CALROC The resulting absolute intensities are accurate to within 30% (rms). The wavelength scale was established using solar lines from neutral and singly ionized atoms as reference lines. From this wavelength scale velocities accurate to 2 km/s can be measured over the entire wavelength range. The measured velocities are, however, relative to the average velocity in the chromosphere where the reference lines are formed. The Atlas contains spectra of three different areas in the sunspot and also of an active region and a quiet region. The selected areas are averaged over several arcsec, ranging from 3.5 arcsec in the sunspot to 18 arcsec in the quiet region. The transition region lines in the Atlas show the most extreme example known of downflowing gas above a sunspot, a phenomenon which seems to be commonly occurring in sunspots. One of the selected areas in the sunspot is a light bridge crossing the spot. This is the most interesting sunspot region where the continuum radiation is enhanced and measurable throughout the HRTS spectral range. A number of lines appear which do not occur in the regular sunspot spectrum. The Atlas is available in a machine readable form together with an IDL program to interactively measure linewidths, total intensities and solar wavelengths. See: http://zeus.nascom.nasa.gov/~pbrekke/HRTS/", "license": "proprietary" }, + { + "id": "HUC250k", + "title": "Hydrologic Units Maps of the Conterminous United States for the EPA Clean Air Mapping and Analysis Program (C-MAP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-127.91, 22.87, -65.32, 48.29", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552976-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552976-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/HUC250k", + "description": "The Geographic Information Retrieval and Analysis System (GIRAS) was developed in the mid 70s to put into digital form a number of data layers which were of interest to the USGS. One of these data layers was the Hydrologic Units. The map is based on the Hydrologic Unit Maps published by the U.S. Geological Survey Office of Water Data Coordination, together with the list descriptions and name of region, subregion, accounting units, and cataloging unit. The hydrologic units are encoded with an eight- digit number that indicates the hydrologic region (first two digits), hydrologic subregion (second two digits), accounting unit (third two digits), and cataloging unit (fourth two digits). The data produced by GIRAS was originally collected at a scale of 1: 250K. Some areas, notably major cities in the west, were recompiled at a scale of 1: 100K. In order to join the data together and use the data in a geographic information system (GIS) the data were processed in the ARC/INFO GUS software package. Within the GIS, the data were edge matched and the neatline boundaries between maps were removed to create a single data set for the conterminous United States. This data set was compiled originally to provide the National Water Quality Assessment (NAWQA) study units with an intermediate- scale river basin boundary for extracting other GIS data layers. The data can also be used for illustration purposes at intermediate or small scales (1:250,000 to 1:2 million). [Summary provided by EPA]", + "license": "proprietary" + }, { "id": "HWSD_1247_1", "title": "Regridded Harmonized World Soil Database v1.2", @@ -86787,6 +89036,45 @@ "description": "Digital data of Administrative Boundaries of Kathmandu Valley: - Districts and Village Development Committee from 1997 map. - Demographic data from 1991 census", "license": "proprietary" }, + { + "id": "ICId0001_202", + "title": "Jhikku Khola Database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1972-12-18", + "end_date": "1998-12-15", + "bbox": "85, 27, 85, 27", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847784-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847784-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0001_202", + "description": "Various GIS datasets on Jhikhu Khola Watershed (see members for details) Members informations: Attached Vector(s): MemberID: 1 Vector Name: Soil map of Arunachal Pradesh Source Map Name: Soil association map of Arunachal Pradesh Source Map Scale: 250000 Source Map Date: ? Projection: polyconic Projection_desc: orig: 87 / 0, sc.: 0.9999, Ell: Everest1830/Nepal Projection_meas: meters Feature_type: polygons Legend_file: lugen.avl Vector The soil resource inventory was carried out following a three tier approach viz. image interpretation, soil survey and chemical analysis and GIS application for thematic mapping and interpretation of database for developing a land use plan. The soil association maps on 1:250,000 scale were digitised toposheetwise (14 toposheets) using polyconic projection to bring out the state soil map. Various thematic maps were generated using 'reclassification' techniques and area calculation was carried out using 'map analysis' tools. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Soil map of Himachal Pradesh Projection_desc: orig: 87 / 0, sc.: 0.9999, Ell: Everest1830/Nepal Feature_type: polygon Members informations: Attached Vector(s): MemberID: 3 Vector Name: Soil map of Jammu&Kashmir Projection: transverse mercator Projection_desc: orig: 87 / 0, sc.: 0.9999, Ell: Everest1830/Nepal Projection_meas: meters Feature_type: polygon Legend_file: lugen.avl Members informations: Attached Vector(s): MemberID: 4 Vector Name: Soil map of Uttar Pradesh Feature_type: polygon Vector Attached Image(s): Member ID: 5 Image Name: Orthophoto mosaic Image Projection: Nepal zone87 Image Source name: camera Image Resolution: 1m Image Number of Rows: 12001 Image Number of Columns: 15201 Image Number of Bits: 8 Image Mosaic of digital orthophotos, 1m resolution, The orthophotos have been prepared from 1996 aerial photographs 1:20000, scanned at 600dpi, using GPS control points and the DEM Accuracy: 10-20m horizontal RMS; maximum errors ca. 50 (absolute) resp. 100m (relative vs the drainage) Members informations: Attached Vector(s): MemberID: 6 Vector Name: Land systems Source Map Name: Land systems map Source Map Scale: 20000 Source Map Date: 1905-06-12 Projection: Nepal 87 Feature_type: polygon Vector Land system classification, soil types Members informations: Attached Vector(s): MemberID: 7 Vector Name: Roads Source Map Name: GPS Source Map Scale: - Source Map Date: 1998/2000 Projection: Nepal 87 Feature_type: lines Vector Road network surveyed by differential GPS Attached Raster(s): Member_ID: 8 Raster Name: Land Capability Evaluation Raster Name: Land systems, DEM Raster Scale: 20000 Raster Date: 1905-06-12 Raster Projection: Nepal 87 Raster Resolution: 20 Number of Rows: 651 Number of Columns: 801 Number of Bits: 8 Raster Land capability evaluation according to refined LRMP-method Members informations: Attached Vector(s): MemberID: 9 Vector Name: Drainage Source Map Name: Jhikhu Khola Base Map Source Map Scale: 20000 Source Map Date: 1905-06-12 Projection: Nepal87 Feature_type: lines Vector Drainage Network; contains some substantial geometric distortions mainly in the upper parts Members informations: Attached Vector(s): MemberID: 10 Vector Name: Contours Source Map Name: Jhikhu Khola Base map Source Map Scale: 20000 Source Map Date: 1905-06-12 Projection: Nepal 87 Projection_meas: meters Feature_type: lines Vector Contours (25m interval), contains some substantial geometric distortions mainly in the upper parts Members informations: Attached Vector(s): MemberID: 11 Vector Name: VDC boundaries Source Map Name: Jhikhu Khola Base Map Source Map Scale: 20000 Source Map Date: 1905-06-12 Projection: Nepal 87 Feature_type: polygon Vector VDC (Village Development Committee) boundaries Members informations: Attached Vector(s): MemberID: 12 Vector Name: settlements Source Map Name: Jhikhu Khola Basemap Source Map Scale: 20000 Source Map Date: 1905-06-12 Projection: Nepal 87 Feature_type: point Vector Location and names of settlements", + "license": "proprietary" + }, + { + "id": "ICId0005_202", + "title": "Kathmandu Valley GIS database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "85, 27, 85, 27", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848526-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848526-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0005_202", + "description": "In the recent past, there has been continuing growth in using GIS and related technologies by many organizations engaged in planning and management of the Kathmandu Valley. As a result, the demand for accurate and homogenous spatial data of the Valley has been realized by government as well as research and development organizations. This study attempts to build a comprehensive GIS Database of the Kathmandu Valley with an aim to bridge the important data gaps in the Valley. The study employs a fresh approach in constructing a GIS database with the available maps and integrates many different kinds of satellite imageries. The maps presented in this publication visualize the different scenarios and raise the awareness of exiting digital database. The application presented in this publication shall increase awareness about the usefulness of digital database and demonstrate what can be achieved with the GIS and related technologies. The database thus developed shall improve the availability of information of the Kathmandu Valley and assist different stakeholders engaged in planning and management of the Valley. Furthermore, the study advocates a building block approach to development, management and revision of database in a complementary way and it hopes to avoid duplication of efforts in costly production of digital data. The study hopes to sensitise senior executives and decision-makers about the need for a sound policy on database sharing, development and standards. Such a policy, at the national level known as National Spatial Database Infrastructure (NSDI) should evolve in order to benefit from the prevailing GIS technology. In using GIS and related technologies, the study facilitated the establishment of Spatial Data Infrastructure of the Kathmandu Valley in a concrete manner. Members informations: Attached Vector(s): MemberID: 1 Vector Name: Contours Source Map Name: topo sheets Source Map Scale: 25000 Source Map Date: 1905-06-17 Projection: transverse mercator Projection_desc: origin 87E/ 0N, false easting=900000, scale=0.9999 Projection_meas: Meter Feature_type: lines Vector Contours digitized from topo sheets Members informations: Attached Vector(s): MemberID: 2 Vector Name: Roads Source Map Name: topo sheet Source Map Scale: 25000 Source Map Date: 1905-06-17 Projection: see member1 Feature_type: lines Vector Road Network Members informations: Attached Vector(s): MemberID: 3 Vector Name: Drainage Source Map Name: topo sheets Source Map Scale: 25000 Source Map Date: 1905-06-17 Projection: see member 1 Feature_type: lines Vector Drainage Network Members informations: Attached Vector(s): MemberID: 4 Vector Name: Land use 78 Source Map Name: LRMP Source Map Scale: 50000 Source Map Date: 1905-05-31 Feature_type: polygon Vector Land use Members informations: Attached Vector(s): MemberID: 5 Vector Name: Land use 1995 Source Map Name: topo sheet Source Map Scale: 25000 Source Map Date: 1905-06-17 Feature_type: polygon Vector Land cover Members informations: Attached Vector(s): MemberID: 6 Vector Name: Administrative boundaries Source Map Name: topo sheet Source Map Scale: 25000 Source Map Date: 1905-06-17 Feature_type: polygon Vector District and VDC boundaries and various socio-economic data Attached Report(s) Member ID: 7 Report Name: Kathmandu Valley GIS database Report Authors: B. Shrestha & S. Pradhan Report Publisher: ICIMOD Report Date: 2000-02-01 Report Report", + "license": "proprietary" + }, + { + "id": "ICId0012_202", + "title": "Districts of Nepal - Indicators of Development", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846594-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846594-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0012_202", + "description": "Atlas of district-based indicators on poverty and deprivation, socio-economic development, women's empowerment, and Natural resource endowment", + "license": "proprietary" + }, { "id": "ICId0013_202", "title": "Climatic and Hydrological Atlas of Nepal", @@ -86800,6 +89088,123 @@ "description": "Monthly averages of Temperature, Precipitation, Humidity, Sunshine etc. have been interpolated spatially from Meteo station data. Also contains some hydrographic charts and data.", "license": "proprietary" }, + { + "id": "ICId0015_202", + "title": "Inventory of Glaciers and Glacial Lakes", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "79.9, 26, 92.37, 30.88", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848528-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848528-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0015_202", + "description": "Inventory of glaciers and glacial lakes from aerial photographs, topo sheets of different years, and satellite images has been prepared. Potentially dangerous lakes (GLOF: Glacial Lake Outburst Floods) will be identified based on air phoitographs and field work.", + "license": "proprietary" + }, + { + "id": "ICId0016_202", + "title": "IRS 1D LISS3 109-50 of 12 July 1998", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "70.83, 15.06, 137.97, 56.58", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848991-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848991-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0016_202", + "description": "IRS 1D LISS3 109-50 of 12 July 1998 Satellite image", + "license": "proprietary" + }, + { + "id": "ICId0017_202", + "title": "Duiling Deqing County GIS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "70.83, 15.06, 137.97, 56.58", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849349-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849349-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0017_202", + "description": "Various datasets on land use and population", + "license": "proprietary" + }, + { + "id": "ICId0018_202", + "title": "IRS WiFS data of HinduKush-Himalayan Region", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-09-30", + "end_date": "1999-02-26", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847560-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847560-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0018_202", + "description": "IRS WiFS coverage of most of the HKH region Attached Image(s): Member ID: 1 Image Name: 86-42 of 30 Sep 96 Image Resolution: 188 Image Number of Rows: 4489 Image Number of Columns: 4904 Image Number of Bits: 8 Image Satellite Image Attached Image(s): Member ID: 2 Image Name: 086-047_961024 Image Resolution: 188 Image Number of Rows: 4492 Image Number of Columns: 4918 Image Number of Bits: 8 Image sat. image Attached Image(s): Member ID: 3 Image Name: 086-052_961024 Image Resolution: 188 Image Number of Rows: 4494 Image Number of Columns: 4929 Image Number of Bits: 8 Image sat. image Attached Image(s): Member ID: 4 Image Name: 092-042_970122 Image Resolution: 188 Image Number of Rows: 4351 Image Number of Columns: 4892 Image Number of Bits: 8 Image sat. image Attached Image(s): Member ID: 5 Image Name: 092-047_961030 Image Resolution: 188 Image Number of Rows: 4492 Image Number of Columns: 4917 Image Number of Bits: 8 Image satellite image Attached Image(s): Member ID: 6 Image Name: 092-052_961205 Image Resolution: 188 Image Number of Rows: 4358 Image Number of Columns: 4899 Image Number of Bits: 8 Image sat. image Attached Image(s): Member ID: 7 Image Name: 098-042_961024 Image Resolution: 188 Image Number of Rows: 4349 Image Number of Columns: 4891 Image Number of Bits: 8 Image sat. img. Attached Image(s): Member ID: 8 Image Name: 098-047_980531 Image Resolution: 188 Image Number of Rows: 4350 Image Number of Columns: 4760 Image Number of Bits: 8 Image sat. img Attached Image(s): Member ID: 9 Image Name: 098-052_961012 Image Resolution: 188 Image Number of Rows: 4494 Image Number of Columns: 4929 Image Number of Bits: 8 Image sat.img. Attached Image(s): Member ID: 10 Image Name: 104-049_981104 Image Resolution: 188 Image Number of Rows: 4359 Image Number of Columns: 4726 Image Number of Bits: 8 Image sat. img. Attached Image(s): Member ID: 11 Image Name: 104-052_961018 Image Resolution: 188 Image Number of Rows: 4494 Image Number of Columns: 4929 Image Number of Bits: 8 Image sat.img. Attached Image(s): Member ID: 12 Image Name: 110-052_961129 Image Resolution: 188 Image Number of Rows: 4354 Image Number of Columns: 4923 Image Number of Bits: 8 Image sat.img. Attached Image(s): Member ID: 13 Image Name: 110-057_980209 Image Resolution: 188 Image Number of Rows: 4337 Image Number of Columns: 4862 Image Number of Bits: 8 Image sat.img. Attached Image(s): Member ID: 14 Image Name: 116-052_961205 Image Resolution: 188 Image Number of Rows: 4353 Image Number of Columns: 4910 Image Number of Bits: 8 Image sat.img. Attached Image(s): Member ID: 15 Image Name: 116-055_990226 Image Resolution: 188 Image Number of Rows: 4350 Image Number of Columns: 4791 Image Number of Bits: 8 Image sat.image Attached Image(s): Member ID: 16 Image Name: 116-062_970404 Image Resolution: 188 Image Number of Rows: 4365 Image Number of Columns: 4934 Image Number of Bits: 8 Image sat.image Attached Image(s): Member ID: 17 Image Name: Mosaic Image Projection: Albers Equal-Area Image Resolution: ? Image Number of Rows: ? Image Number of Columns: ? Image Number of Bits: 8 Image Geometrically controlled Mosaic of all 16 images, radiometrically not adjusted", + "license": "proprietary" + }, + { + "id": "ICId0019_202", + "title": "Landsat TM images of Bhutan", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "88.8, 26.54, 92.37, 28.46", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848112-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848112-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0019_202", + "description": "Landsat TM scenes of Winter 98/99 Attached Image(s): Member ID: 1 Image Name: 137-041_990116 Image Resolution: 30 Image Number of Rows: 5728 Image Number of Columns: 6920 Image Number of Bits: 8 Image TM image Attached Image(s): Member ID: 2 Image Name: 138-041_981104 Image Resolution: 30 Image Number of Rows: 5728 Image Number of Columns: 6920 Image Number of Bits: 8 Image TM image Attached Image(s): Member ID: 3 Image Name: 139-041_981229 (Quadrant 4 only) Image Resolution: 30 Image Number of Rows: 2944 Image Number of Columns: 3500 Image Number of Bits: 7*8 Image Satellite image", + "license": "proprietary" + }, + { + "id": "ICId0020_202", + "title": "Land cover Map of the Hindu Kush-Himalayan region", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849129-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849129-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0020_202", + "description": "Supervised classification of IRS WiFS data; IGBP legend", + "license": "proprietary" + }, + { + "id": "ICId0021_202", + "title": "IRS Pan 104-052 of 23 Nov 1996", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "79.9, 26, 88.84, 30.88", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849217-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849217-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0021_202", + "description": "IRS Panchromatic image of Kathmandu Valley", + "license": "proprietary" + }, + { + "id": "ICId0023_202", + "title": "Landsat TM 140-040 of 22 Sep 1992", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848244-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848244-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0023_202", + "description": "Landsat TM 140-040 of 22 Sep 1992 Satellite image", + "license": "proprietary" + }, + { + "id": "ICId0028_202", + "title": "Landsat TM 141-41 Q2 of 24 Jan 89", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849271-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849271-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/ICId0028_202", + "description": "Landsat TM 141-41 Q2 of 24 Jan 89 Satellite image", + "license": "proprietary" + }, { "id": "ICO_Casey_1", "title": "In situ chemical oxidisation (ICO) of petroleum hydrocarbons at old Casey Station", @@ -86904,6 +89309,19 @@ "description": "Integration of new remote sensing tools for characterization of tidal marsh area extent, vegetation communities and inundation regimes, and advanced retrievals of estuarine biological and biogeochemical processes with multi-disciplinary ecological, paleoecological, and socioeconomic datasets, spatial econometric models of population growth, and a novel coupled hydrodynamic-photo-biogeochemical model specifically designed for the marsh-estuarine continuum in the heavily urbanized Long Island Sound.", "license": "proprietary" }, + { + "id": "IES", + "title": "Irrigation Equipment Supply Database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232284371-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232284371-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/IES", + "description": "The Irrigation Equipment Supply Database is a joint initiative of the Water Resources, Development and Management Service of FAO and the International Programme for Technology and Research in Irrigation and Drainage (IPTRID). It has been developed as part of FAO's mandate to provide information on irrigation. Potential beneficiaries of IES are those who need to locate information on irrigation equipment at regional or country level. IES seeks to establish an up-to-date list of Suppliers/Manufacturers providing irrigation equipment worldwide. National Suppliers/Manufacturers can be displayed by clicking the dark blue countries on the map. Moreover, the website offers a database query facility for identifying Suppliers/Manufactures providing specific irrigation equipment as well as a description of irrigation equipment, a description of standards and links to other related sites. [Summary provided by the FAO.]", + "license": "proprietary" + }, { "id": "IGBGM1B_1", "title": "IceBridge BGM-3 Gravimeter L1B Time-Tagged Accelerations V001", @@ -87528,6 +89946,19 @@ "description": "The CCD camera provides images of a 113 km wide strip with 20m spatial resolution. Since this camera has a sideways pointing capability of \u00b1 32 degrees, it is capable of taking stereoscopic images of a certain region. The CCD camera operates in 5 spectral bands that include a panchromatic one from 0.51 to 0.73 \u00b5m. A complete coverage cycle of the CCD camera takes 26 days.", "license": "proprietary" }, + { + "id": "INPE_CBERS2B_HRC", + "title": "HRC - High Resolution Camera (CBERS 2B) Imagery", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2007-09-25", + "end_date": "2010-03-10", + "bbox": "-79, -36, -33, 10", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456153-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456153-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CBERS2B_HRC", + "description": "HRC camera operates in a single spectral band which covers visible and near-infrared bands. It is only present in CBERS-2B. It generates images of 27km width and resolution 2.7m, which will allow the observation of surface objects with large detail. Given its 27km swath, five 26 days cycles are necessary for the 113km standard CCD swath to be covered by HRC.", + "license": "proprietary" + }, { "id": "INPE_CBERS2_CCD", "title": "CCD - High Resolution CCD Camera (CBERS 2) Imagery", @@ -87541,6 +89972,19 @@ "description": "CBERS-2 CCD - High Resolution CCD Camera. The CCD camera provides images of a 113 km wide strip with 20m spatial resolution. Since this camera has a sideways pointing capability of \u00b1 32 degrees, it is capable of taking stereoscopic images of a certain region. The CCD camera operates in 5 spectral bands that include a panchromatic one from 0.51 to 0.73 \u00b5m. A complete coverage cycle of the CCD camera takes 26 days.", "license": "proprietary" }, + { + "id": "INPE_CBERS2_IRM", + "title": "IRMSS - Infrared Multispectral Scanner (CBERS 2) Imagery", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-10-22", + "end_date": "2009-01-01", + "bbox": "-85, -60, -20, 10", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456081-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456081-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CBERS2_IRM", + "description": "The CBERS-2 satellite is designed for global coverage and include cameras that make optical observations and a Data Collection System transponder to gather data on the environment. They are unique systems due to the use of on board cameras which combine features that are specially designed to resolve the broad range of space and time scales involved in our ecosystem. The IRMSS operates in 4 spectral bands, thus extending the CBERS spectral coverage up to the thermal infrared range. It images a 120 km swath with the resolution of 80m (160m in the thermal channel). In 26 days one obtains a complete Earth coverage that can be correlated with the images of the CCD camera.", + "license": "proprietary" + }, { "id": "INPE_CBERS4_AWFI_1", "title": "AWFI - Wide Field Imaging Camera (CBERS 4) Imagery", @@ -87567,6 +90011,19 @@ "description": "Infrared Medium Resolution Scanner. This camera is built under China responsibility and it is an upgrade of the Infrared Multispectral Scanner (IRMSS) of the CBERS-1 and 2 satellites. It has 4 spectral bands: B09: 0,50 - 0,90 ?m B10: 1,55 - 1,75 ?m B11: 2,08 - 2,35 ?m B12: 10,4 - 12,5 ?m Its spatial resolution is 40 meters in the panchromatic and SWIR (shortwave infrared) bands and 80 meters in the thermal band. A complete coverage cycle of the panchromatic camera takes 26 days.", "license": "proprietary" }, + { + "id": "INPE_CBERS4_MUX_1", + "title": "MUX - MultiSpectral Camera (CBERS 4) Imagery", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2015-01-01", + "end_date": "", + "bbox": "-180, -45, 180, 10", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456083-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456083-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CBERS4_MUX_1", + "description": "CBERS-4 MUX - Multispectral Camera. This camera is built under Brazilian responsibility. It is a multispectral camera with four spectral band covering the wavelength range from blue to near infrared (from 450 nm to 890 nm) with a ground resolution of 20 m and a ground swath width of 120 km A complete coverage cycle of the MUX camera takes 26 days.", + "license": "proprietary" + }, { "id": "INPE_CBERS4_PAN10M_1", "title": "CBERS-4 Multispectral 10 Meters Camera Imagery", @@ -87580,6 +90037,201 @@ "description": "CBERS-4 Multispectral 10 Meters Camera. This camera is built under China responsibility. It is a panchromatic camera with 4 spectral bands: B02: 0,52 - 0,59 ?m B03: 0,63 - 0,69 ?m B04: 0,77 - 0,89 ?m The ground resolution is 10 m and the ground swath width is 60 km. A complete coverage cycle of the panchromatic camera takes 52 days.", "license": "proprietary" }, + { + "id": "INPE_CPTEC_CLIMATE_BRAZIL", + "title": "Monthly Climate Data Products for Brazil (INPE/CPTEC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-74, -34, -34, -6", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456073-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456073-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CPTEC_CLIMATE_BRAZIL", + "description": "This dataset contains monthly precipitation and temperature maps and their respective anomalies in relation to the 30-year climatology (1961-1990). Data was used from the Instituto Nacional de Meteorologia (INMET/BR). This dataset can be obtained via World Wide Web from the CPTEC Home Page. Link to: http://www.cptec.inpe.br/", + "license": "proprietary" + }, + { + "id": "INPE_CPTEC_CLIMATE_GLOBAL", + "title": "Global Monthly Climate Data Products (INPE/CPTEC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456060-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456060-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CPTEC_CLIMATE_GLOBAL", + "description": "This dataset consists of Global monthly fields of several variables and their respective anomalies in relation to the 16 years climatology (1979-1995), using reanalysis data from National Center for Environmental Prediction (NCEP/USA). Variables include Geopotential Height, Streamlines(850hPa, 200hPa), upper level winds(850hPa, 200hPa) , sea level temperature, outgoing long wave radiation, and sea level pressure. This dataset can be obtained via World Wide Web from the CPTEC Home Page. Link to: http://www.cptec.inpe.br/", + "license": "proprietary" + }, + { + "id": "INPE_CPTEC_GLOBAL_METEOGRAM", + "title": "Forecast Model Meteograms For 26 Locations in South America (INPE/CPTEC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-75, -35, -34, 5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456145-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456145-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CPTEC_GLOBAL_METEOGRAM", + "description": "Forecast model meteograms for 26 locations in South America are available from CPTEC (Centro de Previsao de Tempo e Estudos Climaticos) in Brazil. Forecast time steps range from the initial time out to six days. The user may view forecasts from the most recent forecast run back to the previous 36 hours at twelve hour steps. Parameters Forecasted: Relative Humidity (%) Precipitation (mm/h) Mean Sea Level Pressure (mb) Surface Wind (m/s) Surface Temperature (C) Forecasted meteograms may be obtained via World Wide Web from CPTEC's Home Page. Link to: \"http://www.cptec.inpe.br/\"", + "license": "proprietary" + }, + { + "id": "INPE_CPTEC_IR_SAT", + "title": "GOES-8 and Meteosat-5 Infrared Satellite Images of South America (INPE/CPTEC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-76, -56, -34, -15", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456139-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456139-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_CPTEC_IR_SAT", + "description": "GOES-8 and Meteosat-5 infrared images of South America are available from CPTEC (Centro de Previsao de Tempo e Estudos Climaticos) in Brazil. Only the most recent month's images are archived. A new image is provided every three hours. Please read carefully the Disclaimer and Copyright information. All satellite images and additional information may be obtained via the World Wide Web from the CPTEC Home Page. Link to: http://www.cptec.inpe.br/", + "license": "proprietary" + }, + { + "id": "INPE_ER_SAR", + "title": "ERS SAR Data held by the National Institute for Space Research (INPE), Brazil", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1991-08-26", + "end_date": "", + "bbox": "-80, -40, -33, 8", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456064-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456064-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_ER_SAR", + "description": "INPE's only receiving station for ERS-1 and ERS-2 SAR is located in Cuiaba, MT (geographic coordinates approx. 15.5S/56.0W). The SAR tapes recorded at Cuiaba are air shipped to the processing and distribution center in Cachoeira Paulista, SP, where they are kept. A copy of all recorded tapes is sent to the ESA PAF at the DLR facilities in Germany. Early ERS-1 SAR data were acquired primarily for station checkout and Principal Investigator service. A small number of passes, 15 seconds to 2 minutes long, were acquired from August, 1991 to March, 1992, during the Commissioning and Ice phases where the repeat cycle was 3 days but the ground coverage was sparse. More extensive and regular acquisitions began in April, 1992, with the satellite already in the so-called Multidisciplinary phase (full ground coverage and 35-day repeat cycle). INPE is allowed to service user requests originated in Brazil only. Only digital products are available. Requests for products and for image search listings will be handled directly by the processing center (listed at the Data Center entry), through contacts by mail, phone or fax. No online remote access is available, although plans exist to implement it, including the International Directory protocols. No firm date is speculated for that, but hopes are that it can be made before 1998. Requests from countries other that Brazil must be routed to the ESA licensed regional distributors. Information can be obtained with the ESA ERS-1 Order Desk, c/o ESRIN, C.P. 64, I-00044 Frascati, Italy. The phone numbers are +39.6.941-80600 (voice) and +39.6.941-80510 (fax).", + "license": "proprietary" + }, + { + "id": "INPE_IRS_AWIFS", + "title": "IRS AWIFS Imagery", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2009-09-10", + "end_date": "", + "bbox": "-79, -36, -33, 10", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456088-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456088-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_IRS_AWIFS", + "description": "AWIFS, aboard IRS \u2013 P6 (RESOURCESAT-I), imagery held by INPE.", + "license": "proprietary" + }, + { + "id": "INPE_IRS_LISS3", + "title": "IRS LISS3 Imagery", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2009-09-10", + "end_date": "", + "bbox": "-79, -36, -33, 10", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456136-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456136-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_IRS_LISS3", + "description": "LISS 3, aboard IRS \u2013 P6 (RESOURCESAT-I), imagery held by INPE.", + "license": "proprietary" + }, + { + "id": "INPE_LANDSAT1_MSS", + "title": "LANDSAT-1 MSS Imagery", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1973-05-29", + "end_date": "1976-10-19", + "bbox": "-80, -40, -33, 8", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456156-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456156-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LANDSAT1_MSS", + "description": "LANDSAT 1 MSS imagery held by the National Institute for Space Research (INPE), Brazil.", + "license": "proprietary" + }, + { + "id": "INPE_LANDSAT2_MSS", + "title": "LANDSAT-2 MSS Imagery", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1975-07-24", + "end_date": "1982-02-06", + "bbox": "-80, -40, -33, 8", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456137-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456137-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LANDSAT2_MSS", + "description": "LANDSAT 2 MSS imagery held by the National Institute for Space Research (INPE), Brazil.", + "license": "proprietary" + }, + { + "id": "INPE_LANDSAT3_MSS", + "title": "LANDSAT-3 MSS Imagery", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1978-04-05", + "end_date": "1982-07-31", + "bbox": "-80, -40, -33, 8", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456100-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456100-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LANDSAT3_MSS", + "description": "LANDSAT 3 MSS imagery held by the National Institute for Space Research (INPE), Brazil.", + "license": "proprietary" + }, + { + "id": "INPE_LANDSAT5_TM", + "title": "LANDSAT-5 TM Imagery", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1984-04-01", + "end_date": "", + "bbox": "-80, -40, -33, 8", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456071-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456071-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LANDSAT5_TM", + "description": "LANDSAT 5 TM imagery held by the National Institute for Space Research (INPE), Brazil.", + "license": "proprietary" + }, + { + "id": "INPE_LANDSAT7_ETM", + "title": "LANDSAT-7 ETM+ Imagery", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-07-19", + "end_date": "2003-06-01", + "bbox": "-80, -40, -33, 8", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456065-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456065-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LANDSAT7_ETM", + "description": "LANDSAT 7 ETM+ imagery held by the National Institute for Space Research (INPE), Brazil.", + "license": "proprietary" + }, + { + "id": "INPE_LS_MSS", + "title": "LANDSAT MSS Data held by the National Institute for Space Research (INPE), Brazil", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1973-05-14", + "end_date": "1987-10-06", + "bbox": "-80, -40, -33, 8", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456141-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456141-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LS_MSS", + "description": "INPE's only receiving station for Landsat MSS was located in Cuiaba, MT (geographic coordinates approx. 15.5S/56.0W). Data were acquired routinely over the entire range from 1973 until 1986, some time after TM data began being received. MSS data recordings were then reduced to Brazilian territory only. Also, many gaps exist, some of them several months long, related mostly to station downtimes caused by lightning. The MSS tapes recorded at Cuiaba were air shipped to the processing and distribution center in Cachoeira Paulista, SP, where they are kept. The holdings are estimated to be around 75,000 scenes (or 300,000 images, counting individually each of the four spectral bands), not all of them processed. The fifth band (thermal infrared) available on Landsat 3 is not counted for practical purposes. Very few were processed at INPE with disappointing results and it was soon dropped as a product. Demand for MSS products decreased sharply after TM products started being distributed. This determined the reduction and eventually the discontinuing of MSS recordings in 1987. The original processing system, based on 16-bit minicomputers, was dismantled in early 1991. An alternative ingestion system is being developed to allow limited processing of MSS data by the TM system, with a forecast to be ready in late 1998. Meanwhile, available products are limited to reproduction of existing photographic originals (about 150,000 black-and-white and color images). No digital products can be delivered, since no copies were kept from produced CCTs. Requests for products and for image search listings are handled directly by the processing center (listed at the Data Center entry), through contacts by mail, phone or fax. No online remote access is available to the moment, although plans exist to implement it, including the International Directory protocols. No firm date is speculated for that, but hopes are that it can be made before 1998. Information about costs, delivery time and available formats can be requested at the same address.", + "license": "proprietary" + }, + { + "id": "INPE_LS_RBV", + "title": "LANDSAT RBV Data held by the National Institute for Space Research (INPE), Brazil", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1978-11-03", + "end_date": "1983-03-31", + "bbox": "-80, -40, -33, 8", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456063-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456063-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LS_RBV", + "description": "INPE's only receiving station for Landsat RBV was located in Cuiaba, MT (geographic coordinates approx. 15.5S/56.0W). Data were acquired routinely over the entire range during the lifetime of Landsat 3. This means therefore the twin-camera, panchromatic version of the RBV. Some gaps exist, some of them several months long, related mostly to station downtimes caused by lightning. The RBV tapes recorded at Cuiaba were air shipped to the processing and distribution center in Cachoeira Paulista, SP, where they are kept. The holdings (not all of them processed to film) are estimated to be around 100,000 images. Demand for RBV products experienced a brief peak while they were a novelty with higher resolution than MSS (30m vs. 80m), but decreased quickly as the all-analog processing system allowed no digital products and the shading effect could not be effectively corrected, yielding poor quality images. Requests practically vanished after the Thematic Mapper came into scene, and RBV products were taken off the INPE products list in late 1984. The processing system was dismantled in early 1991. Some 50,000 photographic originals are still kept, but no intention of resuming distribution exists in principle.", + "license": "proprietary" + }, + { + "id": "INPE_LS_TM", + "title": "LANDSAT TM Data held by the National Institute for Space Research (INPE), Brazil", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1984-03-24", + "end_date": "", + "bbox": "-80, -40, -33, 8", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456079-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456079-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/INPE_LS_TM", + "description": "INPE's only receiving station for Landsat TM is located in Cuiaba, MT (geographic coordinates approx. 15.5S/56.0W). Data were always acquired in a routine fashion, initially over Brazil only, with extension to the whole range in 1987. A few gaps exist, some of them several months long, related mostly to station downtimes caused by lightning. The TM tapes recorded at Cuiaba are air shipped to the processing and distribution center in Cachoeira Paulista, SP, where they are kept. The holdings are estimated to be around 70,000 scenes (or 500,000 images, counting individually each of the seven spectral bands). About 14,000 scenes have been processed to black-and-white or color photographic originals and can be reproduced as products quicker than unprocessed ones. No copies are kept from delivered digital products. Requests for products and for image search listings are handled directly by the processing center (listed at the Data Center entry), through contacts by mail, phone or fax. No online remote access is available to the moment, although plans exist to implement it, including the International Directory protocols. No firm date is speculated for that, but hopes are that it can be made before 1998. Information about costs, delivery time and available formats can be requested at the same address.", + "license": "proprietary" + }, { "id": "INPE_TOPODATA", "title": "Brazil national full-coverage geomorphometric database", @@ -88594,6 +91246,32 @@ "description": "This file contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid for the INTEXB_2006 theme.", "license": "proprietary" }, + { + "id": "IZIKO_Fish", + "title": "iziko South African Museum - Fish Collection", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1829-08-01", + "end_date": "2005-03-24", + "bbox": "-155.518, -77.85, 179.14, 60.4667", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477688-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477688-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/IZIKO_Fish", + "description": "The iziko South African Museum has a comprehensive holdings comprising of identified bony and cartilaginous fish, mostly from Cape waters, but extending to Angola and Mozambique and the Southern, Indian and Atlantic Oceans. It currently contains 15048 records of 293 families.", + "license": "proprietary" + }, + { + "id": "IZIKO_Marine_Mammals", + "title": "iziko South African Museum - Marine Mammals Collection", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1880-10-14", + "end_date": "1998-01-08", + "bbox": "-170.47888, -70.16666, 174.5, 63.69417", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477689-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477689-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/IZIKO_Marine_Mammals", + "description": "The iziko South African Museum has a comprehensive collection of cetacean and Cape fur seal skeletal material. Skeletal material from other marine mammals is also held. Part of this collection is on exhibition in the museum's Whale Well. It currently contains 14484 records of 46 families.", + "license": "proprietary" + }, { "id": "IceMargin_79E-108E_1", "title": "Margin of the Antarctic ice cover derived from Synthetic Aperture Radar images for the sector 79E-108E", @@ -90388,6 +93066,19 @@ "description": "The KILVOLC_FlowerKahn2021_1 dataset is the MISR Derived Case Study Data for Kilauea Volcanic Eruptions Including Geometric Plume Height and Qualitative Radiometric Particle Property Information version 1 dataset. It comprises MISR-derived output from a comprehensive analysis of Kilauea volcanic eruptions (2000-2018). Data collection for this dataset is complete. The data presented here are analyzed and discussed in the following paper: Flower, V.J.B., and R.A. Kahn, 2021. Twenty years of NASA-EOS multi-sensor satellite observations at K\u012blauea volcano (2000-2019). J. Volc. Geo. Res. (in press). The data is subdivided by date and MISR orbit number. Within each case folder, there are up to 11 files relating to an individual MISR overpass. Files include plume height records (from both the red and blue spectral bands) derived from the MISR INteractive eXplorer (MINX) program, displayed in: map view, downwind profile plot (along with the associated wind vectors retrieved at plume elevation), a histogram of retrieved plume heights and a text file containing the digital plume height values. An additional JPG is included delineating the plume analysis region, start point for assessing downwind distance, and input wind direction used to initialize the MINX retrieval. A final two files are generated from the MISR Research Aerosol (RA) retrieval algorithm (Limbacher, J.A., and R.A. Kahn, 2014. MISR Research-Aerosol-Algorithm: Refinements For Dark Water Retrievals. Atm. Meas. Tech. 7, 1-19, doi:10.5194/amt-7-1-2014). These files include the RA model output in HDF5, and an associated JPG of key derived variables (e.g. Aerosol Optical Depth, Angstrom Exponent, Single Scattering Albedo, Fraction of Non-Spherical components, model uncertainty classifications and example camera views). File numbers per folder vary depending on the retrieval conditions of specific observations. RA plume retrievals are limited when cloud cover was widespread or the solar radiance was insufficient to run the RA. In these cases the RA files are not included in the individual folders. In cases where activity was observed from multiple volcanic zones in a single overpass, individual folders containing data relating to a single region, are included, and defined by a qualifier (e.g. '_1').", "license": "proprietary" }, + { + "id": "KOMPSAT-2", + "title": "KOMPSAT-2 Panchromatic and multispectral imagery", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2006-07-28", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2229454502-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2229454502-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/KOMPSAT-2", + "description": "The KOMPSAT-2 allows the generation of high resolution images with a GSD of better than 1 m for PAN data and 4 m for MS data with nadir viewing condition at the nominal altitude of 685 km. The MSC has a single PAN spectral band between 500 - 900 nm and 4 MS spectral bands between 450-900 nm. PAN imaging and MS imaging can be operated simultaneously during mission operations. The swath width is greater than or equal to 15 km at the mission altitude for PAN data and MS data. The system is equipped with a solid state recorder to record images not less than 1,000km long at the end of life. The satellite can be rolled up to \u00b130 degrees off-nadir to pre-position the MSC swath. The KOMPSAT-2 can provide across-track stereo images by multiple passes of the satellite using off-nadir pointing capability. The satellite is compatible with daily revisit operation by off-nadir pointing with degraded GSD. Also, the image products according to the requested products quality standard can be made within one (1) day after satellite passes over the KGS.", + "license": "proprietary" + }, { "id": "KOMPSAT-2.ESA.archive_NA", "title": "KOMPSAT-2 ESA archive", @@ -114802,6 +117493,32 @@ "description": "This dataset contains CTD (conductivity, temperature, depth) data obtained from the Krill and Rock (KROCK) 92/93 cruise of the Aurora Australis, during Jan - Mar 1993. 62 CTD casts were taken in the Prydz Bay region, as a supplement to the krill and geology research program. Casts were made about 200 m except for one off the shelf. This dataset is a subset of the whole cruise data. The fields in this dataset are: Pressure Temperature Sigma-T Salinity Geopotential Anomaly Specific volume Anomaly samples deviation conduction", "license": "proprietary" }, + { + "id": "KV1_MSS_0.1", + "title": "MSS multispectral images from Kanopus-V", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2012-07-22", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231912416-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231912416-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/KV1_MSS_0.1", + "description": "MSS multispectral images from Kanopus-V Multiband surveying sensor from ?Kanopus-V? satellite that has circular sun synchronous orbit. The sensor is designed for monitoring man-caused and natural-caused emergency situations. The sensor provides earth surface images in 4 spectral bands (blue ? 460-520 nm, green ? 510-600 nm, red ? 630-690 nm, near infrared 750-840 nm). Nadir spatial resolution is 12 m. Swath with of the system is 20 km. The system has the ability to point the sensor to 40\ufffd from nadir in either side, which enables swath view of 920 km. The revisit frequency depends on latitude and can vary from 3 to 16 days. Obtained data can be used for tackling various problems in agriculture, ecology, cartography, construction, forestry, natural resources inventory and emergency situations monitoring.", + "license": "proprietary" + }, + { + "id": "KYOTO_GREENHOUSEGASES", + "title": "Interactive Map of Greenhouse Gas Emissions and the Kyoto Protocol", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2005-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848233-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848233-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/KYOTO_GREENHOUSEGASES", + "description": "This set of interactive graphics was produced in preparation for the seventh Conference of the Parties (COP-7) to the United Nations Framework Convention on Climate Change (UNFCCC) held in The Netherlands in October-November 2001. They are based on several UNFCCC Secretariat documents compiling data from submissions by Annex I countries; these include First and Second National Communications, as well as annual national inventory data. Additional sources include updated reports from individual countries; exceptions are noted on the graphs. The graphs feature actual (1990-2000) and projected (2005, 2010) emissions of the six greenhouse gases: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF6). The emissions are aggregated and represented as CO2 equivalents in million tonnes (1012); please note that in UNFCCC documents, emissions are measured in gigagrams (10**9). For more information on the derivation of GHG emissions statistics, see: \"http://www.grida.no/db/maps/collection/climate6/about.htm\"", + "license": "proprietary" + }, { "id": "Kennebec_0", "title": "Kennebec River Plume - Gulf of Maine ecosystem measurements", @@ -115153,6 +117870,19 @@ "description": "Bio-optical validation observations were made on the CCGS Hudson in spring from 9 May to 11 June 1997 in the Labrador Sea. Stations were occupied along several sections between Labrador and Greenland with some locations revisited more than once during a cruise. The most heavily sampled SW-NE section from Hamilton Bank on the Labrador Shelf to Cape Desolation on the Greenland Shelf is the AR7 line of the World Ocean Circulation Experiment.", "license": "proprietary" }, + { + "id": "LACHYSIS", + "title": "LACHYCIS (Sistema de informacion del Ciclo Hidrologico y las Actividades en recursos Hidricos de America latina)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-105, -60, -35, 30", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232459312-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232459312-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LACHYSIS", + "description": "Information System on Hydrology and Water resources in Latin America and the Caribbean countries.", + "license": "proprietary" + }, { "id": "LADSII_hydrographic_survey_1", "title": "Hydrographic survey LADSII by the RAN Australian Hydrographic Service at Macquarie Island, February to March 1999", @@ -115283,6 +118013,19 @@ "description": "Measurements from the South China Sea (SCS) made by researchers at Columbia University's Lamont-Doherty Earth Observatory (LDEO).", "license": "proprietary" }, + { + "id": "LANDFIRE", + "title": "LANDFIRE National Products", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-138, 13, -54, 68", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554672-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554672-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LANDFIRE", + "description": "LANDFIRE National products comprise a set of 20+ digital maps of vegetation composition and structure; wildland fuel (crown and surface); and current departure from simulated historical vegetation conditions. LANDFIRE National procedures integrate relational databases, remote sensing, systems ecology, gradient modeling, and landscape simulation to create consistent and comprehensive products that are standardized across the entire United States. LANDFIRE will deliver national products on an incremental basis through FY 2009. LANDFIRE national data layers can be obtained through The National Map. ", + "license": "proprietary" + }, { "id": "LANDMET_1", "title": "Land Surface Atmospheric Boundary Interaction Product L3 V1(LANDMET) at GES DISC", @@ -116960,6 +119703,19 @@ "description": "The Landsat-derived Global Rainfed and Irrigated-Cropland Product Level 3 2020 (LGRIP30_L3) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP L3 V2 maps agricultural croplands by dividing them into irrigated and rainfed croplands, and calculates irrigated and rainfed areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. Each LGRIP30 L3 V2 30 m resolution GeoTIFF file contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation without any artificial watering), irrigated cropland (cropland that had at least one irrigation during the crop growing period), non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. Currently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date.", "license": "proprietary" }, + { + "id": "LIDA", + "title": "Lidar Data from Brazil", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1972-02-23", + "end_date": "", + "bbox": "-45, -23, -45, -23", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456105-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456105-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LIDA", + "description": "The FISAT home page on the WWW is http://www.laser.inpe.br/fisat/ . This set contains data obtained at the location of Sao Jose dos Campos (23 degrees S, 45 degrees W), only. >From 1972 to 1981 only night-time data of the Lidar backscatter return at 589.0 nm are available. The periodicity of the data is irregular. Generally short-duration measurements (less than 2 hours) are available at about one measurerent per week. Long-duration data covering most of the night are available in a few campaigns. Data are also given, in processed form, providing aerosol backscatter ratio from 15 to 30 km altitude and sodium density from 75 to 105 km altitude. >From 1981 to 1993, campaigns of sodium measurements taken during the day, including several diurnal cycles are also available. >From 1983 to the present day a new powerful laser at 593.0 nm provides the Rayleigh scatter profiles giving the atmospheric density and temperatures from 35 to nearly 70 km altitude. Data are currently obtained, approximately, on a weekly basis.", + "license": "proprietary" + }, { "id": "LIDAR_0", "title": "Pigment measurements from 1989 and 1990 in the Gulf of St Lawrence", @@ -116986,6 +119742,19 @@ "description": "This data set provides estimates of forest canopy height derived from the Geoscience Laser Altimeter System (GLAS) LiDAR instrument that was aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite. A global GLAS waveform data set (n=12,336,553) from collection periods between October 2004 and March 2008 was processed to obtain canopy height estimates.Estimates of GLAS maximum canopy height and crown-area-weighted Lorey's height are provided for 18,578 statistically-selected globally distributed forested sites in a point shapefile. Country is included as a site attribute.Also provided is the average canopy height for the forested area of each country, plus the number of GLAS data footprints (shots), number of selected sample sites, and estimates of the variance for each country.", "license": "proprietary" }, + { + "id": "LIFE_ECO_NBS_SIER_VEG_FUEL1", + "title": "Fire Fuel Inventory, Yosemite National Park from the U.S. Geological Survey, Biological Resources Division", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1987-05-13", + "end_date": "", + "bbox": "-120, 37, -119, 39", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550682-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550682-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LIFE_ECO_NBS_SIER_VEG_FUEL1", + "description": "The fuel inventory data involves 144 var. radius plots measured for vegetative cover and structure by species and fuel loading. Standing and downed fuel is estimated by size, class and type. Wood and leaf litter fall data are collected annually. This dataset was collected in Yosemite National Park road corridors between 1200 and 2400 meters. This dataset is part of the U.S. Geological Survey, Biological Resources Division, Global Change Program.", + "license": "proprietary" + }, { "id": "LIMSN7L1PROFILER_001", "title": "LIMS/Nimbus-7 Level 1 Profiles of Radiance Data V001 (LIMSN7L1PROFILER) at GES DISC", @@ -117844,6 +120613,19 @@ "description": "LRIRN6L2IPAT is the Nimbus-6 Limb Radiance Inversion Radiometer (LRIR) Level 2 Inverted Profiles of Temperature and Ozone data product. The product contains daily profiles of temperature and ozone concentration profiles that were inverted from radiances measured in four spectral regions: two in the 15 micron carbon dioxide band; one in the 9.7 micron ozone band; and one located in the rotational water vapor band (23 to 27 microns). The calibrated radiances are also included in this product. There are a maximum of 13 orbits per day each with up to 115 profiles per orbit. LRIR is a limb profiler with spatial coverage from latitude -64 to +84 degrees. Vertical profiles are provided at 17 standard pressure levels (from 100 to 0.1 mbar, i.e., from 15 to 64 km) with about 1.5 km vertical resolution. The instrument operated successfully and data are available from 20 June 1975 to 6 January 1976. After this, the detector temperature began to rise rapidly, and the instrument was turned off. The principal investigator for the LRIR experiment was Dr. John Gille from NCAR. This product was previously available from the NSSDC with the identifier ESAD-00037 (old ID 75-052A-04A).", "license": "proprietary" }, + { + "id": "LSATUSERV", + "title": "LANDSAT User Service of the Brazilian National Institute on Space Research (INPE)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1973-05-14", + "end_date": "", + "bbox": "-77, -38, -35, 6", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456155-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456155-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LSATUSERV", + "description": "The Brazilian receiving station for Landsat data is located in Cuiaba, state of Mato Grosso, Midwest region: 56 degrees 5 minutes 30 seconds West longitude, and 15 degrees 32 minutes 30 seconds South latitude. The Cuiaba station started to operate in May 1973. MSS data were acquired on a routine basis over Brazil and by special request over the other countries. RBV images were acquired during the Landsat 3 years. Thematic mapper data have been recorded over Brazil since February 1984. By October 1987, the Cuiaba station stopped recording MSS data and began to acquire Thematic Mapper images on routine fashion over the whole range of the antenna. MSS products are for while available just through the reproduction of existing photographic originals (about 150.000 B/W and color images). A new processing system was designed and began running digital frames in October 1995. Thematic Mapper images are available and can be ordered both in print and digital formats.", + "license": "proprietary" + }, { "id": "LSC_Aeromonas_salmonicida", "title": "Development of DNA primers to identify a Romet-resistance gene in Aeromonas salmonicida and its subsequent use for epidemiological studies", @@ -117883,6 +120665,45 @@ "description": "The eastern coastal rivers of North America have historically supported anadromous populations of Atlantic salmon (Salmo salar). Numbers of these animals have declined due to overfishing and loss of habitat, and population numbers have been supplemented by stocking efforts that span at least the last hundred years. Often, these stockings used fish of diverse origins. This is exemplified by the fact that several Maine rivers were stocked with Canadian fish from at least two locations. Because of this stocking history, it is not known if significant remnants of native Atlantic salmon stocks exist in the coastal rivers of Maine. Atlantic salmon in five Maine rivers were designated as category 2 candidates for listing under the Endangered Species Act in 1991 in response to the precipitous decline in population numbers. In October 1993, all anadromous U.S. Atlantic salmon were included in a petition to the U.S. Fish and Wildlife Service (FWS) for a Rule to List the species under the Endangered Species Act. Information was obtained from http://www.lsc.usgs.gov", "license": "proprietary" }, + { + "id": "LSC_Flavobacteriumpsychrophilum", + "title": "Evaluation of the Genetic Diversity of Flavobacterium Psychrophilum from Various Origins", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-02-01", + "end_date": "2002-09-30", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551122-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551122-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LSC_Flavobacteriumpsychrophilum", + "description": "The US Fish and Wildlife Service along with the Washington Department of Fisheries and Wildlife work in cooperation on the Pacific salmon restoration effort. For a number of reasons, population numbers of some Pacific salmon species have declined dramatically over recent years and a large part of the restoration effort encompasses hatchery propagation of progeny from returning fish for subsequent planting into their natural waters. With hatchery rearing operations fish are maintained in high densities and this situation lends itself to disease problems. One such disease is bacterial cold water disease, caused by the Gram negative bacterium Flavobacterium psychrophilum. Source of the infection is the ubiquitous nature of the pathogen and because of the verticle transmissability. Only recently has the biochemical and taxonomic position of F. psychrophilum been elucidated more accurately. Information was obtained from http://www.lsc.usgs.gov", + "license": "proprietary" + }, + { + "id": "LSC_biomarkers", + "title": "Evaluation of Selected Histologic and Immunologic Biomarkers in Fish Collected for the BEST pilot program in the Mississippi", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-03-01", + "end_date": "2000-10-01", + "bbox": "-82.89, 36.96, -77.48, 40.88", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550647-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550647-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LSC_biomarkers", + "description": "This study is part of a larger project entifled Biomonitoring of Environmental Status and Trends (BEST) Program: Testing and Implementation of Selected Aquatic Ecosystem Indicators in the Mississippi River System, 1995. This pilot project includes assessment of a variety of biomarkers of which we are responsible for the histologic and immunologic markers. During the period in which concentrations of persistent contaminants were declining, the use of and concerns about other chemicals, especially those that do not accumulate in biota, increased. At least part of this concern stemmed from increasingly frequent reports of fish kills and avi an wildlife mortality incidents related to the use of soft pesticides-highly toxic, but short-lived organophosphate and carbamate insecticides that do not accumulate (e.g.; Glaser 1995). Herbicides are also now widely distributed in surface and ground waters of agricultural areas. Information was obtained from http://www.lsc.usgs.gov/", + "license": "proprietary" + }, + { + "id": "LSC_immunereprohistologic", + "title": "Immune, Reproductive and Histologic Biomarker Evaluation in Fish Collected for the Columbia and Rio Grande River Basin BEST Program, 1997", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-08-01", + "end_date": "2001-03-01", + "bbox": "-115, 30, -105, 50", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553382-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553382-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LSC_immunereprohistologic", + "description": "This study is part of a larger project entitled \"Contaminants and Biomarkers in Fish in the Columbia River and Rio Grande Basins, 1997\" ( Mid-Continent Ecological Science Center) This project is part of the Biomonitoring of Environmental Status and Trends (BEST) program. The BEST program incorporates both analytical chemistry arid a suite of biological responses to describe and track contaminant exposure and effects. Our part of this program is to measure and evaluate selected histologic, immunological and reproductive biomarkers. Our objectives are: to document the presence of selected histologic lesions which have been validated or widely accepted as indicators of contaminant exposure; to determine if there is evidence of immunosuppression using immune system biomarkers; evaluate gonad histology utilizing new potential biomarkers; determine if changes in gonad histology correlate with circulating vitellogenin levels; determine if these findings correlate with contaminant presence or concentration. Information was obtained from http://www.lsc.usgs.gov", + "license": "proprietary" + }, { "id": "LSM_807_1", "title": "Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies", @@ -117896,6 +120717,19 @@ "description": "The NCAR LSM 1.0 is a land surface model developed to examine biogeophysical and biogeochemical land-atmosphere interactions, especially the effects of land surfaces on climate and atmospheric chemistry. It can be run coupled to an atmospheric model or uncoupled, in a stand-alone mode, if an atmospheric forcing is provided. The model runs on a spatial grid that can range from one point to global. The model was designed for coupling to atmospheric numerical models. Consequently, there is a compromise between computational efficiency and the complexity with which the necessary atmospheric, ecological, and hydrologic processes are parameterized. The model is not meant to be a detailed micrometeorological model, but rather a simplified treatment of surface fluxes that reproduces at minimal computational cost the essential characteristics of land-atmosphere interactions important for climate simulations. The model is a complete executable code with its own time-stepping driver, initialization (subroutine lsmini), and main calling routine (subroutine lsmdrv). When coupled to an atmospheric model, the atmospheric model is the time-stepping driver. There is one call to subroutine lsmini during initialization to initialize all land points in the domain; there is one call per time step to subroutine lsmdrv to calculate surface fluxes and update the ecological, hydrological, and thermal state for all land points in the domain. The model writes its own restart and history files. These can be turned off if appropriate. Available for downloading from the ORNL DAAC are the LMS Model Documentation and User's Guide, the model source code, input data set, and scripts for running the model. Applications of the model are described in two additional companion files.", "license": "proprietary" }, + { + "id": "LS_TM_ARC", + "title": "Landsat TM Image Data Archived in China Remote Sensing Satellite Ground Station", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1986-06-01", + "end_date": "", + "bbox": "90, 20, 140, 60", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2226631645-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2226631645-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/LS_TM_ARC", + "description": "Landsat 5 was launched on March 1, 1984, carrying a seven-band TM sensor, and still operates properly at present. The satellite takes a sun-synchronized orbit with 705km altitude and 98.22 deg. inclination. A TM scene covers 185km by 170km earth surface approximately, with 30m ground resolution for band 1,2,3,4,5,7 and 120m for band6. For a particular place, the revisit cycle of the satellite is 16 days. Chaina Remote Sensing Satellite Ground Station(CRSGS) was inaugurated and become operational in Dec. 1986. Up to now it is the most important source of remote sensing satellite data in China for earth resouce exploration and environment monitoring. CRSGS has provided a large amount of satellite remote sensing products to more than 400 users, domestic and abroad. Applications of TM images have resulted in great economic and social benefits in a wide range of areas of national economy: resource survey and utilization, environment monitoring, geographic cartography, minerarl exploration, disaster detecting and assessing, etc. TM data received by CRSGS since 1986 have been archived. Through a Catalogue Archive and Browse System(CABS), users can retrieve useful information about data of interests. A image(or a group of images) could be searched according to date, location(latitude-longitude or path-row), and quality, etc. Text catalogue is available for all TM data in the archival. In addition to text contents, sub-sampled browse images are available for data acquired after Apr.,1994. The major products of CRSGS are TM data on CCTs, floppy disks and imagery on films or papaer prints. Products fall into two categories with respect to processing methods. 1. Standard processing includes systematic correction, precision correction, and geocoding, etc. 2.Special product(user dependent) includes multi-scene mosaicking, image classification, user defined annotation or administrative boundary adding, special juts enhancement, etc.", + "license": "proprietary" + }, { "id": "LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001", "title": "Aerosol optical size distribution and soot core size distribution measured by a Single Particle Soot Photometer (SP2) for 30 days in summer 2018-2019", @@ -134588,6 +137422,19 @@ "description": "This data set describes the temperature and relative humidity at 12 locations around Goddard Space Flight Center in Greenbelt MD at 15 minute intervals between November 2013 and November 2015. These data were collected to study the impact of surface type on heating in a campus setting and to improve the understanding of urban heating and potential mitigation strategies on the campus scale. Sensors were mounted on posts at 2 m above surface and placed on 7 different surface types around the centre: asphalt parking lot, bright surface roof, grass field, forest, and stormwater mitigation features (bio-retention pond and rain garden). Data were also recorded in an office setting and a garage, both pre- and post-deployment, for calibration purposes. This dataset could be used to validate satellite-based study or could be used as a stand-alone study of the impact of surface type on heating in a campus setting.", "license": "proprietary" }, + { + "id": "MassBay_LongTerm", + "title": "Long-Term Oceanographic Observations in Massachusetts Bay, 1989-2006", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1989-01-01", + "end_date": "2006-12-31", + "bbox": "-71, 42, -70.5, 42.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552981-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552981-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/MassBay_LongTerm", + "description": "This data report presents long-term oceanographic observations made in western Massachusetts Bay at long-term site LT-A (42\u00b0 22.6' N., 70\u00b0 47.0' W.; nominal water depth 32 meters) from December 1989 through February 2006 and long-term site B LT-B (42\u00b0 9.8' N., 70\u00b0 38.4' W.; nominal water depth 22 meters) from October 1997 through February 2004. The observations were collected as part of a U.S. Geological Survey (USGS) study designed to understand the transport and long-term fate of sediments and associated contaminants in Massachusetts Bay. The observations include time-series measurements of current, temperature, salinity, light transmission, pressure, oxygen, fluorescence, and sediment-trapping rate. About 160 separate mooring or tripod deployments were made on about 90 research cruises to collect these long-term observations. This report presents a description of the 16-year field program and the instrumentation used to make the measurements, an overview of the data set, more than 2,500 pages of statistics and plots that summarize the data, and the digital data in Network Common Data Form (NetCDF) format. This research was conducted by the USGS in cooperation with the Massachusetts Water Resources Authority and the U.S. Coast Guard.", + "license": "proprietary" + }, { "id": "MassGIS_GISDATA.COQHMOSAICSCDS_POLY", "title": "2001 MrSID Mosaics CD-ROM Index", @@ -137032,6 +139879,32 @@ "description": "This NASMo-TiAM (North America Soil Moisture Dataset Derived from Time-Specific Adaptable Machine Learning Models) dataset holds gridded estimates of surface soil moisture (0-5 cm depth) at a spatial resolution of 250 meters over 16-day intervals from mid-2002 to December 2020 for North America. The model employed Random Forests to downscale coarse-resolution soil moisture estimates (0.25 deg) from the European Space Agency Climate Change Initiative (ESA CCI) based on their correlation with a set of static (terrain parameters, bulk density) and dynamic covariates (Normalized Difference Vegetation Index, land surface temperature). NASMo-TiAM 250m predictions were evaluated through cross-validation with ESA CCI reference data and independent ground-truth validation using North American Soil Moisture Database (NASMD) records. The data are provided in cloud optimized GeoTIFF format.", "license": "proprietary" }, + { + "id": "NAWQA", + "title": "GIS Coverage of the National Water-Quality Assessment Study-Unit Investigations in the conterminous United States (NAWQA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-127.88, 22.87, -65.35, 48.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548614-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548614-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NAWQA", + "description": "This is a coverage of the boundaries and codes used for the U.S. Geological Survey National Water-Quality Assessment (NAWQA) Program Study-Unit investigations for the conterminous United States, excluding the High Plains Regional Ground-Water Study. The National Water-Quality Assessment Program is designed to describe the status and trends in the quality of the Nation's ground- and surface-water resources and to provide a sound understanding of the natural and human factors that affect the quality of these resources (Leahy and others, 1990). A \"Study Unit\" is a major hydrologic system in which NAWQA studies are focused. Study Units are geographically defined by a combination of ground- and surface-water features (Gilliom and others, 1995). As part of the NAWQA program, Study-Unit investigations were planned for 60 areas throughout the Nation to provide a framework for national and regional water-quality assessments (Leahy and others, 1990). The 60 planned Study-Units were divided into three groups of 20. Each group would be intensively studied on a rotational basis with 20 studies beginning in fiscal year 1991 (FY 1991 runs from October 1990-September 1991), 20 more studies beginning in fiscal year 1994 (October 1993-September 1994), and the final 20 studies beginning in fiscal year 1997 (October 1996-September 1997). Each study cycle would span 10 years. In 1996, the number of Study-Units was scaled back to 59 when two of the original 60 Study Units combined. Also, because of budgetary restraints, some of the original planned Study Units have been scheduled to start later than originally planned and others have not even been scheduled to start yet. This coverage contains the boundaries for the 57 Study Units within the conterminous United States, excluding the High Plains Regional Ground Water-Study, which was conceived in late 1997. The coverage also includes the name, starting date, and NAWQA standard abbreviation of each Study Unit plus various codes to help display the data. This data set is used primarily to display the location of NAWQA Study Units and for analysis of data at the national scale. It is not recommended for either local or regional analysis due to the small scale of most of the features. This coverage can be used in conjunction with other NAWQA datasets including the point coverage of NAWQA Trace Element Sampling Sites (NAWQA_TE) and the point coverage of NAWQA Nutrients Sampling Sites (NAWQA_NU). Detailed information on these two coverages can be found in their respective metadata. Originally, Study-Unit boundaries in this coverage were composed of 1: 2,000,000-scale hydrologic unit boundaries (Allord, 1992) and state boundaries (Negri, 1994). As the NAWQA project has progressed and Study-Unit Investigations have gotten underway, many Study-Unit boundaries have been modified. In addition, Study Units have enhanced their boundary coverages with features at higher resolutions. As these modifications are made, Study Units submit their new boundary coverages to National Synthesis teams, who are responsible for summarizing the results from all of the Study Units, and the changes are incorporated into this coverage. As a result, this coverage is composed of linear features at various scales (for example, 1: 100,000, 1: 250,000), but the majority remain at the 1: 2,000,000 scale. The original version of this coverage was generated by the the USGS Cartographic and Publishing Program (CAPP) in Madison, Wisconsin, in the fall of 1991. The procedures used to create this coverage are described below. Each NAWQA Study Unit was asked for a description of their boundary definition. Once this information was gathered, CAPP created the coverage by extracting digital features from the 1: 2,000,000 Hydrologic Unit boundaries coverage and the 1: 2,000,000 state boundaries coverage. Since the majority of Study-Unit boundaries are defined from hydrologic unit boundaries, most of the features were directly copied from the Hydrologic Units coverage. An exception to this was the boundary defining the Georgia-Florida Coastal Plain Study Unit where the northern boundary was defined by the northern edge of the Florida Aquifer. To incorporate this boundary into the coverage, the aquifer boundary was digitized from the U.S. Geological Survey's \"Ground-Water Atlas of the United States\", HA-730 (G) (Miller, 1990). In November 1991, responsibility for maintaining the coverage was transferred to NAWQA's National Synthesis staff. Major milestones in the development of the coverage and various revisions to the coverage are listed under the Lineage section. The NAWQA Program has used the coverage for various analyses and displays and for various published reports, for example, Leahy and Thompson (1994) and Gilliom and others (1995). The coverage is reviewed by one of the NAWQA National Synthesis GIS staff members prior to release. Related_Spatial_and_Tabular_Data_Sets: Alaska (Cook Inlet) and Hawaii (Oahu) NAWQA Study-Unit boundaries are maintained in separate data sets. The High Plains Regional Ground-Water Study boundary is in a separate data set. Cook, Oahu, and High Plains study boundaries should be used with this data set to give the full picture of NAWQA Study Units nationwide. [Summary provided by EPA]", + "license": "proprietary" + }, + { + "id": "NAWQAHIS", + "title": "GIS Coverage for the National Water-Quality Assessment (NAWQA) Program Retrospective Database for Nutrients in Surface Water: Monitoring Locations", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-127.88, 22.87, -65.35, 48.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553303-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553303-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NAWQAHIS", + "description": "The retrospective database is a compilation of historical water-quality and ancillary data collected before NAWQA Study Units initiated sampling in 1993. This coverage contains the point locations of monitoring locations where historical water-quality data was collected. Water-quality data were obtained by study-unit personnel from the U.S. Geological Survey (USGS) National Water Information System (NWIS), from records of State water-resource agencies, and from STORET, the U.S. Environmental Protection Agency national database. Ancillary data describing characteristics of sampled sites were compiled by NAWQA Study Units or obtained from national-scale digital maps. Mueller and others (1995) used this data to determine preexisting water-quality conditions in the first 20 NAWQA Study Units that began in 1991. Also, Nolan and Ruddy (1996) used the data to describe areas of the United States at risk of nitrate contamination of ground water. Supplemental_Information: The retrospective database includes over 22,000 surface-water samples. The surface-water data are for samples collected during 1980-90 at sites that had a minimum of 25 monthly samples. Year of sampling is included in the retrospective database because it was reported most often by the various Study Units. Year of sampling also is convenient because some Study Units reported median constituent concentrations. If sampling date ranges for median values fell within a single year, then year of sampling was retained in the national data set for that sample. Because sampling, preservation, and analytical techniques associated with these historical data changed during the period of record and are different for different agencies, reported nutrient concentrations were aggregated into the following groups: (1) ammonia as N, (2) nitrate as N, (3) total nitrogen, (4) orthophosphate as P, and (5) total phosphorus. For example, ammonia includes both ammonium ions and un-ionized ammonia. More information on methods used to aggregate constituent data is available in the report by Mueller and others (1995). Much of the ancillary data, such as well and aquifer descriptions and land-use classification for surface-water drainage basins, were provided by NAWQA Study Units. Data evaluated at the national scale include land use, soil hydrologic group, nitrogen input to the land surface, and the ratios of pasture or woodland to cropland. Land-use classification of surface-water sites is based on Anderson Level I categories (Anderson and others, 1976). Land use at surface-water sites was classified by NAWQA Study Unit personnel based on the Anderson Level I categories. Many surface-water sites were affected by mixed land uses, such as Forest and Agricultural, or Agricultural and Urban. Surface-water sites with very large drainage areas (greater than 10,000 square miles) were considered to be affected by multiple land uses, and were designated as Integrated land use. More detailed descriptions of the land-use categories in the retrospective database are given by Mueller and others (1995). Soil hydrologic group was determined from digital maps compiled by the U.S. Soil Conservation Service (1993). The categorical values (A, B, C, and D) from the digital maps were converted to numbers to permit aggregation (Mueller and others, 1995). Surface-water sites were assigned the area-weighted mean for soil mapping units in the upstream drainage basin. Many surface-water sites did not have digitized basin boundaries available, so hydrologic group could not be evaluated. Fertilizer and manure applications were estimated from national databases of fertilizer sales (U.S. Environmental Protection Agency, 1990) and animal populations (U.S. Bureau of the Census, 1989). Nitrogen input by atmospheric deposition was derived from data provided by the National Atmospheric Deposition Program/National Trends Network (1992). Population data were obtained from the U.S. Bureau of the Census (1991). Total population in the upstream drainage was compiled for the surface-water data set. Within the database, concentrations less than detection are reported as negative values of the detection limit. Missing values are indicated by a decimal point. (During processing of the tabular data, these decimal points were replaced will NULL values; See Data_Quality_Information section. Historical data can be of limited use in national assessments because of inconsistencies between and within agencies in database structure and format and in sample collection, preservation, and analytical procedures. For example, changes in sample collection and analytical procedures can cause shifts in constituent concentrations that are unrelated to possible changes in environmental factors. See Mueller and others (1995) for assumptions and limitations associated with the retrospective database. [Summary provided by the EPA.]", + "license": "proprietary" + }, { "id": "NA_MODIS_Surface_Biophysics_1210_1", "title": "MODIS-derived Biophysical Parameters for 5-km Land Cover, North America, 2000-2012", @@ -137149,6 +140022,19 @@ "description": "New-ID: NBI18 The Africa Major Infrastructure and Human Settlements Dataset Files: TOWNS2.E00 Code: 100022-002 ROADS2.E00 100021-002 Vector Members: The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename The Africa major infrastructure and human settlements dataset form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA. 92373, USA The ROADS2 file shows major roads of the African continent The TOWNS2 file shows human settlements and airports for the African continent References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source: FAO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Points Format: Arc/Info export non-compressed Related Datasets: All UNEP/FAO/ESRI Datasets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments: There is no outline of Africa", "license": "proprietary" }, + { + "id": "NBId0019_101", + "title": "FAO Major Elevation Zones of Africa (GIS Coverage)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-30, -45, 60, 40", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849111-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849111-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0019_101", + "description": "New-ID: NBI19 The Africa Major Elevation Zones Dataset documentation File: ELEVLL Code: 100070-003 Vector Member The above file is in Arc/Info Export format and should be imported using the Arc/Info command Import cover In-Filename Out-Filename The Africa elevation major zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The manuscript derived from the topographic film separates of the UNESCO/FAO Soil Map of the World (1977) in Miller Oblated Stereographic projection was used to provide a generalized coverage of elevation values providing information as both line-related and polygonal form. The map was prepared by overlaying the topography film separate with a matte drafting film and then delineating the selected elevation contours. Some of the line crenulation was removed during the delineation process, because this map was designed to define general elevation zones rather than constitute a true topographic base. Code values were recorded directly on the map and were key-entered during the digitizing process with a spatial resolution of 0.002 inches, as part of the polygon or line sequence indentification number. The map was then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy. ESRI, 380 New York Street, Redlands, CA 92373, USA The ELEVLL2 data shows Major Elevation zones of Africa, in lat/lon References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO/FAO Soil Map of the World(1977). Scale 1:5000000. UNESCO, Paris DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society Washington DC. Source: FAO Soil Map of the World, scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Polygon and line Format: Arc/Info export non compressed Related Datasets: All UNEP/FAO/ESRI Datasets AFELBA elevation and Bathymetry (100048)", + "license": "proprietary" + }, { "id": "NBId0020_101", "title": "Countries, Coasts and Islands of Africa (Global Change Data Base - Digital Boundaries and Coastlines)", @@ -137227,6 +140113,32 @@ "description": "New-ID: NBI36 Africa Lakes and Rivers. Lakes and Rivers Dataset documentation (Micro World Data Bank II) Files: LAKES.VEC Code: 100055-001 RIVERS.VEC 100061-001 AFRIVER.IMG 100002-001 Raster Members The VEC and IMG files are in IDRISI format Africa lakes and rivers datasets are part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The LAKES file shows African lakes The RIVERS file shows African rivers The AFRIVER file shows African rivers References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. vol. 2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : digitized from available sources Publication Date : 1988 Projection : Lat/lon Type : Raster Format : IDRISI", "license": "proprietary" }, + { + "id": "NBId0041_101", + "title": "FNOC Elevation (meters), Terrain and Surface Characteristics for Africa", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-30, -45, 60, 40", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847281-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847281-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0041_101", + "description": "New-ID: NBI41 Africa FNOC Elevation (meters), Terrain and Surface characteristics. Africa Elevation (meters), Terrain, and Surface Characteristics Dataset Documentation Files: AFMAX.IMG Code: 100082-001 AFMIN.IMG 100082-002 AFMOD.IMG 100082-003 Raster Members The IMG files are in IDRISI format Africa elevation dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFMAX file shows maximum elevation (meters) The AFMIN file shows minimum elevation (meters) The AFMOD shows modal elevation (meters) Reference: Cuming, Michael J. and Barbara A. Hawkins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI", + "license": "proprietary" + }, + { + "id": "NBId0042_101", + "title": "NOAA Monthly 10-Minute Normalized Vegetation Index (April 1985-December 1988) for Africa", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-30, -45, 60, 40", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848152-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848152-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0042_101", + "description": "New-ID: NBI42 NOAA monthly Normalized Vegetation Index (NDVI) for Africa. NOAA Monthly 10-Min Normalized Vegetation Index Dataset (APRIL 1985 - DECEMBER 1988) Files: AFAPR85.IMG-AFDEC85.IMG Code: 100041-001 AFJAN86.IMG-AFDEC86.IMG 100041-001 AFJAN87.IMG-AFDEC87.IMG 100041-001 AFJAN88.IMG-AFDEC88.IMG 100041-001 Raster Members The IMG files are in IDRISI format Africa monthly 10-min normalized difference vegetation index dataset is part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA AFAPR85-AFDEC88 (45 months) show monthly Normalized Vegetation Index (NDVI) References: Kidwell, Katherin B. (ed.). Global Vegetion Index User\"'\"s Guide (1990). NOAA/NHESDIS/SDSD. for additional references see Appendix A-26-A32 of the Global Change Data Base documentation Source map : digitized from available maps and reprocessed Publication Date : Jun 1992 Projection : Lat/lon Type : Raster Format : IDRISI", + "license": "proprietary" + }, { "id": "NBId0043_101", "title": "Africa Integrated Elevation and Bathymetry", @@ -137266,6 +140178,175 @@ "description": "New-ID: NBI53 Africa Revised FNOC Percent Water Cover Dataset Documentation File: AFWATER.IMG Code: 100082-005 Raster Member The IMG file is in IDRISI format The percent water cover dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWATER file shows the revised FNOC percent water cover for Africa. Reference: Cuming, Michael J. and Barbara A. Hwakins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lon/lat Type : Raster Format : IDRISI", "license": "proprietary" }, + { + "id": "NBId0079_101", + "title": "Lake Chad Datasets, Africa", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "13, 7, 24, 23", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848788-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848788-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0079_101", + "description": "The Lake Chad Dataset which is a detailed case study of the UNEP/FAO/ESRI Family was developed by UNEP/GRID, on behalf of the UNEP/Fresh Water Unit for the Lake Chad Commission on Sustainable Development. Lake Chad Dataset covers parts of 7 countries: Cameroon, Chad, Nigeria and Niger, Sudan, Central African Republic and Libya and is a clip (regional version) of Africa Outline Dataset (NBI01). The base maps used for the continental version were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit and the Rand-McNally New International Atlas (1982) to clarify unit boundaries. Files: ADMIN.E00 Code: 115001-001 BASE.E00 115002-001 COUNTRIES.E00 115003-001 Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. The ADMIN polygon dataset showing administrative areas for 7 countries around Lake Chad. The BASE is a polygon Dataset showing the countries with inland water bodies. The COUNTRIES is a polygon Dataset showing only the country boundaries. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. FAO/UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris FAO. Maps and Statistical Data by Administrative Unit (unpublished) Rand-McNally. New International Atlas (1982). Rand-McNally & Company. Chicago Source: FAO/UNESCO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1988 Projection: Miller Type: Polygon and line Format: Arc/Info Export, non-compressed Related Datasets: All the Lake Chad Datasets of the UNEP/FAO/ESRI family.", + "license": "proprietary" + }, + { + "id": "NBId0083_101", + "title": "Kenya Administrative Units, Drainage, Agro-Climatic Zones, Rainfall, Infrastructure", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "33, -5, 43, 6", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847488-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847488-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0083_101", + "description": "Description: These datasets (Administrative Units, Drainage, Agro-Climatic Zones, Rainfall, Infrastructure) were scanned by the Canadian Land data Systems Division, Land Directorate, Dept of Environment, Ottawa, Canada. This was in response to the request to GRID by the Kenya Ministry of Agriculture to assist in creating the datasets. The source information and scales are varied; Rivers, Agroecological Zones, Soils, Boundaries, Towns, Lakes, Transport, and the Districts, Provinces (administrative boundary), Elevation were based on the scale of 1: 1 000 000 and of which the source information was derived from Ministry of Agriculture and Survey of Kenya maps. The Landuse dataset was based on the Kenya Rangeland Ecological Monitoring Unit (KREMU now DRSRS) map at the scale of 1: 1 000 000.The Mean Annual Rainfall dataset was based on an East Africa map(1966) at the scale of 1: 2 000 000 Rainfall data was originally provided by Kenya Meteorological Department. These were collected from a total of 79 Stations for the period between 1982-1988. More records were added by GRID which extended the period to 1991 The data consists of the rainfall,Potential Evapotranspiration (PET) and Temperature information. Sample Files: RAINFALL.E00 FILL8291.PLU, PETALL.DBF/.NDX, ADD82,83,84,85,86.DAT (Others available on request) Vector Members: - Files are in an ArcInfo Export format", + "license": "proprietary" + }, + { + "id": "NBId0089_101", + "title": "Kenya Soils (GIS Coverage from UNEP/GRID Nairobi)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1979-12-30", + "end_date": "1982-12-30", + "bbox": "33, -5, 43, 6", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848109-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848109-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0089_101", + "description": "New-ID: NBI89 SOIL MAP OF KENYA. Produced by the Republic of Kenya, Kenya Soil Survey in the Ministry of Agriculture Nairobi. Agro-climatic classification and map preparation was done by H. M. H. Braun and other staff of the Kenya soil survey. Cartography and lithography was done by the Soil Survey Insitute Wageningen, The Netherlands. There are three items in the info table which are of importance namely TYPE1, TYPE2 and SOIL. TYPE1 and TYPE2 are an alpha-numeric code which represent the soil type in the item SOIL. This code was given in order to facilitate manipulation and calculations of the info tables, which is more easily done using integers rather than using character strings. TYPE1 is the first part of the character string in the item SOIL and TYPE2 is the second part of the character string in the item SOIL, as seen in the info table below in SOIL# 19. For details on the actual soil types and associated information see the documentation \"Exploratory Soil Map and Agro-climatic Zone Map of Kenya, 1980. MAP TITLE Exploratory Soil Map and Agro-climatic Zone Map of Kenya, 1980. Arc/info table AREA PERIMETER SOIL# SOIL-ID TYPE1 TYPE2 SOIL -47.552 39.567 1 0 0 0 '' 0.068 3.258 2 9009 479 0 ' H9' 0.013 0.634 3 9010 645 0 ' Y5' 0.000 0.053 4 9011 60937 0 ' Ux7' 0.001 0.132 5 9012 403 0 ' A3' 0.002 0.284 6 9013 645 0 ' Y5' 0.009 0.524 7 9014 60937 0 ' Ux7' 0.001 0.150 8 9015 479 0 ' H9' 0.009 0.602 9 9016 516 0 ' L6' 0.052 1.562 10 9017 645 0 ' Y5' 0.022 0.975 11 9018 558821 0 ' Ps21' 0.127 2.573 12 9019 558821 0 ' Ps21' 0.000 0.085 13 9020 479 0 ' H9' 0.073 4.595 14 9021 403 0 ' A3' 0.238 5.943 15 9022 60937 0 ' Ux7' 0.002 0.231 16 9023 458 0 ' F8' 0.142 3.913 17 9024 408 0 ' A8' 0.004 0.263 18 9025 479 0 ' H9' 0.004 0.249 19 9026 431 55813 ' D1 + Pl3' 0.018 0.855 20 9027 408 0 ' A8' 0.044 1.360 21 9028 479 0 ' H9'", + "license": "proprietary" + }, + { + "id": "NBId0093_101", + "title": "Kenya Coastal Zone - International/Administrative Boundaries and Schools", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "34, -5, 42, 5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847490-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847490-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0093_101", + "description": "The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data.", + "license": "proprietary" + }, + { + "id": "NBId0098_101", + "title": "Kenya Coastal Zone - Coastal Features, Landuse, Marine/Terrestrial Parks, Elevation, Bathymetry", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "39, -5, 41, -1", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847562-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847562-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0098_101", + "description": "The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data.", + "license": "proprietary" + }, + { + "id": "NBId0106_101", + "title": "Kenya Coastal Zone - Scientific Cruise", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "39, -4, 42, -1", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847399-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847399-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0106_101", + "description": "The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data.", + "license": "proprietary" + }, + { + "id": "NBId0110_101", + "title": "Kenya Coastal Zone - Lakes, Rivers, Watersheds, Boreholes", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "38, -5, 42, -1", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848268-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848268-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0110_101", + "description": "The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data.", + "license": "proprietary" + }, + { + "id": "NBId0115_101", + "title": "Kenya Coastal Zone - Marine Life, Fishing, Sport, Aquaculture, Vessels, Lighthouses", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "38, -5, 42, -1", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848234-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848234-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0115_101", + "description": "The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data.", + "license": "proprietary" + }, + { + "id": "NBId0127_101", + "title": "Kenya Coastal Zone - Dumping Grounds, Pollution, Erosion", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "39, -4, 40, -3", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848654-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848654-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0127_101", + "description": "The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data.", + "license": "proprietary" + }, + { + "id": "NBId0128_101", + "title": "Kenya Coastal Zone - Wildlife: Bird Species and Mammals", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "39, -4, 41, -1", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846793-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846793-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0128_101", + "description": "The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data.", + "license": "proprietary" + }, + { + "id": "NBId0129_101", + "title": "Kenya Coastal Zone - Infrastructure and Hotel, Health, Recreation Facilities", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "38, -5, 41, -1", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847291-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847291-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0129_101", + "description": "The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data.", + "license": "proprietary" + }, + { + "id": "NBId0131_101", + "title": "Kenya Archeological Sites the Coastal Zone", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "39, -4, 41, -1", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846610-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846610-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0131_101", + "description": "The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data.", + "license": "proprietary" + }, + { + "id": "NBId0136_101", + "title": "Kenya Coastal Zone - Mining, Towns, Industry, Utilities", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "39, -4, 41, -1", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848495-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848495-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0136_101", + "description": "The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data.", + "license": "proprietary" + }, { "id": "NBId0153_101", "title": "Benito River dataset of Equatorial Guinea", @@ -137305,6 +140386,19 @@ "description": "The purpose of the Kenya Pilot Study was to evaluate the FAO/UNEP Provisional Methodology for Assessment and Mapping of Desertification, and to recommend an effective, simple methodology for desertification assessment within Kenya. The FAO/UNEP Provisional Methodology (1984) proposes seven processes for consideration in desertification assessment: degradation of vegetation, water erosion, wind erosion, salinization, reduction of organic content, soil crusting and compaction. In late 1985, a pilot project for the assessment of the FAO/UNEP Methodology within Kenya was proposed, and in 1987 a memorandum of understanding between the Government of Kenya and UNEP for the implementation of that study was signed. The study areas were: 1) Models can be useful to assist in desertification assessment. Models can be developed from FAO/UNEP Methodology. 2) Any modeling output requires verification. 3) Ground survey and remote sensing can be important sources of data. 4) An evaluation of data and methodologies necessary to allow verification of desertification assessment modeling is required. 5) A human use component should be incorporated into desertification assessment that considers management implications and social, as well as, economic context. 6) Computer implementation of desertificaiton assessment can be effective, however, procedures should be well defined. This study within the Baringo Study Area was designed to address these concerns. The Baringo Study Area identified in this study would be typical of such a training area. The models developed during this study could be applied to the general region. The study area lies between 0 15'-1 N and 35 30' -36 30' E. It is located between the Laikipia escarpment to the East and the Tugen Hills to the West. Topographic elevations vary from 900m on the Njemps flats to 2000m in the Puka, Tangulbei and Pokot highlands. The size of the study area is approximately 15ookm2. 4.0 DATA COLLECTION A wide variety of data was collected. Detailed data was required to provide a basis for evaluating more general cost effective data gathering techniques and to provide a basis for model verification, particularly the socio/economic data. Physical Environment Topographic Data Topographic contours were digitized directly from 1:250,000 Survey of Kenya topographic maps. The contour interval was 200 feet. A digital elevation model was constructed using triangular irregular networks (TIN). Soil Data Soil types were mapped at 1:100,000 scale using existing soil maps, manual interpretation of SPOT imagery, and field investigations (Figure 3). During field trips, soil samples were taken from each soil unit and analyzed by the Kenya National Agricultural Center. 4.2 Climate Data 4.2.1 Rainfall Data Rainfall data from the Kenya Meteorological Department was analyzed for 33 stations within and surrounding the study area. A rainfall erosivity index was calculated based on the Fourier Index (R). 12 RE (p /P) 12 where P = annual rainfall p = monthly rainfall A relationship between this erosivity index and the annual rainfall for each station was calculated using linear regression (Bake, 1988). A map of rainfall erosivity was generated for the study area by relating annual rainfall isoheyts to the following: y = 0.108x - 0.68 This data was coded and digitized. Wind Erosion Potential The following required conditions were determined to create high wind erosion potential (Kinuthia, 1989): 1) Annual rainfall less than 300mm. 2) P/E greater than zero and less than 1, where: P=mean monthly rainfall (cm). E=mean monthly PET (cm). 3) Wind velocity greater than 4 m/s at 10m height. Vegetation Data A vegetation map for the study area was produced at a scale of 1:100,000 through manual interpretation of a SPOT image and field investigations (Figure 6). A structural classification system as adopted by DRSRS was used for naming vegetation types (Grunb). Systematic Reconnaissance Flight Data Since 1977, DRSRS has been conducting aerial surveys of Kenyan rangelands. In addition to data on the number of wildlife and livestock, observations of land use and environmental condition are also made. Socio/economic Data Social Factors A wide variety of data was collected through literature review and a field administered questionnaire. Nutritional status was estimated by measurement of childrens' mid upper arm. Such data is useful for a Level 1 type assessment. Permanent Structures Data For the Level 2 assessment, data on permanent structures was extracted from DRSRS SRF data. This data was used to indicate presence and concentration of sedentary populations. Example Files: VDS.E00 (Vegetation degradation) DES.E00 (Plant Species) Others available on request.", "license": "proprietary" }, + { + "id": "NBId0177_101", + "title": "Laikipia (Kenya) Research Programme GIS Datasets", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1994-12-30", + "bbox": "36, 0, 37, 1", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848187-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848187-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0177_101", + "description": "Laikipia Research Programme GIS Datasets are divided into two main different study area scales: the Regional level [Laikipia district, the Ewaso Ng'iro Basin] and the Local level [Land parcels-farm(s), catchments of a few kilometer square]. Coordinate Reference System Coverage data is organized thematically as a series of layers. The coordinate reference systems used in LRP dataset are:- (a) global coordinate system - Universal Transverse Mercator (UTM), (b) Local coordinate system. Digitizing Scale and Fuzzy Tolerance The initial digitizing scale for the LRP GIS Dataset is dependent on the scale of the study areas. There are two major research levels carried by LRP namely Regional and Local. The scales used for regional level are 1:250,000 and 1:50,000. FUZZY TOLERANCE is the minimum distance between coordinates in a coverage. The resolution of a coverage is defined by the minimum distance separating the coordinates used to store coverage features. Resolution is limited by the map scale in initial digitizing. The fuzzy tolerance can be calculated as follows for digitizing table: Initial Scale for Coverage of Fuzzy Tolerance Digitizing Units Value 1;250,000 Meters 6.35 1:50,000 Meters 1.25 1:10,000 Meters 0.25 1:5,000 Meters 0.125 1:2,500 Meters 0.0625 Files: Roads.E00 (Roads) Settle.E00 (Settlement Pattern) Centres.E00 (Urban Centres) (other files exist also)", + "license": "proprietary" + }, { "id": "NBId0203_101", "title": "Africa Water Balance high/lowland crops, 1987", @@ -137318,6 +140412,19 @@ "description": "The Africa Water Balance data set which is prepared by watershed and by country, belongs to the group of \"Irrigation and Water Resources Potential\" study. It covers 55 countries and 25 major basins which contain 335 watersheds. The digitized data base for Africa and the World was originally prepared for an FAO/UNEP project on Desertification in 1982-1984. UNEP financed preparation and analysis of the digitized map data and FAO prepared the data and methodology. The main input maps (all in Miller Oblated Stereographic projection) are the 1975 UNESCO Geological Map of Africa (originally at a scale of 1:10 million); the FAO/UNESCO Soil Map of Africa; Mean Annual Rainfall Map from hand drawn FAO/AGS climate maps; Template; Watersheds; and Administrative Units map - all at a scale 1:5 m. The methodology was based on water balance approach. This determines the suitability of the soil for irrigation and estimates the amount of water the soil requires. Estimates of the surface and groundwater are then compared to the potential irrigation use. If use exceeds available water resources, the irrigable area is correspondingly reduced; in the event of water surplus, some of the water is routed to the downstream basin. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except lowland crops). For further details refer to FAO contact for the 1987 FAO Irrigation and Water Resources Potential for Africa AGL/MISC/11/87. FAO, Land and Water Development Division via Delle Terme di Caracalla, 00100, Rome, Italy Vector Member The file is in Arc/Info Export format. Reference: FAO. Irrigation and Water Resources Potential for Africa. (1987) FAO. Final Report UNEP/FAO world and Africa GIS data base (1984), unpublished publication of ESRI, FAO and UNEP. UNESCO. Geological Map of Africa (1975). Scale 1:5 000 000.", "license": "proprietary" }, + { + "id": "NBId0207_101", + "title": "IGADD Member Countries Crop types and distribution by administrative units, 1987", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "22, -12, 51, 23", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849119-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849119-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NBId0207_101", + "description": "The IGADD (Inter-Governmental Authority on Drought and Development) crop zones dataset is part of the Africa UNEP/FAO/ESRI Crops Data. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. The data was provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service, Land and Water Development Division, Italy. The datasets were then developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Administrative Units map and the World Atlas of Agriculture (1969). All sources were re-registered to the base map by comparing known features on the base map and the source maps. In the original Database (Africa), a considerable study was made of crop water requirements for a range of crops in the various African climates during the time of the year when irrigation would be required. It was found that a relatively simple relationship exists between annual rainfall and the crop irrigation water requirements for the African food grain crops. It was also observed that water requirements for food grains vary between fruit and vegetable crops on the one side and fiber crops and fodder on the other. No attempt was made to produce complex crop patterns. There is a maximum of 13 crop types in one country. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO/UNESCO Soil Map of the Africa (1977). Scale 1:5000000. UNESCO, Paris. FAO. Administration units map. Scale 1:5 000 000. Rome. FAO. Irrigation and Water Resources Potential for Africa. (1987) Source :UNESCO/FAO Soil Map of the World. Scale 1:5000000 Publication Date :Nov 1987 Projection :Miller Type :Polygon Format :Arc/Info Export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets FAO Irrigable Data sets 100050: \" IRRIGLB lowland crops, best soils \" IRRIGLT lowland crops, best plus suitable soils \" IRRIGUB upland crops, best soils \" IRRIGUT upland crops, best plus suitable soils FAO Soil water balance 100053: \" WATBALLB lowland crops, best soils \" WATBALLT lowland crops, best plus suitable soils \" WATBALUB upland crops, best soils \" WATBALUT upland crops, best plus suitable soils FAO Agro-ecological zones AEZBLL08 North-west of continent AEZBLL09 North-east of continent AEZBLL10 South of continent", + "license": "proprietary" + }, { "id": "NBId0208_101", "title": "Africa Major Human Settlements and Landuse, 1984", @@ -138904,6 +142011,19 @@ "description": "This product is monthly Ice Cover Statistics. The dataset was prepared by Dr. Peter Romanov at Cooperative Institute for Climate Studies(CICS) of the University of Maryland for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program. The product includes the monthly ice statistics (frequency of occurrence) for Northern Hemisphere at 1x1 degree spatial resolution. The dataset covers the time period starting January 2000 to November 2014. The data was derived from daily ice cover charts produced at NOAA/NESDIS within Interactive Multisensor Ice Mapping System (IMS).", "license": "proprietary" }, + { + "id": "NHS", + "title": "National Hydrological Services (NHS)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-30, -45, 60, 40", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848605-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848605-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NHS", + "description": "The National Hydrological and Hydrometeorological Services of the World Meteorological Organization (WMO) provides data on hydrology and water resources assessment activities. The data are available regionally: Region I - Africa Benin - Service de l'Hydrologie Botswana - Department of Water Affairs Burkina Faso - Direction G?n?rale de l'Hydraulique Cameroun - Centre de Recherches Hydrologiques Congo - Direction G?n?rale de la Recherche Scientifique et Technique Egypt - Ministry of Public Work and Water Resources Guinee - Direction nationale de la gestion des ressources en eau Mali - Direction Nationale de l'Hydraulique et de l'Energie Morocco - Direction G?n?rale de l'Hydraulique Mozambique - Direc??o nacional de ?guas Niger - Direction des ressources en eau Republique Centrafricaine - Direction de la M?t?orologie Nationale, Service de l'Hydrologie South Africa - Department of Water Affairs and Forestry Tanzania - Ministry of Water Tchad - Direction des Ressources en Eau et de la M?t?orologie Uganda - Directorate of Water Development Region II - Asia Bangladesh Water Development Board - Flood Forecasting and Warning Centre China - Ministry of Water Resources India - Central Water Commission Islamic Republic of Iran - Water Resources Management Organization Japan River Bureau Mongolia Institute of Meteorology and Hydrology Nepal Department of Hydrology and Meteorology Pakistan Flood Forecasting Bureau Republic of Korea Water Resources Bureau Saudi Arabia Ministry of Agriculture and Water Region III - South America Argentina - Instituto nacional del agua Bolivia Servicio nacional de meteorolog?a e hidrolog?a Brazil ANA - National Water Agency (in Portuguese) Chile - Direcci?n General de Aguas Colombia IDEAM - Institute of Hydrology, Meteorology and Environment Studies Ecuador INAMHI - National Institute of Meteorology and Hydrology Guyana Hydrometeorological Service Peru SENAMHI - National Meteorological and Hydrological Service Venezuela Ministry of Environment and Natural Resources Region IV - North and Central America Bahamas - Water and Sewerage Corporation British Caribbean Territories - Caribbean Institute for Meteorology and Hydrology Canada Environment Canada Dominica - Caribbean Institute for Meteorology and Hydrology El Salvador Servicio Nacional De Estudios Territoriales Jamaica Water Resources Authority Mexico Comisi?n nacional del agua Panama Departamento de Hidrometeorolog?a Republica Dominicana INDRHI - Instituto Nacional de Recursos Hidraulicos USA United States Geological Survey Region V - South-West Pacific Australia Hydrometeorological Advisory Service (HAS) - Bureau of Meteorology Malaysia Department of Irrigation and Drainage New Zealand National Institute of Water and Atmospheric Research Philippines National Water Resources Board Region VI - Europe (including Middle East) Armenia Department of Hydrometeorology ARMHYDROMET Austria BMLF Hydrological Service Azerbaijan State Hydrometeorological Committee of the Azerbaijan Republic Bosnia and Herzegovina - Federal Meteorological Institute Bulgaria National Institute of Meteorology and Hydrology Croatia Meteorological and Hydrological Service Cyprus Water Development Sector Czech Republic Czech Hydrometeorological Institute Denmark Geological Survey of Denmark and Greenland Estonia Estonian Meteorological and Hydrological Institute Finland Finnish Environment Institute - Hydrology and Water Management Division France R?seau National des Donn?es sur l'Eau Germany BfG - Federal Institute for Hydrology Hungary VITUKI RT (mainly in Hungarian) Iceland Hydrological Service Ireland Office of Public Works Italy National Hydrographic and Oceanographic Service (in Italian) Latvia Latvian Hydrometeorological Agency Lithuania Lithuanian Hydrometeorological Service The former Yugoslav Republic of Macedonia Hydrometeorological Institute Malta Water Services Corporation Netherlands Institute for Inland Watermanagement and Wastewater Treatment (RIZA) Norway NVE - Norwegian Water Resources and Energy Administration Poland IMGW - Institute of Meteorology and Water Management Portugal Instituto da ?gua - Water Institute Romania National Institute of Meteorology and Hydrology Russian Federation State Hydrological Institute Slovakia Slovak Hydrometeorological Institute Slovenia Hydrometeorological Service Spain Ministerio de Medio Ambiente Sweden Swedish Meteorological and Hydrological Institute Switzerland Swiss Federal Office for Water and Geology Turkey DSI General Directorate of State Hydraulic Works United Kingdom Centre for Ecology and Hydrology Yugoslavia Federal Hydrometeorological Institute Information taken from \"http://www.wmo.ch/web/homs/links/linksnhs.html\" Data link: \"http://www.wmo.ch/web/homs/links/linksnhs.html\"", + "license": "proprietary" + }, { "id": "NHSNOWM_001", "title": "Northern Hemisphere Snow Cover Monthly Statistics at 1 Degree Resolution V001 (NHSNOWM) at GES DISC", @@ -139528,6 +142648,19 @@ "description": "This collection is composed of AVHRR L1B products (1.1 km) reprocessed from the NOAA POES and Metop AVHRR sensors data acquired at the University of Dundee and University of Bern ground stations and from the ESA and University of Bern data historical archive. The product format is the NOAA AVHRR Level 1B that combines the AVHRR data from the HRPT stream with ancillary information like Earth location and calibration data which can be applied by the user. Other appended parameters are time codes, quality indicators, solar and satellite angles and telemetry. Two data collections cover the Europe and the neighbouring regions in the period of 1 January 1981 to 31 December 2020 and the acquired data in the context of the 1-KM project in the \u201890s. During the early 1990\u2019s various groups, including the International Geosphere-Biosphere Programme (IGBP), the Commission of the European Communities (CEC), the Moderate Resolution Imaging Spectrometer (MODIS) Science Team and ESA concluded that a global land 1-KM AVHRR data set would have been crucial to study and develop algorithms for several land products for the Earth Observing System. USGS, NOAA, ESA and other non-U.S. AVHRR receiving stations endorsed the initiative to collect a global land 1-km multi-temporal AVHRR data set over all land surfaces using NOAA's TIROS \"afternoon\" polar-orbiting satellite. On the 1st of April 1992, the project officially began up to the end of 1999 with the utilisation of 23 stations worldwide plus the NOAA local area coverage (LAC) on-board recorders. The global land 1-km AVHRR dataset is composed of 5 channels, raw AVHRR dataset at 1.1km resolution from the NOAA-11 and NOAA-14 satellites covering land surfaces, inland water and coastal areas. Spatial coverage: Check the spatial coverage of the collection on a map available on the Third Party Missions Dissemination Service: _$$AVHRR L1B 1.1 KM$$ https://tpm-ds.eo.esa.int/socat/AVHRR_L1B_1_1KM _$$AVHRR L1B LAC Out-of-Europe$$ https://tpm-ds.eo.esa.int/socat/NOAA_AVHRR_L1B_LAC_out-of-Europe", "license": "proprietary" }, + { + "id": "NOAA_CDR_NDVI", + "title": "NOAA Climate Data Record Normalized Difference Vegetation Index", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1981-01-01", + "end_date": "2013-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552492-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552492-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NOAA_CDR_NDVI", + "description": "National Oceanic and Atmospheric Administration (NOAA) Climate Data Records (CDR) provide historical climate information using data from weather satellites. This dataset contains daily Normalized Difference Vegetation Index (NDVI) derived from surface reflectance data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor. This long-term record spans from 1981 to 2013 and utilizes AVHRR data from seven NOAA polar orbiting satellites: NOAA 7, 9, 11, 14, 16, and 18. This NDVI collection provides the global change and resource management communities with vegetation data for historical trend analysis and vegetation monitoring studies for land surfaces around the globe.", + "license": "proprietary" + }, { "id": "NOAA_ToF_CIMS_Instrument_Data_1921_2", "title": "ATom: L2 Measurements from NOAA ToF Chemical Ionization Mass Spectrometer, Version 2", @@ -140711,6 +143844,19 @@ "description": "The Alien Plants Ranking System (APRS) is a computer-implemented system to help land managers make difficult decisions concerning invasive nonnative plants. The management of invasive plants is difficult, expensive, and requires a long-term commitment. Therefore, land managers must focus their limited resources, targeting the species that cause major impacts or threats to resources within their management, or the species that impede attainment of management goals. APRS provides an analytical tool to separate the innocuous species from the invasive ones (typically around 10% of the nonnative species). APRS not only helps identify those species that currently impact a site, but also those that have a high potential do so in the future. Finally, the system addresses the feasibility of control of each species, enabling the manager to weigh the costs of control against the level of impact. This system has been developed and tested primarily in grassland and prairie parks in the central United States.", "license": "proprietary" }, + { + "id": "NPWRC_effectsoffireonbirdpops", + "title": "Effects of Fire on Bird Populations in Mixed-grass Prairie", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-01-01", + "end_date": "1997-12-31", + "bbox": "-101, 46.5, -97, 48.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549248-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549248-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NPWRC_effectsoffireonbirdpops", + "description": "The mixed-grass prairie is one of the largest ecosystems in North America, originally covering about 69 million hectares (Bragg and Steuter 1995). Although much of the natural vegetation has been replaced by cropland and other uses (Samson and Knopf 1994, Bragg and Steuter 1995), significant areas have been preserved in national wildlife refuges, waterfowl production areas, state game management areas, and nature preserves. Mixed-grass prairie evolved with fire (Bragg 1995), and fire is frequently used as a management tool for prairie (Berkey et al. 1993). Much of the mixed-grass prairie that has been protected is managed to enhance the reproductive success of waterfowl and other gamebirds, but nongame birds now are receiving increasing emphasis. Despite the importance of the area to numerous species of birds and the aggressive management applied to many sites, relatively little is known about the effects of fire on the suitability of mixed-grass prairie for breeding birds. Several studies have examined effects of fire on breeding birds in the tallgrass prairie (e.g., Tester and Marshall 1961, Eddleman 1974, Halvorsen and Anderson 1983, Westenmeier and Buhnerkempe 1983, Zimmerman 1992, Herkert 1994), in western sagebrush grasslands (Peterson and Best 1987), and in shrubsteppe (Bock and Bock 1987). Studies of fire effects in the mixed-grass prairie are limited. Huber and Steuter (1984) examined the effects on birds during the breeding season following an early-May prescribed burn on a 122-ha site in South Dakota. They contrasted the bird populations on that site to those on a nearby 462-ha unburned site that had been lightly grazed by bison (Bison bison). Pylypec (1991) monitored breeding bird populations occurring in fescue prairies of Canada on a single 12.9-ha burned area and on an adjacent 5.6-ha unburned fescue prairie for three years after a prescribed burn.", + "license": "proprietary" + }, { "id": "NRMSC_carnivorerecolonisation", "title": "Carnivore Re-Colonisation: Reality, Possibility and a Non-Equilibrium Century for Grizzly Bears in the Southern Yellowstone Ecosystem", @@ -144260,6 +147406,19 @@ "description": "This data set is derived by downscaling Soil Moisture Active Passive (SMAP) enhanced Level-3 9 km brightness temperatures (TB) using Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data and employing a slightly modified version of the baseline SMAP active-passive TB algorithm, the Single Channel Algorithm \u2013 Vertical polarization (SCA-V). The main parameter of this data set is surface soil moisture presented on the Global EASE-Grid 2.0 projection, with each data point representing the top 5 cm of the soil column. For SMAP-derived data, see SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture, Version 5; for CYGNSS-derived data, see CYGNSS Level 1, Version 2.1.", "license": "proprietary" }, + { + "id": "NURE_SEDIMENT_CHEM", + "title": "National Uranium Resource Evaluation Program: Sediment Chemistry of the Conterminous United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1964-01-01", + "end_date": "1996-01-01", + "bbox": "-179, 19, -68, 70", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552690-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552690-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NURE_SEDIMENT_CHEM", + "description": "From NURE Sediment Chemistry FAQ: These maps are derived from a subset of the National Uranium Resource Evaluation (NURE) Hydrogeochemical and Stream Sediment Reconnaissance (HSSR) data. Approximately 260,000 samples were analyzed in the continental U.S. and consisted of solid samples, including stream, lake, pond, spring, and playa sediments, and soils. Data for eleven elements: Na, Ti, Fe, Cu, Zn, As, Ce, Hf, Pb, Th, and U were analyzed and included on the National Geochemical Atlas CD and the digital release NURE Sediment Chemistry. These publications are intended to allow the rapid visualization of the geochemical landscape of the conterminous U.S. using NURE HSSR data. The raw data used in the production of these publications are available on the following CD-ROM: Hoffman, J.D., Kim P. Buttleman, Russell A. Ambroziak, and Christine A. Cook, 1996, National Uranium Resource Evaluation (NURE) Hydrogeochemical and Stream Sediment Reconnaissance (HSSR) data. available ", + "license": "proprietary" + }, { "id": "NVAP_CLIMATE_Layered-Precipitable-Water_1", "title": "NASA Water Vapor Project MEaSUREs (NVAP-M) CLIMATE Layered Precipitable Water", @@ -144364,6 +147523,19 @@ "description": "Geochemical, microbial and 14C data on remediation of petroleum hydrocarbons in Antarctica. This record is part of ASAC project 1163 (ASAC_1163). Microcosm study using Old Casey petroleum hydrocarbon contaminated sediment investgating the effect of water, nutrients and freze/thaw cycles on biodegradation. Temperature range -4 to 28 degrees. Microcosms with three different levels of nutrients and three different levels of water were investigated. The experiment was run over 95 days. Degradation was traced by radiometric methods and total aliphatic hydrocarbons were measured by gas chromatography. Radiometric data in file radiometric_01.xls, Gas Chromatography data in file gc_01.xls. This work was completed as part of ASAC project 1163 (ASAC_1163). The radiometric spreadsheet is divided up as follows: CODES is a summary of what went into each microcosm. CALCULATIONS is how much nutrients, water, radioactivity was added to the sediment. SUMMARY is what went into each microcosm flask. CT1, CT2 etc is the raw data, what was measured and calculations of radioactivity and recovery of isotope. Note that the Evaporation flasks (i.e., E10a) the number refers to the temperature that the flasks were incubated at, 'a' and 'b' refer to duplicates. AVERAGE is the average recoveries and first order rates of the triplicate microcosm for each treatment. GRAPHS is the graphs. The fields in this dataset are: Days Hours Initial flask weight NaOH removed NaOH added Weight of NaOH (g) Count (dpm) Discarded dpm's Volume NaOH (ml) dpm in trap Absolute dpm's %dpm recovered millimole octadecane mineralised", "license": "proprietary" }, + { + "id": "NatalMuseum", + "title": "Natal Museum - Mollusc Collection (Bivalvia and Gastropoda)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1894-01-01", + "end_date": "2005-07-09", + "bbox": "11.38667, -43.19167, 55.13334, -11", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477685-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477685-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/NatalMuseum", + "description": "The Natal Museum's Department of Mollusca had its origins in the shell collection and library of Henry Burnup, a dedicated amateur who was honorary curator of molluscs until his death in 1928. Subsequently, the collection has been expanded many times over through field work, donation, exchange and purchase. Its historical value was greatly increased by absorption of important shell collections housed the Transvaal Museum (1978) and Albany Museum (1980), as well as the Rodney Wood collection from the Seychelles received from the Mutare Museum in Zimbabwe and the Kurt Grosch collection, built up over 25 years of residence in northern Mozambique. The mollusc collection now ranks among the 15 largest in the world and is certainly the largest both in Africa and on the Indian Ocean rim. It currently contains 7233 Bivalvia records, and 20112 Gastropoda records (total 27345 records of 282 families). The collection will be updated in the near future.", + "license": "proprietary" + }, { "id": "Nested_DGGE_1", "title": "Molecular comparison of bacterial diversity in uncontaminated and hydrocarbon contaminated marine sediment", @@ -146821,6 +149993,19 @@ "description": "This report contains all of the available daily sulfur dioxide and carbon dioxide emission rates from Cook Inlet volcanoes as determined by the U.S. Geological Survey (USGS) from March 1990 through July 1994. Airborne sulfur dioxide gas sampling of the Cook Inlet volcanoes (Redoubt, Spurr, Iliamna, and Augustine) began in 1986 when several measurements were carried out at Augustine volcano during the eruption of 1986. Systematic monitoring for sulfur dioxide and carbon dioxide began in March 1990 at Redoubt volcano and continues to the present. Intermittent measurements at Augustine and Iliamna volcanoes began in 1990 and continues to the present. Intermittent measurements began at Spurr volcano in 1991, and were continued at more regular intervals from June, 1992 through the 1992 eruption at the Crater Peak vent to the present.", "license": "proprietary" }, + { + "id": "OFR_95-78_1", + "title": "Geometeorological data collected by the USGS Desert Winds Project at Gold Spring, Great Basin Desert, northeastern Arizona, 1979-1992", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1979-01-27", + "end_date": "1992-12-31", + "bbox": "-111, 35, -111, 35", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550505-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550505-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/OFR_95-78_1", + "description": "This data set contains meteorological data files pertaining to the Gold Spring Geomet research site. Documentation files and data-accessing display software are also included. The meteorological data are wind speed, peak gust, wind direction, precipitation, air temperature, soil temperature, barometric pressure, and humidity. Data from the monitoring station are voluminous; 14 observations from each station are made as often as ten times per hour, totaling more than a million observations per station per year.", + "license": "proprietary" + }, { "id": "OISSS_L4_multimission_7day_v1_1.0", "title": "Multi-Mission Optimally Interpolated Sea Surface Salinity Global Dataset V1", @@ -152307,6 +155492,19 @@ "description": "This data set provides the mean diurnal cycle of precipitation, near-surface thermodynamics, and surface fluxes generated from short-term forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) model.The model outputs were 12- to 36-hour short-range forecasts, run at a triangular truncation of T319 and a vertical resolution of 60 levels, from each daily 1200 (UTC) analysis. The version of the forecast model used to prepare this data product was the operational ECMWF model in fall 2000, which included the tiled land-surface scheme (TESSEL) (Van den Hurk et al., 2000) and recent revisions to the convection, radiation, and cloud schemes described by Gregory et al., (2000). The ECMWF model was run for two Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) campaigns conducted in Rondonia, Brazil, during January and February of 1999: the Wet Season Atmospheric Mesoscale Campaign (WETAMC) and the Tropical Rainfall Measuring Mission (TRMM). See Silva Dias et al.,(2002) for additional information regarding the WETAMAC and TRMM campaigns. There are two comma-delimited data files with this data set: the ECMWF model output data and a file containing the mean hourly precipitation observations used to check the model output for biases.", "license": "proprietary" }, + { + "id": "PCD_INPE_web", + "title": "Meteorological Data Collection Platform Network from Brazilian Institute for Space Research", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "", + "bbox": "-75.64, -35.81, -32.74, 7.12", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456061-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456061-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/PCD_INPE_web", + "description": "Web access to data of a network of Meteorological Automatic Stations covering the Brazilian area", + "license": "proprietary" + }, { "id": "PEACETIME_0", "title": "ProcEss studies at the Air-sEa Interface after dust deposition in the MEditerranean sea project (PEACETIME)", @@ -154374,6 +157572,19 @@ "description": "This data set contains calculated balance velocity of the Greenland Ice Sheet during the last three quarters of the Holocene epoch (9ka).", "license": "proprietary" }, + { + "id": "RDK1_GTNL1_0.1", + "title": "Geoton-L1 multispectral images from Resurs-DK", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2006-06-15", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231912423-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231912423-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/RDK1_GTNL1_0.1", + "description": "Geoton-L1 multispectral images from Resurs-DK Multispectral highly detailed-resolution optoelectronic sensor from \"Resurs-DK\" satellite that has circular sun synchronous orbit. Archival satellite images are presented by panchromatic data (580-800 nm) with 2,8 m spatial resolution and multispectral data (green - 500-600 nm, red - 600-700 nm, near infrared - 700-800 nm) with 3-5 m spatial resolution. Swath with of the system is 8-16 km. Data can be used for solving disparate issues in agriculture, ecology, cartography, construction, forestry, natural resources inventory and emergency situations monitoring.", + "license": "proprietary" + }, { "id": "RDSISC4_1", "title": "IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Images V001", @@ -154621,6 +157832,58 @@ "description": "Measurements made by the NOAA research vessel, the Ron H. Brown between 2000 and 2002.", "license": "proprietary" }, + { + "id": "RP1_GSA_0.1", + "title": "Hyperspectral imaging from Resurs-P N1", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2013-06-25", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231912409-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231912409-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/RP1_GSA_0.1", + "description": "Hyperspectral imaging from Resurs-P N1 Hyperspectral sensor from ?Resurs-P N1? satellite that has circular sun synchronous orbit. The sensor surveys earth surface in the 96 narrow spectral bands from 400 to 1100 nm with 30 m spatial resolution and 3 days revisit frequency. Swath with of the sensor is 25 km. The data can be used for solving wide variety of problems in agriculture, ecology, cartography, construction, forestry, natural resources inventory, emergency situations monitoring. Collected data allows identifying vegetation composition, pollution films composition, mineral composition of soils, subsoils, rocks and other parameters of natural and anthropogenic objects.", + "license": "proprietary" + }, + { + "id": "RP1_GTNL1_0.1", + "title": "Geoton-L1 multispectral images from Resurs-P N1", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2013-06-25", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231912397-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231912397-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/RP1_GTNL1_0.1", + "description": "Geoton-L1 multispectral images from Resurs-P N1 Multispectral high-resolution optoelectronic sensor from ?Resurs-P N1? satellite that has circular sun synchronous orbit. The sensor can survey earth surface in 7 spectral bands (450-520 nm, 520-600 nm, 610-680 nm, 670-700 nm, 700-730nm, 720-800 nm, 800-900 nm) with 3 m spatial resolution and panchromatic band (580-800 nm) with 1 m spatial resolution. Swath with of the sensor is 38 km. The revisit frequency is less than 3 days. Data can be used for infrastructure monitoring, cadastral and topographical surveying, engineering surveying, natural resources inventory and emergency situation monitoring.", + "license": "proprietary" + }, + { + "id": "RP2_GSA_0.1", + "title": "Hyperspectral imaging from Resurs-P N2", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2015-12-26", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231912393-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231912393-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/RP2_GSA_0.1", + "description": "Hyperspectral imaging from Resurs-P N2 Hyperspectral sensor from ?Resurs-P N2? satellite that has circular sun synchronous orbit. The sensor allows surveying earth surface in the 96-255 narrow spectral bands from 400 to 1100 nm with 30 m spatial resolution and 3 days revisit frequency. Swath with of the sensor is 25 km. Received data can be used to address different problems in agriculture, ecology, cartography, construction, forestry, natural resources inventory, emergency situations monitoring. This data allows identification of vegetation composition, pollution, mineral composition of soils, subsoils, rock. Many other parameters of natural and anthropogenic objects can also be determined.", + "license": "proprietary" + }, + { + "id": "RP2_GTNL1_0.1", + "title": "Geoton-L1 multispectral images from Resurs-P N2", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2014-12-26", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231912426-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231912426-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/RP2_GTNL1_0.1", + "description": "Geoton-L1 multispectral images from Resurs-P N2 Multispectral high resolution optoelectronic sensor from ?Resurs-P N2? satellite that has circular sun synchronous orbit. The sensor allows surveying earth surface in 7 spectral bands (450-520 nm, 520-600 nm, 610-680 nm, 670-700 nm, 700-730nm, 720-800 nm, 800-900 nm) with 3 m spatial resolution and panchromatic band (580-800 nm) with 1 m spatial resolution. Swath with of the sensor is 38 km. The revisit frequency is less than 3 days. Data from the sensor can be used for infrastructure monitoring, cadastral and topographical surveying, engineering surveying, natural resources inventory, emergency situation monitoring.", + "license": "proprietary" + }, { "id": "RRRAG4_1", "title": "Radiostratigraphy and Age Structure of the Greenland Ice Sheet V001", @@ -166295,6 +169558,19 @@ "description": "The Shuttle Solar Backscatter Ultraviolet (SSBUV) Level-2 Ozone data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flying on the NOAA satellites. Data are available in the ASCII AMES text format. Ozone profiles of the upper atmosphere and total column ozone values are available for the following time periods: Flight #1: 1989 October 19, 20, 21. Flight #2: 1990 October 7, 8, 9. Flight #3: 1991 August 3, 4, 5, 6. Flight #4: 1992 March 29, 31. Flight #5: 1993 April 9, 11, 13, 15, 16. Flight #6: 1994 March 14, 15, 17. Flight #7: 1994 November 5, 7, 10, 13. Flight #8: 1996 January 12, 16, 18. SSBUV measures spectral ultraviolet radiances backscattered by the earth's atmosphere. For the ozone measurements the instrument steps over wavelengths between 252.2 and 339.99 nm while viewing the earth in the nadir position (50 km x 50 km footprint at nadir) at 19 pressure levels between 0.3 mb and 100 mb.", "license": "proprietary" }, + { + "id": "SSDP_HAZARD_EARTHQUAKE", + "title": "Earthquakes and Planning for Ground Rupture Hazards", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-116, 33, -115.5, 33.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553786-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553786-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/SSDP_HAZARD_EARTHQUAKE", + "description": "Detailed maps bring a greater resolution to the number and locations of active faults. Preparing maps at a higher resolution requires extensive field study, and with a GIS, information, such as tract and parcel data, utility corridors, and flood hazard zones, can be incorporated to help decision makers in locating remediation facilities. After the Sylmar earthquake in 1972, building codes were strengthened, and the Alquist-Priolo Special Studies Zone Act was passed. Its purpose is to mitigate the hazard of fault rupture by prohibiting the location of most human occupancy structures across the traces of active faults. Earthquake fault zones are regulatory zones that encompass surface traces of active faults with a potential for future surface fault rupture. The zones are generally established about 500 feet on either side of the surface trace of active faults. Active faults and strips of state-mandated zoning along faults (Alquist-Priolo zones) riddle the Salton Sea Basin. The primary fault, the San Andreas, steps from the northeast side of the Salton Sea across the southern end, along a series of poorly understood faults, to the Brawley and Imperial fault systems. This stepover region has not had a historic ground-rupturing earthquake. Alquist-Priolo zones could not be defined because the faults are not well-located. Faults parallel to, and splaying from, the San Andreas are also capable of major earthquakes. Initial plans for remediation facilities take into account the generalized information (at 1:750,000 scale) on active faults, and the fault maps do not provide information on strong ground shaking. The shaking can damage facilities that lie far from an earthquake epicenter and far from active faults. Information on near-surface materials is required to estimate the ground-shaking hazards.", + "license": "proprietary" + }, { "id": "SSEC-AMRC-AIRCRAFT", "title": "Aircraft meteorological reports over Antarctica", @@ -169987,6 +173263,19 @@ "description": "The data collected by Spire from it's 110 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height. The following products can be requested: ADS-B Data Stream Global ADS-B satellite data observed by Spire satellites and processed through the ground stations network. Historical ADS-B data older than 6 months can be delivered as data cuts containing CSV file(s) accessible through a Web Service or Cloud storage solutions. Live ADS-B data is available through a streaming API, and recent historical data can be accessed through a REST API. Data is distributed as a monthly subscription: historical data can be requested starting from 3 December 2008, the time period for live data starts from a user-defined date and continues for 30 days. AIS AIS messages include satellite AIS (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third party sensor stations (up to 40 million messages per day). Historical AIS data are delivered as a cvs file with availability back to June 2016 or via Historical API from December 2018; live AIS data are pushed to end users via TCP or through Messages API. Data is distributed as a monthly subscription, from a user-defined date and continues for a 30 day period. GNSS-Radio Occultation GNSS Radio Occultation (GNSS-RO) measurements are collected globally on a continuous basis, generating profiles of the Earth\u2019s atmosphere. Derived Level 1 and Level 2 products include both atmospheric and ionospheric products. Historical data for most of the GNSS-RO products are available from December 2018 to the present. Near real-time (within 90 minutes or less latency from collection to delivery) GNSS-RO profiles are also available upon request. GNSS Reflectometry GNSS Reflectometry (GNSS-R) is a technique to measure Earth\u2019s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: conventional, near-nadir incidence LHCP reflections collected by the Spire GNSS-R satellites (e.g., Spire GNSS-R \u201cBatch-1\u201d satellites) and grazing angle (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. Historical grazing angle GNSS-R data are available from May 2019 to the present, while conventional GNSS-R data are available from December 2020 to the present. Name: Automatic Identification System (AIS) Description: The automatic identification system (AIS) is an automatic tracking system that uses transponders on ships and is used by vessel traffic services. Spire data includes satellite AIS (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third party sensor stations. Data format and content: .parquet.gz files The AIS files contain time-series data on received AIS messages, both the raw NMEA message and added post-processing data for each message. Application: Supply chain analysis, commodity trading, identification of illegal fishing or dark targets, ship route and fuel use optimization, analysis of global trade patterns, anti-piracy, autonomous vessel software, ocean currents. Name: Automatic Dependent Surveillance-Broadcast (ADS-B) Description: Spire AirSafe ADS-B products give access to satellite and terrestrial ADS-B data from captured aircrafts. Data format and content: .csv.gz files The decompressed csv file contains a list of hexadecimal representations of ADS-B messages associated with the timestamp they were received on the satellite. Application: Fleet management, ICAO regulatory compliance, route optimization, predictive maintenance, global airspace, domain awareness. Name: Global Navigation Satellite System Radio Occultation (GNSS-RO) Description: GNSS atmospheric radio occultation (GNSS-RO) relies on the detection of a change in a radio signal as it passes through a planet's atmosphere, i.e. as it is refracted by the atmosphere. This data set contains precise orbit determination (POD) solutions, satellite attitude information, high-rate occultation observations, excess phase, and derived atmospheric dry temperature profiles. Data format and content: podObs*.rnx This file contains raw pseudorange, carrier phase, Doppler frequency, and signal-to-noise measurements for each observed GPS signal from a single Spire satellite which allow to estimate the positions and velocities of each Spire satellite and also used to derive ionospheric total electron content data. leoOrb*.sp3 This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file leoAtt*.log It contains 1 Hz rate quaternion information measured from a single Spire satellite describing the satellite orientation. opnGns*ro.bin, opnGns*rst.bin these files contain raw measurements from the occulting GNSS satellite (one for each signal frequency) and raw phase data from one or more reference GNSS satellites. atmPhs* The file contains occultation excess phase delay. Also contains SNR values, ransmitter and receiver positions and open loop model information. atmPrf*.nc The file contains profiles of atmospheric dry pressure, dry temperature and neutral refractivity as a function of altitude produced from full processing of one occultation event. bfrPrf*.bufr The file contains derived profiles of dry pressure, dry temperature, refractivity and bending angle for each occultation. Application:\tAtmospheric profiles of pressure, dry temperature, bending angle, and refractivity used in numerical weather prediction data assimilation and climate change studies. Name: Raw IF samples from GNSS-RO satellites Description: Raw intermediate frequency (IF) sampled data (I/Q) from the GNSS receiver front-end of GNSS-RO satellites. Data format and content: rocRIF*.zip Binary raw IF data and associated ancillary data (e.g., POD data) in a zip archive per collection event. Application: GNSS-RO studies, GNSS RFI and jamming monitoring, research. Name: Raw IF samples from GNSS-R satellites Description: Raw intermediate frequency (IF) sampled data (I/Q) from the GNSS receiver front-end of conventional GNSS-R satellites. Data format and content: gbrRIF*.zip Binary raw IF data and associated ancillary data (e.g., POD data) in a zip archive per collection event. Application: GNSS-R studies, GNSS RFI and jamming monitoring, research, etc. Name: Grazing angle GNSS-R observations Description: During grazing angle GNSS-R events, signal reflection at two frequencies is observed through the limb-facing antenna and is trackedusing an open-loop tracking technique thatrelies on a model topredict the propagationdelay and Doppler of thereflected signal. Simultaneous open-looptracking of the signaldirectly along theline-of-sight from thetransmitter to thereceiver is alsoperformed to provideadditional data that maybenecessary for signalcalibration. The mainoutput of the open-looptracking are in-phase (I)and quadrature (Q)accumulation samples(nominally at 50 Hz),which represent the residual Doppler (phase) from the model. Data format and content: grzObs*.nc L1A filecontains rawopen loopcarrier phasemeasurementsat 50 Hzsampling forgrazingangleGNSS-Rreflectionscaptured in the GNSS-RO RHC Pantennas, (bothdirect andreflectedsignals) on GNSS-RO satellites. Application: Sea surface and sea ice height extent, and classification. Name: Georeferenced grazing angle GNSS-R observations Description: The low-levelobservations of the high-rate grazing angle GNSS-R observationsbut withthegeoreferenced bistatic radar parameters of the satellite receiver,specular reflection, and GNSS transmitter included. Data format and content: grzRfl*.nc L1B file contains the georeferenced grazing angle GNSS-R data collected by Spire GNSS-RO satellites, including the low-level observables and bistatic radar geometries (e.g., receiver, specular reflection, and the transmitter locations). Application: Sea surface and sea ice height extent, and classification Name: GNSS-R calibrated bistatic radar reflectivities Description: Higher level product used to derive land-surface reflectivity. Data format and content: gbrRfl*.nc L1A along-track calibrated relative power between reflected and direct signals (e.g., bistatic radar reflectivities) measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies, soil moisture, ocean wind, and sea ice applications Name: GNSS-R calibrated bistatic radar cross-sections Description: Higher level product used to derive ocean surface roughness products. Data format and content: gbrRCS*.nc L1B along-track calibrated and normalized bistatic radar cross-sections measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies, ocean wind and sea ice applications Name: Combined Surface Soil Moisture Description: Combined CYGNSS and SMAP soil moisture data are provided as a combined surface soil moisture (COMB-SSM) product in two data level formats: L2U1 and L3U1. 6 x 6 km grid cell. L-band measurements of surface soil moisture benefit from better vegetation penetration in comparison to traditional C-band measurements. Data format and content: COMB-SSM.nc This file contains the combined data product containing measurements from both CYGNSS and SMAP reported on a 6 km global Equi7Grid grid. Application: Agriculture, crop insurance, farming solutions, climatology, terrain awareness, peatlands and wetlands monitoring etc. Name: Ionosphere total electron content Description: Spire routinely collects and processes a large volume of total electron content (TEC) data, representing the line-of-sight integration of electron density between a Spire satellite and a GNSS satellite. Each file contains line-of-sight ionospheric total electron content (TEC) estimates derived for a \u2018single viewing arc\u2019 contained in the POD observation file. Viewing arcs are at least 10 minutes in duration. Data format and content: podTec*.nc This file contains the line-of-sight total electron content with associated orbital information. Application: Space weather research, tsunamigenic earthquakes, weather applications, space situational awareness (SSA), autonomous vehicles etc Name: Ionosphere scintillation Description: The scintillation index for each GNSS frequency is computed onboard the spacecraft. This index provides a measure of the fluctuations of the GNSS signal over the course of 10 seconds caused by propagation of the radio signals through electron density irregularities in the ionosphere. After the raw indices are downlinked to the ground, they are packaged along with associated metadata such as orbit position to create the final scintillation data product. Data format and content: scnLv1*.nc This file contains on-board computed scintillation data (S4 only) with associated orbital information Application: Space weather research, solar events, TIDs, weather applications positioning and navigation, communications etc. Name: Electron density profile Description: Electron density profiles are retrieved as a function of altitude. Electron density profiles are processed from podTec netcdf files, which span a sufficient elevation angle range. A standard Abel inversion algorithm is applied to retrieve the profiles. Data format and content: ionPrf*.nc This file contains electron density profile retrieved from podTec files spanning appropriate elevation angle range Application: Space weather research, solar events, TIDs, weather applications positioning and navigation, communications. The products are available as part of the Spire provision with worldwide coverage. All details about the data provision, data access conditions and quota assignment procedure are described in the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639).", "license": "proprietary" }, + { + "id": "Stream_GIS_USGS", + "title": "Digital Line Graphs of U.S. Streams for the EPA Clean Air Mapping and Analysis Program (C-MAP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-127.77, 23.25, -65.71, 48.15", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553171-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553171-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/Stream_GIS_USGS", + "description": "This is a 1:2,000,000 coverage of streams for the conterminous United States. This coverage was intended for use as a background display for the National Water Summary program. The stream layer was extracted from the 1:2,000,000 Digital Line Graph files. Originally, each state was stored as a separate coverage. In this version, the individual state coverages all have been appended. [Summary provided by EPA]", + "license": "proprietary" + }, { "id": "Surface_Oligo_Med_Sea_0", "title": "Surface oligotrophic measurements in the West-central Mediterranean Sea", @@ -179646,6 +182935,32 @@ "description": "The Asian administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. This project (which has been carried out as a cooperative activity between NCGIA, CGIAR and UNEP/GRID between Oct. 1995 and present) has pooled available data sets, many of which had been assembled for the global demography project. All data were checked, international boundaries and coastlines were replaced with a standard template, the attribute database was redesigned, and new, more reliable population estimates for subnational units were produced for all countries. From the resulting data sets, raster surfaces representing population distribution and population density were created in collaboration between NCGIA and GRID-Geneva.", "license": "proprietary" }, + { + "id": "UNEP_GRID_SF_GLOBAL", + "title": "Global Population Distribution Database from UNEP/GRID-Sioux Falls", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849256-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849256-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UNEP_GRID_SF_GLOBAL", + "description": "Population databases are forming the backbone of many important studies modelling the complex interactions between population growth and environmental degradation, predicting the effects of global climate change on humans, and assessing the risks of various hazards such as floods, air pollution and radiation. Detailed information on population size, growth and distribution (along with many other environmental parameters) is of fundamental importance to such efforts. This database includes rural population distributions, population distrbution for cities and gridded global population distributions. This project has provided a population database depicting the worldwide distribution of population in a 1X1 latitude/longitude grid system. The database is unique, firstly, in that it makes use of the most recent data available (1990). Secondly, it offers true apportionment for each grid cell that is, if a cell contains populations from two different countries, each is assigned a percentage of the grid cell area, rather than artificially assigning the whole cell to one or the other country (this is especially important for European countries). Thirdly, the database gives the percentage of a country's total population accounted for in each cell. So if a country's total in a given year around 1990 (1989 or 1991, for example) is known, then population in each cell can be calculated by using the percentage given in the database with the assumption that the growth rate in each cell of the country is the same. And lastly, this dataset is easy to be updated for each country as new national population figures become available.", + "license": "proprietary" + }, + { + "id": "UNEP_GRID_SF_LATINAMERICA_1.0", + "title": "Latin America and Caribbean Population Distribution Database from UNEP/GRID-Sioux Falls", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "1990-12-31", + "bbox": "-120, -60, -31, 36", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848778-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848778-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UNEP_GRID_SF_LATINAMERICA_1.0", + "description": "The Latin America population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. This documentation describes the Latin American Population Database, a collaborative effort between the International Center for Tropical Agriculture (CIAT), the United Nations Environment Program (UNEP-GRID, Sioux Falls) and the World Resources Institute (WRI). This work is intended to provide a population database that compliments previous work carried out for Asia and Africa. This data set is more detailed than the Africa and Asia data sets. Population estimates for 1960, 1970, 1980, 1990 and 2000 are also provided. The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a).", + "license": "proprietary" + }, { "id": "UNEP_SDG14_2022_0", "title": "United Nations Environment Programme - Sustainable Development Goal 14(2022): Index of coastal eutrophication in Latin America", @@ -180634,6 +183949,58 @@ "description": "This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location.", "license": "proprietary" }, + { + "id": "USDA0113", + "title": "Groundwater Quality in Beaver Creek Watershed, Tennessee", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-07-01", + "end_date": "1992-08-31", + "bbox": "-90.74, 34.56, -81.22, 37.12", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411621-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411621-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USDA0113", + "description": "Analysis for 400 domestic wells for selected constituents. Reconnaissance of Ground Water Quality in Beaver Creek Watershed, Shelby, Tipton, Fayette, and Haywood counties, Tennessee. Collection Organization: USDA-CSREES/USGS - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by UTAES staff, trained volunteers, and USGS Personnel - USGS conducted field and laboratory analysis. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 400 wells; 20 parameters per sample. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Dissemination Media: USGS Data Base Access Instructions: Contact the data center.", + "license": "proprietary" + }, + { + "id": "USDA0114", + "title": "Groundwater Quality in Bedford and Coffee Counties, Tennessee", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1991-06-01", + "end_date": "1991-07-31", + "bbox": "-90.74, 34.56, -81.22, 37.12", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411616-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411616-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USDA0114", + "description": "Analysis for 200 domestic wells and springs for selected constituents. Reconnaissance of Ground Water Quality in Bedford and Coffee Counties, TN. Collection Organization: USDA-CSREES/USGS - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by UTAES staff, trained volunteers, and USGS Personnel - USGS conducted field and laboratory analysis. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 200 wells/springs; 7 parameters per sample. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Media: USGS Data Base Access Instructions: Contact the data center.", + "license": "proprietary" + }, + { + "id": "USDA0115", + "title": "Groundwater Quality in Tennessee", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1984-01-01", + "end_date": "1990-12-31", + "bbox": "-90.74, 34.56, -81.22, 37.12", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411608-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411608-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USDA0115", + "description": "Analysis of 150 wells for selected constituents, reconnaissance of Ground Water Quality in Tennessee. Collection Organization: USDA-CSREES - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by USGS staff. USGS conducted field and laboratory analysis at their national lab. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 150 wells on farmsteads across Tennessee; 7 parameters per well. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Media: USGS Data Base. Access Instructions: Contact the data center.", + "license": "proprietary" + }, + { + "id": "USGS-DDS-058_1.0", + "title": "Geologic and Geophysical Characterization Studies of Yucca Mountain, Nevada, A Potential High-Level Radioactive-Waste Repository", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-120.35, 34.65, -113.69, 42.34", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553976-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553976-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-058_1.0", + "description": "The safe disposal of high-level radioactive wastes is one of the most pressing environmental issues of modern times. At present, most of these materials are being stored under temporary conditions at many of the individual nuclear power plants where they were produced. In recognition of the need for permanent waste storage, Yucca Mountain in southwestern Nevada has been investigated by Federal agencies since the 1970's as one of the Nation's potential geologic disposal sites. In 1987, Congress selected Yucca Mountain for an expanded and more detailed site characterization effort, and a broad multidisciplinary program of studies was developed by the U.S. Department of Energy to further evaluate the suitability of the mountain as a safe and permanent underground disposal facility. The scope and objectives of the many kinds of investigations to be pursued were guided in large measure by regulations governing the siting of geologic repositories for high-level radioactive wastes that were issued by the U.S. Nuclear Regulatory Commission (Code of Federal Regulations 10CFR60) and supplmented by further requirements set forth by the U.S. Department of Energy (Code of Federal Regulations 10CFR960). As an integral part of the planned site-characterization program, the U.S. Geological Survey began a series of detailed geologic, geophysical, and related investigations designed to characterize the tectonic setting, fault behavior, and seismicity of the Yucca Mountain area. A broad goal was to provide essential data for assessing the possible risks posed by future seismic and fault activity in the area that may affect the design and long-term performance, and the safe operation, of the potential surface and subsurface repository facilities. The results of 13 of the many studies undertaken to increase understanding of the tectonic environment of Yucca Mountain and the adjacent area are presented in this report. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS-DDS-066_1.0", "title": "Assessment of the Alluvial Sediments in the Big Thompson River Valley, Colorado", @@ -180647,6 +184014,71 @@ "description": "To obtain subsurface geologic information about the alluvium in the Big Thompson River valley, S -wave refraction data were collected along three roads that cross the valley. The traveltimes were processed to estimate velocities and thicknesses for a layered-earth model; from these models, three cross sections of the river valley were constructed. The river valleys are covered by a layer of soil, which is 0.2 to 1.5 m thick. Beneath the soil, there is one layer of alluvium at some locations and two layers at other locations. For the two westernmost cross sections, the total thickness of the alluvium ranges from about 6 to 10 m near the center of the valley and from about 2 to 6 m near the sides of the valley. The easternmost cross section is somewhat more complex than the other two, because it is near the confluence of the Big Thompson and the Little Thompson Rivers. In this cross section, the thickness of the alluvium ranges from about 8 to 10 m in the southern half of the valley and from about 3 to 13 m in the northern half. In all three cross sections, the alluvium overlies bedrock, which is the upper transition member of the Pierre Shale. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS-DDS-067", + "title": "Geologic Studies of Deep Natural Gas Resources", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-177.1, 13.71, -61.48, 76.63", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554437-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554437-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-067", + "description": "In 1997, the U. S. Geological Survey published USGS Bulletin 2146, comprising 12 chapters dealing with geologic, geochemical, and assessment issues related to deep gas resources (Dyman and others, 1997). A primary goal of that report was to provide geology-based information that might aid in future improvements to technology for deep gas exploration and development. Chapters of this report represent a continuation of that work. The current work is funded by the U. S. Department of Energy, National Energy Technology Laboratory, Morgantown, W. Va. (contract No. DE-AT26-98FT40032), Gas Technology Institute (GTI), Chicago, Ill. (contract No. 5094-210-3366 through a Cooperative Research and Development Agreement with Advanced Resources International, Arlington, Va.), and the U. S. Geological Survey, Denver, Colo. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS-DDS-11", + "title": "Geology of the Conterminous United States at 1:2,500,000 Scale -- A Digital Representation of the 1974 P.B. King and H.M. Beikman Map", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-162, 24, -66, 60", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549169-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549169-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-11", + "description": "Conversion of the geologic map of the U.S. to a digital format was undertaken to facilitate the presentation and analysis of earth-science data. Digital maps can be displayed at any scale or projection, whereas a paper map has a fixed scale and projection. However, the geology on this disc is not intended to be used at any scale finer than 1:2,500,000. This CD-ROM contains a digital version of the Geologic Map of the United States, originally published at a scale of 1:2,500,000 (King and Beikman, 1974b). It excludes Alaska and Hawaii. In addition to the graphical formats, the map key is included in ASCII text. A geographic information system (GIS) allows combining and overlaying of layers for analysis of spatial relations not readily apparent in the standard paper publication. This disc contains only geology. However, digital data on geology, geophysics, and geochemistry can be combined to create useful derivative products-- for example, see Phillips and others (1993). This CD-ROM contains a copy of the text and figures from Professional Paper 901 by King and Beikman (1974a). This text describes the historical background of the map, details of the compilation process, and limitations to interpretation. The digital version of the text can be searched for keywords or phrases. For DOS users, the CD-ROM contains menu-driven analytical software, in which the user selects from an array of topics. The CD-ROM also contains MAPPER display software, a user-friendly package that displays the interactive vector map. The raster image of the geologic map can be displayed with VIEWLBL. For other types of computer users, the map must be converted from one of the following formats included on the CD-ROM: ARC/INFO 6.1.1 Export Digital Line Graph (DLG) Optional Drawing Exchange File (DXF) Map Overlay Statistical System (MOSS)", + "license": "proprietary" + }, + { + "id": "USGS-DDS-18-A_1.0", + "title": "National Geochemical Database: National Uranium Resource Evaluation Data for the Conterminous United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-162, 24, -66, 60", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552333-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552333-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-18-A_1.0", + "description": "This is an online version of a CD-ROM publication. It is intended for use only on DOS-based computer systems. The files must be downloaded onto your computer before they can be used. The files are presented here in two forms: as the original folders that were published on the CD-ROM and as a large zip file that you can use to download the entire product in one step. This publication contains National Uranium Resource Evaluation (NURE) data for the conterminous United States. The data has been compressed and requires GSSEARCH software for access. GSSEARCH, supplied below, runs only under DOS. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS-DDS-19", + "title": "Geology and Resource Assessment of Costa Rica at 1:500,000 Scale", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-86, 8, -82, 11", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554233-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554233-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-19", + "description": "PROJECT OVERVIEW Conversion of the information from the original folio to a computerized digital format was undertaken to facilitate the presentation and analysis of earth-science data. Digital maps can be displayed at any scale or projection, whereas a paper map has a fixed scale and projection. However, most of the maps on this disc are not intended to be used at any scale more detailed than 1:500,000. A geographic information system (GIS) allows combining and overlaying of layers for analysis of spatial relations not readily apparent in the standard paper publication. Digital information on geology, geophysics, and geochemistry can be combined to create useful derivative products. HISTORY OF THE MAPS In 1986 and 1987, the U.S. Geological Survey (USGS), the Dirección General de Geología, Minas e Hidrocarburos, and the Universidad de Costa Rica conducted a mineral-resource assessment of the Republic of Costa Rica. The results were published as a large 80- by 50-cm color folio (U.S. Geological Survey and others, 1987). The 75-page document consists of maps as well as descriptive and interpretive text in English and Spanish covering physiographic, geologic, geochemical, geophysical, and mineral site themes as well as a mineral-resource assessment. The following maps are present in the original folio: 1) Physiographic base map at a scale of 1:500,000 with hypsography, place names, and drainage. 2) Geologic map at a scale of 1:500,000. 3) Regional geophysical maps, including gravity, aeromagnetic, and seismicity maps at various scales. 4) Mineral sites map at a scale of 1:500,000 showing mines, prospects, and occurrences. 5) Volcanological framework of the Tilarán region important for epithermal gold deposits at a scale of 1:100,000. 6) Rock sample locations, mining areas, and vein locations for several parts of the country. 7) Permissive areas delineated for selected mineral deposit types. 8) Digital elevation model. This CD-ROM contains most of the above maps; it lacks items 1 and 8 and the seismicity and aeromagnetic maps of item 3. The linework was digitized from preliminary and printed maps with GSMAP (Selner and Taylor, 1987), a USGS-authored program for map editing and publishing. Conversion from GSMAP to ARC/INFO was accomplished through the use of the GSMARC program (Green and Selner, 1988). The arcs and polygons were tagged using Alacarte (Wentworth and Fitzgibbon, 1991). [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS-DDS-27_1", + "title": "Monthly average polar sea-ice concentration - USGS-DDS-27", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1978-10-25", + "end_date": "1991-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553834-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553834-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-27_1", + "description": "The purpose of this data set is to provide paleoclimate researchers with a tool for estimating the average seasonal variation in sea-ice concentration in the modern polar oceans and for estimating the modern monthly sea-ice concentration at any given polar oceanic location. It is expected that these data will be compared with paleoclimate data derived from geological proxy measures such as faunal census analyses and stable-isotope analyses. The results can then be used to constrain general circulation models of climate change. This data set represents the results of calculations carried out on sea-ice-concentration data from the SMMR and SSM/I instruments. The original data were obtained from the National Snow and Ice Data Center (NSIDC). The data set also contains the source code of the programs that made the calculations. The objective was to derive monthly averages for the whole 13.25-year series (1978-1991) and to derive a composite series of monthly averages representing the variation of an average year. The resulting file set contains monthly images for each of the polar regions for each year, yielding 160 files for each pole, and composite monthly averages in which the years are combined, yielding 12 more files. Averaging the images in this way tends to reduce the number of grid cells that lack valid data; the composite averages are designed to suppress interannual variability. Also included in the data set are programs that can retrieve seasonal ice-concentration profiles at user-specified locations. These nongraphical data retrieval programs are provided in versions for UNIX, extended DOS, and Macintosh computers. Graphical browse utilities are included for the same computing platforms but require more sophisticated display systems. The data contained in this data set are derived from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/ Imager (SSM/I) data produced by the National Snow and Ice Data Center (NSIDC) at the University of Colorado in cooperation with the U.S. National Aeronautics and Space Administration (NASA) and the U.S. National Oceanic and Atmospheric Administration (NOAA). The basic data come from satellites of the U.S. Air Force Defense Meteorological Satellite Program. NSIDC distributes three collections of sea- ice-concentration grids on CD-ROM: data from the Nimbus-7 SMMR (October 25, 1978 through August 20, 1987) are provided on volume 7 of the SMMR Polar Data series (NASA, 1992); data from the SSM/I are provided on two separate volumes, covering the periods from July 9 of 1987 to December 31 of 1989, and from January 1 of 1990 through December 31 of 1991, respectively. The NASATEAM data from revision 2 of the SSM/I CD-ROM's were used to create the present data set. SMMR images were collected every 2 to 3 days, whereas SSM/I data are provided in daily ice-concentration grids. Apart from a number of small gaps (5 or fewer days) in the record, the only long period for which no data are available is December 3 of 1987 through January 12 of 1988, inclusive. As ancillary data, the ETOPO5 global gridded elevation and bathymetry data (Edwards, 1989) were interpolated to the resolution of the NSIDC data; the interpolated topographic data are included. The images are provided in three formats: Hierarchical Data Format (HDF), a flexible scientific data format developed at the National Center for Supercomputing Applications; Graphics Interchange Format (GIF); and Macintosh PICT format. The ice- concentration grids are distributed by NSIDC in HDF format.", + "license": "proprietary" + }, { "id": "USGS-DDS-3", "title": "A Geologic Map of the Sea Floor in Western Massachusetts Bay, Constructed from Digital Sidescan-Sonar Images, Photography, and Sediment Samples", @@ -180673,6 +184105,19 @@ "description": "The Upper Cretaceous Sussex \"B\" sandstone was deposited as a probable transgressive-marine sand-ridge complex in a mid-shelf position. The \"B\" sandstone is bounded by upper and basal disconformities and encased in mudstones and low-porosity and low-permeability sandstones of the Cody Shale. Reservoir characteristics are controlled primarily by depositional and diagenetic heterogeneity at megascopic (field), macroscopic (well), and microscopic (rock sample) levels. To simplify, this means production of oil is controlled by stacking and interbedding of sandstone and mudstone beds and by geochemical changes through time that affect flow of fluids through the rock. More than 24.8 million barrels of oil (MMBO) have been produced from the Sussex \"B\" sandstone in the House Creek field, Powder River Basin, Wyoming. Greatest oil production, porosity, and permeability, the thickest reservoir sandstone intervals, and best lateral continuity of the primary reservoir facies are all located parallel and proximal to field axes. Decrease in reservoir quality west of the axes is due to greater heterogeneity from interbedding of low- and moderate-depositional-energy facies, with associated drop in porosity and permeability. Decrease in production east of the axes results primarily from a combination of seaward thinning of the primary reservoir facies and non-deposition of sand ridges. The House Creek field has two axis orientations; these are related to depositional patterns of the four sand ridges. Deposition of the \"B\" sandstone began in the southeastern corner of the field with sand ridge 1; axis orientation is about north 20 degrees west. Later-deposited sand ridges 2 through 4 are located west and north of sand ridge 1; their axis orientations are approximately north 32 degrees west. Progressive northward deposition of later sand ridges is probably concurrent with uplift of the northeast-trending Belle Fourche arch. Movement along the arch and of lineaments may have caused topographic highs that localized Sussex and Shannon deposition proximal to the arch. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS-DDS-74_2.0", + "title": "Long-term Oceanographic Observations in Western Massachusetts Bay Offshore of Boston, Massachusetts: Data Report for 1989-2002", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1989-12-01", + "end_date": "2002-12-01", + "bbox": "-71, 42, -70.5, 42.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551840-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551840-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-DDS-74_2.0", + "description": "Long-term oceanographic observations have been made at two locations in western Massachusetts Bay: (1) Site A (42\u00fd 22.6' N, 70\u00fd 47.0' W, 33 m water depth) from from 1989 to 2002, and (2) Site B (42\u00fd 9.8' N, 70\u00fd 38.4' W, 21 m deter depth) from 1997 to 2002. Site A is approximately 1 km south of the new ocean outfall that began discharging treated sewage effluent from the Boston metropolitan area into Massachusetts Bay in September 2000. These long-term oceanographic observations have been collected by the U.S. Geological Survey (USGS) in partnership with the Massachusetts Water Resources Authority (MWRA) and with logistical support from the U. S. Coast Guard (USCG). This report presents time series data collected through December 2002, updating a similar report that presented data through December 2000 (Butman and others, 2002). The long-term observations at these two stations are part of a USGS study designed to understand the transport and long-term fate of sediments and associated contaminants in the Massachusetts Bays (see //woodshole.er.usgs.gov/project-pages/bostonharbor / and Butman and Bothner, 1997). The long-term observations document seasonal and inter-annual changes in currents, hydrography, and suspended-matter concentration in western Massachusetts Bay, and the importance of infrequent catastrophic events, such as major storms or hurricanes, in sediment resuspension and transport. They also provide observations for testing numerical models of circulation. This data report presents a description of the field program and instrumentation, an overview of the data through summary plots and statistics, and the data in NetCDF and ASCII format for the period December 1989 through December 2002. The objective of this report is to make the data available in digital form, and to provide summary plots and statistics to facilitate browsing of the long-term data set . [Summary provided by the USGS.] ", + "license": "proprietary" + }, { "id": "USGS-DDS-79", "title": "Coastal Erosion and Wetland Change in Louisiana: Selected USGS Products", @@ -180725,6 +184170,32 @@ "description": "The USGS presents an updated model of the Juan de Fuca slab beneath southern British Columbia, Washington, Oregon, and northern California, and use this model to separate earthquakes occurring above and below the slab surface. The model is based on depth contours previously published by Fl\u00fcck and others (1997). Our model attempts to rectify a number of shortcomings in the original model and to update it with new work. The most significant improvements include (1) a gridded slab surface in geo-referenced (ArcGIS) format, (2) continuation of the slab surface to its full northern and southern edges, (3) extension of the slab surface from 50-km depth down to 110-km beneath the Cascade arc volcanoes, and (4) revision of the slab shape based on new seismic-reflection and seismic-refraction studies. We have used this surface to sort earthquakes and present some general observations and interpretations of seismicity patterns revealed by our analysis. In addition, we provide files of earthquakes above and below the slab surface and a 3-D animation or fly-through showing a shaded-relief map with plate boundaries, the slab surface, and hypocenters for use as a visualization tool. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS-OFR-92-299_1.0", + "title": "Molecular and Isotopic Analyses of the Hydrocarbon Gases within Gas Hydrate-Bearing Rock Units of the Prudhoe Bay-Kuparuk River Area in Northern Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1979-05-01", + "end_date": "1990-09-01", + "bbox": "-150, 70, -148, 71", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550014-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550014-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-OFR-92-299_1.0", + "description": "Information about and data from the USGS Open-File Report 92-299 (Molecular and isotopic analyses of the hydrocarbon gases within gas hydrate-bearing rock units of the Prudhoe Bay-Kuparuk River area in northern Alaska) are available On-line via the World Wide Web: \"http://pubs.usgs.gov/of/of92-299//\" or \"http://pubs.usgs.gov/of/1992/of92-299/\" The following information about the data set was provided by the data center contact: The objective of this study was to document the molecular and isotopic composition of the gas trapped within the gas hydrate-bearing stratigraphic intervals overlying the Prudhoe Bay and Kuparuk River oil fields. To reach this objective, we have analyzed cuttings gas and free gas samples collected from 10 drilling-production wells in the Prudhoe Bay and Kuparuk River fields. The dataset includes a report documenting the materials, the procedures used to analyze them, and the results. Results are given in tabular form as spreadsheets showing headspace, headspace/free gas, and blended headspace analyses. Gas characteristics analyzed include nitrogen, carbon dioxide, methane, ethane, ethene, propane, propene, isobutane, n-butane, isopentane, n-pentane, stable carbon isotope composition of the methane, ethane, and carbon dioxide fractions, and deuterium isotope composition of the methane fraction. Methane is the most abundant hydrocarbon gas within the gas hydrate- bearing rock units of the Prudhoe Bay-Kuparuk River area in the North Slope of Alaska. Isotopic analysis indicates that both microbial and thermogenic processes have contributed to the formation of this methane. The thermogenic component probably migrated into the rock units from greater depths, since vitrinite reflectance measurements show that the units never endured temperatures within the thermogenic range. Approximately 50 to 70 percent of the methane within the gas hydrate units is thermogenic in origin. This is U.S. Geological Survey Open-File Report 92-299 This report is preliminary and has not been reviewed for conformity with U.S. Geological Survey editorial standards or with the North American Stratigraphic Code. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.", + "license": "proprietary" + }, + { + "id": "USGS-PRISM-PACIFIC-OSTRACODES", + "title": "Modern and fossil ostracode census data from the Western Pacific Ocean and seas around Japan", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1993-12-31", + "bbox": "122, 25, 165, 63", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551101-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551101-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS-PRISM-PACIFIC-OSTRACODES", + "description": "This data set is part of the Pliocene Research, Interpretation, and Synoptic Mapping (PRISM) Project. This data set describes marine ostracode species and related sample and stratigraphic information produced as part of the USGS PRISM Project (Pliocene Research, Interpretation, and Synoptic Mapping). The general goals of PRISM are to reconstruct global climate during a period of extreme warmth about 3 million years ago and to determine the causes of the warmth and the subsequent climatic change towards colder climates about 2.5 million years ago. To do this, PRISM has been studying Pliocene deposits and their microfaunas and, by comparison with modern assemblages, estimating past boundary conditions such as ocean temperatures. To obtain more reliable estimates of past environments in paleoclimate studies, the use of ecologically sensitive species requires extensive modern datasets on living species with limited environmental tolerances. Thus, much of the data generated by PRISM consists of species counts from modern samples that form a \"coretop\" dataset applicable not only to PRISM Pliocene assemblages but also to Quaternary assemblages as well. This situation was especially true for ostracodes, a group of Crustacea that includes many species that have limited range of water temperatures required for survival, reproduction, or both. Fossil assemblages of ostracodes can therefore yield information on past bottom water conditions on continental shelves in the mixed ocean layer above the thermocline and they are especially useful where planktic foraminifers are rare or absent. However comprehensive datasets with quantitative ostracode data were not available for application to regional paleoceanographic studies. Further, because of the endemic nature of ostracodes living on continental shelves, separate modern datasets needed to be developed for regions of the Pacific, Atlantic and Arctic Oceans. The data contained in the files in this folder come from the western North Pacific Ocean, mainly the seas around Japan. These regions encompass subtropical to cold temperate and subfrigid marine climate zones and include faunas from the major Western North Pacific water masses such as the Oyashio and Kuroshio current systems. The ostracode data sets were developed in collaboration with Prof. Noriyuki Ikeya, Institute of Geosciences, Shizuoka University, Shizuoka, Japan, Prof. Ikeya's students, and other Japanese colleagues, with support from the USGS Global Change and Climate History Program and grants from the National Science Foundation (NSF grant INT: LTV-9013402) and the Japanese Society for the Promotion of Science (JSPS grant EPAR- 093). Most of the faunal slides are housed at Shizuoka University. Separate PRISM ostracode data sets contain modern and Pliocene species data from continental shelves of the Arctic and Atlantic Oceans and from deep sea environments. Among the various types of quantitative analyses used to evaluate the ostracode data, the Squared Chord Distance (SCD) coefficient of dissimilarity was found to be useful in identifying modern analog assemblages for fossil assemblages on the basis of the proportions of shared species between two samples. The ostracode data and analyses of them are discussed in detail in the following published scientific papers: Ikeya, Noriyuki and Cronin, Thomas. M., 1993, Quantitative analysis of Ostracoda and water masses around Japan: Application to Pliocene and Pleistocene paleoceanography: Micropaleontology, v. 39, p. 263-281. Cronin, T.M., Kitamura, A., Ikeya, N., Watanabe, M., and Kamiya, T., in press. Late Pliocene climate change 3.4-2.3 Ma: Paleoceanographic record from the Yabuta Formation, Sea of Japan: Palaeogeography, Palaeoclimatology, Palaeoecology.", + "license": "proprietary" + }, { "id": "USGSPHOTOS", "title": "U.S. Geological Survey Aerial Photography", @@ -180764,6 +184235,45 @@ "description": "[From Arsenic in ground water of the United States, \"http://water.usgs.gov/nawqa/trace/arsenic/\" Arsenic is a naturally occurring element in the environment. Arsenic in ground water is largely the result of minerals dissolving naturally from weathered rocks and soils. Several types of cancer have been linked to arsenic in water. The US Environmental Protection Agency is currently reviewing the maximum contaminant level of arsenic permitted in drinking water, and will likely lower it, as recommended last year by the National Research Council. The USGS has developed a map that shows where and to what extent arsenic occurs in ground water across the country. Highest concentrations were found throughout the West and in parts of the Midwest and Northeast.", "license": "proprietary" }, + { + "id": "USGS_ASC_MarineEcoregionsLayer_1.0", + "title": "Marine_Ecoregions_AK", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2007-01-01", + "end_date": "", + "bbox": "-180, 42.42584, 180, 74.238594", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549548-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549548-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ASC_MarineEcoregionsLayer_1.0", + "description": "ABSTRACT: To better understand of how and why marine ecosystems vary, we developed a map of \"Large Marine Ecosystems\" (LME) for the area surrounding Alaska. These LMEs were constructed using the best information available on bathymetry, currents, temperature, and primary productivity.", + "license": "proprietary" + }, + { + "id": "USGS_ASTER_HydrothermalAlterationMaps", + "title": "Hydrothermal Alteration Maps of the Central and Southern Basin and Range Province of the United States Compiled From Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2013-01-01", + "end_date": "", + "bbox": "-120.40977, 30.652391, -107.4039, 42.39188", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554154-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554154-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ASTER_HydrothermalAlterationMaps", + "description": "ABSTRACT: Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and Interactive Data Language (IDL) logical operator algorithms were used to map hydrothermally altered rocks in the central and southern parts of the Basin and Range province of the United States. The hydrothermally altered rocks mapped in this study include (1) hydrothermal silica-rich rocks (hydrous quartz, chalcedony, opal, and amorphous silica), (2) propylitic rocks (calcite-dolomite and epidote-chlorite mapped as separate mineral groups), (3) argillic rocks (alunite-pyrophyllite-kaolinite), and (4) phyllic rocks (sericite-muscovite). A series of hydrothermal alteration maps, which identify the potential locations of hydrothermal silica-rich, propylitic, argillic, and phyllic rocks on Landsat Thematic Mapper (TM) band 7 orthorectified images, and shape files of hydrothermal alteration units are provided. ", + "license": "proprietary" + }, + { + "id": "USGS_BIO_KATRINA", + "title": "Hurricane Katrina - Biological Resources", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-97.87498, 26.042156, -82.72492, 32.819813", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549509-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549509-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_BIO_KATRINA", + "description": "This website provides information regarding the emergency response and rescue efforts provided by USGS personnel from the National Wetlands Research Center and USGS Louisiana Water Science Center to the population and area impacted by Hurricane Katrina. This website also chronicles the activities by the USGS to provide geospatial technology to aid in locating stranded hurricane victims. Impacts to the biological resources affected by Hurricane Katrina are also being assessed. Information on these resources can be accessed from this website.", + "license": "proprietary" + }, { "id": "USGS_BISON", "title": "Biodiversity Information Serving Our Nation (BISON)", @@ -180777,6 +184287,19 @@ "description": "The USGS Biodiversity Information Serving Our Nation (BISON) project is an online mapping information system consisting of a large collection of species occurrence datasets (e.g., plants and animals) found in the United States, with relevant geospatial layers. Species occurrences are records of organisms at a particular time and location that are often collected as part of biological field studies and taxonomic collections. These data serve as a foundation for biodiversity and conservation research.", "license": "proprietary" }, + { + "id": "USGS_BRD_SageSTEP", + "title": "Joint Fire Science SageSTEP (Sagebrush Steppe Treatment Evaluation Project)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2006-01-01", + "end_date": "2011-12-31", + "bbox": "-120, 35, -110, 50", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554186-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554186-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_BRD_SageSTEP", + "description": "To study the effects of land management options, two experiments will be conducted across a regional network of sites in sagebrush communities. Using this regional network of sites will allow us to understand the thresholds between healthy and unhealthy sagebrush communities over a broad range of conditions across the Great Basin. Management treatment effects on plants, potential for wildfire, soils and nutrients, water runoff/erosion, and birds and insects will be documented. Additionally, an economic analysis will be conducted to assist managers in selecting optimal management strategies, and citizens\u2019 and managers\u2019 views about the treatments will be explored. The first experiment is focused on cheatgrass invasion (Cheatgrass Network), and the second experiment is focused on woodland encroachment (Woodland Network). Cheatgrass Network: For this experiment, sites will be located in sagebrush communities threatened by cheatgrass invasion, and we will study the effects of four land management options: control (no management action), prescribed fire, mechanical thinning of sagebrush by mowing, and herbicide application (to thin old, unproductive sagebrush plants and encourage growth of young sagebrush and native understory grasses). An additional herbicide application to control cheatgrass will be applied within portions of treated areas. The objective is to address the question of what amount of native perennial bunchgrasses needs to be present in the understory of a sagebrush community in order for managers to improve land health without having to conduct expensive restoration, such as reseeding of native grasses. Woodland Network: For this experiment, sites will be located in sagebrush communities threatened by woodland encroachment, and we will study the effects of no management action (control), prescribed fire, and mechanical removal of trees (chainsaw cutting). The objective is to address the question of what amount of the native sagebrush/bunchgrass community there needs to be in order for managers to improve land health without having to conduct expensive restoration.", + "license": "proprietary" + }, { "id": "USGS_BioData", "title": "BioData - Aquatic Bioassessment Data for the Nation", @@ -180790,6 +184313,32 @@ "description": "The U.S. Geological Survey (USGS) BioData Retrieval system provides access to aquatic bioassessment data (biological community and physical habitat data) collected by USGS scientists from stream ecosystems across the nation. USGS scientists collect fish-, aquatic macroinvertebrate-, and algae-community samples and conduct stream physical habitat surveys as part of its fundamental mission to describe and understand the Earth. The publicly available BioData Retrieval system disseminates data from over 15,000 fish, aquatic macroinvertebrate, and algae community samples. Additionally, the system serves data from over 5000 physical data sets (samples), such as reach habitat and light availability, that were collected to support the community sample analyses. The system contains sample data that were collected and processed since 1991 using the protocols of the National Water-Quality Assessment (NAWQA). As of 2010, the system has added data collected by USGS scientists using the procedures and protocols of the U.S. Environmental Protection Agency National Rivers and Streams Assessment program (NRSA).", "license": "proprietary" }, + { + "id": "USGS_Bulletin_2064-A_1.0", + "title": "Map Showing Geologic Terranes of the Hailey 1 deg. x 2 deg. Quadrangle and the western part of the Idaho Falls 1 deg. x 2 deg. Quadrangle, south-central Idaho", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-116, 43, -113.25, 44", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549382-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549382-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Bulletin_2064-A_1.0", + "description": "This dataset was developed to provide a geologic GIS database of the terranes of the Hailey 1x2 quadrangle and the western part of the Idaho Falls 1x2 quadrangle in south-central Idaho for use in spatial analysis. The paper version of Map Showing Geologic Terranes of the Hailey 1x2 Quadrangle and the western part of the Idaho Falls 1x2 Quadrangle, south-central Idaho was compiled by Ron Worl and Kate Johnson in 1995. The plate was compiled on a 1:250,000 scale topographic base map. TechniGraphic System, Inc. of Fort Collins Colorado digitized this map under contract for N.Shock. G.Green edited and prepared the digital version for publication as a geographic information system database. The digital geologic map database can be queried in many ways to produce a variety of geologic maps.", + "license": "proprietary" + }, + { + "id": "USGS_Bulletin_2064-C_1.0", + "title": "Geologic map of outcrop areas of sedimentary units in eastern Hailey 1 deg. x 2 deg. Quadrangle and southern Challis 1 deg. x 2 deg. Quadrangle, Idaho", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-115, 43.25, -114, 44.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548472-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548472-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Bulletin_2064-C_1.0", + "description": "This dataset was developed to provide a geologic GIS database of the Geologic map of outcrop areas of sedimentary units in the eastern part of the Hailey 1 deg. x 2 deg. Quadrangle and part of the southern part of the Challis 1 deg. x 2 deg. Quadrangle, south-central Idaho for use in spatial analysis. The paper version of the Geologic map of outcrop areas of sedimentary units in the eastern part of the Hailey 1 deg. x 2 deg. Quadrangle and part of the southern part of the Challis 1 deg. x 2 deg. Quadrangle, south-central Idaho was compiled by Paul Link and others in 1995. The plate was compiled on a 1:100,000 scale topographic base map. TechniGraphic System, Inc. of Fort Collins Colorado digitized this map under contract for N.Shock. G.Green edited and prepared the digital version for publication as a GIS database. The digital geologic map database can be queried in many ways to produce a variety of geologic maps.", + "license": "proprietary" + }, { "id": "USGS_CLUES", "title": "Climate, Land Use, and Environmental Sensitivity (CLUES)", @@ -180816,6 +184365,84 @@ "description": "Descriptive data of core samples housed within the Core Research Center. The database contains information about drill hole locations, intervals of core availability, formation names, and geologic ages. CORE information sets also indicate availability of non-automated information including analyses, photographs, cuttings, and thin sections.", "license": "proprietary" }, + { + "id": "USGS_CT_NATTEN", + "title": "Nitrogen Transport and Attenuation in the Connecticut River Basin, New Hampshire, Vermont, and Massachusetts", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2005-01-01", + "end_date": "2005-12-31", + "bbox": "-130, 33, -46, 57", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550196-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550196-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_CT_NATTEN", + "description": "The objective of this project is to estimate the rate of nitrogen loss in selected reaches of the Connecticut River. In-stream loss of nitrogen may influence the total nitrogen loads being input to Long Island Sound (LIS); therefore, an improved understanding of nitrogen attenuation is needed to plan effective strategies for meeting the goals of the LIS Total Maximum Daily Load (TMDL) allocation plan approved by the U.S. Environmental Protection Agency (USEPA) in 2001. The TMDL plan was instituted to reduce the problem of chronic seasonal hypoxia (low dissolved oxygen) that results from excessive nitrogen loading in Long Island Sound. Two study methods were used to measure nitrogen loss in selected study reaches of the Connecticut River during 2005: a mass-balance study to observe in-stream changes in total nitrogen, and a dissolved nitrogen gas study to measure denitrification. For the mass-balance study, samples were collected from all major tributaries and at the upstream and downstream ends of two 30- to 40-mile study reaches, and were analyzed for total nitrogen (including ammonia, nitrite, nitrate, and organic nitrogen). Streamflow data (from USGS gaging stations or manual measurements) were also taken at the time of sampling so that the mass flux of nitrogen could be computed at each site. To assess the effects of different hydrologic conditions and water temperatures on nitrogen attenuation in the Connecticut River, the study reaches were sampled two times in the spring and summer. The calculations of nitrogen mass flux entering and exiting each study reach will indicate when and where nitrogen removal processes are significant. The study of dissolved nitrogen gas was performed on a 6-mile sub-reach of the Connecticut River during a period of late summer when warm temperatures and low-flow conditions are most conducive to observing measurable rates of denitrification. Denitrification is estimated by measuring the downstream change in dissolved nitrogen after compensating for gas exchange with the atmosphere and dilution from inflows. Gas exchange is computed from the downstream concentration changes of SF6 gas and Bromide, which are injected at the head of the study reach. The data from this study will be useful for verifying predictions of nitrogen inputs, transport, and loss from water-quality models such as the New England SPARROW model and the RivR-N model. The results will assist state resource managers in the development of nitrogen reduction strategies for the Connecticut River Watershed, including the selection of sources in which to target these strategies. Results of the study will be presented in a journal paper in 2007. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_CascadeRange_HydrothermalMonitoring", + "title": "Hydrothermal monitoring data from the Cascade Range, northwestern United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2009-06-01", + "end_date": "", + "bbox": "-124, 40, -120, 50", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552978-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552978-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_CascadeRange_HydrothermalMonitoring", + "description": "Traditionally, most measurement and sampling of hydrothermal fluids has been on a highly intermittent basis. Such intermittent data, with sampling frequencies typically >1 year, are not well-suited for comparison with continuous seismic and geodetic monitoring data. Further, when volcanic unrest becomes evident from other geophysical observations, baseline hydrothermal observations are sometimes non-existent, and are often limited to the season when weather conditions are most amenable to field work. The preponderance of field-season, daytime data means that there is limited information on seasonal or diurnal variability. Beginning in the summer of 2009, motivated by the dramatic hydrothermal anomalies associated with volcanic unrest at South Sister volcano (Wicks and others, 2002; Evans and others, 2004), the USGS made a concerted effort to develop hourly hydrothermal records in the Cascade Range. The 25 selected monitoring sites show evidence of magmatic influence in the form of high 3He/4He ratios and (or) large fluxes of magmatic CO2 or heat. The monitoring sites can be grouped into three broad categories (Fig. 1): (1) sites with continuous pressure-temperature-conductivity monitoring and intermittent liquid sampling and discharge measurements; (2) sites with continuous temperature monitoring and intermittent gas sampling; and (3) sites that lack hourly data, but where the USGS has carried out intermittent flux measurements over a period of several decades. For most sites, correlations have been developed to convert pressure-temperature-conductivity data into a flux of heat or (more often) to the flux of a solute species of interest. We relate (1) specific electrical conductance to lab-measured concentrations of dissolved constituents and (2) pressure (depth of water) to field-measured discharge. The metadata includes descriptions of the sites and methods and plots of the calculated fluxes. The workbook files contain all of the data and correlations upon which those fluxes are based. Part of the database compilation is a list of relevant references for each area. These lists include all references cited in the metadata.", + "license": "proprietary" + }, + { + "id": "USGS_DDS-27_1", + "title": "Monthly average polar sea-ice concentration", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1978-10-25", + "end_date": "1991-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553986-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553986-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS-27_1", + "description": "The purpose of this data set is to provide paleoclimate researchers with a tool for estimating the average seasonal variation in sea-ice concentration in the modern polar oceans and for estimating the modern monthly sea-ice concentration at any given polar oceanic location. It is expected that these data will be compared with paleoclimate data derived from geological proxy measures such as faunal census analyses and stable-isotope analyses. The results can then be used to constrain general circulation models of climate change. This data set represents the results of calculations carried out on sea-ice-concentration data from the SMMR and SSM/I instruments. The original data were obtained from the National Snow and Ice Data Center (NSIDC). The data set also contains the source code of the programs that made the calculations. The objective was to derive monthly averages for the whole 13.25-year series (1978-1991) and to derive a composite series of monthly averages representing the variation of an average year. The resulting file set contains monthly images for each of the polar regions for each year, yielding 160 files for each pole, and composite monthly averages in which the years are combined, yielding 12 more files. Averaging the images in this way tends to reduce the number of grid cells that lack valid data; the composite averages are designed to suppress interannual variability. Also included in the data set are programs that can retrieve seasonal ice-concentration profiles at user-specified locations. These nongraphical data retrieval programs are provided in versions for UNIX, extended DOS, and Macintosh computers. Graphical browse utilities are included for the same computing platforms but require more sophisticated display systems. The data contained in this data set are derived from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/ Imager (SSM/I) data produced by the National Snow and Ice Data Center (NSIDC) at the University of Colorado in cooperation with the U.S. National Aeronautics and Space Administration (NASA) and the U.S. National Oceanic and Atmospheric Administration (NOAA). The basic data come from satellites of the U.S. Air Force Defense Meteorological Satellite Program. NSIDC distributes three collections of sea- ice-concentration grids on CD-ROM: data from the Nimbus-7 SMMR (October 25, 1978 through August 20, 1987) are provided on volume 7 of the SMMR Polar Data series (NASA, 1992); data from the SSM/I are provided on two separate volumes, covering the periods from July 9 of 1987 to December 31 of 1989, and from January 1 of 1990 through December 31 of 1991, respectively. The NASATEAM data from revision 2 of the SSM/I CD-ROM's were used to create the present data set. SMMR images were collected every 2 to 3 days, whereas SSM/I data are provided in daily ice-concentration grids. Apart from a number of small gaps (5 or fewer days) in the record, the only long period for which no data are available is December 3 of 1987 through January 12 of 1988, inclusive. As ancillary data, the ETOPO5 global gridded elevation and bathymetry data (Edwards, 1989) were interpolated to the resolution of the NSIDC data; the interpolated topographic data are included. The images are provided in three formats: Hierarchical Data Format (HDF), a flexible scientific data format developed at the National Center for Supercomputing Applications; Graphics Interchange Format (GIF); and Macintosh PICT format. The ice- concentration grids are distributed by NSIDC in HDF format.", + "license": "proprietary" + }, + { + "id": "USGS_DDS-46", + "title": "Geology and resource assessment of the Venezuelan Guayana Shield at 1:500,000 scale", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-75, 0, -57, 13", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552370-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552370-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS-46", + "description": "Conversion of the Venezuela maps to a computerized digital format was undertaken for the following reasons: 1) The digital format facilitates the presentation and analysis of earth-science data. Digital maps can be displayed at any scale or projection, whereas a paper map has a fixed scale and projection. However, the maps on this disc are not intended to be used at any scale more detailed than 1:500,000. A geographic information system (GIS) allows combining and overlaying of layers for analysis of spatial relations not readily apparent in the standard paper publication. Digital data on geology, geophysics, and geochemistry can be combined to create useful derivative products. 2) The digital format was used to facilitate publication in both paper and electronic form. For the Rio Caura paper map publication (Brooks and others, 1995), digital images were sent to the Gerber plotter, a vector-to-film processor. The other 1:500,000-scale MF maps were reproduced photographically from electrostatic plotter output on clear mylar. The published digital formats include this CD-ROM and ARC/INFO Export files to be located on the World Wide Web on the Internet. The data in this CD-ROM are based on a mineral resource assessment of the Venezuelan Guayana Shield, conducted between 1987 and 1991 by the U.S. Geological Survey and Corporacion Venezolana de Guayana, Tecnica Minera, (USGS, 1993). The Venezuelan Shield occupies about 415,000 sq km in the south and east part of Venezuela. The study area is bounded on the north by the Rio Orinoco. It includes all of the Territorio Federal Amazonas, Estado Bolivar, and part of Estado Delta Amacuro. The original resource assessment publication USGS Bulletin 2062 consists of 121 pages of text and figures as well as eight full-color maps: Geographic Geologic and tectonic Bouguer gravity Two mineral-occurrence maps Side-looking airborne radar image Two permissive domain maps The side-looking airborne radar image and the Bouguer gravity map are not included in this CD-ROM. The geology layer from the 1993 Bulletin was revised and published as a series of MF and I maps.", + "license": "proprietary" + }, + { + "id": "USGS_DDS-55_EF_1.0", + "title": "Gulf of Mexico Marine Geology and Geophysics from Field Activity: A-1-97-GM: East Flower Garden Bank bathymetry and backscatter data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-01-01", + "end_date": "1997-12-31", + "bbox": "-132, 30, -114, 50", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551486-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551486-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS-55_EF_1.0", + "description": "Accurate base maps are a prerequisite for any geological study, regardless of the objectives. Land-based studies commonly utilize aerial photographs, USGS 7.5-minute quadrangle maps, and satellite images as base maps. Until now, studies that involve the ocean floor have been at a disadvantage due to an almost complete lack of accurate marine base maps. Many base maps of the sea floor have been constructed over the past century but with a wide range in navigational and depth accuracies. Only in the past few years has marine surveying technology advanced far enough to produce navigational accuracy of 1 meter and depth resolutions of 50 centimeters. The Pacific Seafloor Mapping Project, U.S. Geological Survey, Western Coastal and Marine Geology Program, Menlo Park, California, U.S.A. in cooperation with the Ocean Mapping Group, University of New Brunswick, Canada is using this new technology to systematically map the ocean floor and lakes. This type of marine surveying, called Multibeam surveying, collects high-resolution bathymetry and backscatter data that can be used for a variety of basemaps, GIS coverages, and scientific visualization methods. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_DDS-55_WF", + "title": "Gulf of Mexico Marine Geology and Geophysics from Field Activity: A-1-97-GM: West Flower Garden Bank bathymetry and backscatter data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-01-01", + "end_date": "1997-12-31", + "bbox": "-132, 30, -114, 50", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548694-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548694-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS-55_WF", + "description": "These data and information are intended for science researchers, students from elementary through college, policy makers, and general public. Pacific Seafloor Mapping Project Test cruise. Bathymetry and seafloor backscatter data for the Flower Gardens National Marine Sanctuary are provided in TIFF image format. This data set contains data, metadata, and formal metadata associated with a marine data collection activity referred to by the USGS/CMG Activity ID: A-1-97-GM Similar information is available for over 1500 other USGS/CMG-related Activities. If known, available are Activity-specific navigation, gravity, magnetic, bathymetry, seismic, and sampling data; track maps; and equipment information; as well as summary overviews, crew lists, and information about analog materials. Primary access to the USGS/CMG Information Bank's digital data, analog data, and metadata is provided through \"http://walrus.wr.usgs.gov/infobank/programs/html/main/activities.html\" This page accommodates a variety of search approaches (e.g., by ship, by region, by scientist, by equipment type, etc.). Please recognize the U.S. Geological Survey (USGS) as the source of this information. Physical materials are under controlled on-site access. Some USGS information accessed through this means may be preliminary in nature and presented without the approval of the Director of the USGS. This information is provided with the understanding that it is not guaranteed to be correct or complete and conclusions drawn from such information are the responsibility of the user. This information is not intended for navigational purposes. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.", + "license": "proprietary" + }, { "id": "USGS_DDS-66_1.0", "title": "Assessment of the Alluvial Sediments in the Big Thompson River Valley, Colorado - USGS_DDS-66", @@ -180855,6 +184482,19 @@ "description": "These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) or other software to display bathymetry and backscatter data of Crater Lake, Oregon. These data include high-resolution bathymetry and calibrated acoustic backscatter in XYZ ASCII and ArcInfo GRID format generated from the 2000 multibeam sonar survey of Crater Lake, Oregon. Information for USGS Coastal and Marine Geology related activities are online at \"http://walrus.wr.usgs.gov/infobank/s/s100or/html/s-1-00-or.meta.html\" These data not intended for navigational purposes. Please recognize the U.S. Geological Survey (USGS) as the source of this information. USGS-authored or produced data and information are in the public domain. Although these data have been used by the U.S. Geological Survey, U.S. Department of the Interior, these data and information are provided with the understanding that they are not guaranteed to be usable, timely, accurate, or complete. Users are cautioned to consider carefully the provisional nature of these data and information before using them for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences. Conclusions drawn from, or actions undertaken on the basis of, such data and information are the sole responsibility of the user. Neither the U.S. Government nor any agency thereof, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any data, software, information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights. Trade, firm, or product names and other references to non-USGS products and services are provided for information only and do not constitute endorsement or warranty, express or implied, by the USGS, USDOI, or U.S. Government, as to their suitability, content, usefulness, functioning, completeness, or accuracy.", "license": "proprietary" }, + { + "id": "USGS_DDS_10_1", + "title": "Modern Average Global Sea-Surface Temperature", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1981-10-01", + "end_date": "1989-12-31", + "bbox": "-180, -66, 180, 72", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552931-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552931-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS_10_1", + "description": "The purpose of this data set is to provide paleoclimate researchers with a tool for estimating the average seasonal variation in sea-surface temperature (SST) throughout the modern world ocean and for estimating the modern monthly and weekly sea-surface temperature at any given oceanic location. It is expected that these data will be compared with temperature estimates derived from geological proxy measures such as faunal census analyses and stable isotopic analyses. The results can then be used to constrain general circulation models of climate change. The data contained in this data set are derived from the NOAA Advanced Very High Resolution Radiometer Multichannel Sea Surface Temperature data (AVHRR MCSST), which are obtainable from the Distributed Active Archive Center at the Jet Propulsion Laboratory (JPL) in Pasadena, Calif. The JPL tapes contain weekly images of SST from October 1981 through December 1990 in nine regions of the world ocean: North Atlantic, Eastern North Atlantic, South Atlantic, Agulhas, Indian, Southeast Pacific, Southwest Pacific, Northeast Pacific, and Northwest Pacific. This data set represents the results of calculations carried out on the NOAA data and also contains the source code of the programs that made the calculations. The objective was to derive the average sea-surface temperature of each month and week throughout the whole 10-year series, meaning, for example, that data from January of each year would be averaged together. The result is 12 monthly and 52 weekly images for each of the oceanic regions. Averaging the images in this way tends to reduce the number of grid cells that lack valid data and to suppress interannual variability. As ancillary data, the ETOPO5 global gridded elevation and bathymetry data (Edwards, 1989) were interpolated to the resolution of the SST data; the interpolated topographic data are included. The images are provided in three formats: a modified form of run-length encoding (MRLE), Graphics Interchange Format (GIF), and Macintosh PICT format. Also included in the data set are programs that can retrieve seasonal temperature profiles at user-specified locations and that can decompress the data files. These nongraphical SST retrieval programs are provided in versions for UNIX, MS-DOS, and Macintosh computers. Graphical browse utilities are included for users of UNIX with the X Window System, 80386- based PC's, and Macintosh computers.", + "license": "proprietary" + }, { "id": "USGS_DDS_P12_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Santa Maria Basin Province", @@ -181128,6 +184768,19 @@ "description": "The U.S. Geological Survey conducted a literature review to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet called the 2014 USGS Pier-Scour Database (PSDb-2014) consisting of 569 laboratory and 1,858 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 23 States within the United States and from 6 other countries. The digital spreadsheet is available on the Internet and offers a valuable resource to engineers and researchers seeking to understand pier-scour relations in the laboratory and field.", "license": "proprietary" }, + { + "id": "USGS_DS_2006_171", + "title": "JAMSTEC multibeam surveys and submersible dives around the Hawaiian Islands: A collaborative Japan-USA exploration of Hawaii's deep seafloor", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1998-01-01", + "end_date": "2002-12-31", + "bbox": "-161, 16.75, -152.99988, 25.25005", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554487-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554487-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2006_171", + "description": "This database release, USGS Data Series 171, contains data collected during four Japan-USA collaborative cruises that characterize the seafloor around the Hawaiian Islands. The Japan Agency for Marine-Earth Science and Technology (JAMSTEC) sponsored cruises in 1998, 1999, 2001, and 2002, to build a greater understanding of the deep marine geology around the Hawaiian Islands. During these cruises, scientists surveyed over 600,000 square kilometers of the seafloor with a hull-mounted multibeam seafloor-mapping sonar system (SEA BEAM\u00ae 2112), observed the seafloor and collected samples using robotic and manned submersible dives, collected dredge and piston-core samples, and performed single-channel seismic surveys. To date, 32 research papers have been published describing results from these cruises. For a list of these articles see the bibliography. This digital database was compiled with ESRI ArcInfo version 7.2.2 and ArcGIS 9.0. The GIS files contain multibeam bathymetry, and acoustic backscatter data in ESRI grid format, and dive, seafloor sampling, and siesmic location data in ESRI shapefile format; ArcInfo-compatible GIS software is therefore required to use the files of this database. Metadata for the GIS files are available as text files. The GIS files were also symbolized and used to create Portable Document Format (PDF) files that are ready to be printed. Adobe Reader or other software that can translate PDFs is necessary to print these files. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_DS_2006_177", "title": "Digital database of recently active traces of the Hayward Fault, California", @@ -181180,6 +184833,19 @@ "description": "More than 1,200 water-level measurements from 1957 to 2005 in the Rainier Mesa area of the Nevada Test Site were quality assured and analyzed. Water levels were measured from 50 discrete intervals within 18 boreholes and from 4 tunnel sites. An interpretive database was constructed that describes water-level conditions for each water level measured in the Rainier Mesa area. Multiple attributes were assigned to each water-level measurement in the database to describe the hydrologic conditions at the time of measurement. General quality, temporal variability, regional significance, and hydrologic conditions are attributed for each water-level measurement. The database also includes hydrograph narratives that describe the water-level history of each well. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_DS_2006_199_1.0", + "title": "Digital Geologic Map and GIS Database of Venezuela", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-74, 0, -60, 12", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554824-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554824-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2006_199_1.0", + "description": "The digital geologic map and GIS database of Venezuela captures GIS compatible geologic and hydrologic data from the \"Geologic Shaded Relief Map of Venezuela,\" which was released online as U.S. Geological Survey Open-File Report 2005-1038. Digital datasets and corresponding metadata files are stored in ESRI geodatabase format; accessible via ArcGIS 9.X. Feature classes in the geodatabase include geologic unit polygons, open water polygons, coincident geologic unit linework (contacts, faults, etc.) and non-coincident geologic unit linework (folds, drainage networks, etc.). Geologic unit polygon data were attributed for age, name, and lithologic type following the L\u00e9xico Estratigr\u00e1fico de Venezuela. All digital datasets were captured from source data at 1:750,000. Although users may view and analyze data at varying scales, the authors make no guarantee as to the accuracy of the data at scales larger than 1:750,000. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_DS_2006_203", "title": "Archive of Digital Boomer Seismic Reflection Data Collected During USGS Cruise 97CCT01 Offshore of Central South Carolina, June 1997", @@ -181206,6 +184872,32 @@ "description": "Base-flow yields at approximately 50 percent of the annual mean ground-water recharge rate were estimated for watersheds in the Berkeley County area, W.Va. These base-flow yields were determined from two sets of discharge measurements made July 25-28, 2005, and May 4, 2006. Two sections of channel along Opequon Creek had net flow losses that are expressed as negative base-flow watershed yields; these and other base-flow watershed yields in the eastern half of the study area ranged from -940 to 2,280 gallons per day per acre ((gal/d)/acre) and averaged 395 (gal/d)/acre. The base-flow yields for watersheds in the western half of the study area ranged from 275 to 482 (gal/d)/acre and averaged 376 (gal/d)/acre. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_DS_2006_220", + "title": "Hurricane Rita Surge Data, Southwestern Louisiana and Southeastern Texas, September to November 2005", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-98, 29, -90, 33", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548576-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548576-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2006_220", + "description": "Pressure transducers and high-water marks were used to document the inland water levels related to storm surge generated by Hurricane Rita in southwestern Louisiana and southeastern Texas. On September 22-23, 2005, an experimental monitoring network consisting of 47 pressure transducers (sensors) was deployed at 33 sites over an area of about 4,000 square miles to record the timing, extent, and magnitude of inland hurricane storm surge and coastal flooding. Sensors were programmed to record date and time, temperature, and barometric or water pressure. Water pressure was corrected for changes in barometric pressure and salinity. Elevation surveys using global-positioning systems and differential levels were used to relate all storm-surge water-level data, reference marks, benchmarks, sensor measuring points, and high-water marks to the North American Vertical Datum of 1988 (NAVD 88). The resulting data indicated that storm-surge water levels over 14 feet above NAVD 88 occurred at three locations and rates of water-level rise greater than 5 feet per hour occurred at three locations near the Louisiana coast. Quality-assurance measures were used to assess the variability and accuracy of the water-level data recorded by the sensors. Water-level data from sensors were similar to data from co-located sensors, permanent U.S. Geological Survey streamgages, and water-surface elevations performed by field staff. Water-level data from sensors at selected locations were compared to corresponding high-water mark elevations. In general, the water-level data from sensors were similar to elevations of high quality high-water marks, while reporting consistently higher than elevations of lesser quality high-water marks. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_DS_2006_221", + "title": "Land-Cover and Imperviousness Data for Regional Areas near Denver, Colorado; Dallas-Fort Worth, Texas; and Milwaukee-Green Bay, Wisconsin - 2001", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-01-01", + "end_date": "2002-12-31", + "bbox": "-106, 31, -86, 46", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548697-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548697-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2006_221", + "description": "This report describes the processing and results of land-cover and impervious surface derivation for parts of three metropolitan areas being studied as part of the U.S. Geological Survey's (USGS) National Water-Quality Assessment (NAWQA) Program Effects of Urbanization on Stream Ecosystems (EUSE). The data were derived primarily from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) satellite imagery from the period 1999-2002, and are provided as 30-meter resolution raster datasets. Data were produced to a standard consistent with data being produced as part of the USGS National Land Cover Database 2001 (NLCD01) Program, and were derived in cooperation with, and assistance from, NLCD01 personnel. The data were intended as surrogates for NLCD01 data because of the EUSE Program's time-critical need for updated land-cover for parts of the United States that would not be available in time from the NLCD01 Program. Six datasets are described in this report: separate land-cover (15-class categorical data) and imperviousness (0-100 percent continuous data) raster datasets for parts of the general Denver, Colorado area (South Platte River Basin), Dallas-Fort Worth, Texas area (Trinity River Basin), and Milwaukee-Green Bay, Wisconsin area (Western Lake Michigan Drainages). [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_DS_2006_224", "title": "Aeromagnetic Survey of Taylor Mountains Area in Southwest Alaska, A Website for the Distribution of Data", @@ -181219,6 +184911,19 @@ "description": "An airborne high-resolution magnetic and coincidental horizontal magnetic graviometer survey was completed over the Taylor Mountains area in southwest Alaska. The flying was undertaken by McPhar Geosurveys Ltd. on behalf of the United States Geological Survey (USGS). First tests and calibration flights were completed by April 7, 2004, and data acquisition was initiated on April 17, 2004. The final data acquisition and final test/calibrations flight was completed on May 31, 2004. Data acquired during the survey totaled 8,971.15 line-miles. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_DS_2006_234_1.0", + "title": "Nevada Magnetic and Gravity Maps and Data: A Website for the Distribution of Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-120, 35, -114, 42", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548572-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548572-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2006_234_1.0", + "description": "Magnetic anomalies are due to variations in the Earth's magnetic field caused by the uneven distribution of magnetic minerals (primarily magnetite) in the rocks that make up the upper part of the Earth's crust. The features and patterns of the magnetic anomalies can be used to delineate details of subsurface geology, including the locations of buried faults and magnetite-bearing rocks and the depth to the base of sedimentary basins. This information is valuable for mineral exploration, geologic mapping, and environmental studies. The Nevada magnetic map is constructed from grids that combine information (see data processing details) collected in 82 separate magnetic surveys conducted between 1947 and 2004. The data from these surveys are of varying quality. The design and specifications (terrain clearance, sampling rates, line spacing, and reduction procedures) varied from survey to survey depending on the purpose of the project and the technology of that time. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_DS_2007_119", "title": "Archive of Digital Boomer Seismic Reflection Data Collected During USGS Field Activity 04SGI01 in the Withlacoochee River of West-Central Florida, March 2004", @@ -181245,6 +184950,45 @@ "description": "In August of 2005, the U.S. Geological Survey conducted geophysical surveys offshore of Port Fourchon and Timbalier Bay, Louisiana, and in nearby waterbodies. This report serves as an archive of unprocessed digital chirp seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_DS_2007_244", + "title": "Geochemical Database for Intrusive Rocks of North-Central and Northeast Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2007-12-31", + "bbox": "-119, 38, -114, 42", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550906-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550906-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2007_244", + "description": "North-central and northeast Nevada contains numerous large plutons and smaller stocks but also contains many small, shallowly emplaced intrusive bodies, including dikes, sills, and intrusive lava dome complexes. Decades of geologic investigations in the study area demonstrate that many ore deposits, representing diverse ore deposit types, are spatially, and probably temporally and genetically, associated with these igneous intrusions. However, despite the number and importance of igneous intrusions in the study area, no synthesis of geochemical data available for these rocks has been completed. This report presents a synthesis of geochemical data for these rocks. The product represents the first phases of an effort to evaluate the time-space-compositional evolution of Mesozoic and Cenozoic magmatism in the study area and identify genetic associations between magmatism and mineralizing processes in this region. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_DS_2007_246_1.0", + "title": "Flow Velocity and Sediment Data Collected During 1990 and 1991 at National Canyon, Colorado River, Arizona", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1991-12-31", + "bbox": "-114, 35, -111, 37", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550397-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550397-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2007_246_1.0", + "description": "During 1990 and 1991, a series of research flows were released from Glen Canyon Dam. Data collected at the streamflow-gaging station on the Colorado River above National Canyon near Supai from that period have been compiled and entered into the U.S. Geological Survey database. The data consist of measurements of suspended-sediment concentration and sand sizes in suspension, sand sizes of streambed sediment, and velocity of the Colorado River above National Canyon near Supai streamflow-gaging site. Velocity and sediment data are available upon request from the Arizona Water Science Center and from the U.S. Geological Survey water-quality database (http://waterdata.usgs.gov/az/nwis/qw). [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_DS_2007_250", + "title": "Modal Composition and Age of Intrusions in North-central and Northeast Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-119, 38, -114, 42", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550917-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550917-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DS_2007_250", + "description": "North-central and northeast Nevada contains numerous large plutons and smaller stocks but also contains many small, shallowly emplaced intrusive bodies, including dikes, sills, and intrusive lava dome complexes. Decades of geologic investigations in the study area demonstrate that many ore deposits, representing diverse ore deposit types, are spatially, and probably temporally and genetically, associated with these igneous intrusions. However, despite the number and importance of igneous instrusions in the study area, no synthesis of geochemical data available for these rocks has been completed. This report presents a synthesis of composition and age data for these rocks. The product represents the first phases of an effort to evaluate the time-space-compositional evolution of Mesozoic and Cenozoic magmatism in the study area and identify genetic associations between magmatism and mineralizing processes in this region. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_DS_2007_254", "title": "Archive of Digital CHIRP Seismic Reflection Data Collected During USGS Cruise 06FSH01 Offshore of Siesta Key, Florida, May 2006", @@ -181310,6 +185054,32 @@ "description": "The unique landscape of South Dakota, known for its diverse wetlands and large areas of native prairie, provides critical habitat for many of the nation\u2019s migratory birds, including grassland birds.", "license": "proprietary" }, + { + "id": "USGS_FORT_Mesa_Verda_NP_veg", + "title": "Mesa Verde National Park Vegetation Mapping Project - Spatial Vegetation Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-01-01", + "end_date": "2006-12-31", + "bbox": "-108.57052, 37.14272, -108.32347, 37.36232", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554965-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554965-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_FORT_Mesa_Verda_NP_veg", + "description": "The Mesa Verde National Park Vegetation Map Database was developed as a primary product in the Mesa Verde National Park Vegetation Classification, Distribution, and Mapping project. The map database maps vegetation at three levels of thematic organization at the park: the base, group, and management map classes. Most of the base map classes represent plant communities identified to National Vegetation Classification associations. The associated report, Vegetation Classification and Distribution Mapping Report: Mesa Verde National Park, describes in detail the methods used to develop the map database and map classes. The project was sponsored by the USA-National Vegetation Mapping Program and the National Park Service (NPS) Southern Colorado Plateau Network and the work was executed by a multi-agency and organizational team. The vegetation map database covers the park and an approximately 1 kilometer buffer around the park boundary.", + "license": "proprietary" + }, + { + "id": "USGS_FORT_WY_WindTurbines2012", + "title": "Locations and Attributes of Wind Turbines in Wyoming, 2012", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-110.83864, 41.119976, -104.89066, 43.134483", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551793-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551793-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_FORT_WY_WindTurbines2012", + "description": "These data represent locations of wind turbines found within Wyoming as of August 2012. We assigned each wind turbine to a wind farm and, in these data, provide information about each turbine\u2019s potential megawatt output, rotor diameter, hub height, rotor height, the status of the land ownership where the turbine exists, the county each turbine is located in, wind farm power capacity, the number of units currently associated with each wind farm, the wind turbine manufacturer and model, the wind farm developer, the owner of the wind farm, the current purchaser of power from the wind farm, the year the wind farm went online, and the status of its operation. Some of the attributes are estimates based on the information we found via the American Wind Energy Association and other on-line reports. The locations are derived from National Agriculture Imagery Program (2009 and 2012) true color aerial photographs and have a positional accuracy of approximately +/-5 meters. These data will provide a planning tool for wildlife- and habitat-related projects underway at the U.S. Geological Survey\u2019s Fort Collins Science Center and other government and non-government organizations. Specifically, we will use these data to support quantifying disturbances of the landscape as related to wind energy as well as to quantify indirect disturbances to flora and fauna. This data set represents an update to a previous version by O\u2019Donnell and Fancher (2010).", + "license": "proprietary" + }, { "id": "USGS_FRESC_Columbia_Basin_sagebrush_1.0", "title": "Current Distribution of Sagebrush and Associated Vegetation in the Columbia Basin and Southwestern Regions", @@ -181869,6 +185639,32 @@ "description": "The Global Land Cover Characterization Project was established to meet science data requirements identified by the International Geosphere and Biosphere Programme (IGBP), and the U. S. Global Change Research Program. The overall goal is to produce flexible large-area land cover databases to meet evolving requirements of the earth science research community. The project was implemented by the United States Geological Survey/EROS Data Center (EDC), the University of Nebraska-Lincoln (UNL), and the Joint Research (JRC) of European Commission. This effort is part of the National Aeronautic's and Space Administration (NASA) Earth Observing System Pathfinder Program. Funding for the project was provided by the USGS, NASA, the U.S. Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), U.S. Forest Service (USFS) , and the United Nations Environment Programme. The data base has been adopted by the International Geosphere-Biosphere Programme Data and Information System office (IGBP-DIS) to fill its requirement for a global 1-km land cover data set. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_GLOBAL_CRUST", + "title": "Global Crustal Database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549336-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549336-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_GLOBAL_CRUST", + "description": "In 1988, work was started on a global database intended to characterize the Earth's crust. Today, this database has over 10,000 entries, covering a large portion of the Earth's surface. The primary data source is from published literature detailing the results of seismic refraction profiles, although some unpublished results have been used as well, especially in Russia and China. From these seismic profiles, we extract a 1-D seismic velocity model (Vp and Vs if available) for a specific latitude and longitude. The 1-D model includes the thickness and seismic velocity for each crustal layer as well as annotations of sedimentary layers, velocity gradients, and the moho depth. Other crustal parameters are added to each point to create a complete image of the Earth's crust. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_GLSC_GreatLakesCopepods", + "title": "Free-living and Parasitic Copepods of the Laurentian Great Lakes: Keys and Details on Individual Species", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551007-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551007-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_GLSC_GreatLakesCopepods", + "description": "We intend that this website provide individuals interested in copepod and branchiuran crustaceans of the Great Lakes with the best taxonomic information currently available, a brief introduction to the known distributions and ecology of the various species, and some of the most relevant literature. This product reflects our belief that the taxonomy of even \"difficult\" groups can be made understandable, interesting, and informative, especially in this digital age. Most of the information on the copepod fauna of the Great Lakes can be accessed directly from the Main Menu. To access information on identification nuances, distribution, life history, ecology, and synonymies for each species, there are two routes available. You can go to the Species List of Major Groups and Distribution Within the Great Lakes and in the table click on the name of the species in which you are interested, or you can click on the species name within the key when you reach the end of the identification process. Photographs, drawings, and text can be printed by placing the cursor over the object or page of interest, right-clicking, and then selecting the appropriate option from the drop down menu.", + "license": "proprietary" + }, { "id": "USGS_IndianapolisMetroStreams", "title": "Benthic-invertebrate, fish-community, and streambed-sediment-chemistry data for steams in the Indianapolis metropolitan area, Indiana, 2009-2012", @@ -181895,6 +185691,149 @@ "description": "Joint Experiment for Crop Assessment and Monitoring The overarching goal of JECAM is to reach a convergence of approaches, develop monitoring and reporting protocols and best practices for a variety of global agricultural systems. JECAM will enable the global agricultural monitoring community to compare results based on disparate sources of data, using various methods, over a variety of global cropping systems. It is intended that the JECAM experiments will facilitate international standards for data products and reporting, eventually supporting the development of a global system of systems for agricultural crop assessment and monitoring. The JECAM initiative is developed in the framework of GEO Global Agricultural Monitoring (GEOSS Task AG0703 a) and Agricultural Risk Management (GEOSS Task AG0703 b).", "license": "proprietary" }, + { + "id": "USGS_KATRINA_COASTAL_IMPACT_LIDAR", + "title": "Hurricane Katrina Impact Studies: Pre- and Post-Storm 3D Topography", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-89, 30, -87, 31", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548501-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548501-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_KATRINA_COASTAL_IMPACT_LIDAR", + "description": "In a cooperative research program, the USGS, NASA and the US Army Corps of Engineers (USACE) are using airborne laser mapping systems to survey coastal areas before and after hurricanes. As the aircraft flies along the coast, a laser altimeter (lidar) scans a several hundred meter wide swath of the earth's surface acquiring an estimate of ground elevation approximately every square meter. The elevation data from different flights can be compared to determine the patterns and magnitudes of coastal change (erosion, overwash, etc.) and the loss (or gain) of buildings and infrastructure. Results come from two lidar systems, the USACE's Compact Hydrographic Airborne Rapid Total Survey (CHARTS) and NASA's Experimental Advanced Airborne Research Lidar (EAARL). [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_Katrina_Coastal_Impact", + "title": "Hurricane Katrina Impact Studies", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2005-08-29", + "end_date": "", + "bbox": "-94, 29, -86, 36", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231758498-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231758498-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Katrina_Coastal_Impact", + "description": "Hurricane Katrina made landfall as a category 4 storm in Plaquemines Parish, LA on August 29, 2005. The U.S. Geological Survey (USGS), NASA, the U.S. Army Corps of Engineers, and the University of New Orleans are cooperating in a research project investigating coastal change that occurred as a result of Hurricane Katrina. Aerial video, still photography, and laser altimetry surveys of post-storm beach conditions were collected August 31 and September 1, 2005 for comparison with earlier data. The comparisons will show the nature, magnitude, and spatial variability of coastal changes such as beach erosion, overwash deposition, and island breaching. These data will also be used to further refine predictive models of coastal impacts from severe storms. The data are being made available to local, state, and federal agencies for purposes of disaster recovery and erosion mitigation. ", + "license": "proprietary" + }, + { + "id": "USGS_MAP_MF-2336_1.0", + "title": "Geologic map of the Cape Mendocino, Eureka, Garberville, & southwestern part of the Hayfork 30 X 60 Quadrangles and Adjacent Offshore Area, Northern California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-125.029884, 39.982655, -123, 41.017353", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548610-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548610-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF-2336_1.0", + "description": "This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:100,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (ceghmf.ps, ceghmf.pdf, ceghmf.txt), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:100,000 or smaller. This is the pre-release version of the report. The accompanying text file mf2336.rev contains version numbers for each part of the data set. This report consists of a set of geologic map database files (Arc/ Info coverages) and supporting text and plotfiles. In addition, the report includes two sets of plotfiles (PostScript and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described in the explanatory pamphlets (ceghdesc and ceghdb). The base map layer used in the preparation of the geologic map plotfiles was downloaded from the web (www.gisdatadepot.com) as Digital Raster Graphic files of scale-stable versions of the USGS 1:100,000 topographic maps and coverted to TIFF images which were then converted to GRIDs. These grids contain no database information other than position, and are included for reference only. The base maps used were the Cape Mendocino (1989 edition), Eureka (1987 edition), Garberville (1979 edition), Hayfork (1978 edition) 1:100,000 topographic maps, which all have a 50-meter contour interval. The bathymetry maps were converted from the Coast and Geodetic Survey hydrographic chart 1308 N-12, 1969.", + "license": "proprietary" + }, + { + "id": "USGS_MAP_MF-2349_1.0", + "title": "Geologic map and map database of the Spreckels 7.5-minute quadrangle, Monterey County, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-121.75, 36.62, -121.62, 36.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552246-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552246-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF-2349_1.0", + "description": "This database and accompanying plot files depict the distribution of geologic materials and structures at a scale of 1:24,000. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be used to accurately identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (skmf.txt, skmf.pdf, or skmf.ps), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller. This report consists of a set of geologic map database files (Arc/Info coverages) and supporting text and plotfiles. In addition, the report includes two sets of plotfiles (PostScript and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described below: >ARC/INFO Resultant Description of Coverage >export file Coverage >----------- ----------- -------------------------------- >sk-geol.e00 sk-geol/ Polygon and line coverage showing > faults, depositional contacts, and > rock units in the map area. > >sk-strc.e00 sk-strc/ Point and line coverage showing > strike and dip information and fold axes. > >sk-lnds.e00 sk-lnds/ Point and line coverage showing arrows > indicating landslide directions as well > as the locations of wells and springs > not included in the topographic base map. ASCII text files, including explanatory text, ARC/INFO key files, PostScript and PDF plot files, and a ARC Macro Language file for conversion of ARC export files into ARC coverages: >skmf.ps A PostScript plot file of the pamphlet > containing detailed unit descriptions > and geological information, a description > of the digital files associated with the > publication, plus references cited. > >skmf.pdf A PDF version of mamf.ps. > >skmf.txt A text-only file containing an unformatted > version of skmf.ps. > >import.aml ASCII text file in ARC Macro Language to > convert ARC export files to ARC coverages > in ARC/INFO. > >skmap.ps A PostScript plottable file containing > an image of the geologic map and base > maps at a scale of 1:24,000, along with > a simple map key. > >skmap.pdf A PDF file containing an image of the > geologic map and base maps at a scale > of 1:24,000, along with a simple map key. Base maps Base Map layers used in the preparation of the geologic map plotfiles were derived from published digital maps (Aitken, 1997) obtained from the U.S. Geological Survey Geologic Division Website for the Western Region (http://wrgis.wr.usgs.gov). Please see the website for more detailed information about the original databases. Because the base map digital files are already available at the website mentioned above, they are not included in the digital database package.", + "license": "proprietary" + }, + { + "id": "USGS_MAP_MF-2352_Version 1.0", + "title": "Geologic map of the Tetilla Peak quadrangle, Santa Fe and Sandoval Counties, New Mexico", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-106.25, 35.5, -106.125, 35.625", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551508-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551508-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF-2352_Version%201.0", + "description": "The purpose of this mapping was to determine the bedrock geology that would control or impact ground-water flow from the Espanola basin into the Santo Domingo basin. As it is a multi-purpose geologic map, it is suitable as the geologic layer for any variety of interdisciplinary investigations incorporating geology as a theme. This digital geologic map summarizes all available geologic information for the Tetilla Peak quadrangle located immediately southwest of Santa Fe, New Mexico. The geologic map consists of new polygon (geologic map units) and line (contact, fault, fold axis, dike, flow contact, hachure) data, as well as point data (locations for structural measurements, geochemical and geochronologic data, geophysical soundings, and water wells). The map database has been generated at 1:24,000 scale, and provides significant new geologic information for an area of the southern Cerros del Rio volcanic field, which sits astride the boundary of the Espanola and Santo Domingo basins of the Rio Grande rift. The quadrangle includes the west part of the village of La Cienega along its eastern border and includes the southeasternmost part of the Cochiti Pueblo reservation along its northwest side. The central part of the quadrangle consists of Santa Fe National Forest and Bureau of Land Management lands, and parts of several Spanish-era land grants. Interstate 25 cuts through the southern half of the quadrangle between Santa Fe and Santo Domingo Pueblo. Canada de Santa Fe, a major river tributary to the Rio Grande, cuts through the quadrangle, but there is no dirt or paved road along the canyon bottom. A small abandoned uranium mine (the La Bajada mine) is found in the bottom of the Canada de Santa Fe about 3 km east of the La Bajada fault zone; it has been partially reclaimed. The surface geology of the Tetilla Peak quadrangle consists predominantly of a thin (1-2 m generally, locally as thick as 10? m) layer of windblown surficial deposits that has been reworked colluvially. Locally, landslide, fluvial, and pediment deposits are also important. These colluvial deposits mantle the principal bedrocks units, which are (from most to least common): (1) basalts, basanites, andesite, and trachyte of the Pliocene (2.7-2.2 Ma) Cerros del Rio volcanic field; (2) unconsolidated deposits of the Santa Fe Group, mainly along the western border, in the hanging wall of the La Bajada fault zone, but locally extending 2-3 km east under the Cerros del Rio volcanic field; (3) older Tertiary volcanic and sedimentary rocks (Abiquiu?, Espinaso, and Galisteo Formations); (4) intrusive rocks of the Cerrillos intrusive center that are roughly coeval with the Espinaso volcanic rocks; and (5) Mesozoic sedimentary rocks ranging in age from the Upper Triassic Chinle Formation to the Upper Cretaceous Mancos Shale. GEOSPATIAL DATAFILES AND OTHER FILES INCLUDED IN THIS DATA SET: Map political location: Santa Fe and Sandoval Counties, New Mexico Compilation scale: 1:24,000 Geology mapped: 1996-1998 >tepk_geol: geologic units, faults, dikes, volcanic flow boundaries >tepk_struct: bearing and attitude measurements of structural features >tepk_bed: attitude measurements of geologic units >tepk_chem: geochemical and geochronologic data by sample >tepk_amt: audio-magneto-telluric (AMT) geophysical sample data >tepk_wells: water well locations >tepk_marker: cartographic decorations (bar and ball symbol, etc.) >color524.shd: ArcInfo shadeset used to color geology polygons >geoscamp1.mrk: ArcInfo markerset used to plot geologic symbols >geoscamp1.lin: ArcInfo lineset used to plot geologic line symbols >tepk_base.tif,.tfw: 1:24,000-scale topographic base", + "license": "proprietary" + }, + { + "id": "USGS_MAP_MF-2354_Version 1.0", + "title": "Geologic map of the Chewelah 30' x 60' quadrangle, Washington and Idaho", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1963-07-01", + "end_date": "1989-10-09", + "bbox": "-117.9, 41.8, -117, 42.31", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550449-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550449-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF-2354_Version%201.0", + "description": "The data set for the Chewelah 30' X 60' quadrangle has been jointly prepared by the U.S. Geological Survey Mineral Resource Program, the Southern California Areal Mapping Project (SCAMP), and the Washington Division of Geology and Earth Resources, as part of an ongoing effort to utilize a Geographical Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Data Base of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the Chewelah 30' X 60' quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, it can be used for mineral resource evaluation studies, animal and plant habitat studies, and soil studies in the Colville and Kaniksu National Forests. The database is not suitable for site-specific geologic evaluations. This data set maps and describes the geology of the Chewelah 30' X 60' quadrangle, Washington and Idaho. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) a point coverage containing site-specific geologic structural data, (3) two coverages derived from 1:100,000 Digital Line Graphs (DLG); one of which represents topographic data, and the other, cultural data, (4) two line coverages that contain cross-section lines and unit-label leaders, respectively, and (5) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, and two cross sections, and on a separate sheet, a Correlation of Map Units (CMU) diagram, an abbreviated Description of Map Units (DMU), modal diagrams for granitic rocks, an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of the Readme text-file and expanded Description of Map Units (DMU), and (3) this metadata file. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. The map was compiled from geologic maps of eight 1:48,000 15' quadrangle blocks, each of which was made by mosaicing and reducing the four constituent 7.5' quadrangles. These 15' quadrangle blocks were mapped chiefly at 1:24,000 scale, but the detail of the mapping was governed by the intention that it was to be compiled at 1:48,000 scale. The compilation at 1:100,000 scale entailed necessary simplification in some areas and combining of some geologic units. Overall, however, despite a greater than two times reduction in scale, most geologic detail found on the 1:48,000 maps is retained on the 1:100,000 map. Geologic contacts across boundaries of the eight constituent quadrangles required minor adjustments, but none significant at the final 1:100,000 scale. The geologic map was compiled on a base-stable cronoflex copy of the Chewelah 30' X 60' topographic base and then scribed. The scribe guide was used to make a 0.007 mil-thick blackline clear-film, which was scanned at 1200 DPI by Optronics Specialty Company, Northridge, California. This image was converted to vector and polygon GIS layers and minimally attributed by Optronics Specialty Company. Minor hand-digitized additions were made at the USGS. Lines, points, and polygons were subsequently edited at the USGS by using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:100,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum. Data package contents: >chew_geo.e00 Contacts, faults, geologic unit labels >chew_pts.e00 Attitudes and their dip values. Dip values plotted > as annotation. >chew_xs.e00 lines of cross sections >chew_ldr.e00 unit label leaders >chew_hyps.e00 Topography >chew_trans.e00 Roads, cultural information >lines.rel.e00 Line dictionary >points.rel.e00 Point dictionary >scamp2.shd.e00 SCAMP shade set", + "license": "proprietary" + }, + { + "id": "USGS_MAP_MF-2356_1.0", + "title": "Geologic map of the Jasper quadrangle, Newton and Boone Counties, Arkansas", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-93.25, 36, -93.125, 36.125", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550760-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550760-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF-2356_1.0", + "description": "To provide a digital geologic map database of the quadrangle that improves understanding of the regional geologic framework and its influence on the regional groundwater flow system. This digital geologic map compilation presents new polygon (i.e., geologic map unit contacts), line (i.e., fault, fold axis, and structure contour), and point (i.e., structural attitude, contact elevations) vector data for the Jasper 7 1/2' quadrangle in northern Arkansas. The map database, which is at 1:24,000-scale resolution, provides geologic coverage of an area of current hydrogeologic, tectonic, and stratigraphic interest. The Jasper quadrangle is located in northern Newton and southern Boone Counties about 20 km south of the town of Harrison. The map area is underlain by sedimentary rocks of Ordovician, Mississippian, and Pennsylvanian age that were mildly deformed by a series of normal and strike-slip faults and folds. The area is representative of the stratigraphic and structural setting of the southern Ozark Dome. The Jasper quadrangle map provides new geologic information for better understanding groundwater flow paths in and adjacent to the Buffalo River watershed. The current map database incorporates geologic data from: (1) early geologic mapping (1906) by Purdue and Miser and (2) more recent field mapping (1995-1998) by M. R. Hudson. Buffalo National River, under the auspices of the National Park Service, occupies the central part of the map area. >FILES INCLUDED WITH THIS DATA SET: >jsp24k: geology polygon coverage >jsppnt: strike/dip point locations and data >jspcontrol: field elevation control points >jspcontour: structure contours on the top of the Boone Formation >geoscamp1.lin: geologic line symbols >geoscamp1.mrk: geologic marker symbols >fnt037: font used with geoscamp1.mrk >wpgcmykg.shd: shadeset used to color polygons in jsp24k coverage >fnt027: font containing geologic age symbols", + "license": "proprietary" + }, + { + "id": "USGS_MAP_MF-2359_1.0", + "title": "Geologic Map of the Clifton Quadrangle, Mesa County, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-108.5, 39, -108.375, 39.125", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553308-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553308-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF-2359_1.0", + "description": "To update earlier small-scale geologic mapping, and to provide sufficient geologic information for land-use decisions. 1:24,000-scale geologic mapping in the Clifton 7.5' quadrangle, in support of the USGS Colorado River/I-70 Corridor Cooperative Geologic Mapping Project, provides interpretations of the Quaternary stratigraphy and geologic hazards in this area of the Grand Valley. The Clifton 1:24,000 quadrangle is in Mesa County in western Colorado. Because the map area is dominated by various surficial deposits, the map depicts 16 different Quaternary units. Five prominent river terraces are present in the quadrangle containing gravels deposited by the Colorado River. The map area contains a large landslide deposit on the southern slopes of Mount Garfield. The landslide developed in the Mancos Shale and contains large blocks of the overlying Mesaverde Group. In addition, the landslide is a source of debris flows that have closed I-70 in the past. The major bedrock unit in the quadrangle is the Mancos Shale of Upper Cretaceous age. The map is accompanied by text containing unit descriptions, and sections on geologic hazards (including landslides, piping, gullying, expansive soils, and flooding), and economic geology (including sand and gravel). A table indicates what map units are susceptible to a given hazard. Approximately 20 references are cited at the end of the report. Map political location: Mesa County, Colorado Compilation scale: 1:24,000 Geology mapped in 1996 to 1998. Compilation completed March 1999. DATASETS INCLUDED IN THIS GEOSPATIAL DATABASE: > clifpoly: geology polygons, contacts, and other linear features > clifline: line of cross-section A-A' > clifpnt: point features - bedding attitudes, drillholes", + "license": "proprietary" + }, + { + "id": "USGS_MAP_MF2329_1.0", + "title": "Map showing inventory and regional susceptibility for Holocene debris flows and related fast moving landslides in the conterminous United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1928-01-01", + "end_date": "1999-12-31", + "bbox": "-127.6, 31.8, -69.5, 48.8", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554010-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554010-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MAP_MF2329_1.0", + "description": "These data are intended for geographic display and analysis at the national level, and for large regional areas. It is not intended for hazard evaluation or other site-specific work, and should not be used for such. It can be used to determine where debris flow processes may be a problem and where additional information and investigation are warranted. Although the digital form of the data removes the constraint imposed by the scale of a paper map, the detail and accuracy inherent in map scale are also present in the digital data. The fact that this database was edited at a scale of 1:2,500,000 means that higher resolution information is not present in the data. Plotting at scales larger than 1:2,500,000 will not yield greater real detail, and it may reveal fine-scale irregularities below the intended resolution of the database. Similarly, where this database is used in combination with other data of higher resolution, the resolution of the combined output will be limited by the lower resolution of these data. No responsibility is assumed by the U.S. Geological Survey in the use of these data. Debris flows, debris avalanches, mud flows and lahars are fast-moving landslides that occur in a wide variety of environments throughout the world. They are particularly dangerous to life and property because they move quickly, destroy objects in their paths, and can strike with little warning. The purpose of this map is to show where debris flows have occurred in the conterminous United States and where these slope movements might be expected in the future.", + "license": "proprietary" + }, + { + "id": "USGS_MASSBAY", + "title": "Massachusetts Bay Circulation and Effluent Modeling", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1989-10-01", + "end_date": "1992-12-31", + "bbox": "-71.104, 41.685, -69.82, 42.66", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549081-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549081-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MASSBAY", + "description": "Understanding the circulation of water in Massachusetts and Cape Cod Bays is of critical importance for determining how nutrients, sediment, contaminants and other water-borne materials are transported. Numerical circulation models represent a powerful tool to build understanding of transport processes in these bays, as well as for synthesis, scenario testing and prediction. The U.S. Geological Survey has developed a three-dimensional model of circulation in Massachusetts Bay driven by tides, wind, river runoff, surface heating and cooling and remote forcing from the Gulf of Maine. The circulation calculated from this model was used as input to the HydroQual water quality model. The USGS is currently using the model in a Regional Marine Research Program in the Gulf of Maine funded study of sources, transport and nutrient environment of red tide populations in the western gulf. Together with investigators from WHOI and UNH, this work seeks to characterize the physical transport mechanisms that influence the distribution and fate of toxic Alexandrium cells in this region, and the processes by which cells are transported to Massachusetts Bay. The ability of the regional model to represent the movement of fresh water from the Kennebec and Androscoggin rivers will be determined. Over the next three years, the USGS will be developing a regional sediment transport model by interfacing existing surface wave, bottom boundary layer and sediment erosion models into the current hydrodynamic model. Current and suspended sediment data from the long-term mooring (as well as other sites) will be used for calibration and verification. When the new outfall comes online, additional hydrodynamic model runs in Massachusetts Bay will be performed to test the ability of the model to simulate the effects of the relocated effluent discharge.", + "license": "proprietary" + }, + { + "id": "USGS_MF-2323_1.0", + "title": "Extent of Pleistocene Lakes in the Western Great Basin", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-01-01", + "end_date": "1999-12-31", + "bbox": "-121.319, 36.934, -113.445, 42.973", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552513-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552513-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_MF-2323_1.0", + "description": "The purpose of this map is to show the differences between the extents of late Pleistocene pluvial lakes and older, larger lakes caused by much higher effective moisture during past glacial-pluvial episodes. During the Pliocene to middle Pleistocene, pluvial lakes in the western Great Basin repeatedly rose to levels much higher than those of the well-documented late Pleistocene pluvial lakes, and some presently isolated basins were connected. Sedimentologic, geomorphic, and chronologic evidence at sites shown on the map indicates that Lakes Lahontan and Columbus-Rennie were as much as 70 m higher in the early-middle Pleistocene than during their late Pleistocene high stands. Lake Lahontan at its 1400-m shoreline level would submerge present-day Reno, Carson City, and Battle Mountain, and would flood other now-dry basins. To the east, Lakes Jonathan (new name), Diamond, Newark, and Hubbs also reached high stands during the early-middle(?) Pleistocene that were 25-40 m above their late Pleistocene shorelines; at these very high levels, the lakes became temporarily or permanently tributary to the Humboldt River and hence to Lake Lahontan. Such a temporary connection could have permitted fish to migrate from the Humboldt River southward into the presently isolated Newark Valley and from Lake Lahontan into Fairview Valley. The timing of drainage integration also provides suggested maximum ages for fish to populate the basins of Lake Diamond and Lake Jonathan. Reconstructing and dating these lake levels also has important implications for paleoclimate, tectonics, and drainage evolution in the western Great Basin. For example, shorelines in several basins form a stair-step sequence downward with time from the highest levels, thought to have formed at about 650 ka, to the lowest, formed during the late Pleistocene. This descending sequence indicates progressive drying of pluvial periods, possibly caused by uplift of the Sierra Nevada and other western ranges relative to the western Great Basin. However, these effects cannot account for the extremely high lake levels during the early middle Pleistocene; rather, these high levels were probably due to a combination of increased effective moisture and changes in the size of the Lahontan drainage basin.", + "license": "proprietary" + }, { "id": "USGS_MOJAVE_CLIM", "title": "Climate History of the Mojave Desert Region, 1892 - 1996", @@ -181908,6 +185847,58 @@ "description": "The Climate History of the Mojava Desert Region provides an overview of regional climate variations including precipitation and temperature information. To evaluate climate variation, weather data was compiled from 48 long-term weather stations across the Mojave Desert. The stations are in western Arizona, eastern California, southern Nevada, and southwest Utah. The primary data set consists of about 1.2 and 1.8 million daily observations of precipitation and temperature, respectively. These data were collected mainly at weather stations staffed by volunteers (NOAA, 1986). Some of the raw data were purchased in electronic form from EarthInfo, Inc. who obtained it from the National Climate Data Center (NCDC), Asheville, North Carolina. These data do not contain the entire record of a particular station, as the available electronic record typically begins in 1948. To evaluate climate variation, the longest possible record is necessary. Thus, the USGS obtained from the NCDC the complete National Weather Service reports on microfiche for the four-state region of the Mojave Desert. These data were entered into the computer manually, producing a record of precipitation beginning in 1892. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_Map-MF-2377_1.0", + "title": "Generalized Geologic Map of Part of the Upper Animas River Watershed and Vicinity, Silverton, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-107.875, 37.75, -107.5, 38", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552017-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552017-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map-MF-2377_1.0", + "description": "This map data was compiled for the purpose of comparing multiple Animas River Watershed Abandoned Mine Lands Project datasets such as geophysical, biologic, remote sensing, and geochemical datasets in a geologic context. This dataset represents geology compiled for the upper Animas River Watershed near Silverton, Colorado. The source data used are derived from 1:24,000, 1:20,000, 1:48,000 and 1:250,000-scale geologic maps by geologists who have worked in this area since the early 1960's. This product consists of seven vector coverages. These separate coverages include the geology, faults, veins, andesite dikes, dacite dikes, rhyolite dikes, and San Juan Caldera topographic margin.", + "license": "proprietary" + }, + { + "id": "USGS_Map-MF-2387_1.0", + "title": "Geologic Map and Digital Database of Hidden Hills and Vicinity, Mohave County, Northwestern Arizona", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-113.759, 36.245, -113.492, 36.506", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552810-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552810-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map-MF-2387_1.0", + "description": "The geologic map of Hidden Hills and vicinity covers part of the Arizona Strip north of Grand Canyon and several large tributary canyons that make up the western part of Arizona's Grand Canyon. The map is part of a cooperative U.S. Geological Survey, National Park Service, and Bureau of Land Management project to provide geologic information for areas within the newly established Grand Canyon-Parashant National Monument. This map fills in one of the remaining areas where uniform quality geologic mapping was needed. The geologic information will be useful for future resource management studies for federal, state, and private agencies. This digital map database is compiled from unpublished data and new mapping by the authors and represents the general distribution of surficial and bedrock geology in the mapped area. Together with the accompanying pamphlet, it provides current information on the geologic structure and stratigraphy of the area. The database delineates map units that are identified by age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution of the database to 1:31,680 or smaller.", + "license": "proprietary" + }, + { + "id": "USGS_Map-MF-2388_1.0", + "title": "Generalized Surficial Geologic Map of the Pueblo 1 degree x 2 degrees Quadrangle, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-106, 38, -104, 39", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552005-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552005-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map-MF-2388_1.0", + "description": "The report may be used for land-use planning (e.g., selecting land-fill sites, greenbelts, avoiding geologic hazards), for finding aggregate resources (crushed rock, sand, and gravel), and for study of geomorphology and Quaternary geology. The report identifies geologic hazards (e.g., landslides, swelling soils, heaving bedrock, and flooding) if they are known to be located in, or characteristic of, mapped units. Surficial deposits in the quadrangle are evidence of depositional events of the Quaternary Period (the most recent 1.8 million years). Some events such as floods are familiar to persons living in the area, while others preceded human occupation. The latter include glaciation, probable large earthquakes, protracted drought, and widespread deposition of sand and silt by wind. At least twice in the past 200,000 years (most recently from about 30,000 to 12,000 years ago) global cooling caused glaciers to form on Pikes Peak and in the high parts of the Sangre de Cristo Mountains. Some glaciers advanced down valleys, deeply eroded the bedrock, and deposited moraines (map units tbk, tbg, tbj, tbi) and deposited outwash (ggq, gge), in the Wet Mountain Valley. On the plains (east part of map area), eolian sand (es), stabilized dune sand (ed), and loess (elb) are present and in places contain buried paleosols, which indicate sand dune deposition alternating with periods of stabilized landscape during which soils developed. Fifty-three types of surficial geologic deposits and residual materials of Quaternary age are described in a pamphlet and located on a map of the greater Pueblo area, in part of the Front Range, in the Wet and Sangre de Cristo Mountains, and on the plains east of Colorado Springs and Pueblo. Deposits formed by landslides, wind, and glaciers, as well as colluvium, residuum, alluvium, and others are described in terms of predominant grain size, mineral or rock composition (e.g., gypsiferous, calcareous, granitic, andesitic), thickness, and other physical characteristics. Origins and ages of the deposits and geologic hazards related to them are noted. Many lines drawn between units on our map were placed by generalizing contacts on published maps. However, in 1997-1999 we mapped new boundaries as well. The map was projected to the UTM projection. This large map area extends from near Salida (on the west edge), eastward about 107 mi (172 km), and from Antero Reservoir and Woodland Park on the north edge to near Colorado City at the south edge (68 mi; 109 km). Compilation scale: 1:250,000. Map is available in digital and print-on-demand paper formats. Deposits are described in terms of predominant grain size, mineralogic and lithologic composition, general thickness, and geologic hazards, if any, and relevant geologic historical information and paleosoil information, if any. Fifty-three map units of deposits include alluvium, colluvium, residuum, eolian deposits, periglacial/disintegrated deposits, tills, landslide units, glaciofluvial units, and a diamicton. A bedrock map unit depicts large areas of mostly bare bedrock. The physical properties of materials were compiled from published soil and geologic maps and reports, our field observations, and from earth science journal articles. Selected deposits in the field were checked for conformity to descriptions of map units by the Quaternary geologist who compiled the surficial geologic map units. >puebpoly: polygon coverage containing geologic unit contacts and labels. >puebline: arc coverage containing faults. >puebpnt: point coverage containing point locations of decorative > bar-and-ball symbols for faults. >geol_sfo.lin: This lineset file defines geologic line types in the > geologically themed coverages. >geoscamp2.mrk: This markerset file defines the geologic markers in the > geologically themed coverages. >color524.shd: This shadeset file defines the cmyk values of colors > assigned to polygons in the geologically themed coverages.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2326_1.0", + "title": "Geologic map of the Palisade Quadrangle, Mesa County, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-108.375, 39, -108.25, 39.125", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550131-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550131-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2326_1.0", + "description": "This map has been prepared to provide the first detailed view of the Palisade 1:24,000-scale quadrangle. Previous geologic mapping that encompassed the map area was at scales of 1:100,000 and 1: 250,000. The Palisade area is an important agricultural region of Colorado, fruit orchards were first established in the area in the late 19th century. In addition, the Palisade quadrangle is undergoing rapid growth, as is the rest of the Grand Valley. Because of this rapid growth, the recognition of geologic hazards is important. The map depicts many surficial units associated with geologic hazards. The map is accompanied by a separate leaflet containing a section on geologic hazards (including landslides, piping, gullying, expansive soils, and flooding). A table indicates what map units are susceptible to a given hazard. The map will be of interest to town and county officials, land- use planners, as well as the general public. The Palisade 1:24,000 quadrangle is in Mesa County in western Colorado. Because the map area is dominated by various surficial deposits, the map depicts 22 different Quaternary units. Two prominent river terraces are present in the quadrangle containing gravels deposited by the Colorado River. The map area contains many mass movement deposits. Extensive landslide deposits are present along the eastern part of the quadrangle. These massive landslides originate on the flanks of Grand Mesa, in the Green River and Wasatch Formations, and flow west onto the Palisade quadrangle. In addition, large areas of the eastern and southern parts of the map are covered by extensive pediment surfaces. These pediment surfaces are underlain by debris flow deposits also originating from Grand Mesa. Material in these deposits consists of mainly subangular basalt cobbles and boulders and indicate that these debris flow deposits have traveled as much as 10 km from their source area. The pediment surfaces have been divided into 5 age classes based on their height above surrounding drainages. Two common bedrock units in the map area are the Mancos Shale and the Mesaverde Group both of Upper Cretaceous age. The Mancos shale is common in low lying areas near the western map border. The Mesaverde Group forms prominent sandstone cliffs in the north-central map area. The map is accompanied by a separate pamphlet containing unit descriptions, a section on geologic hazards (including landslides, piping, gullying, expansive soils, and flooding), and a section on economic geology (including sand and gravel, and coal). A table indicates what map units are susceptible to a given hazard. Approximately twenty references are cited at the end of the report. Map political location: Mesa County, Colorado Compilation scale: 1:24,000 Geology mapped in 1996 and 1997", + "license": "proprietary" + }, { "id": "USGS_Map_MF-2330", "title": "Bituminous coal production in the Appalachian basin--Past, present, and future", @@ -181921,6 +185912,240 @@ "description": "Maps Provide an overview of coal production from the Appalachian basin, by county. This report on Appalachian basin coal production consists of four maps and associated graphs and tables, with links to the basic data that were used to construct the maps. Plate 1 shows the time (year) of maximum coal production, by county. For illustration purposes, the years of maximum production are grouped into decadal units. Plate 2 shows the amount of coal produced (tons) during the year of maximum coal production for each county. Plate 3 illustrates the cumulative coal production (tons) for each county since about the beginning of the 20th century. Plate 4 shows 1996 annual production by county. During the current (third) cycle of coal production in the Appalachian basin, only seven major coal-producing counties (those with more than 500 million tons cumulative production), including Greene County, Pa.; Boone, Kanawha, Logan, Mingo, and Monongalia Counties, W. Va.; and Pike County, KY., exhibit a general increase in coal production. Other major coal-producing counties have either declined to a small percentage of their maximum production or are annually maintaining a moderate level of production. In general, the areas with current high coal production have large blocks of coal that are suitable for mining underground with highly efficient longwall methods, or are occupied by very large scale, relatively low cost surface mining operations. The estimated cumulative production for combined bituminous and anthracite coal is about 100 billion tons or less for the Appalachian basin. In general, it is anticipated that the remaining resources will be progressively of lower quality, will cost more to mine, and will become economical only as new technologies for extraction, beneficiation, and consumption are developed, and then only if prices for coal increase.", "license": "proprietary" }, + { + "id": "USGS_Map_MF-2331_1.0", + "title": "Geologic Map of the Silt Quadrangle, Garfield County, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-107.75, 39.5, -107.625, 39.625", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549606-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549606-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2331_1.0", + "description": "To update and reinterpret earlier geologic mapping, and to provide sufficient geologic information for land-use decisions. New 1:24,000-scale geologic mapping in the Silt 7.5' quadrangle, in support of the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping Project, provides new interpretations of the stratigraphy, structure, and geologic hazards in the area of the southwest flank of the White River uplift, the Grand Hogback, and the eastern Piceance Basin. The Wasatch Formation was subdivided into three formal members, the Shire, Molina, and Atwell Gulch Members. Also a sandstone unit within the Shire Member was broken out. The Mesaverde Group consists of the upper Williams Fork Formation and the lower Iles Formation. Members for the Iles Formation consist of the Rollins Sandstone, the Cozzette Sandstone, and the Corcoran Sandstone Members. The Cozzette and Corcoran Sandstone Members were mapped as a combined unit. Only the upper part of the Upper Member of the Mancos Shale is exposed in the quadrangle. From the southwestern corner of the map area toward the northwest, the unfaulted early Eocene to Paleocene Wasatch Formation and underlying Mesaverde Group gradually increase in dip to form the Grand Hogback monocline that reaches 45-75 degree dips to the southwest (section A-A'). The shallow west- northwest-trending Rifle syncline separates the northern part of the quadrangle from the southern part along the Colorado River. Geologic hazards in the map area include erosion, expansive soils, and flooding. Erosion includes mass wasting, gullying, and piping. Mass wasting involves any rock or surficial material that moves downslope under the influence of gravity, such as landslides, debris flows, or rock falls, and is generally more prevalent on steeper slopes. Locally, where the Grand Hogback is dipping greater than 60 degrees and the Wasatch Formation has been eroded, leaving sandstone slabs of the Mesa Verde Group unsupported over vertical distances as great as 500 m, the upper part of the unit has collapsed in landslides, probably by a process of beam-buckle failure. In the source area of these landslides strata are overturned and dip shallowly to the northeast. Landslide deposits now armor Pleistocene pediment surfaces and extend at least 1 km into Cactus Valley. Gullying and piping generally occur on more gentle slopes. Expansive soils and expansive bedrock are those unconsolidated materials or rocks that swell when wet and shrink when dry. Most floods are restricted to low-lying areas. Several gas-producing wells extract methane from coals from the upper part of the Iles Formation. Map political location: Garfield County, Colorado Compilation scale: 1:24,000 Geology mapped in 1992 to 1996. Compilation completed March 1997.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2337_1.0", + "title": "Digital geologic map and map database of parts of Marin, San Francisco, Alameda, Contra Costa, and Sonoma Counties, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-01-01", + "end_date": "1999-12-31", + "bbox": "-123, 37.7, -122.2, 38.3", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548662-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548662-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2337_1.0", + "description": "This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:62,500) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (mageo.txt, mageo.pdf, or mageo.ps), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:62,500 or smaller. The databases in this report were compiled in ARC/INFO, a commercial Geographic Information System (Environmental Systems Research Institute, Redlands, California), with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files are in either GRID (ARC/INFO raster data) format or COVERAGE (ARC/INFO vector data) format. Coverages are stored in uncompressed ARC export format (ARC/INFO version 7.x). ARC/INFO export files (files with the .e00 extension) can be converted into ARC/INFO coverages in ARC/INFO (see below) and can be read by some other Geographic Information Systems, such as MapInfo via ArcLink and ESRI's ArcView (version 1.0 for Windows 3.1 to 3.11 is available for free from ESRI's web site: http://www.esri.com ). The digital compilation was done in version 7.1.1 of ARC/INFO with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The geologic map information was digitized from stable originals of the geologic maps at 1:62,500 scale. The author manuscripts (pen on mylar) were scanned using a Altek monochrome scanner with a resolution of 800 dots per inch. The scanned images were vectorized and transformed from scanner coordinates to projection coordinates with digital tics placed by hand at quadrangle corners. The scanned lines were edited interactively by hand using ALACARTE, color boundaries were tagged as appropriate, and scanning artifacts visible at 1:24,000 were removed. Revisions: 8/31/99 This is the pre-release version of the report. There have been no revisions to any part of the report. Data Revision List > File Report Version Last Update > Last Updated > > mamap.ps 1.0 > maexpl.ps 1.0 > mageo.ps 1.0 > mamap.pdf 1.0 > maexpl.pdf 1.0 > mageo.pdf 1.0 > ma-geol.e00 1.0 > ma-strc.e00 1.0 > ma-blks.e00 1.0 > ma-altr.e00 1.0 > ma-quad.e00 1.0 > ma-corr.e00 1.0 > ma-so.e00 1.0 > ma-terr.e00 1.0 > mageo.txt 1.0 > mafig1.tif 1.0 > mafig2.tif 1.0 > madb.ps 1.0 > madb.pdf 1.0 > madb.txt 1.0 > import.aml 1.0 > mageol.met 1.0 Reviews_Applied_to_Data: This report has undergone two scientific peer reviews, one digital database review, one review for conformity with geologic names policy, and review of the plotfiles for conformity with USGS map standards. Related_Spatial_and_Tabular_Data_Sets: This report consists of a set of geologic map database files (Arc/Info coverages) and supporting text and plotfiles. In addition, the report includes two sets of plotfiles (PostScript and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described below: > ARC/INFO Resultant Description of Coverage > export file Coverage > ----------- ----------- -------------------------------- > ma-geol.e00 ma-geol/ Polygon and line coverage showing faults, > depositional contacts, and rock units > in the map area. > > ma-strc.e00 ma-strc/ Point and line coverage showing strike and dip > information and fold axes. > > ma-blks.e00 ma-blks/ Point coverage showing location of high-grade > blocks in Franciscan rock units. > > ma-altr.e00 ma-altr/ Polygon coverage showing areas of hydrothermal > alteration. > > ma-quad.e00 ma-quad/ Line coverage showing index map of quadrangles > in the map area. Lines and annotation only. > > ma-corr.e00 ma-corr/ Polygon and line coverage of the correlation > table for the units in this map database. > This database is not geospatial. > > ma-so.e00 ma-so/ Line coverage showing sources of data index > map for this map database. > > ma-terr.e00 ma-terr/ Polygon and line coverage of the index map of > tectonostratigraphic terranes in the map area. > (Terranes are described in mageo.txt, > mageo.ps, or mageo.pdf). ASCII text files, including explanatory text, ARC/INFO key files, PostScript and PDF plot files, and a ARC Macro Language file for conversion of ARC export files into ARC coverages: > mageo.ps A PostScript plot file of a report containing > detailed unit descriptions and geological > information, plus sources of data and references > cited, with two figures. > > mageo.pdf A PDF version of mageo.ps. > > mageo.txt A text-only file containing an unformatted > version of mageo.ps without figures. > > mafig1.tif A TIFF file of Figure 1 from mageo.ps > > mafig2.tif A TIFF file of Figure 2 from mageo.ps > > madb.ps A PostScript plot file of a pamphlet containing > detailed information about the contents and > availability of this report. > > madb.pdf A PDF version of madb.ps. > > madb.txt A text-only file containing an unformatted > version of madb.ps. > > import.aml ASCII text file in ARC Macro Language to convert > ARC export files to ARC coverages in ARC/INFO. > > mamap.ps A PostScript plottable file containing an image > of the geologic map and base maps at a scale of > 1:62,500, along with a simple map key. > > maexpl.ps A PostScript plot file containing an image of > the explanation sheet, including terrane map, > index maps, correlation chart, and unit > descriptions. > > mamap.pdf A PDF file containing an image of the geologic > map and base maps at a scale of 1:62,500, along > with a simple map key. > > maexpl.pdf A PDF file containing an image of the > explanation sheet, including terrane map, index > maps, correlation chart, and unit descriptions. Base maps Base Map layers used in the preparation of the geologic map plotfiles were derived from published digital maps (Aitken, 1997) obtained from the U.S. Geological Survey Geologic Division Website for the Western Region (http://wrgis.wr.usgs.gov). Please see the website for more detailed information about the original databases. Because the base map digital files are already available at the website mentioned above, they are not included in the digital database package. ", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2341_1.0", + "title": "Geologic map of the Rifle Falls quadrangle, Garfield County, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "1998-12-31", + "bbox": "-107.75, 39.625, -107.625, 39.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550390-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550390-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2341_1.0", + "description": "New 1:24,000-scale geologic map of the Rifle Falls 7.5' quadrangle, in support of the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping Project, provides new interpretations of the stratigraphy, structure, and geologic hazards in the area of the southwest flank of the White River uplift. Bedrock strata include the Upper Cretaceous Iles Formation through Ordovician and Cambrian units. The Iles Formation includes the Cozzette Sandstone and Corcoran Sandstone Members, which are undivided. The Mancos Shale is divided into three members, an upper member, the Niobrara Member, and a lower member. The Lower Cretaceous Dakota Sandstone, the Upper Jurassic Morrison Formation, and the Entrada Sandstone are present. Below the Upper Jurassic Entrada Sandstone, the easternmost limit of the Lower Jurassic and Upper Triassic Glen Canyon Sandstone is recognized. Both the Upper Triassic Chinle Formation and the Lower Triassic(?) and Permian State Bridge Formation are present. The Pennsylvanian and Permian Maroon Formation is divided into two members, the Schoolhouse Member and a lower member. All the exposures of the Middle Pennsylvanian Eagle Evaporite intruded into the Middle Pennsylvanian Eagle Valley Formation, which includes locally mappable limestone beds. The Middle and Lower Pennsylvanian Belden Formation and the Lower Mississippian Leadville Limestone are present. The Upper Devonian Chaffee Group is divided into the Dyer Dolomite, which is broken into the Coffee Pot Member and the Broken Rib Member, and the Parting Formation. Ordovician through Cambrian units are undivided. The southwest flank of the White River uplift is a late Laramide structure that is represented by the steeply southwest-dipping Grand Hogback, which is only present in the southwestern corner of the map area, and less steeply southwest-dipping older strata that flatten to nearly horizontal attitudes in the northern part of the map area. Between these two is a large-offset, mid-Tertiary(?) Rifle Falls normal fault, that dips southward placing Leadville Limestone adjacent to Eagle Valley and Maroon Formations. Diapiric Eagle Valley Evaporite intruded close to the fault on the down-thrown side and presumably was injected into older strata on the upthrown block creating a blister-like, steeply north-dipping sequence of Mississippian and older strata. Also, removal of evaporite by either flow or dissolution from under younger parts of the strata create structural benches, folds, and sink holes on either side of the normal fault. A prominent dipslope of the Morrison-Dakota-Mancos part of the section forms large slide blocks that form distinctly different styles of compressive deformation called the Elk Park fold and fault complex at different parts of the toe of the slide. The major geologic hazard in the area consist of large landslides both associated with dip-slope slide blocks and the steep slopes of the Eagle Valley Formation and Belden Formation in the northern part of the map. Significant uranium and vanadium deposits were mined prior to 1980.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2342_1.0", + "title": "Geologic map and map database of the Oakland metropolitan area, Alameda, Contra Costa, and San Francisco Counties, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-122.4, 37.6, -122, 38", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550741-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550741-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2342_1.0", + "description": "This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:24,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (oakmf.ps, oakmf.pdf, oakmf.txt), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller. The databases in this report were compiled in ARC/INFO, a commercial Geographic Information System (Environmental Systems Research Institute, Redlands, California), with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files are in either GRID (ARC/INFO raster data) format or COVERAGE (ARC/INFO vector data) format. Coverages are stored in uncompressed ARC export format (ARC/INFO version 7.x). ARC/INFO export files (files with the .e00 extension) can be converted into ARC/INFO coverages in ARC/INFO (see below) and can be read by some other Geographic Information Systems, such as MapInfo via ArcLink and ESRI's ArcView (version 1.0 for Windows 3.1 to 3.11 is available for free from ESRI's web site: http://www.esri.com ). The digital compilation was done in version 7.1.1 of ARC/INFO with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The geologic map information was digitized from stable originals of the geologic maps at 1:62,500 and 1:24,000 scale. The author manuscripts (pen on mylar) were scanned using a Altek monochrome scanner with a resolution of 800 dots per inch. The scanned images were vectorized and transformed from scanner coordinates to projection coordinates with digital tics placed by hand at quadrangle corners. The scanned lines were edited interactively by hand using ALACARTE, color boundaries were tagged as appropriate, and scanning artifacts visible at 1:24,000 were removed.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2343_1.0", + "title": "Geologic Map and Digital Database of the Upper Parashant Canyon and Vicinity, Mohave County, Northwestern Arizona", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-113.5, 36.2, -113.2, 36.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550938-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550938-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2343_1.0", + "description": "The geologic map of the upper Parashant Canyon area covers part of the Colorado Plateau and several large tributary canyons that make up the western part of Arizona's Grand Canyon. The map is part of a cooperative U.S. Geological Survey and National Park Service project to provide geologic information for areas within the newly established Grand Canyon/Parashant Canyon National Monument. Most of the Grand Canyon and parts of the adjacent plateaus have been geologically mapped; this map fills in one of the remaining areas where uniform quality geologic mapping was needed. The geologic information presented may be useful in future related studies as to land use management, range management, and flood control programs for federal and state agencies, and private concerns. This digital map database is compiled from unpublished data and new mapping by the authors, represents the general distribution of surficial and bedrock geology in the mapped area. Together with the accompanying pamphlet, it provides current information on the geologic structure and stratigraphy of the area. The database dilineate map units that are identified by age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution of the database to 1:31,680 or smaller. This report consists of a set of geologic map database files (ARC/ INFO coverages) and supporting text and plot files. In addition, the report includes two sets of plot files (Post Script and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described in the explanatory pamphlets (para.eps, para.pdf, or para.txt). The base layer used in the preparation of the geologic map plot files was derived from four Digital Raster Graphic versions of standard USGS 7.5' quadrangles. These raster images where converted to Grid format in ARC/INFO, trimmed and seamed together, then converted to a GeoTIFF image. The resultant TIFF image was combined with geologic data to produce the final map image in Illustrator 8.0.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2347_1.0", + "title": "Generalized Surficial Geologic Map of the Denver 1 degree x 2 degree Quadrangle, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-106, 39, -104, 40", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551427-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551427-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2347_1.0", + "description": "The map and descriptions offer information that may be used for: land-use planning (e.g. selecting land fill sites, greenbelts, avoiding geologic hazards), for finding aggregate resources (crushed rock, sand, and gravel), for study of geomorphology and Quaternary geology. Geologic hazards (e.g., landslides, swelling soils, heaving bedrock, and flooding) known to be located in, or characteristic of some mapped units, were identified. Surficial deposits in the quadrangle partially record depositional events of the Quaternary Period (the most recent 1.8 million years). Some events such as floods are familiar to persons living in the area, while other recorded events are pre-historical. The latter include glaciation, probable large earthquakes, protracted drought, and widespread deposition of sand and silt by wind. At least twice in the past 200,000 years (most recently about 30,000 to 12,000 years ago) global cooling caused glaciers to form along the Continental Divide. The glaciers advanced down valleys in the Front Range, deeply eroded the bedrock, and deposited moraines (map units tbg, tbj) and outwash (ggq, gge). On the plains (east part of map), eolian sand (es), stabilized dune sand (ed), and loess (elb) are present and in places contain buried paleosols. These deposits indicate that periods of sand dune deposition alternated with periods of stabilized dunes and soil formation. Thirty-nine types of surficial geologic deposits and residual materials of Quaternary age are described and mapped in the greater Denver area, in part of the Front Range, and in the piedmont and plains east of Denver, Boulder, and Castle Rock. Descriptions appear in the pamphlet that accompanies the map. Landslide deposits, colluvium, residuum, alluvium, and other deposits or materials are described in terms of predominant grain size, mineral or rock composition (e.g., gypsiferous, calcareous, granitic, andesitic), thickness of deposits, and other physical characteristics. Origins and ages of the deposits and geologic hazards related to them are noted. Many lines between geologic units on our map were placed by generalizing contacts on published maps. However, in 1997-1999 we mapped new boundaries, as well. The map was projected to the UTM projection. This large map area extends from the Continental Divide near Winter Park and Fairplay ( on the west edge), eastward about 107 mi (172 km); and extends from Boulder on the north edge to Woodland Park at the south edge (68 mi; 109 km). Compilation scale: 1:250,000. Map is available in digital and print-on-demand paper formats. Deposits are described in terms of predominant grain size, mineralogic and lithologic composition, general thickness, and geologic hazards, if any, relevant geologic historical information and paleosoil information, if any. Thirty- nine map units of deposits include 5 alluvium types, 15 colluvia, 6 residua, 3 types of eolian deposits, 2 periglacial/disintegrated deposits, 3 tills, 2 landslide units, 2 glaciofluvial units, and 1 diamicton. An additional map unit depicts large areas of mostly bare bedrock. The physical properties of the surficial materials were compiled from published soil and geologic maps and reports, our field observations, and from earth science journal articles. Selected deposits in the field were checked for conformity to descriptions of map units by the Quaternary geologist who compiled the surficial geologic map units. FILES INCLUDED IN THIS DATA SET: >denvpoly: polygon coverage containing geologic unit contacts and labels. >denvline: arc coverage containing faults. >geol_sfo.lin: This lineset file defines geologic line types in the > geologically themed coverages. >geoscamp2.mrk: This markerset file defines the geologic markers in the > geologically themed coverages. >color524.shd: This shadeset file defines the cmyk values of colors > assigned to polygons in the geologically themed coverages.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2361_1.0", + "title": "Geologic map of the Eagle quadrangle, Eagle County, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-01-01", + "end_date": "1997-12-31", + "bbox": "-106.875, 39.625, -106.75, 39.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552216-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552216-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2361_1.0", + "description": "This map was funded by the National Cooperative Geologic Program as part of the geologic mapping studies conducted along the I-70 urban corridor. This corridor is experiencing rapid urban growth and geologic mapping is needed to aid in land-use planning in order to address, avoid, and mitigate known and potential geologic hazards. The Eagle quadrangle covers an area that straddles the Eagle River and Interstate 70 (I-70) and it includes the town of Eagle, Colo., which is located in the southwestern part of the quadrangle, just south of I-70 and the Eagle River, about 37 km west of Vail, Colo. The map area is part of the I-70 urban corridor, which is experiencing rapid and escalating urban growth. Geologic mapping along this corridor is needed for ongoing land-use planning. A variety of rocks and deposits characterize the map area and areas nearby. Sedimentary rocks present in the map area range in age from Pennsylvanian rocks, which were deposited in the ancestral Eagle basin during the formation of the ancestral Rocky Mountains, to Late Cretaceous rocks that were deposited just prior to the formation of the present Rocky Mountains. The Pennsylvanian rocks in the map area include a thick sequence of evaporitic rocks (Eagle Valley Evaporite). These evaporitic rocks are commonly complexly folded throughout the southern part of the quadrangle where they are exposed. In general, in the central and northern parts of the quadrangle, the sedimentary rocks overlying the evaporite dip gently to moderately northward. Consequently, the youngest sedimentary rocks (Late Cretaceous rocks) are exposed dipping gently to the north in the northern part of the quadrangle; landslide complexes are widespread along the northerly dipping, dip slopes in shaly rocks of the Cretaceous sequence in the northeastern part of the map area. During the Early Miocene, basaltic volcanism formed extensive basaltic flows that mantled the previously deformed and eroded sedimentary rocks. Erosional remnants of the basaltic flows are preserved in the southeastern, west-central, and north-central parts of the map area. Some of these basaltic flows are faulted and downdropped in a manner that suggests they were downdropped in areas where large volumes of the underlying evaporitic rocks were removed from the subsurface, beneath the basaltic rocks, by dissolution or flowage of the evaporite in the subsurface. Quaternary and late Tertiary(?) surficial deposits in the map area consist mainly of Quaternary alluvium and colluvium, late and middle Pleistocene terrace gravels of the Eagle River, Miocene(?) gravel remnants of the ancestral Eagle River and its tributaries, and Pleistocene to recent mass movement deposits that include landslides and debris flows. Potential geologic hazards in the map area include landslides, debris flows, rockfalls, local flooding, ground subsidence, and expansive and corrosive soils. Map political location: Eagle County, Colorado Compilation scale: 1:24,000 Geology mapped in 1997. GEOSPATIAL DATA FILES INCLUDED IN THIS DATA SET: eaglepy: polygon coverage containing geologic unit contacts and labels. eagleln: arc coverage containing fold axes and other line entities. eaglept: point coverage containing bedding attitude measurements and other point entities. eaglepit: polygon coverage containing gravel pits.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2363_1.0", + "title": "Geologic map of the Grand Junction quadrangle, Mesa County, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-108.625, 39, -108.5, 39.125", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552505-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552505-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2363_1.0", + "description": "To update and reinterpret earlier geologic mapping, and provide sufficient geologic information for land-use decisions for private land and for areas managed by the Bureau of Land Management and the National Park Service. Use of these data at scales greater than 1:24,000 would be inappropriate because mapping was performed at that scale. This 1:24,000-scale geologic map of the Grand Junction 7.5' quadrangle, in support of the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping Project, provides new interpretations of the stratigraphy, structure, and geologic hazards in the area of the junction of the Colorado River and the Gunnison River. Bedrock strata include the Upper Cretaceous Mancos Shale through the Lower Jurassic Wingate Sandstone units. Below the Mancos Shale, which floors the Grand Valley, the Upper and Lower(?)Cretaceous Dakota Formation and the Lower Cretaceous Burro Canyon Formation hold up much of the resistant northeast- dipping monocline along the northeast side of the Uncompahgre uplift. The impressive sequence of Jurassic strata below include the Brushy Basin, Salt Wash, and Tidwell Members of the Upper Jurassic Morrison Formation, the Middle Jurassic Wanakah Formation and informal \"board beds\" unit and Slick Rock Member of the Entrada Formation, and the Lower Jurassic Kayenta Formation and Wingate Sandstone. The Upper Triassic Chinle Formation and Early Proterozoic meta-igneous gneiss and migmatitic meta- sedimentary rocks, which are exposed in the Colorado National Monument quadrangle to the west, do not crop out here. The monoclinal dip slope of the northeastern margin of the Uncompahgre uplift is apparently a Laramide structural feature. Unlike the southwest-dipping, high-angle reverse faults in the Proterozoic basement and s-shaped fault- propagation folds in the overlying strata found in the Colorado National Monument 7.5' quadrangle along the front of the uplift to the west, the monocline in the map area is unbroken except at two localities. One locality displays a small asymmetrical graben that drops strata to the southwest. This faulted character of the structure dies out to the northwest into an asymmetric fault-propagation fold that also drops strata to the southwest. Probably both parts of this structure are underlain by a northeast-dipping high-angle reverse fault. The other locality displays a second similar asymmetric fold. No evidence of post-Laramide tilting or uplift exists here, but the antecedent Unaweep Canyon, only 30 km to the south-southwest of the map area, provides clear evidence of Late Cenozoic, if not Pleistocene, uplift. The major geologic hazards in the area include large landslides associated with the dip-slope-underlain, smectite-rich Brushy Basin Member of the Morrison Formation and overlying Dakota and Burro Canyon Formations. Active landslides affect the southern bank of the Colorado River where undercutting by the river and smectitic clays in the Mancos trigger landslides. The Wanakah, Morrison, and Dakota Formations and the Mancos Shale create a significant hazard to houses and other structures by containing expansive smectitic clay. In addition to seasonal spring floods associated with the Colorado and Gunnison Rivers, a serious flash flood hazard associated with sudden summer thunderstorms threatens the intermittent washes that drain the dip slope of the monocline. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology. Geospatial data files of this data set: > gj24k: geology polygons, contacts, faults > gjpnt: point data representing bedding attitudes > gjline: line representing location of the cross section, > and fold axes Symbolsets used for plotting in ArcInfo: > wpgcmykg.shd: shadeset > geol_sfo.lin: lineset > geoscamp1.mrk: markerset", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2364_1.0", + "title": "Geologic Map and Digital Database of the House Rock Quadrangle, Coconino County, Northern Arizona", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-112.127, 36.624, -111.998, 36.751", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553373-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553373-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2364_1.0", + "description": "This geologic map is part of a cooperative project between the U.S. Geological Survey and the Kaibab National Forest Service to provide geologic information for the Paradine Plains Cactus (Pediocactus pardinei Benson, 1957) Conservation Assessment and Strategy conducted by the Kaibab National Forest, Williams, Arizona. The map area includes part of House Rock Valley and part of the Kaibab Plateau, sub-physiographic provinces of the Colorado Plateau. This part of the Colorado Plateau was not previously mapped in adequate geologic detail. This map completes one of several remaining areas where uniform quality geologic mapping was needed. The geologic information in this report may be useful to future biological studies, land management, range management, and flood control programs for all federal, state, and private agencies. The map area is in the North Kaibab Ranger District of the Kaibab National Forest and the Arizona Strip Field Office of the Bureau of Land Management (BLM). The nearest settlement is Jacob Lake about 8 km (5 mi) west of the map area (fig. 1). Elevations range from about 2,305 m (7,560 ft) on the Kaibab Plateau in the northwest corner of the map area to about 1,555 m (5,100 ft) in House Rock Valley in the east-central edge of the map area. Primary vehicle access is by U.S. Highway 89A in the northern part of the map area. Four-wheel-drive roads access most of the map area. Dirt roads are not passable in winter snow conditions. The Bureau of Land Management Arizona Strip Field Office in St. George, Utah, manages the public lands, and the North Kaibab Ranger District in Fredonia, Arizona manages the U.S. National Forest system land. Other lands include one quarter of a section belonging to the State of Arizona, about 0.7 of a section of private land, and about 1.5 sections within the BLM-administered Paria Canyon-Vermilion Cliffs Wilderness Area (U.S. Department of the Interior, 1993). The private land is in House Rock Valley near State Highway 89A. Lower elevations within upper House Rock Valley support a sparse growth of cactus, grass, and a variety of desert shrubs. Sagebrush, grass, cactus, cliffrose bush, pinion pine trees, juniper trees, ponderosa pine, and oak trees thrive at elevations above 1,830 m (6,000 ft). Surface runoff in the map area drains eastward toward the Colorado River through House Rock Valley and into Marble Canyon of the Colorado River at Mile 17 (17 miles downstream from Lees Ferry, Arizona).", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2366_1.0", + "title": "Geologic Map and Digital Database of the Cane Quadrangle, Coconino County, Northern Arizona", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-112.127, 36.499, -111.998, 36.626", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552953-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552953-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2366_1.0", + "description": "This geologic map is part of a cooperative project between the U.S. Geological Survey and the Kaibab National Forest Service to provide geologic information for the Paradine Plains Cactus (Pediocactus pardinei B,W. Benson) Conservation Assessment and Strategy conducted by the Kaibab National Forest, Williams, Arizona. The map area includes part of House Rock Valley and part of the Kaibab Plateau, sub- physiographic provinces of the Colorado Plateau. This part of the Colorado Plateau was not previously mapped in adequate geologic detail. This map completes one of several remaining areas where uniform quality geologic mapping was needed. The geologic information in this report may be useful to land management, range management, and flood control programs for all federal and state agencies, and private affairs. This digital map database is compiled from unpublished data and new mapping by the authors, represents the general distribution of surficial and bedrock geology in the mapped area. Together with the accompanying pamphlet, it provides current information on the geologic structure and stratigraphy of the area. The database delineate map units thatare identified by age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution of the database to 1:24,000 or smaller. This report consists of a set of geologic map database files (ARC/ INFO coverages) and supporting text and plot files. In addition, the report includes two sets of plot files (Post Script and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described in the explanatory pamphlets (canegeo.doc, canegeo. pdf, or canegeo.txt). The base layer used in the preparation of the geologic map plot files was derived from a Digital Raster Graphic of a standard USGS 7.5' quadrangle. This raster image was converted to Grid format in ARC/INFO, trimmed and rotated, then converted to a GeoTIFF image. The resultant TIFF image was combined with geologic data to produce the final map layout in Illustrator 8.0.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2367_1.0", + "title": "Geologic Map and Digital Database of the House Rock Spring Quadrangle, Coconino County, Northern Arizona", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-112.127, 36.749, -111.998, 36.876", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553061-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553061-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2367_1.0", + "description": "The digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the House Rock Spring area. Together with the accompanying text, it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age, lithology, and geomorphology following the spatial resolution (scale) of the database to 1:24,000. The content and character of the database, as well as three methods of obtaining the database, are described below. This digital map database is compiled from unpublished data and new mapping by the authors, represents the general distribution of surficial and bedrock geology in the mapped area. Together with the accompanying pamphlet, it provides current information on the geologic structure and stratigraphy of the area. The database delineate map units that are identified by age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution of the database to 1:24,000 or smaller. This report consists of a set of geologic map database files (ARC/ INFO coverages) and supporting text and plot files. In addition, the report includes two sets of plot files(Post Script and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described in the explanatory pamphlets (hrsgeo.doc, hrsgeo. pdf, or hrsgeo.txt). The base layer used in the preparation of the geologic map plot files was derived from a Digital Raster Graphic version of a standard USGS 7.5' quadrangle. This raster image was converted to Grid format in ARC/INFO, trimmed and converted to a GeoTIFF image. The resultant TIFF image was combined with geologic data to produce the final map image in Illustrator 8.0.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2368_1.0", + "title": "Geologic Map and Digital Database of Part of the Uinkaret Volcanic Field, Mohave County, Northwestern Arizona", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-113.257, 36.246, -112.994, 36.504", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550460-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550460-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2368_1.0", + "description": "The geologic map of the Uinkaret volcanic field area covers part of the Colorado Plateau and several large tributary canyons that make up the western part of Arizona's Grand Canyon. The map is part of a cooperative U.S. Geological Survey and National Park Service project to provide geologic information for areas within the newly established Grand Canyon/Parashant Canyon National Monument. Most of the Grand Canyon and parts of the adjacent plateaus have been geologically mapped; this map fills in one of the remaining areas where uniform quality geologic mapping was needed. The geologic information presented may be useful in future related studies as to land use management, range management, and flood control programs for federal and state agencies, and private concerns. This digital map database is compiled from unpublished data and new mapping by the authors, represents the general distribution of surficial and bedrock geology in the mapped area. Together with the accompanying pamphlet, it provides current information on the geologic structure and stratigraphy of the area. The database dilineate map units that are identified by age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution of the database to 1:31,680 or smaller. This report consists of a set of geologic map database files (ARC/ INFO coverages) and supporting text and plot files. In addition, the report includes two sets of plot files (Post Script and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described in the explanatory pamphlets (uink.eps, uink.pdf, or uink.txt). The base layer used in the preparation of the geologic map plot files was derived from four Digital Raster Graphic versions of standard USGS 7.5' quadrangles. These raster images where converted to Grid format in ARC/INFO, trimmed and seamed together, then converted to a GeoTIFF image. The resultant TIFF image was combined with geologic data to produce the final map image in Illustrator 9.0.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2369_1.0", + "title": "Geologic Map of the Vail West quadrangle, Eagle County, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-106.5, 39.625, -106.375, 39.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550380-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550380-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2369_1.0", + "description": "This map was funded by and is a product of the National Cooperative Geologic Mapping Program. This corridor is experiencing rapid urban growth. Geologic mapping is needed to aid in land development planning in order to address, avoid, or mitigate known and potential geologic hazards. This new 1:24,000-scale geologic map of the Vail West 7.5' quadrangle, as part of the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping Project, provides new interpretations of the stratigraphy, structure, and geologic hazards in the area on the southwest flank of the Gore Range. Bedrock strata include Miocene tuffaceous sedimentary rocks, Mesozoic and upper Paleozoic sedimentary rocks, and undivided Early(?) Proterozoic metasedimentary and igneous rocks. Tuffaceous rocks are found in fault-tilted blocks. Only small outliers of the Dakota Sandstone, Morrison Formation, Entrada Sandstone, and Chinle Formation exist above the redbeds of the Permian-Pennsylvanian Maroon Formation and Pennsylvanian Minturn Formation, which were derived during erosion of the Ancestral Front Range east of the Gore fault zone. In the southwestern area of the map, the proximal Minturn facies change to distal Eagle Valley Formation and the Eagle Valley Evaporite basin facies. The Jacque Mountain Limestone Member, previously defined as the top of the Minturn Formation, cannot be traced to the facies change to the southwest. Abundant surficial deposits include Pinedale and Bull Lake Tills, periglacial deposits, earth-flow deposits, common diamicton deposits, common Quaternary landslide deposits, and an extensive, possibly late Pliocene landslide deposit. Landscaping has so extensively modified the land surface in the town of Vail that a modified land-surface unit was created to represent the surface unit. Laramide movement renewed activity along the Gore fault zone, producing a series of northwest-trending open anticlines and synclines in Paleozoic and Mesozoic strata, parallel to the trend of the fault zone. Tertiary down-to-the-northeast normal faults are evident and are parallel to similar faults in both the Gore Range and the Blue River valley to the northeast; presumably these are related to extensional deformation that occurred during formation of the northern end of the Rio Grande rift system in Colorado. In the southwestern part of the map area, a diapiric(?) exposure of the Eagle Valley Evaporite exists and chaotic faults and folds suggest extensive dissolution and collapse of overlying bedrock, indicating the presence of a geologic hazard. Quaternary landslides are common and indicate that landslide hazards are widespread in the area, particularly where old slide deposits are disturbed by construction. The late Pliocene(?) landslide that consists largely of a smectitic upper Morrison Formation matrix and boulders of Dakota Sandstone is readily reactivated. Debris flows are likely to invade low-standing areas within the towns of Vail and West Vail where tributaries of Gore Creek issue from the mountains on the north side of the valley. DATASETS INCLUDED IN THIS GEOSPATIAL DATABASE: > vwpoly: geologic polygons, contacts, faults, marker beds, and intra-unit scarps > vwline: fold axes, concealed linear features, limits of abundant chert fragments in the Maroon Formation, and cross-section lines > vwpoint: bedding and foliation attitudes, and miscellaneous point data", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2371", + "title": "Geologic map of the Silver Lake quadrangle, Cowlitz County, Washington", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-122.875, 46.25, -122.749, 46.375", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553299-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553299-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2371", + "description": "This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:24,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the Oregon State Geologist. Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits of the Silver lake 7.5 minute quadrangle. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2372_1.0", + "title": "Hydrostructural Maps of the Death Valley Regional Flow System, Nevada and California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-118, 35, -115, 38.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552955-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552955-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2372_1.0", + "description": "These maps (maps A and B) were prepared in support of a regional three-dimensional ground-water model currently being constructed by the U.S. Geological Survey (USGS) for the DVRFS. The maps identify regional geologic structures whose possible hydrologic significance merits their inclusion in the HFM for the DVRFS. The locations of principal faults and structural zones that may influence ground-water flow were compiled in support of a three-dimensional ground-water model for the Death Valley regional flow system (DVRFS), which covers 80,000 square km in southwestern Nevada and southeastern California. Faults include Neogene extensional and strike-slip faults and pre-Tertiary thrust faults. Emphasis was given to characteristics of faults and deformed zones that may have a high potential for influencing hydraulic conductivity. These include: (1) faulting that results in the juxtaposition of stratigraphic units with contrasting hydrologic properties, which may cause ground-water discharge and other perturbations in the flow system; (2) special physical characteristics of the fault zones, such as brecciation and fracturing, that may cause specific parts of the zone to act either as conduits or as barriers to fluid flow; (3) the presence of a variety of lithologies whose physical and deformational characteristics may serve to impede or enhance flow in fault zones; (4) orientation of a fault with respect to the present-day stress field, possibly influencing hydraulic conductivity along the fault zone; and (5) faults that have been active in late Pleistocene or Holocene time and areas of contemporary seismicity, which may be associated with enhanced permeabilities. The faults shown on maps A (Structural Framework, Neogene Basins, and Potentiometric Surface) and B (Structural Framework, Earthquake Epicenters, and Potential Zones of Enhanced Hydraulic Conductivity) are largely from Workman and others (in press), and fit one or more of the following criteria: (1) faults that are more than 10 km in map length; (2) faults with more than 500 m of displacement; and (3) faults in sets that define a significant structural fabric that characterizes a particular domain of the DVRFS. The following fault types are shown: Neogene normal, Neogene strike-slip, Neogene low-angle normal, pre-Tertiary thrust, and structural boundaries of Miocene calderas. We have highlighted faults that have late Pleistocene to Holocene displacement (Piety, 1996). Areas of thick Neogene basin-fill deposits (thicknesses 1-2 km, 2-3 km, and >3 km) are shown on map A, based on gravity anomalies and depth-to-basement modeling by Blakely and others (1999). We have interpreted the positions of faults in the subsurface, generally following the interpretations of Blakely and others (1999). Where geophysical constraints are not present, the faults beneath late Tertiary and Quaternary cover have been extended based on geologic reasoning. Nearly all of these concealed faults are shown with continuous solid lines on maps A and B, in order to provide continuous structures for incorporation into the hydrogeologic framework model (HFM). Map A also shows the potentiometric surface, regional springs (25-35 degrees Celsius, D'Agnese and others, 1997), and cold springs (Turner and others, 1996). A composite base map is included based upon published 83-m DEM data from USGS 1:250,000-scale quadrangles, as well as road lines and political boundaries from published USGS 1:100,000-scale DLG data. The 1:100,000-scale data were generalized to 1:250,000 scale for inclusion with the 1:250,000-scale database. Additional coverages include a ground-water model area coverage, and text labels for structural features. Files necessary for printing the map are also included such as text fonts, linesets, shadesets, projection files, and AML files. These files are all explained in the included README.txt file.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2373_1.0", + "title": "Geologic maps and structure sections of the southwestern Santa Clara Valley and southern Santa Cruz Mountains, Santa Clara and Santa Cruz Counties, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1988-01-01", + "end_date": "1997-12-31", + "bbox": "-122, 36.998, -121.548, 37.252", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553047-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553047-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2373_1.0", + "description": "This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:24,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be substituted for comprehensive maps that systematically identify and classify landslide hazards. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (scvmf.ps, scvmf.pdf, scvmf.txt), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2381-A_1.0", + "title": "Geologic Map of the Death Valley Ground-water Model Area, Nevada and California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-118, 35, -115, 38.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554614-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554614-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2381-A_1.0", + "description": "This digital geologic and tectonic database of the Death Valley ground-water model area, as well as its accompanying geophysical maps, are compiled at 1:250,000 scale. The map compilation presents new polygon, line, and point vector data for the Death Valley region. The map area is enclosed within a 3 degree X 3 degree area along the border of southern Nevada and southeastern California. In addition to the Death Valley National Park and Death Valley-Furnace Creek fault systems, the map area includes the Nevada Test Site, the southwest Nevada volcanic field, the southern end of the Walker Lane (from southern Esmeralda County, Nevada, to the Las Vegas Valley shear zone and Stateline fault system in Clark County, Nevada), the eastern California shear zone (in the Cottonwood and Panamint Mountains), the eastern end of the Garlock fault zone (Avawatz Mountains), and the southern basin and range (central Nye and western Lincoln Counties, Nevada). This geologic map improves on previous geologic mapping in the area by providing new and updated Quaternary and bedrock geology, new interpretation of mapped faults and regional structures, new geophysical interpretations of faults beneath the basins, and improved GIS coverages. The basic geologic database has tectonic interpretations imbedded within it through attributing of structure lines and unit polygons which emphasize significant and through-going structures and units. An emphasis has been put on features which have important impacts on ground-water flow. Concurrent publications to this one include a new isostatic gravity map (Ponce and others, 2001), a new aeromagnetic map (Ponce and Blakely, 2001), and contour map of depth to basement based on inversion of gravity data (Blakely and Ponce, 2001). This map compilation was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the Department of Energy in conjunction with the U. S. Geological Survey and National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants off of the Nevada Test Site and Yucca Mountain high-level waste repository. The geologic compilation and tectonic interpretations contained within this database will serve as the basic framework for the flow model. The database also represents a synthesis of many sources of data compiled over many years in this geologically and tectonically significant area.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2381-C_1.0", + "title": "Isostatic Gravity Map of the Death Valley Ground-water Model Area, Nevada and California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-118, 35, -115, 38.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555211-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555211-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2381-C_1.0", + "description": "An isostatic gravity map of the Death Valley groundwater model area was prepared from over 40,0000 gravity stations as part of an interagency effort by the U.S. Geological Survey and the U.S. Department of Energy to help characterize the geology and hydrology of southwest Nevada and parts of California. This dataset was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the U.S. Department of Energy in conjunction with the U. S. Geological Survey and U.S. National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants out of the Nevada Test Site and Yucca Mountain high-level waste repository.", + "license": "proprietary" + }, { "id": "USGS_Map_MF-2381-D_1.0", "title": "Aeromagnetic Map of the Death Valley Ground-water Model Area, Nevada and California", @@ -181934,6 +186159,32 @@ "description": "An aeromagnetic map of the Death Valley groundwater model area was prepared from published aeromagnetic surveys as part of an interagency effort by the U.S. Geological Survey and the U.S. Department of Energy to help characterize the geology and hydrology of southwest Nevada and parts of California. This dataset was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the U.S. Department of Energy in conjunction with the U. S. Geological Survey and U.S. National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants off of the Nevada Test Site and Yucca Mountain high-level waste repository.", "license": "proprietary" }, + { + "id": "USGS_Map_MF-2381-E_1.0", + "title": "Map Showing Depth to Pre-Cenozoic Basement in the Death Valley Ground-water Model Area, Nevada and California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-118, 35, -115, 38.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554277-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554277-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2381-E_1.0", + "description": "A depth to basement map of the Death Valley groundwater model area was prepared using over 40,0000 gravity stations as part of an interagency effort by the U.S. Geological Survey and the U.S. Department of Energy to help characterize the geology and hydrology of southwest Nevada and parts of California. This dataset was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the U.S. Department of Energy in conjunction with the U. S. Geological Survey and U.S. National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants off of the Nevada Test Site and Yucca Mountain high-level waste repository.", + "license": "proprietary" + }, + { + "id": "USGS_Map_MF-2385_1.0", + "title": "Map and map database of susceptibility to slope failure by sliding and earthflow in the Oakland area, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-122.375, 37.625, -122, 38", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551540-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551540-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Map_MF-2385_1.0", + "description": "Mitigation is superior to post-disaster response in reducing the billions of dollars in losses resulting from U.S. natural disasters, and information that predicts the varying likelihood of geologic hazards can help public agencies improves the necessary decision making on land use and zoning. Accordingly, this map was created to increase the resistance of one urban area, metropolitan Oakland, California, to land sliding. Prepared in a geographic information system from a statistical model, the map estimates the relative likelihood of local slopes to fail by two processes common to this area of diverse geology, terrain, and land use. Map data that predict the varying likelihood of land sliding can help public agencies make informed decisions on land use and zoning. This map, prepared in a geographic information system from a statistical model, estimates the relative likelihood of local slopes to fail by two processes common to an area of diverse geology, terrain, and land use centered on metropolitan Oakland. The model combines the following spatial data: (1) 120 bedrock and surficial geologic-map units, (2) ground slope calculated from a 30-m digital elevation model, (3) an inventory of 6,714 old landslide deposits (not distinguished by age or type of movement and excluding debris flows), and (4) the locations of 1,192 post-1970 landslides that damaged the built environment. The resulting index of likelihood, or susceptibility, plotted as a 1:50,000-scale map, is computed as a continuous variable over a large area (872 km2) at a comparatively fine (30 m) resolution. This new model complements landslide inventories by estimating susceptibility between existing landslide deposits, and improves upon prior susceptibility maps by quantifying the degree of susceptibility within those deposits. Susceptibility is defined for each geologic-map unit as the spatial frequency (areal percentage) of terrain occupied by old landslide deposits, adjusted locally by steepness of the topography. Susceptibility of terrain between the old landslide deposits is read directly from a slope histogram for each geologic-map unit, as the percentage (0.00 to 0.90) of 30-m cells in each one-degree slope interval that coincides with the deposits. Susceptibility within landslide deposits (0.00 to 1.33) is this same percentage raised by a multiplier (1.33) derived from the comparative frequency of recent failures within and outside the old deposits. Positive results from two evaluations of the model encourage its extension to the 10-county San Francisco Bay region and elsewhere. A similar map could be prepared for any area where the three basic constituents, a geologic map, a landslide inventory, and a slope map, are available in digital form. Added predictive power of the new susceptibility model may reside in attributes that remain to be explored-among them seismic shaking, distance to nearest road, and terrain elevation, aspect, relief, and curvature.", + "license": "proprietary" + }, { "id": "USGS_NAWQA_HG_DEP", "title": "Atmospheric Deposition of Mercury in the Boston Area", @@ -181960,6 +186211,19 @@ "description": "The National Earthquake Information Center (NEIC of the U.S. Geological Survey provides current earthquake information and data including interactive earthquake maps, near real time earthquake data, fast moment and broadband solutions, and lists of earthquakes for the past 3 weeks. Current earthquake information and data are located at: http://earthquake.usgs.gov/ Near real time earthquake data is located at: http://earthquake.usgs.gov/ Archives of past earthquakes can be found at: http://earthquake.usgs.gov/earthquakes/eqinthenews/", "license": "proprietary" }, + { + "id": "USGS_NHD_CATCH", + "title": "National Hydrography Dataset Catchment Delineations", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-170, 17, -46, 78", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554271-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554271-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_NHD_CATCH", + "description": "Topographically-based catchments will be delineated for all stream-reach segments of the National Hydrography Dataset (NHD) within the entire conterminous United States. The NHD is a digital hydrographic dataset produced by the USGS, in cooperation with the U.S. Environmental Protection Agency (USEPA), that shows streams, lakes, ponds, and wetlands for the Nation at an initial scale of 1:100,000. This effort is being supported by the USEPA and USGS and is intended to benefit a wide variety of water-quality and stream-flow studies across the nation. The catchment-delineation technique is the same as that developed for use in the New England SPARROW model. The New England SPARROW model was the first to utilize the detail of the National Hydrography Dataset (NHD) as the underlying stream-reach network. Final products for this project will be the completion of NHD catchment delineations for the conterminous United States, which will be part of the NHDPlus project to be completed and made available in 2006.", + "license": "proprietary" + }, { "id": "USGS_NPS_AcadiaAccuracy_Final", "title": "Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points", @@ -182012,6 +186276,45 @@ "description": "ABSTRACT: The U.S. Geological Survey (USGS) Upper Midwest Environmental Sciences Center (UMESC) has produced the Vegetation Spatial Database Coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). The vegetation map is of Acadia National Park (NP) and extended environs, providing 99,693 hectares (246,347 acres) of map data. Of this coverage, 52,872 hectares (130,650 acres) is non-vegetated ocean, bay, and estuary (53% of coverage). Acadia NP comprises 19,276 hectares (47,633 acres) of the total data coverage area (19%, 40% not counting ocean and estuary data). Over 7,120 polygons make up the coverage, each with map class description and, for vegetation classes, physiognomic feature information. The spatial database provides crosswalk information to all National Vegetation Classification System (NVCS) floristic and physiognomic levels, and to other established classification systems (NatureServe's U.S. Terrestrial Ecological System Classification, Maine Natural Community Classification, and the USGS Land Use and Land Cover Classification). This mapping project has identified 53 NVCS associations (vegetation communities) at Acadia National Park through analyses of vegetation sample data. These associations are represented in the map coverage with 33 map classes. With all vegetation types, land use classes, and park specific categories combined, 57 map classes define the ground features within the project area (58 classes including the class for no map data). Each polygon within the spatial database map is identified with one of these map classes. In addition, physiognomic modifiers are added to map classes representing vegetation to describe the vegetation structure within a polygon (density, pattern, and height). The spatial database was produced from the interpretation of spring 1997 1:15,840-scale color infrared aerial photographs. The standard minimum mapping unit (MMU) applied is 0.5 hectares (1.25 acres). The interpreted data were transferred and automated using base maps produced from USGS digital orthophoto quadrangles. The finished spatial database is a single seamless coverage, projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. The estimated overall thematic accuracy for vegetation map classes is 80%. ", "license": "proprietary" }, + { + "id": "USGS_NSHMP", + "title": "National Seismic Hazard Maps from the USGS National Seismic Hazard Mapping Project", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "170, 18, -65, 72", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550531-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550531-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_NSHMP", + "description": "The National Seismic Hazard Mapping Project (NSHMP) provides online maps. The hazard maps depict probabilistic ground motions and spectral response with 10%, 5%, and 2% probabilities of exceedance (PE) in 50 years. These maps correspond to return times of approximately 500, 1000, and 2500 years, respectively. The maps are based on the assumption that earthquake occurrence is Poissonian, so that the probability of occurrence is time-independent. The maps cover all of the U.S. including Hawaii and Alaska along with other pertinent information related to earthquake hazards.", + "license": "proprietary" + }, + { + "id": "USGS_NWRC_LA_LandChange_1932-2010", + "title": "Land Area Change in Coastal Louisiana from 1932 to 2010", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1932-01-01", + "end_date": "2010-12-31", + "bbox": "-94, 29, -89, 31", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549617-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549617-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_NWRC_LA_LandChange_1932-2010", + "description": "The analyses of landscape change presented in this dataset use historical surveys, aerial data, and satellite data to track landscape changes in coastal Louisiana. Persistent loss and gain data are presented for 1932-2010. The U.S. Geological Survey (USGS) analyzed landscape changes in coastal Louisiana by determining land and water classifications for 17 datasets. These datasets include survey data from 1932, aerial data from 1956, and Landsat Multispectral Scanner System (MSS) and Thematic Mapper (TM) data from the 1970s to 2010.", + "license": "proprietary" + }, + { + "id": "USGS_OF99-535_1.0", + "title": "Middle Pliocene Paleoenvironmental Reconstruction: PRISM2", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553168-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553168-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OF99-535_1.0", + "description": "As part of the USGS Global Change Research effort, the PRISM (Pliocene Research, Interpretation and Synoptic Mapping) Project has documented the characteristics of middle Pliocene climate on a global scale. The middle Pliocene was selected for detailed study because it spans the transition from relatively warm global climates when glaciers were absent or greatly reduced in the Northern Hemisphere to the generally cooler climates of the Pleistocene with expanded Northern Hemisphere ice sheets and prominent glacial-interglacial cycles. The purpose of this report is to document and explain the PRISM2 mid Pliocene reconstruction. The PRISM2 reconstruction consists of a series of 28 global scale data sets (Table 1) on a 2\u00b0 latitude by 2\u00b0 longitude grid. As such, it is the most complete and detailed global reconstruction of climate and environmental conditions older than the last glacial. PRISM2 evolved from a series of studies that summarized conditions at a large number of marine and terrestrial sites and areas (eg. Cronin and Dowsett, 1991; Poore and Sloan, 1996). The first global reconstruction of mid Pliocene climate (PRISM1) was based upon 64 marine sites and 74 terrestrial sites and included data sets representing annual vegetation and land ice, monthly sea surface temperature (SST) and sea-ice, sea level and topography on a 2\u00b0x2\u00b0 grid (Dowsett et al. (1996) and Thompson and Fleming (1996)). The current reconstruction (PRISM2) is a revision of PRISM1 that incorporates several important differences: 1) Additional sites were added to the marine portion of the reconstruction to improve previous coverage. Sites from the Mediterranean Sea and Indian Ocean are incorporated for the first time in PRISM2. 2) All Pliocene sea surface temperature (SST) estimates were recalculated based upon a new core top calibration to the Reynolds and Smith (1995) adjusted optimum interpolation (AOI) SST data set. This reduced some of the problems previously encountered when different fossil groups were calibrated to different modern climatologies (Climate / Long Range Investigation Mapping and Predictions [CLIMAP], Goddard Institute for Space Sciences [GISS], Advanced Very High Resolution Radiometer [AVHRR], etc.). 3) PRISM2 uses a +25m rise in sea level for the Pliocene (PRISM1 used +35m), in keeping with much new data that has become available. 4) Although the change in global ice volume between PRISM1 and PRISM2 is minor, PRISM2 uses model results from Prentice (personal communication) to guide the areal and topographic distribution of Antarctic ice. This results in a more realistic Antarctic ice configuration in tune with the +25m sea level rise.", + "license": "proprietary" + }, { "id": "USGS_OFR-03-13", "title": "Cascadia Tsunami Deposit Database", @@ -182090,6 +186393,32 @@ "description": "In 1995, the USGS Woods Hole Field Center in Cooperation with the U.S. Army Corps of Engineers, began a program designed to map the seafloor offshore of the New York-New Jersey metropolitan area; the most heavily populated, and one of the most impacted coastal regions of the United States. The ultimate goal of this program is to provide an overall synthesis of the sea floor environment, including surficial sediment texture, subsurface geometry, and anthropogenic impact (e.g. ocean dumping, trawling, channel dredging), through the use and analysis of sidescan-sonar and subbottom mapping techniques. This regional synthesis will support a wide range of management decisions and will provide a basis for further process-oriented investigations. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS DIAN 96040 cruise. The coverage is the nearshore of Long Island, NY in the vicinity of Fire Island. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed.", "license": "proprietary" }, + { + "id": "USGS_OFR00-494", + "title": "High-Resolution Marine Seismic Reflection Data From the San Francisco Bay Area, USGS/OFR 00-494", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-122.77, 37.49, -121.68, 38.16", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549874-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549874-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR00-494", + "description": "Marine seismic reflection data are used to image and map sedimentary and structural features of the seafloor and subsurface. These data are useful in mapping faults (such as the San Andreas and Hayward Faults) where they pass under the waters of the San Francisco Bay, and in assessing other submarine geologic characteristics and features. Particularattention was devoted to investigating the offshore confluence of the San Andreas and San Gregorio fault zones. These data were collected under the auspices of the auspices of the Central California/San Francisco Bay Earthquake Hazards Project of the Western Coastal and Marine Geology Program. Further information concerning the objectives and efforts of this project may be found at: \"http://walrus.wr.usgs.gov/earthquakes/cencal/\" This report consists of two-dimensional marine seismic reflection profile data from the San Francisco Bay area. These data were acquired between 1993 and 1997 with the Research Vessels David Johnston and Robert Gray. The data are available in a variety of formats, including binary, postscript and GIF image. Binary data are in Society of Exploration Geologists (SEG) SEG-Y format and may be downloaded for further processing or display. Reference maps and GIF images othe profiles may be viewed with your Web browser. Seismic reflection profiles are acquired by means of an acoustic source (usually generated electromagnetically or with compressed air), and a hydrophone or hydrophone array. Both elements are typically towed in the waterbehind a survey vessel. The sound source emits a short acoustic pulse, which propogates through the water and sediment columns. The acoustic energy is reflected at density boundaries (such as the seafloor or sediment layers beneath the seafloor), and detected at the hydrophone. As the vessel moves, this process is repeated at intervals ranging between 0.5 and 20 meters depending on the source type. In this way a two-dimensional image of the geologic structure beneath the ship track is constructed.", + "license": "proprietary" + }, + { + "id": "USGS_OFR00-495_1.0", + "title": "Geologic Data Sets for Weights-of-evidence Analysis in Northeast Washington--1. Geologic Raster Data, USGS/OFR 00-495", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1998-01-01", + "end_date": "1998-12-31", + "bbox": "-120, 48, -117, 49", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550193-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550193-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR00-495_1.0", + "description": "This dataset contains the combination of geology data (geologic units, faults, folds, and dikes) from 6 1;100,000 scale digital coverages in eastern Washington (Chewelah, Colville, Omak, Oroville, Nespelem, Republic). The data was converted to an Arc grid in ArcView using the Spatial Analyst extension.", + "license": "proprietary" + }, { "id": "USGS_OFR0047", "title": "Coal-Bearing Regions and Structural Sedimentary Basins of China and Adjacent Seas", @@ -182181,6 +186510,19 @@ "description": "The purpose of this project is to map the surficial geology of the sea floor of Historic Area Remediation Site (HARS) and changes in surficial characteristics over time. This GIS project presents multibeam and other data in a digital format for analysis and display by scientists, policy makers, managers and the general public. This data set includes the boundaries of the Cellar Dirt Disposal site, located offshore of New York and New Jersey.", "license": "proprietary" }, + { + "id": "USGS_OFR00503dredgesite", + "title": "Locations of Dredged Material Placed in the Historic Area Remediation Site Offshore of New York 1996-2000, USGS OFR 00-503", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "2000-12-31", + "bbox": "-73.90012, 40.349976, -73.81226, 40.433605", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554757-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554757-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR00503dredgesite", + "description": "The purpose of this project is to map the surficial geology of the sea floor of Historic Area Remediation Site (HARS) and changes in surficial characteristics over time. This GIS project presents multibeam and other data in a digital format for analysis and display by scientists, policy makers, managers and the general public. This data set includes the location and volume of material placed on the sea floor in the Historic Area Remediation Site between November 1996 and April 2000, extracted from records maintained by the U.S. Army Corps of Engineers. These data are maintained in a system called DAN-NY (Disposal Analysis System NY). DAN-NY includes data from Inspector Logs (data recorded by inspectors on the barges), and more recently data acquired by NYDISS (New York Disposal Surveillance System). The NYDISS automatically records the location of the barge when placement begins and ends. For material placed between November 1996 and November 1998, the plotted locations are from Inspector Logs. For the material placed between November 1998 and April 2000, the placement location was determined by NYDISS. This Open-File Report utilizes the location (latitude and longitude), date of placement, and volume of material in the scow from these data bases.", + "license": "proprietary" + }, { "id": "USGS_OFR00503epabndry", "title": "Boundaries of the Historic Area Remediation Site, Offshore New York, USGS OFR 00-503", @@ -182220,6 +186562,32 @@ "description": "This study was completed as part of an ongoing project in the field of natural gas hydrate research. Natural gas hydrates are an ice-like crystalline combination of water and gas, most commonly methane. The data included in this report were collected in an effort to understand a site where we believe large quantities of methane, approximately 4% of the present atmospheric total, was released from seafloor sediments. This site is known as the Blake Ridge collapse structure, located 300 km off the South Carolina coast at approximately 2600 m of water depth. This CD-ROM contains copies of the navigation and deep-towed chirp subbottom data collected aboard the R/V Cape Hatteras on cruises 92023 and 95023 in 1992 and 1995 respectively. This CD-ROM is (Compact Disc-Read Only Memory UDF (Universal Disc Format) CD-ROM Standard (ISO 9660 equivalent). The HTML documentation is written utilizing some HTML 4.0 enhancements. The disk should be viewable by all WWW browsers but may not properly format on some older WWW browsers. Also, some links to USGS collaborators and other agencies are available on this CD-ROM. These links are only accessible if access to the Internet is available during browsing of the CD-ROM. On cruise 92023, 58 km of deep-towed chirp data were recorded on 4 lines and broken into a total of 8 files. 78 square kilometers of sidescan mosaic and approximately 1000 km of air gun single channel seismic reflection data were recorded as well but are not achived on this report. On cruise 95023, 100km of deep- towed chirp data were recorded on 5 lines and broken into 18 files. 152 square kilometers of sidescan mosaic and 244.3 km of GI gun single channel seismic reflection were also recorded but are not archived on this report. The archived Chirp subbottom data are in standard Society of Exploration Geologists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded for processing with software such as Seismic Unix or SIOSEIS. The subbottom data were recorded on the ISIS data acquisition system in QMIPS format. Chirp subbottom channel extracted from raw QMIPS format sonar files and converted to 16-bit Int. SEG-Y format using the program QMIPSTOSEGY. Even though the data are in SEG-Y format, it is not the conventional time series data (e.g. voltages or pressures), but rather instantaneous amplitude or envelope detected and therefore all of the amplitudes are positive (though not simply rectified). Seismic reflection profiles are acquired by means of an acoustic source (usually generated electromagnetically or with compressed air), and a hydrophone or hydrophone array. Both elements are typically towed in the water behind a survey vessel, or some cases, mounted on side of the hull. The sound source emits a short acoustic pulse, which propagates through the water and sediment columns. The acoustic energy is reflected at density boundaries (such as the seafloor or sediment layers beneath the seafloor), and detected at the hydrophone. As the vessel moves, this process is repeated at intervals ranging between 0.5 and 20 meters depending on the source type. In this way, a two-dimensional image of the geologic structure beneath the ship track is constructed. For more information concerning seismic reflection profiling at the USGS Woods Hole \"http://woodshole.er.usgs.gov/operations/sfmapping/\"", "license": "proprietary" }, + { + "id": "USGS_OFR01-131_Version 1.0", + "title": "Geologic Map of the San Bernardino North 7.5' Quadrangle, San Bernardino County, California, USGS OFR 01-131", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1974-01-01", + "end_date": "1981-12-31", + "bbox": "-117.37509, 34.124985, -117.24991, 34.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549362-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549362-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-131_Version%201.0", + "description": "The data set for the San Bernardino North 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) a part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographical Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the San Bernardino North 7.5' quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, itcan be used for groundwater studies in the San Bernardino basin, and for mineral resource evaluation studies, animal and plant habitat studies, and soil studies in the San Bernardino National Forest. The database is not suitable for site-specific geologic evaluations. This data set maps and describes the geology of the San Bernardino North 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of this Readme (including the metadata file as an appendix), Description of Map Units (DMU), and the graphic produced by the PostScript plot file. The geologic map covers a part of the southwestern San Bernardino Mountains and the northwestern San Bernardino basin. Granitic and metamorphic rocks underlie most of the mountain area, and a complex array of Quaternary deposits fill the basin. These two areas are separated by strands of the seismically active San Andreas Fault. Bedrock units in the San Bernardino Mountains are dominate by large Cretaceous and Jurassic granitic bodies, ranging in composition from onzogranite to monzodiorite, and include lesser Triassic monzonite. The younger of these granitic rocks intrude a complex assemblage of gneiss, marble, and granitic rock of probable early Mesozoic age; the relationship between these metemorphic rocks and the Triassic rocks is unknown. Spanning the Pleistocene in age, large and small alluvial bodies emerge from the San Bernardino Mountains, and and fill the San Bernardino basin. In the southwestern part of the quadrangle, Cajon Wash carries sediments from both the San Bernardino and San Gabriel Mountains, and Lytle Creek heads in the eastern San Gabriel Mountains. Limite bedrock areas showing through the Quaternary sediments of the basin consist exclusively of Mesozoic Pelona Schist locally intruded by Tertairy dikes. Youthful-appearing fault scarps discontinuously mark the traces of the San Andreas Fault along the southern edge of the San Bernardino Mountains. Unnamed Tertiary sedimentary rocks are bounded by two strands of the fault between Badger Canyon and the east edge of the quadrangle. Young and old high-angle faults cut bedrock units within the San Bernardino Mountains, and the buried, seismically active San Jacinto Fault traverses the southwestern part of the quadrangle. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. This digital Open-File map superceeds an older analog Open-File map of the quadrangle, and includes extensive new data on the Quaternary deposits, and revises some fault and bedrock distribution within the San Bernardino Mountains. The digital map was compiled on a base-stable cronoflex copy of the San Bernardino North 7.5' topographic base and then scribed. This scribe guide was used to make a 0.007 mil blackline clear-film, which was scanned at 1200 DPI by Optronics Specialty Company, Northridge, California; minor hand-digitized additions were made at the USGS. Lines, points, and polygonswere subsequently edited at the USGS using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum.", + "license": "proprietary" + }, + { + "id": "USGS_OFR01-132_Version 1.0", + "title": "Geologic Map of the Fifteenmile Valley 7.5' Quadrangle, San Bernardino County, California, USGS OFR 00-132", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-01-01", + "end_date": "2000-12-31", + "bbox": "-117.12509, 34.374985, -116.99991, 34.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548788-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548788-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-132_Version%201.0", + "description": "The data set for the Fifteenmile Valley 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographical Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the Fifteenmile Valley 7.5' quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, it can be used for mineral resource evaluation studies, animal and plant habitat studies, and soil studies in the San Bernardino National Forest. The database is not suitable for site-specific geologic evaluations. This data set maps and describes the geology of the Fifteenmile Valley 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Descriptionof Map Units (DMU), an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of this Readme (including the metadata file as an appendix), Description of Map Units (DMU), and a screen graphic of the plot produced by the PostScript plot file. The geologic map covers the northernmost part of the San Bernardino Mountain and the southern Granite Mountains. These two bedrock areas are separated by the wide, alluviated Fifteenmile Valley. Bedrock units in the San Bernardino Mountains are dominated by large Cretaceous granitic bodies ranging in composition from monzogranite to gabbro, and include lesser Triassic monzonite. The Granite Mountains are underlain chiefly by large Triassic monzonite bodies, and in the western part, by Cretaceous and possibly Jurassic monzogranite to monzodiorite. Spanning the Pleistocene in age, large alluvial fans, flank the north side of the San Bernardino Mountains, and are dominated by debris flow deposits. The central part of Fifteenmile Valley is covered by fine grained alluvial material deposited by streams flowing into Rabbit Lake and an unnamed dry lake in the northwestern part of the quadrangle. Young, south dipping reverse faults, some with moderately to well eroded fault scarps, discontinuously flank the northern edge of the San Bernardino Mountains. Young and old high-angle faults are mapped within both the San Bernardino and Granite Mountains. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. The map was compiled on a base-stable cronoflex copy of the Fifteenmile Valley 7.5' topographic base and then scribed. This scribe guide was used to make a0.007 mil blackline clear-film, which was scanned at 1200 DPI by Optronics Specialty Company, Northridge, California; minor hand-digitized additions were madeat the USGS. Lines, points, and polygons were subsequently edited at the USGS using standard ARC/INFO commands. Digitizing and editing artifacts significan enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum.", + "license": "proprietary" + }, { "id": "USGS_OFR01-142_1", "title": "Digital Database of Mining-related Features at Selected Historic and Active Phosphate Mines in Idaho, USGS OFR 01-142", @@ -182233,6 +186601,19 @@ "description": "This is a spatial database that delineates mining-related features in areas of historic and active phosphate mining in the core of the southeastern Idaho phosphate resource area. The data has varying degrees of accuracy and attribution detail. The breakdown of areas by type of activity at active mines is detailed; however, the disturbed areas at many of the closed or inactive mines are not subdivided into specific categories detailing the type of activity that occurred. Nineteen phosphate mine sites are included in the study. A total of 5,728 hc (14,154 ac), or more than 57 km2 (22 mi2), of phosphate mining-related surface disturbance are documented in the spatial coverage of the core of the southeast Idaho phosphate resource area. The study includes 4 active phosphate minebsDry Valley, Enoch Valley, Rasmussen Ridge, and Smoky Canyobnand 15 historic phosphate minebsBallard, Champ, Conda, Diamond Gulch, Gay, Georgetown Canyon, Henry, Home Canyon, Lanes Creek, Maybe Canyon, Mountain Fuel, Trail Canyon, Rattlesnake Canyon, Waterloo, and Wooley Valley. Spatial data on the inactive historic mines is relatively up-to-date; however, spatially described areas for active mines are based on digital maps prepared in early 1999. The inactive Gay mine has the largest total area of disturbance: 1,917 hc (4,736 ac) or about 19 km2 (7.4 mi2). It encompasses over three times the disturbance area of the next largest mine, the Conda mine with 607 hc (1,504 ac), and it is nearly four times the area of the Smoky Canyon mine, the largest of the active mines with 497 hc (1,228 ac). The wide range of phosphate mining-related surface disturbance features (approximately 80) were reduced to 13 types or features used in this studbyadit and pit, backfilled mine pit, facilities, mine pit, ore stockpile, railroad, road, sediment catchment, tailings or tailings pond, topsoil stockpile, water reservoir, and disturbed land (undifferentiated). In summary, the spatial coverage includes polygons totaling 1,114 hc (2,753 ac) of mine pits, 272 hc (671 ac) of backfilled mine pits, 1,570 hc (3,880 ac) of waste dumps, 26 hc (64 ac) of ore stockpiles, and 44 hc (110 ac) of tailings or tailings ponds. Areas of undifferentiated phosphate mining-related land disturbances, called bed land,b site-specific studies to delineate distinct mine features will allow modification of this preliminary spatial database.", "license": "proprietary" }, + { + "id": "USGS_OFR01-153_Version 1.0, February 13, 2001", + "title": "Heavy Minerals from the Palos Verdes Margin, Southern California: Data and Factor Analysis, USGS OFR 00-153", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "1992-12-31", + "bbox": "-118.44776, 33.67033, -118.14747, 33.8569", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550564-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550564-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-153_Version%201.0%2C%20February%2013%2C%202001", + "description": "Heavy or high-density minerals in the 63-250-um (micron) size fraction (very fine and fine sand) were analyzed from beach and offshore sites to determine the areal and temporal mineralogic distributions and the relation of those distributions to the deposit affected by effluent discharged from the Los Angeles County Sanitation District sewage system. Heavy or high-density minerals in the 63-250-_m (micron) size fraction (very fine and fine sand) were analyzed from 36 beach and offshore sites (38 samples) of the Palos Verdes margin to determine the areal and temporal mineralogic distributions and the relation of those distributions to the deposit affected by material discharged from the Los Angeles County Sanitation District sewage system (Lee, 1994) (Figure 1). Data presented here were tabulated for a report to the Department of Justice (Wong, 1994). The results of the data analysis are discussed in Wong (in press).", + "license": "proprietary" + }, { "id": "USGS_OFR01-157", "title": "Archive of Water Gun Subbottom Data Collected During USGS Cruise SEAX 95007 New York Bight, 7-25 May, 1995", @@ -182246,6 +186627,97 @@ "description": "Beginning in 1995, the USGS, in cooperation with the U.S Army Corps of Engineers (USACE), New York District, began a program to generate reconnaissance maps of the sea floor offshore of the New York-New Jersey metropolitan area, one of the most populated coastal regions of the United States. The goal of this mapping program is to provide a regional synthesis of the sea-floor environment, including a description of sedimentary environments, sediment texture, seafloor morphology, and geologic history to aid in understanding the impacts of anthropogenic activities, such as ocean dumping. This mapping effort differs from previous studies of this area by obtaining digital, sidescan sonar images that cover 100 percent of the sea floor. This investigation was motivated by the need to develop an environmentally acceptable solution for the disposal of dredged material from the New York - New Jersey Port, by the need to identify potential sources of sand for renourishment of the southern shore of Long island, and by the opportunity to develop a better understanding of the transport and long-term fate of contaminants by investigations of the present distribution of materials discharged into the New York Bight over the last 100+ years (Schwab and others, 1997). This DVD-ROM contains copies of the navigation and field Water Gun subbottom data collected aboard the R/V Seaward Explorer, from 7-25 May, 1995. The coverage is in the New York Bight area. This DVD-ROM (Digital Versatile Disc-Read Only Memory) has been produced in accordance with the UDF (Universal Disc Format) DVD-ROM Standard (ISO 9660 equivalent) and is therefore capable of being read on any computing platform that has appropriate DVD-ROM driver software installed. Access to the data and information contained on this DVD-ROM was developed using the HyperText Markup Language (HTML) utilized by the World Wide Web (WWW) project. Development of the DVD-ROM documentation and user interface in HTML allows a user to access the information by using a variety of WWW information browsers to facilitate browsing and locating information and data. To access the information contained on this disk with a WWW client browser, open the file'index.htm' at the top level directory of this DVD-ROM with your selected browser. The HTML documentation is written utilizing some HTML 4.0 enhancements. The disk should be viewable by all WWW browsers but may not properly format on some older WWW browsers. Also, some links to USGS collaborators and other agencies are available on this DVD-ROM. These links are only accessible if access to the Internet is available during browsing of the DVD-ROM.", "license": "proprietary" }, + { + "id": "USGS_OFR01-173_Version 1.0", + "title": "Geologic Map of the Devore 7.5' quadrangle, San Bernardino County, California, USGS, OFR 00-173", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1974-01-01", + "end_date": "1981-12-31", + "bbox": "-117.50009, 34.124985, -117.37491, 34.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552829-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552829-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-173_Version%201.0", + "description": "The data set for the Devore 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographical Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the Devore 7.5' quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, it can be used for groundwater studies in the San Bernardino basin, and for mineral resource evaluation studies, animal and plant habitat studies, and soil studies in the San Bernardino National Forest. The database is not suitable for site-specific geologic evaluations. This data set maps and describes the geology of the Devore 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of this Readme (including the metadata file as an appendix), Description of Map Units (DMU), and the graphic produced by the PostScript plot file. The Devore quadrangle straddles part of the boundary between two major physiographic provinces of California, the Transverse Ranges Province to the north and the Peninsular Ranges Province to the south. The north half of the quadrangle includes the eastern San Gabriel Mountains and a small part of the western San Bernardino Mountains, both within the east-central part of the Transverse Ranges Province. South of the Cucamonga and San Andreas Fault zones, the extensive alluviated area in the south half of the quadrangle lies within the upper Santa Ana River Valley, and represents the northernmost part of the Peninsular Ranges Province. There are numerous active faults within the quadrangle, including right-lateral strike-slip faults of the San Andreas Fault system, which dominate the younger structural elements, and separate the San Gabriel from the San Bernardino Mountains. The active San Jacinto Fault zone projects toward the quadrangle from the southeast, but its location is poorly constrained not only within the quadrangle, but for at least several kilometers to the southeast. As a result, the interrelation between it, the Glen Helen Fault, and the probable easternmost part of the San Gabriel Fault is intrepretive. Thrust faults of the Cucamonga Fault zone along the south margin of the San Gabriel Mountains, represent the rejuvinated eastern end of a major old fault zone that bounds the south side of the western and central Transverse Ranges (Morton and Matti, 1993). Rejuvenation of this old fault zone, including the Cucamonga Fault zone, is apparently in response to compression in the eastern San Gabriel Mountains resulting from initiation of right-lateral slip on the San Jacinto Fault zone in the Peninsular Ranges.The structural grain within the San Gabriel Mountains, as defined by basement rocks, is generally east striking. Within the Devore quadrangle, these basement rocks include a Paleozoic (?) schist, quartzite, and marble metasedimentary sequence, which occurs as discontinuous lenses and septa within Cretaceous granitic rocks. Most of the granitic rocks are of tonalitic composition, and much of them are mylonitic. South of the granitic rocks is a complex assemblage of Proterozoic (?) metamorphic rocks, at least part of which is metasedimentary. The assemblage was metamorphosed to upper amphibolite and lower granulite grade, and subsequently remetamorphosed to a lower metamorphic grade. It is also intensely deformed by mylonitization which is characterized by an east striking, north dipping foliation, and by a pronounced lineation that plunges shallowly east and west. East of Lytle Creek and west of the San Andreas Fault zone, the predominant basement lithology is Mesozoic Pelona Schist, which consists mostly of greenschist grade metabasalt and metagraywacke. Intruding the Pelona Schist, between Lytle Creek and Cajon Canyon, is the granodiorite of Telegraph Peak of Oligocene age (May and Walker, 1989). East of the San Andreas Fault in the San Bernardino Mountains, basement rocks consist of amphibolite grade gneiss and schist intermixed with concordant and discordant tonalitic rock and pegmatite. Tertiary conglomerate and sandstone occur in the Cucamonga Fault zone and in a zone 200 to 700 m wide between strands of the San Andreas Fault zone and localized thrust faults northeast of the San Andreas. Most of the conglomerate and sandstone within the Cucamonga Fault zone is overturned forming the north limb of an overturned syncline. Clasts in the conglomerate are not derived from any of the basement rocks in the eastern San Gabriel Mountains. Clasts in the conglomerate and sandstone northeast of the San Andreas Fault zone do not appear to be locally derived either. The south half of the quadrangle is dominated by the large symmetrical alluvial-fan emanating from the canyon of Lytle Creek, and by the complex braided stream sediments of Lytle Creek and Cajon Wash. The San Andreas Fault is restricted to a relatively narrow zone marked by a pronounced scarp that is especially well exposed near the east margin of the quadrangle. Two poorly exposed, closely spaced, north-dipping thrust faults northeast of the San Andreas Fault have dips that appear to range from 55? to near horizontal. These hallower dips probably are the result of rotation of initially steeper fault surfaces by downhill surface creep. Between the San Andreas and Glen Helen Fault zones, there are several faults that have north facing scarps, the largest of which are the east striking Peters Fault and the northwest striking Tokay Hill Fault. The Tokay Hill Fault is at least in part a reverse fault. Scarps along both faults are youthful appearing. The Glen Helen Fault zone along the west side of Cajon Creek, is well defined by a pronounced scarp from the area north of Interstate 15, south through Glen Helen Regional Park; an elongate sag pond is located within the park. The large fault zone along Meyers Canyon, between Penstock and Lower Lytle Ridges, is probably the eastward extension of the San Gabriel Fault zone that is deformed into a northwest orientation due to compression in the eastern San Gabriel Mountains (Morton and Matti, 1993). At the south end of Sycamore Flat, this fault zone consists of three discreet faults distributed over a width of 300 m. About 2.5 km northwest of Sycamore Flats, it consists of a 300 m wide shear zone. At the north end of Penstock Ridge, the fault zone has bifurcated into four strands, which at the northwest corner of the quadrangle are distributed over a width of about one kilometer. From the northern part of Sycamore Flat, for a distance of nearly 5 km northwestward, a northeast dipping reverse fault is located along the east side of the probable San Gabriel Fault zone. This youthful reverse fault has locally placed the Oligocene granodiorite of Telegraph Peak over detritus derived from the granodiorite. The Lytle Creek Fault, which is commonly considered the western splay of the San Jacinto Fault zone, is located on the west side of Lytle Creek. Lateral displacement on the Lytle Creek Fault has offset parts of the old Lytle Creek channel; this offset gravel-filled channel is best seen at Texas Hill, near the mouth of Lytle Creek, where the gravel was hydraulic mined for gold in the 1890s. The Cucamonga Fault zone consists of a one kilometer wide zone of northward dip-ping thrust faults. Most splays of this fault zone dip north 25 to 35. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. This digital Open-File map supercedes an older analog Open-File map of the quadrangle, and includes extensive new data on the Quaternary deposits, and revises some fault and bedrock distribution within the San Gabriel Mountains. The digital map was compiled on a base-stable cronoflex copy of the Devore 7.5 deg. topographic base and then scribed. This scribe guide was used to make a 0.007 mil blackline clear-film, from which lines and point were hand digitized. Lines, points, and polygons were subsequently edited at the USGS using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum.", + "license": "proprietary" + }, + { + "id": "USGS_OFR01-227_1.0", + "title": "Geologic Map Database of the Washington DC Area Featuring Data From Three 30' X 60' Quadrangles: Frederick, Washington West, and Fredericksburg, USGS OFR 01-227", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1988-01-01", + "end_date": "2001-12-31", + "bbox": "-78, 38, -77, 39.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550463-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550463-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-227_1.0", + "description": "Geology was researched and compiled for use in studies of ecosystem health, environmental impact, soils, groundwater, land use, tectonics, crustal genesis, sedimentary provenance, and any others that could benefit from geographically referenced geological data. The Washington DC Area geologic map database (DCDB) provides geologic map information of areas to the NW, W, and SW of Washington, DC to various professionals and private citizens who have uses for geologic data. Digital, geographically referenced, geologic data is more versatile than traditional hard copy maps, and facilitates the examination of relationships between numerous aspects of the geology and other types of data such as: land-use data, vegetation characteristics, surface water flow and chemistry, and various types of remotely sensed images. The DCDB was created by combining Arc/Info coverages, designing a Microsoft (MS) Access database, and populating this database. Proposed improvements to the DCDB include the addition of more geochemical, structural, and hydrologic data. Data are provided in several common GIS formats and MS Access database files. The geologic data themes included are bedrock, surficial, faults and fold axes, neat line, structural data, and sinkholes; the base themes are political boundaries, roads, elevation contours, and hydrography. Data were originally collected in UTM coordinates, zone 18, NAD 1927, and projected to geographic coordinates (Lat/Long), NAD 1983. The data base is accompanied by large format color maps, a readme.txt file, and a explanatory PDF pamphlet.", + "license": "proprietary" + }, + { + "id": "USGS_OFR01-290_1.0", + "title": "Geologic map and digital database of the San Rafael Mtn. 7.5- minute Quadrangle, Santa Barbara County, California, USGS OFR 01-290", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2001-12-31", + "bbox": "-119.875, 34.624985, -119.74991, 34.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549269-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549269-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-290_1.0", + "description": "This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:24,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, mineral and energy resources, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be substituted for comprehensive maps that systematically identify and classify landslide hazards. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (srm_expl.txt, srm_expl.pdf), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller. The databases in this report were compiled in ARC/INFO, a commercial Geographic Information System (Environmental Systems Research Institute, Redlands, California), with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files are COVERAGE (ARC/INFO vector data) format. Coverages are stored in uncompressed ARC export format (ARC/INFO version 8.0. 2). ARC/INFO export files (files with the .e00 extension) can be converted into ARC/INFO coverages in ARC/INFO (see below) and can be read by some other Geographic Information Systems, such as MapInfo via ArcLink and ESRI's ArcView (version 1.0 for Windows 3.1 to 3.11 is available for free from ESRI's web site: http://www.esri.com.) The digital compilation was done in version 7.2.1 of ARC/INFO with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The geologic map information was digitized from stable originals of the geologic maps at 1:24,000 scale. The author manuscripts (pen on mylar) were scanned using a Altek monochrome scanner with a resolution of 800 dots per inch. The scanned images were vectorized and transformed from scanner coordinates to projection coordinates with digital tics placed by hand at quadrangle corners. The scanned lines were edited interactively by hand using ALACARTE, color boundaries were tagged as appropriate, and some scanning artifacts visible at 1:24,000 were removed. This report consists of a set of geologic map database files (Arc/Info coverages) and supporting text and plotfiles. In addition, the report includes two sets of plotfiles (PostScript and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Open-File Report. These files are described in the explanatory pamphlets (srm.ps, srm.pdf, and srm.txt). The base map layers used in the preparation of the geologic map plotfiles were scanned from a scale-stable version of the USGS 1:24,000 topographic maps of the San Rafael Mtn. (1959, photorevised 1988) 7.5-minute quadrangle. The map has a 40 foot contour interval.", + "license": "proprietary" + }, + { + "id": "USGS_OFR01-293_Version 1.0", + "title": "Geologic Map of the Telegraph Peak 7.5 min. quadrangle, San Bernardino County, California, USGS OFR 01-293", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1974-01-01", + "end_date": "1981-12-31", + "bbox": "-117.62509, 34.249985, -117.49991, 34.375", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548656-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548656-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-293_Version%201.0", + "description": "The data set for the Telegraph 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) and the California Division of Mines as part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographical Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the Telegraph 7.5' quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, the U.S. Forest Service, San Bernardino National Forest, may use the map and database as a basic geologic data source for soil studies, mineral resource evaluations, road building, biological surveys, and general forest management. The database is not suitable for site-specific geologic evaluations at scales greater than 1:24,000 (1 in = 2,000 ft). This data set maps and describes the geology of the Telegraph 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a double precision map coverage containing geologic contacts and units, (2) a coverage containing site-specific structural data, (3) a coverage containing geologic-unit label leaders and their associated attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of this Readme (including the metadata file as an appendix), Description of Map Units (DMU), and the graphic produced by the PostScript plot file. The Telegraph Peak quadrangle is located in the eastern San Gabriel Mountains part of the Transverse Ranges Province of southern California. The generally east-striking structural grain characteristic of the crystalline rocks of much of the San Gabriel Mountains is apparent, but not well developed in the Telegraph Peak quadrangle. Here, the east-striking structural grain is somewhat masked by the northwest-striking grain associated with the San Andreas Fault zone. Faults within the quadrangle include northwest-striking, right-lateral strike-slip faults of the San Andreas system. The active San Andreas Fault, located in the northern part of the quadrangle, dominates the younger structural elements. North of the San Andreas Fault is the inactive Cajon Valley Fault that was probably an early strand of the San Andreas system. It was active during deposition of the middle Miocene Cajon Valley Formation. South of the San Andreas, the Punchbowl Fault, which is probably a long-abandoned segment of the San Andreas Fault (Matti and Morton, 1993), has a sinuous trace apparently due to compression in the eastern San Gabriel Mountains that post-dates displacement on the fault. The Punchbowl Fault separates two major subdivisions of the Mesozoic Pelona Schist and is left-laterally offset by a northeast-striking fault in the northwestern part of the quadrangle. Within the Punchbowl Fault zone is a thin layer of highly deformed basement rock, which is clearly not part of the Pelona Schist. To the southeast, in the Devore quadrangle, this included basement rock attains a thickness of several hundred feet. Along strike to the northwest, Tertiary sedimentary rocks are included within the fault zone. South of the Punchbowl Fault are several arcuate (in plan) faults that are part of an antiformal schuppen-like fault complex of the eastern San Gabriel Mountains. Most of these arcuate faults are reactivated and deformed older faults, and probably include the eastern part of the San Gabriel Fault. The Vincent Thrust of late Cretaceous or early Tertiary age separates the Pelona Schist in the lower plate from a heterogeneous basement complex in the upper plate. Immediately above the Vincent Thrust is a variable thickness of mylonitic rock generally interpreted as a product of displacement on the thrust. The upper plate includes two Paleozoic units, a schist and gneiss sequence and a schist, quartzite, and marble metasedimentary sequence. Both sequences are thrust over the Mesozoic Pelona Schist along the Vincent Thrust, and intruded by Tertiary (late Oligocene) granitic rocks, granodiorite of Telegraph Peak, that also intrude the Vincent Thrust. The Pelona Schist consists mostly of greenschist to amphibolite metamorphic grade meta-basalt (greenschist and amphibolite) and meta-graywacke (siliceous and white mica schist), with minor impure quartzite and marble, in which all primary structures have been destroyed and all layering transposed. Cretaceous granitic rocks, chiefly tonalite, intrude the schist and gneiss sequence, but not the Pelona Schist or the Vincent Thrust. North of the San Andreas Fault, bedrock units consist of undifferentiated Cretaceous tonalite, here informally named tonalite of Circle Mountain, with some included small boldies of gneiss and marble. These basement rocks are the westward continuation of rocks of the San Bernardino Mountains and not rocks of the San Gabriel Mountains south of the San Andreas Fault. Also north of the San Andreas Fault are the Oligocene Vaqueros Formation, middle Miocene Cajon Valley Formation, and Pliocene rocks of Phelan Peak. The latter two formations are divided into several conglomerate and arkosic sandstone subunits. In the northeastern corner of the quadrangle, the rocks of Phelan Peak are unconformably overlain by the Quaternary Harold Formation and Shoemaker Gravel. Quaternary units ranging from early Pleistocene to recent are mapped, and represent alluvial fan, landslide, talus, and wash environments. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. This digital Open-File map supercedes an older analog Open-File map of the quadrangle, and includes extensive new data on the Quaternary deposits, and revises some fault and bedrock distribution within the San Gabriel Mountains. The digital map was compiled on a base-stable cronoflex copy of the Telegraph 7.5' topographic base and then scribed. This scribe guide was used to make a 0.007 mil blackline clear-film, from which lines and point were hand digitized. Lines, points, and polygons were subsequently edited at the USGS using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum.", + "license": "proprietary" + }, + { + "id": "USGS_OFR01-308_Version 1.0", + "title": "Hawaii Beach Monitoring Program: Beach Profile Data for Maui and Oahu, Hawaii, USGS OFR 01-308", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-08-03", + "end_date": "1999-12-31", + "bbox": "-158.29727, 20.571814, -155.95554, 21.746782", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549926-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549926-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-308_Version%201.0", + "description": "This data set is intended for scientific research of beach morphology and volume changes. Biannual beach profiles were collected at 42 Oahu and 36 Maui Locations between August 1994 and August 1999. Surveys were conducted at approximately summer-winter intervals and extend from landward of the active beach to about -4 meters water depth. Profile data on this CDROM are presented in both Microsoft EXCEL 97/98 & 5.0/95 Workbook (.xls) format and comma separated value (.csv) format. Graphical representation of the surveys (x vs. z and x vs. y) are presented in EXCEL format only. Site descriptions, including beach location, directions to site, GPS information, and a description of Reference Points used, are available in both EXCEL and ADOBE ACROBAT .pdf format. Cross-shore beach profile data were collected as a component of the Hawaii Coastal Erosion Study, a cooperative effort by U.S. Geological Survey and University of Hawaii in order to document seasonal and longer-term variations in beach volume and behavior. The overall objectives of the Hawaii Coastal Erosion Study are to document the recent history of shoreline change in Hawaii and to determine the primary factor(s) responsible for coastal erosion in low-latitude environments for the purpose of predicting future changes and to provide quality scientific data that is useful to other scientists, planners, engineers, and coastal managers. The overall strategy consists of first quantifying the magnitude and location of serious erosion problems followed by close monitoring of coastal change in critical areas. Bi-annual beach profiles have been collected at over 40 critical beach sites on the islands of Oahu and Maui. Once sufficient background information is analyzed and key problems are defined, field sites will be selected for detailed process- oriented studies (both physical and biological) to gain an understanding of the complex relationships between reef carbonate production, sediment dispersal, and the interaction of man-made structures with sediment movement along the shore. Information derived from this project will be used to develop general guidelines for sediment production, transport, and deposition of low- latitude coasts. Planned major products include a comprehensive atlas of coastal hazards, journal articles and reports presenting results of our studies, and a \"living\" database of shoreline history and changes based on results of the beach profile monitoring and softcopy photogrammetric analysis.", + "license": "proprietary" + }, + { + "id": "USGS_OFR01-30_1.0", + "title": "Geologic Map and Digital Database of the Porcupine Wash 7.5 minute Quadrangle, Riverside County, California, USGS OFR 01-30", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1973-01-01", + "end_date": "2000-12-31", + "bbox": "-115.87509, 33.749985, -115.74991, 33.875", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552435-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552435-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-30_1.0", + "description": "The data set for the Porcupine Wash quadrangle has been prepared by the Southern California Areal Mapping Project (SCAMP), a cooperative project sponsored jointly by the U.S. Geological Survey and the California Division of Mines and Geology. The Porcupine Wash data set represents part of an ongoing effort to create a regional GIS geologic database for southern California. This regional digital database, in turn, is being developed as a contribution to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The Porcupine Wash database has been prepared in cooperation with the National Park Service as part of an ongoing project to provide Joshua Tree National Park with a geologic map base for use in managing Park resources and developing interpretive materials. The digital geologic map database for the Porcupine Wash quadrangle has been created as a general-purpose data set that is applicable to land-related investigations in the earth and biological sciences. Along with geologic map databases in preparation for adjoining quadrangles, the Porcupine Wash database has been generated to further our understanding of bedrock and surficial processes at work in the region and to document evidence for seismotectonic activity in the eastern Transverse Ranges. The database is designed to serve as a base layer suitable for ecosystem and mineral resource assessment and for building a hydrogeologic framework for Pinto Basin. This data set maps and describes the geology of the Porcupine Wash 7.5 minute quadrangle, Riverside County, southern California. The quadrangle, situated in Joshua Tree National Park in the eastern Transverse Ranges physiographic and structural province, encompasses parts of the Hexie Mountains, Cottonwood Mountains, northern Eagle Mountains, and south flank of Pinto Basin. It is underlain by a basement terrane comprising Proterozoic metamorphic rocks, Mesozoic plutonic rocks, and Mesozoic and Mesozoic or Cenozoic hypabyssal dikes. The basement terrane is capped by a widespread Tertiary erosion surface preserved in remnants in the Eagle and Cottonwood Mountains and buried beneath Cenozoic deposits in Pinto Basin. Locally, Miocene basalt overlies the erosion surface. A sequence of at least three Quaternary pediments is planed into the north piedmont of the Eagle and Hexie Mountains, each in turn overlain by successively younger residual and alluvial deposits. The Tertiary erosion surface is deformed and broken by north-northwest-trending, high-angle, dip-slip faults and an east-west trending system of high-angle dip- and left-slip faults. East-west trending faults are younger than and perhaps in part coeval with faults of the northwest-trending set. The Porcupine Wash database was created using ARCVIEW and ARC/INFO, which are geographical information system (GIS) software products of Environmental Systems Research Institute (ESRI). The database consists of the following items: (1) a map coverage showing faults and geologic contacts and units, (2) a separate coverage showing dikes, (3) a coverage showing structural data, (4) a scanned topographic base at a scale of 1:24,000, and (5) attribute tables for geologic units (polygons and regions), contacts (arcs), and site-specific data (points). The database, accompanied by a pamphlet file and this metadata file, also includes the following graphic and text products: (1) A portable document file (.pdf) containing a navigable graphic of the geologic map on a 1:24,000 topographic base. The map is accompanied by a marginal explanation consisting of a Description of Map and Database Units (DMU), a Correlation of Map and Database Units (CMU), and a key to point-and line-symbols. (2) Separate .pdf files of the DMU and CMU, individually. (3) A PostScript graphic-file containing the geologic map on a 1:24,000 topographic base accompanied by the marginal explanation. (4) A pamphlet that describes the database and how to access it. Within the database, geologic contacts , faults, and dikes are represented as lines (arcs), geologic units as polygons and regions, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum and link it to other tables (.rel) that provide more detailed geologic information. Map nomenclature and symbols Within the geologic map database, map units are identified by standard geologic map criteria such as formation-name, age, and lithology. The authors have attempted to adhere to the stratigraphic nomenclature of the U.S. Geological Survey and the North American Stratigraphic Code, but the database has not received a formal editorial review of geologic names. Special symbols are associated with some map units. Question marks have been added to the unit symbol (e.g., QTs?, Prpgd?) and unit name where unit assignment based on interpretation of aerial photographs is uncertain. Question marks are plotted as part of the map unit symbol for those polygons to which they apply, but they are not shown in the CMU or DMU unless all polygons of a given unit are queried. To locate queried map-unit polygons in a search of database, the question mark must be included as part of the unit symbol. Geologic map unit labels entered in database items LABL and PLABL contain substitute characters for conventional stratigraphic age symbols: Proterozoic appears as 'Pr' in LABL and as '<' in PLABL, Triassic appears as 'Tr' in LABL and as '^' in PLABL. The substitute characters in PLABL invoke their corresponding symbols from the GeoAge font group to generate map unit labels with conventional stratigraphic symbols.", + "license": "proprietary" + }, + { + "id": "USGS_OFR01-311_Version 1.0", + "title": "Geologic Map of the Cucamonga Peak 7.5' Quadrangle, San Bernardino County, California, USGS OFR 01-311", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1974-01-01", + "end_date": "1981-12-31", + "bbox": "-117.62509, 34.124985, -117.49991, 34.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553911-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553911-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-311_Version%201.0", + "description": "The data set for the Cucamonga Peak 7.5' quadrangle has been prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as part of an ongoing effort to develop a regional geologic framework of southern California, andto utilize a Geographical Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the Cucamonga Peak 7.5' quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, the U.S. Forest Service and the San Bernardino National Forest may use the map and data base as a basic geologic data source for soil studies, mineral resource evaluations, road building, biologicalsurveys, and general forest management. The Cucamonga Peak database is not suitablefor site-specific geologic evaluations at scales greater than 1:24,000 (1in = 2,000 ft.). This data set maps and describes the geology of the Cucamonga Peak 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the database consists of the following items: (1) a map coverage containing geologic contacts and units, (2) a coverage containing site-specific structural data, (3) a coverage containing geologic-unit label leaders and their associated attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of this Readme (including the metadata file as an appendix) and the graphic produced by the PostScript plot file. The Cucamonga Peak quadrangle includes part of the boundary between two major physiographic provinces of California, the Transverse Ranges Province to the north and the Peninsular Ranges Province to the south. The north part of the quadrangle isin the eastern San Gabriel Mountains, and the southern part includes an extensive quaternary alluvial-fan complex flanking the upper Santa Ana River valley, the northernmost part of the Peninsular Ranges Province. Thrust faults of the active Cucamonga Fault zone along the the south margin of the San Gabriel Mountains are the rejuvenated eastern terminus of a major old fault zone that bounds the south side of the western and central Transverse Ranges (Morton and Matti, 1993). Rejuvenation of this old fault zone, including the Cucamonga Fault zone, is apparently in response to compression in the eastern San Gabriel Mountains resulting from initiation of right-lateral slip on the San Jacinto Fault zone in the Peninsular Ranges. Within the northern part of the quadrangle are several arcuate-in-plan faults that are part of an antiformal, schuppen-like fault complex of the eastern San Gabriel Mountains. Most of these arcuate faults are reactivated and deformed older faults that probably include the eastern part of the San Gabriel Fault. The structural grain within the San Gabriel Mountains, as defined by basement rocks, is generally east striking. Within the Cucamonga Peak quadrangle, these basement rocks include a Paleozoic schist and gneiss sequence which occurs as large, continuous and discontinuous bodies intruded by Cretaceous granitic rocks. Most of the granitic rocks are of tonalitic composition, and many are mylonitic. South of the granitic rocks is a comple assemblage of Proterozoic(?) metamorphic rocks, at least part of which is metasedimentary. This assemblage is intruded by Cretaceous tonalite on its north side, and by charnockitic rocks near the center of the mass. The charnockitic rocks are in contact with no other Cretaceous granitic rocks. Consequently, their relative position in the intrusive sequence is unknown. The Proterozoic(?) assemblage was metamorphosed to upper amphibolite and lower granulite grade, and subsequently to a lower metamorphic grade. It is also intensely deformed by mylonitization characterized by an east-striking, north-dipping foliation, and by a pronounced subhorizontal lineation that plunges shallowly east and west. The southern half of the quadrangle is dominated by extensive, symmetrical alluvial-fan complexes, particularly two emanating from Day and Deer Canyons. Other Quaternary units ranging from early Pleistocene to recent are mapped, and represent alluvial-fan, landslide, talus, and wash environments. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. This digital Open-File map supercedes an older analog Open-File map of the quadrangle, and includes extensive new data on the Quaternary deposits, and revises some fault and bedrock distribution within the San Gabriel Mountains. The digital map was compiled on a base-stable cronoflex copy of the Cucamonga Peak 7.5' topographic base and then scribed. This scribe guide was used to make a 0.007 mil blackline clear-film, from which lines and point were hand digitized. Lines, points, and polygons were subsequently edited at the USGS using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum.", + "license": "proprietary" + }, { "id": "USGS_OFR01-318", "title": "Coal Geology, Land Use, and Human Health in the Peoples Republic of China", @@ -182324,6 +186796,19 @@ "description": "The purpose of this dataset is to give geologists and other scientists a spatial database of major coal mines and their production in China. This data set will be utilized in energy research and cartographic projects. This dataset is a collection of major coal mine locations and production data for The Peoples Republic of China. Included in this dataset are the locations of major coal mines, Coal Mining Administrations, and the approximate annual amount of coal and type mined annually in millions of metric tons. Procedures_Used: The major coal mine production points were digitized utilizing ARC/VIEW from page-size maps found in the U.S. Environmental Protection Agency Report ERP 430-R-96-005. Revisions: none Reviews_Applied_to_Data: None", "license": "proprietary" }, + { + "id": "USGS_OFR01-31_Version 1.0", + "title": "Geologic Map and Digital Database of the Conejo Well 7.5 Minute Quadrangle, Riverside County, California, USGs OFR 01-31", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1973-01-01", + "end_date": "2000-12-31", + "bbox": "-115.75009, 33.749985, -115.62491, 33.875", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552761-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552761-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01-31_Version%201.0", + "description": "The data set for the Conejo Well quadrangle has been prepared by the Southern California Areal Mapping Project (SCAMP), a cooperative project sponsored jointly by the U.S. Geological Survey and the California Division of Mines and Geology. The Conejo Well data set represents part of an ongoing effort to create a regional GIS geologic database for southern California. This regional digital database, in turn, is being developed as a contribution to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The Conejo Well database has been prepared in cooperation with the National Park Service as part of an ongoing project to provide Joshua Tree National Park with a geologic map base for use in managing Park resources and developing interpretive materials. The digital geologic map database for the Conejo Well quadrangle has been created as a general-purpose data set that is applicable to land-related investigations in the earth and biological sciences. Along with geologic map databases in preparation for adjoining quadrangles, the Conejo Well database has been generated to further our understanding of bedrock and surficial processes at work in the region and to document evidence for seismotectonic activity in the eastern Transverse Ranges. The database is designed to serve as a base layer suitable for ecosystem and mineral resource assessment and for building a hydrogeologic framework for Pinto Basin. This data set maps and describes the geology of the Conejo Well 7.5 minute quadrangle, Riverside County, southern California. The quadrangle, situated in Joshua Tree National Park in the eastern Transverse Ranges physiographic and structural province, encompasses part of the northern Eagle Mountains and part of the south flank of Pinto Basin. It is underlain by a basement terrane comprising Proterozoic metamorphic rocks, Mesozoic plutonic rocks, and Mesozoic and Mesozoic or Cenozoic hypabyssal dikes. The basement terrane is capped by a widespread Tertiary erosion surface preserved in remnants in the Eagle Mountains and buried beneath Cenozoic deposits in Pinto Basin. Locally, Miocene basalt overlies the erosion surface. A sequence of at least three Quaternary pediments is planed into the north piedmont of the Eagle Mountains, each in turn overlain by successively younger residual and alluvial deposits. The Tertiary erosion surface is deformed and broken by north-northwest-trending, high-angle, dip-slip faults in the Eagle Mountains and an east-west trending system of high-angle dip- and left-slip faults. In and adjacent to the Conejo Well quadrangle, faults of the northwest-trending set displace Miocene sedimentary rocks and basalt deposited on the Tertiary erosion surface and Pliocene and (or) Pleistocene deposits that accumulated on the oldest pediment. Faults of this system appear to be overlain by Pleistocene deposits that accumulated on younger pediments. East-west trending faults are younger than and perhaps in part coeval with faults of the northwest-trending set. The Conejo Well database was created using ARCVIEW and ARC/INFO, which are geographical information system (GIS) software products of Environmental Systems Research Institute (ESRI). The database consists of the following items: (1) a map coverage showing faults and geologic contacts and units, (2) a separate coverage showing dikes, (3) a coverage showing structural data, (4) a point coverage containing line ornamentation, and (5) a scanned topographic base at a scale of 1:24,000. The coverages include attribute tables for geologic units (polygons and regions), contacts (arcs), and site-specific data (points). The database, accompanied by a pamphlet file and this metadata file, also includes the following graphic and text products: (1) A portable document file (.pdf) containing a navigable graphic of the geologic map on a 1:24,000 topographic base. The map is accompanied by a marginal explanation consisting of a Description of Map and Database Units (DMU), a Correlation of Map and Database Units (CMU), and a key to point-and line-symbols. (2) Separate .pdf files of the DMU and CMU, individually. (3) A PostScript graphic-file containing the geologic map on a 1:24,000 topographic base accompanied by the marginal explanation. (4) A pamphlet that describes the database and how to access it. Within the database, geologic contacts , faults, and dikes are represented as lines (arcs), geologic units as polygons and regions, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum and link it to other tables (.rel) that provide more detailed geologic information. Map nomenclature and symbols Within the geologic map database, map units are identified by standard geologic map criteria such as formation-name, age, and lithology. The authors have attempted to adhere to the stratigraphic nomenclature of the U.S. Geological Survey and the North American Stratigraphic Code, but the database has not received a formal editorial review of geologic names. Special symbols are associated with some map units. Question marks have been added to the unit symbol (e.g., QTs?, Jmi?) and unit name where unit assignment based on interpretation of aerial photographs is uncertain. Question marks are plotted as part of the map unit symbol for those polygons to which they apply, but they are not shown in the CMU or DMU unless all polygons of a given unit are queried. To locate queried map-unit polygons in a search of database, the question mark must be included as part of the unit symbol. In some polygons, multiple units crop out in individual domains that are too small or too intricately intermingled to distinguish at 1:24,000, or for which relations are not well documented. For these polygons, unit symbols are combined using plus (+) signs (e.g., Qyaos + Qyas2) in the LABL and PLABL items. Geologic map unit labels entered in database items LABL and PLABL contain substitute characters for conventional stratigraphic age symbols: Proterozoic appears as 'Pr' in LABL and as '<' in PLABL, Triassic appears as 'Tr' in LABL and as '^' in PLABL. The substitute characters in PLABL invoke their corresponding symbols from the GeoAge font group to generate map unit labels with conventional stratigraphic symbols.", + "license": "proprietary" + }, { "id": "USGS_OFR01-321_Version 1.0", "title": "Chromite Deposits in Stillwater Complex, Sweet Grass Co., MT: A Digital Database for the Geologic Map of the East Slope of Iron Mountain, USGS OFR 01-321", @@ -182350,6 +186835,45 @@ "description": "Chemical Composition of Samples Collected from Waste Rock Dumps and Other Mining-Related Features at Selected Phosphate Mines in Southeastern Idaho, Western Wyoming, and Northern Utah The sampling effort was undertaken as a reconnaissance and does not constitute a characterization of mine wastes. Twenty-five samples were collected from waste rock dumps, 2 from stockpiles, and 1 each from slag, tailings, mill shale, and an outcrop. All samples were analyzed for a suite of major, minor, and trace elements. This text file contains chemical analyses for 31 samples collected from various phosphate mine sites in southeastern Idaho (25), northern Utah (2), and western Wyoming (4).", "license": "proprietary" }, + { + "id": "USGS_OFR01139_Version 1.0", + "title": "Geochemical Analysis of Soils and Sediments, Coeur d'Alene Drainage Basin, Idaho: Sampling, Analytical Methods, and Results, USGS OFR 00-139", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1993-01-01", + "end_date": "2000-12-31", + "bbox": "-116.73, 47.47, -115.72, 47.58", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549496-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549496-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR01139_Version%201.0", + "description": "This report was prepared to document the chemical composition of sediments and soils in the Coeur d'Alene (CdA) drainage basin in northern Idaho. These compositions are of interest because of the potential for human and wildlife health impacts from high metal contents of some sediments and soils from over 100 years of mining activity. This report presents the results of over 1100 geochemical analyses of samples of soil and sediment from the Coeur d'Alene (CdA) drainage basin in northern Idaho. The location (in 3 dimensions) and a lithological description of each sample is included with the laboratory analytical data. Methods of sample location, collection, preparation, digestion and geochemical analysis are described. Five different laboratories contributed geochemical data for this report and the quality control procedures used by each laboratory are described. Comparison of the analytical accuracy and precision of each laboratory is given by comparing analyses of standard reference materials and of splits of CdA samples. These geochemical data are presented in seven MS Excel tables and seven dBase4 tables. The seven dBase4 files allow users to more easily import these geochemical data into a GIS. Only one of these seven tables includes geospatial data AppendixB. However, in AppendixB there is a Site ID column that will allow users to link or join the matching Site Id columns in the six associated lithologic and geochemical tables. Due to format constraints of dBase4, the column names (headers) had to be modified to a maximum of only ten ASCII characters. As a result, some of the dBase4 column header names can be rather cryptic. To assist dBase files users, this ten digit dBase4 column name is also found directly under the more descriptive column names found in the MS Excel tables packaged with this report. Additional formatting requirements such as changing the below detection limit symbol (<) to a negative symbol (-) were used to accurately display the data the dBase4 format. This dataset consists of the following MS Excel 2000 spreadsheets and equivalent dBase4 files: AppendixB.xls, AppendixB.dbf: sample location and site description AppendixC.xls, AppendixC.dbf: lithologic descriptions of samples AppendixD.xls, AppendixD.dbf: USGS EDXRF Data AppendixE.xls, AppendixE.dbf: EWU 4-acid ICP-MS, ICP-AES, and FAA Data AppendixF.xls, AppendixF.dbf: CHEMEX nitric/aqua regia ICP-AES Data AppendixG.xls, AppendixG.dbf: XRAL 4-acid ICP-AES Data AppendixH.xls, AppendixH.dbf: ACZ microwave assisted nitric acid ICP-AES Data", + "license": "proprietary" + }, + { + "id": "USGS_OFR_00-376_1.0", + "title": "Geologic map and database of the Roseburg 30 x 60 minute quadrangle, Douglas and Coos Counties, Oregon, USGS OFR 00-376", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-123.99993, 42.995632, -122.99986, 43.504375", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548720-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548720-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00-376_1.0", + "description": "This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:100,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the Oregon State Geologist. Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, largely compiled from new mapping by the authors, represents the general distribution of bedrock and surficial deposits of the Roseburg 30 x 60 minute quadrangle along the southeastern margin of the Oregon Coast Range and its tectonic boundary with Mesozoic terranes of the Klamath Mountains. Together with the accompanying text files as PDF (rb_geol.pdf), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps is 1:24,000, but the Quaternary contacts and structural data have been much simplified for the 1:100,000-scale map and database. The spatial resolution (scale) of the database is 1:100,000 or smaller.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_00135_Version 1.0", + "title": "Digital geologic map of the Coeur d'Alene 1:100,000 quadrangle, Idaho and Montana", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-10-05", + "end_date": "2000-10-05", + "bbox": "-117, 47.5, -116, 48", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550217-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550217-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00135_Version%201.0", + "description": "This data set was developed to provide geologic map GIS of the Coeur d'Alene 1:100,000 quadrangle for use in future spatial analysis by a variety of users. These data can be printed in a variety of ways to display various geologic features or used for digital analysis and modeling. This database is not meant to be used or displayed at any scale larger than 1:100,000 (e.g. 1:62,500 or 1:24,000). The digital geologic map of the Coeur d'Alene 1:100,000 quadrangle was compiled from preliminary digital datasets [Athol, Coeur d'Alene, Kellogg, Kingston, Lakeview, Lane, and Spirit Lake 15-minute quadrangles] prepared by the Idaho Geological Survey from A. B. Griggs (unpublished field maps), supplemented by Griggs (1973) and by digital data from Bookstrom and others (1999) and Derkey and others (1996). The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps. This GIS consists of two major Arc/Info data sets: one line and polygon file (cda100k) containing geologic contacts and structures (lines) and geologic map rock units (polygons), and one point file (cda100kp) containing structural data.", + "license": "proprietary" + }, { "id": "USGS_OFR_00142", "title": "Archive of Boomer and Sparker Subbottom Data Collected During USGS Cruise DIAN 97032 Long Island, NY, Inner Shelf-Fire Island, NY, September 24-October 19, 1997", @@ -182363,6 +186887,32 @@ "description": "This project will generate reconnaissance maps of the sea floor offshore of the New York - New Jersey metropolitan area -- the most heavily populated, and one of the most impacted coastal regions of the United States. The surveys will provide an overall synthesis of the sea floor environment, including seabed texture and bed forms, Quaternary strata geometry, and anthropogenic impact (e.g., ocean dumping, trawling, channel dredging). The goal of this project is to survey the offshore area, the harbor, and the southern shore of Long Island, providing a regional synthesis to support a wide range of management decisions and a basis for further process-oriented investigations. The project is conducted cooperatively with the U.S. Army Corps of Engineer (USACE). This CD-ROM contains digital high resolution seismic reflection data collected during the USGS Diane G 97032 cruise. The coverage is the nearshore of Long Island, NY in the vicinity of Fire Island. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed.", "license": "proprietary" }, + { + "id": "USGS_OFR_00145_Version 1.0", + "title": "Digital Geologic Map of the Butler Peak 7.5' Quadrangle, San Bernardino County, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-117.12509, 34.249985, -116.99991, 34.375004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549734-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549734-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00145_Version%201.0", + "description": "The data set for the Butler Peak quadrangle has been prepared by the Southern California Areal Mapping Project (SCAMP), a cooperative project sponsored jointly by the U.S. Geological Survey and the California Division of Mines and Geology, as part of an ongoing effort to utilize a Geographical Information System (GIS) format to create a regional digital geologic database for southern California. This regional database is being developed as a contribution to the National Geologic Map Data Base of the National Cooperative Geologic Mapping Program of the USGS. Development of the data set for the Butler Peak quadrangle has also been supported by the U.S. Forest Service, San Bernardino National Forest. The digital geologic map database for the Butler Peak quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, the U.S. Forest Service, San Bernardino National Forest, is using the database as part of a study of an endangered plant species that shows preference for particular rock type environments. The Butler Peak database is not suitable for site-specific geologic evaluations at scales greater than 1:24,000 (1 in = 2,000 ft). This data set maps and describes the geology of the Butler Peak 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage showing geologic contacts and units,(2) a scanned topographic base at a scale of 1:24,000, and (3) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map on a 1:24,000 topographic base accompanied by a Description of Map Units (DMU), a Correlation of Map Units (CMU), and a key to point and line symbols; (2) PDF files of the DMU and CMU, and of this Readme, and (3) this metadata file. The geologic map data base contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. The map was created by transferring lines from the aerial photographs to a 1:24,000 mylar orthophoto-quadrangle and then to a base-stable topographic map. This map was then scribed, and a .007 mil, right-reading, black line clear film made by contact photographic processes.The black line was scanned and auto-vectorized by Optronics Specialty Company, Northridge, CA. The non-attributed scan was imported into ARC/INFO, where the database was built. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum and link it to other tables (.rel) that provide more detailed geologic information.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_0014_version 1.0", + "title": "Geologic datasets for weights of evidence analysis in northeast Washington--4. Mineral industry activity in Washington, 1985-1997.", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1985-01-01", + "end_date": "1997-12-31", + "bbox": "-123, 45, -117, 49", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551581-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551581-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_0014_version%201.0", + "description": "This dataset was developed to provide mineral resource data for the region of northeast WA for use in future spatial analysis by a variety of users. This database is not meant to be used or displayed at any scale larger than 1:24,000 This report is a tabular presentation of mineral activities for mining and exploration in Washington during 1985 to 1997. The data may be incomplete as it depended on published data or data volunteered by operators.", + "license": "proprietary" + }, { "id": "USGS_OFR_00152", "title": "Archive of Chirp Subbottom Data Collected During USGS Cruise DIAN 97032, Long Island, NY Inner Shelf -- Fire Island, NY, 25 September-19 October, 1997", @@ -182389,6 +186939,19 @@ "description": "In November 1999, the U. S. Geological Survey, in cooperation with Coastal Carolina University, began a program to produce geologic maps of the nearshore regime off northern South Carolina, utilizing high resolution sidescan sonar, interferometric (direct phase methods) swath bathymetry, and seismic subbottom profiling systems. The study areas extends from the ~7m isobath to about 10km offshore (water depths <12m). The goals of the investigation are to determine regional scale sand resource availability needed for planned beach nourishment programs, to investigate the roles that the inner shelf morphology and geologic framework play in the evolution of this coastal region, and to provide baseline geologic maps for use in proposed biologic habitat studies. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS ATSV 99044 cruise. The coverage is the nearshore of the northern South Carolina. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed.", "license": "proprietary" }, + { + "id": "USGS_OFR_00175_Version 1.0", + "title": "Geologic Map and Digital Database of the Cougar Buttes 7.5' Quadrangle, San Bernardino County, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-116.87509, 34.374985, -116.74991, 34.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554454-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554454-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00175_Version%201.0", + "description": "The data set for the Cougar Buttes quadrangle has been prepared by the Southern California Areal Mapping Project (SCAMP), a cooperative project sponsored jointly by the U.S. Geological Survey and the California Division of Mines and Geology, as part of an ongoing effort to utilize a Geographical Information System (GIS) format to create a regional digital geologic database for southern California. This regional database is being developed as a contribution to the National Geologic Map Data Base of the National Cooperative Geologic Mapping Program of the USGS. Development of the data set for the Cougar Buttes quadrangle has also been supported by the Mojave Water Agency and U.S. Forest Service, San Bernardino National Forest. The digital geologic map database for the Cougar Buttes quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. In cooperation with the Water Resources Division of the U.S. Geological Survey, we have used our mapping in the Cougar Buttes and adjoining quadrangles together with well log data to develop a hydrogeologic framework for the basin. In an effort to understand surficial processes and to provide a base suitable for ecosystem assessment, we have differentiated surficial veneers on piedmont and pediment surfaces and distinguished the various substrates found beneath these veneers. Currently, the geologic database for the Cougar Buttes quadrangle is being applied in groundwater investigations in the Lucerne Valley basin (USGS, Water Resources Division), in biological species studies of the Cushenbury Canyon area (U.S. Forest Service, San Bernardino National Forest), and in the study of soils on various Quaternary landscape surfaces on the north piedmont of the San Bernardino Mountains (University of New Mexico). The Cougar Buttes database is not suitable for site-specific geologic evaluations at scales greater than 1:24,000 (1 in = 2,000 ft). This data set maps and describes the geology of the Cougar Buttes 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage showing geologic contacts and units, (2) a separate coverage layer showing structural data, (3) a scanned topographic base at a scale of 1:24,000, and (4) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). The data base is accompanied by a readme file and this metadata file. In addition, the data set includes the following graphic and text products: (1) A portable document file (.pdf) containing a browse-graphic of the geologic map on a 1:24,000 topographic base. The map is accompanied by a marginal explanation consisting of a Description of Map Units (DMU), a Correlation of Map Units (CMU), and a key to point and line symbols. (2) Separate .pdf files of the DMU and CMU, individually. (3) A PostScript graphic plot-file containing the geologic map on a 1:24,000 topographic base accompanied by the marginal explanation. (4) A pamphlet that summarizes the late Cenozoic geology of the Cougar Buttes quadrangle. The geologic map data base contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs, including low-altitude color and black-and-white photographs and high-altitude infrared photographs. The map was created by transferring lines from the aerial photographs to a 1:24,000 topographic base via a mylar orthophoto-quadrangle or by using a PG-2 plotter. The map was then scribed, scanned, and imported into ARC/INFO, where the database was built. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum and link it to other tables (.rel) that provide more detailed geologic information.", + "license": "proprietary" + }, { "id": "USGS_OFR_00177", "title": "Archive of Sidescan-Sonar Data and DGPS Navigation Data Collected During USGS Cruise ATSV 99045 North Carolina Coast, NC, 7-27 October, 1999", @@ -182402,6 +186965,32 @@ "description": "In 1999, the USGS began developing a cooperative mapping program in North Carolina, with collaborators at the North Carolina Geological Survey (NCGS), and academic institutions. The goal of the program is to develop a refined understanding of the regional geological framework and non-living resources of the North Carolina coastal area, including the emerged and submerged portions of the Coastal Plain. The USGS Coastal and Marine Geology Program is focusing on nearshore morphologic evolution (using LIDAR), short-term shoreline change (with SWASH), and with the present cruise, collecting data on the geologic framework of the shoreface and inner continental shelf. The goal of the inner shelf mapping program is to provide a regional synthesis of the seafloor environment, including a description of sedimentary environments, sediment texture, seafloor morphology, and shallow stratigraphy to aid in understanding the long- and short-term evolution of the coastal system, the form and distribution of sand and gravel resources, and to provide a basis for sediment dynamics studies.", "license": "proprietary" }, + { + "id": "USGS_OFR_00192_Version 1.0", + "title": "Geologic Map of the Christian Quadrangle, Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-147, 67, -144, 68", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549713-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549713-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00192_Version%201.0", + "description": "Digital representation of geologic mapping facilitates the presentation and analysis of earth-science data. Digital maps may be displayed at any scale or projection, however the geologic data in this coverage is not intended for use at a scale larger than 1:250,000. This data set represents reconnaissance geologic mapping of the Christian quadrangle, Alaska. It is used to create the mapsheet in USGS OFR 00-192, which shows bedrock and surficial deposits of the 1:250,000 scale Christian quadrangle in northern Alaska.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_00222_1.0", + "title": "Geologic Map Database of the El Mirage Lake Area, San Bernardino and Los Angeles Counties, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-01-01", + "end_date": "1999-12-31", + "bbox": "-117.690704, 34.49907, -117.50146, 34.73541", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549328-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549328-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00222_1.0", + "description": "This geologic map database for the El Mirage Lake area describes geologic materials for the dry lake, parts of the adjacent Shadow Mountains and Adobe Mountain, and much of the piedmont extending south from the lake upward toward the San Gabriel Mountains. This area lies within the western Mojave Desert of San Bernardino and Los Angeles Counties, southeastern California (see Fig. 1). The area is traversed by a few paved highways that service the community of El Mirage, and by numerous dirt roads that lead to outlying properties. An off-highway vehicle area established by the Bureau of Land Management encompasses the dry lake and much of the land north and east of the lake. The physiography of the area consists of the dry lake, flanking mud and sand flats and alluvial piedmonts, and a few sharp craggy mountains. This digital geologic map database, intended for use at 1:24,000-scale, describes and portrays the rock units and surficial deposits of the El Mirage Lake area. The map database was prepared to aid in a water-resource assessment of the area by providing surface geologic information with which deepergroundwater-bearing units may be understood. The area mapped covers the Shadow Mountains SE and parts of the Shadow Mountains, Adobe Mountain, and El Mirage 7.5-minute quadrangles (see Fig. 2). The map includes detailed geology of surface and bedrock deposits, which represent a significant update from previous bedrock geologic maps by Dibblee (1960) and Troxel and Gunderson (1970), and the surficial geologic map of Ponti and Burke (1980); it incorporates a fringe of the detailed bedrock mapping in the Shadow Mountains by Martin (1992). The map data were assembled as a digital database using ARC/INFO to enable wider applications than traditional paper-product geologic maps and to provide for efficient meshing with other digital data bases prepared by the U.S. Geological Survey's Southern California Areal Mapping Project. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_00241", "title": "Archive of Boomer and Sparker Subbottom Data Collected During USGS Cruise DIAN 97011, Long Island, NY Inner Shelf -- Fire Island, New York, 5-26 May, 1997", @@ -182441,6 +187030,19 @@ "description": "In 1995, the USGS Woods Hole Field Center, in Cooperation with the U.S. Army Corps of Engineers, began a program designed to map the seafloor offshore of the New York-New Jersey metropolitan area, the most heavily populated, and one of the most impacted coastal regions of the United States. The ultimate goal of this program is to provide an overall synthesis of the sea floor environment, including surficial sediment texture, subsurface geometry, and anthropogenic impact (e.g. ocean dumping, trawling, channel dredging). Sidescan sonar and high-resoluion seismic reflection profiling have been used to map the region north of about 42o10' and west of about 73o15'. The Hudson Shelf Valley, a shallow topographic feature that cuts across the shelf from offshore of New York to the shelf edge, was mapped using multibeam. This regional synthesis will support a wide range of management decisions and will provide a basis for further process-oriented investigations. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS MGNM 99023 cruise. The coverage is the nearshore of Southern Long Island and the Hudson Shelf Valley. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed.", "license": "proprietary" }, + { + "id": "USGS_OFR_00304", + "title": "Georeferenced Sea-Floor Mapping and Bottom Photography in Long Island Sound, USGS OFR 00-304", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1952-01-01", + "end_date": "", + "bbox": "-73.0991, 41.0167, -72.7566, 41.2256", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552915-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552915-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304", + "description": "This Open File Report (00-304) consists of over 30 digital data sets representing GIS layers assessing the benthic communities and spatial seafloor structures of the Long Island Sound. These data sets are made available as a USGS Open File Report and correspond to research arctiles published in a thematic section of the Journal of Coastal Research (Vol. 16 (3), 2000).", + "license": "proprietary" + }, { "id": "USGS_OFR_00304_BENTHOS", "title": "Detailed analysis of 35 most common species found in Long Island Sound benthic communities, USGS OFR 00-304", @@ -182454,6 +187056,45 @@ "description": "This GIS layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. This GIS layer provides the location where samples from Pellegrino and Hubbard were summarized to provide detailed analysis of 35 common species found in Long Island Sound benthic communities.", "license": "proprietary" }, + { + "id": "USGS_OFR_00304_BUZAS", + "title": "M.A. Buzas benthic foraminiferal samples (1965) from Long Island Sound, USGS OFR 00-304", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1965-01-01", + "end_date": "1965-12-31", + "bbox": "-73.7, 40.8917, -72.4811, 41.2778", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550485-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550485-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_BUZAS", + "description": "The purpose of this layer is to disseminate a digital version of the location of samples collected and analyzed by M. A. Buzas in 1965. This GIS layer contains a point overlay showing the the distribution of benthic foraminiferal samples collected in 1965 by M. A. Buzas in Long Island Sound. Sediment samples were washed on a 0.062 mm sieve to separate the foraminifera from the silt and clay. Foraminifera were picked from the fraction retained on the sieve and individually identified and counted with a binocular microscope using reflected light. The foraminifera data and navigation were entered into a flat-file database (Excel) and inported into Mapview for graphical analysis. The files were subsequently exported into MIDMIF files, and converted into shape files with the Arc utility MIFSHAPE.EXE.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_00304_CPERFLOC", + "title": "Locations of sediment samples with Clostridium perfringens in Long Island Sound, USGS OFR 00-304", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-73.7469, 40.8405, -72.2128, 41.2919", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551771-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551771-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_CPERFLOC", + "description": "The purpose of this layer is to disseminate a digital version of the location of samples containing Clostridium perfringens, and concentrations of Clostridium perfringens in those samples. This GIS layer contains a point layer showing the location of surficial sediment samples in Long Island Sound containing Clostridium perfringens and the concentration of Clostridium perfringens in those samples. Grab samples were frozen at sea, and freeze-dried in the lab. Analyses for Clostridium perfringens were performed by Biological Analytical Labs of North Kingston, RI, according to methods described by Emerson and Cabelli (1982) and Bisson and Cabelli (1979). Data consisting of station navigation and Clostridium perfringens concentrations in the surficial sediments were inmported as text files.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_00304_CST27", + "title": "Medium resolution shoreline for the Long Island Sound study area, USGS OFR 00-304", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-01-01", + "end_date": "1994-12-31", + "bbox": "-74.4422, 40.2499, -71.4471, 41.5513", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551911-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551911-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_CST27", + "description": "This data layer provides a medium resolution coastline for the Long Island Sound Study Area in OFR 00-304. NOAA's Medium Resolution Digital Vector Shoreline is a high quality, GIS-ready, general-use digital vector data set created by the Strategic Environmental Assessments (SEA) Division of NOAA's Office of Ocean esources Conservation and Assessment. The coastlines are compiled from the NOAA coast charts. The specified section of NOAA's medium resolution shoreline was downloaded from their website. That file was clipped to include the are of interest for the Long Island Sound studies. NOAA's Medium Resolution Digital Vector Shoreline was compiled from hundreds of NOAA coast charts and comproses over 75,000 nautical miles of coastline. The portion contained here is part of the EC80_04 - Chincoteague Inlet Virginia to Block Island Sound Rhode Island data layer, which is part of the Atlantic East-Coast Section.", + "license": "proprietary" + }, { "id": "USGS_OFR_00304_FRANZ", "title": "Benthic community sediment samples in Long Island Sound collected by D. Franz (1976), USGS OFR 00-304", @@ -182467,6 +187108,58 @@ "description": "This GIS layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. This GIS layer provides the location where samples were taken in a survey conducted by D. Franz (1976).", "license": "proprietary" }, + { + "id": "USGS_OFR_00304_GRAVITY", + "title": "Free-air gravity of Long Island and Block Island Sounds, USGS OFR 00-304", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1975-01-01", + "end_date": "1975-12-31", + "bbox": "-73.7099, 40.8512, -71.5563, 41.3909", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554660-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554660-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_GRAVITY", + "description": "The purpose is to disseminate the only existing free-air gravity information in digital form to the research community, and to facilitate modern geophysical and environmental studies of the Long Island and Block Island Sounds. This GIS layer contains an interpretive layer represented by contour lines (2-mgal intervals) of the free-air gravity of Long Island and Block Island Sounds.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_00304_LISGRABS", + "title": "Long Island Sound metals sample distribution locations, USGS OFR 00-304", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-73.7469, 40.8363, -72.042, 41.3379", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550528-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550528-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_LISGRABS", + "description": "The purpose of this datalayer is to disseminate a digital version of the map showing the locations of surficial samples used in the analysis of metal distributions in Long Island Sound. This GIS layer contains a point overlay showing the location of surficial samples used in the analysis of metal distributions in Long Island Sound. Attribute information containing the chemical analysis values are also included in the data layer.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_00304_LISTEX", + "title": "Distribution of Surficial Sediments in Long Island Sound, USGS OFR 00-304", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-07-21", + "end_date": "2000-07-21", + "bbox": "-74.0491, 40.5031, -71.8185, 41.4348", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549629-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549629-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_LISTEX", + "description": "The purpose is to disseminate a digital version of a regional map showing the distribution of surficial sediments in Long Island Sound. Grain size is the most basic attribute of sediment texture, and texture controls many benthic ecological and chemical processes. This GIS layer contains an computer generated model of the distribution of surficial sediments in Long Island Sound.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_00304_LISTOC", + "title": "Distribution of Total Organic Carbon (TOC) in Long Island Sound, USGS OFR 00-304", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-06-01", + "end_date": "1998-03-31", + "bbox": "-74.131, 40.5037, -71.8412, 41.4201", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552639-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552639-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_LISTOC", + "description": "The purpose of this layer is to disseminate a digital version of the regional total organic carbon distribution in Long Island Sound. This GIS layer contains a polygon overlay showing the distribution of Total Organic Carbon (TOC) in the sediments of Long Island Sound. These data, which represent the only regional total organic carbon study of Long Island Sound, were originally published in USGS Open-File Report 98-502.", + "license": "proprietary" + }, { "id": "USGS_OFR_00304_MARINET", "title": "Depth of the Marine Transgressive Surface in Long Island Sound, USGS OFR 00-304", @@ -182493,6 +187186,19 @@ "description": "This GIS layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. This GIS layer provides the location where samples were taken in a survey conducted by P.L. McCall (1975).", "license": "proprietary" }, + { + "id": "USGS_OFR_00304_MOSAREA", + "title": "Extent of the area covered by the sidescan sonar mosaic from the study area off New London, CT, USGS OFR 00-304", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "2000-12-31", + "bbox": "-72.1383, 41.2512, -72.0307, 41.3143", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550556-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550556-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_MOSAREA", + "description": "This GIS layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. This GIS layer shows the extent of the area covered by the sidescan sonar mosaic from the study area off New London, CT.", + "license": "proprietary" + }, { "id": "USGS_OFR_00304_NLBENTHS", "title": "Benthic communities in the New London sidescan mosaic study area, USGS OFR 00-304", @@ -182506,6 +187212,32 @@ "description": "This data layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. The original studies were conducted to describe the benthic communities in Long Island Sound; the corresponding data layer is presented to show the available species richness data in eastern Long Island Sound. This data layer depicts benthic communities found in the New London sidescan sonar mosaic study area.", "license": "proprietary" }, + { + "id": "USGS_OFR_00304_NLMOSINT", + "title": "Interpretation of the sidescan sonar mosaic from the study area off New London, CT, USGS OFR 00-304", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "2000-12-31", + "bbox": "-72.1383, 41.2512, -72.0307, 41.3143", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552755-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552755-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_NLMOSINT", + "description": "This data layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. Mapping was performed on a sidescan sonar survey. This survey was processed at 3,479-scale utilizing the U.S.G.S. Mini Image Processing system (MIPS) in an Equatorial Mercator Projection. Processing included bottom, ratio, and radiometric corrections; sectioning the survey area; \"Geoming\" individual map sections; \"stenciling\" and \"mosaicing\"; and building the final image. The shading convention for this mosaic is that dark tones are interpreted as fine sediment (fine sand, silt and clay); and light tones are interpreted as coarse sediment. Rough and \"grainy\" patches are interpreted as glacial drift or bedrock outcrops.The image files contained here have been modified, using Arc/Info software, from the three original TIFFs delivered by University of Rhode Island. The images were converted to grids, geo-referenced, and individually reclassified in a manner similar to linear stretching to account for variations in gray scales among the three sections of the mosaic. The grids were then converted back to TIFF format with world files in Latitude/Longitude decimal degrees (no projection). Pixel size is approximately 0.8 meters. The original studies were conducted to describe the benthic communities in Long Island Sound; the corresponding data layer is presented to show the extent of the sidescan sonar mosaic off New London, in eastern Long Island Sound, and the distribution of habitats on the mosaic. This data layer is an interpretation of the sidescan sonar mosaic from the study area off New London, CT.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_00304_PARKER", + "title": "F. L. Parker benthic foraminiferal samples (1952) in Long Island Sound, USGS OFR 00-304", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1952-01-01", + "end_date": "1952-12-31", + "bbox": "-75.9367, 41.0617, -72.305, 41.26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555079-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555079-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_PARKER", + "description": "The purpose of this layer is to disseminate a digital version of the location of samples collected and analyzed by F. L. Parker in 1952 in Long Island Sound. This GIS layer contains a point overlay showing the the distribution of benthic foraminiferal samples collected in 1952 by F. L. Parker.", + "license": "proprietary" + }, { "id": "USGS_OFR_00304_PELLEGRI", "title": "Benthic community samples collected by Pellegrino and Hubbard (1983) in Long Island Sound, USGS OFR 00-304", @@ -182545,6 +187277,58 @@ "description": "This data layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. This GIS layer provides the location where samples were taken in a survey conducted by H.L. Sanders (1956). ", "license": "proprietary" }, + { + "id": "USGS_OFR_00304_TOCPNT1", + "title": "Location of Long Island Sound samples with Total Organic Carbon (TOC), USGS OFR 00-304", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-06-01", + "end_date": "1998-03-31", + "bbox": "-73.7482, 40.8367, -72.126, 41.3439", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553922-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553922-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00304_TOCPNT1", + "description": "The purpose of this data layer is to disseminate a digital version of the map showing the locations of surficial total organic carbon sampling stations in Long Island Sound. This GIS layer contains a point overlay showing the location of samples with Total Organic Carbon (TOC). This layer shows the distribution of samples used in the creation of the TOC polygon layer, listoc.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_00351_1.0", + "title": "Geologic map and database of the Salem East and Turner 7.5 minute quandrangles, Marion County, Oregon: A digital database, USGS OFR 00-351", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-123.00002, 44.874996, -122.87471, 44.999996", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549933-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549933-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00351_1.0", + "description": "This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:24,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the Oregon State Geologist. Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits of the Salem East and Turner 7.5 minute quadrangles. A previously published adjacent geologic map and database by Tolan, Beeson, and Wheeler (1999) contains a text file (geol.txt or geol.ps), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller. The Salem East and Turner 7.5-minute quadrangles are situated in the center of the Willamette Valley near the western margin of the Columbia River Basalt Group (CRBG) distribution. The terrain within the area is of low to moderate relief, ranging from about 150 to almost 1,100-ft elevation. Mill Creek flows northward from the Stayton basin (Turner quadrangle) to the northern Willamette Valley (Salem East quadrangle) through a low that dissects the Columbia River basalt that forms the Salem Hills on the west and the Waldo Hills to the east. Approximately eight flows of CRBG form a thickness of up to 700 in these two quadrangles. The Ginkgo intracanyon flow that extends from east to west through the south half of the Turner quadrangle is exposed in the hills along the southeast part of the quadrangle. The major emphasis of this study was to identify and map CRBG units within the Salem East and Turner Quadrangles and to utilize this detailed CRBG stratigraphy to identify and characterize structural features. Water well logs were used to provide better subsurface stratigraphic control. Three other quadrangles (Scotts Mills, Silverton, and Stayton NE) in the Willamette Valley have been mapped in this way (Tolan and Beeson, 1999). The databases in this report were compiled in ARC/INFO, a commercial Geographic Information System (Environmental Systems Research Institute, Redlands, California), with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files are in either GRID (ARC/ INFO raster data) format or COVERAGE (ARC/INFO vector data) format. Coverages are stored in uncompressed ARC export format (ARC/INFO version 7.x). ARC/INFO export files (files with the .e00 extension) can be converted into ARC/INFO coverages in ARC/INFO (see below) and can be read by some other Geographic Information Systems, such as MapInfo via ArcLink and ESRI's ArcView (version 1.0 for Windows 3.1 to 3.11 is available for free from ESRI's web site: \"http://www.esri.com\"). The digital compilation was done in version 7.1.1 of ARC/INFO with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The geologic map information was digitized from stable originals of the geologic maps at 1:24,000 scale. The author manuscripts (pen on mylar and pen on paper) were scanned using a Anatek rasterizing color scanner with a resolution of 600 and 400 dots per inch. The scanned images were vectorized and transformed from scanner coordinates to projection coordinates with digital tics placed by hand at quadrangle corners. The scanned lines were edited interactively by hand using ALACARTE, color boundaries were tagged as appropriate, and scanning artifacts visible at 1:24,000 were removed.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_00356_Version 1.0", + "title": "Geologic map of the Wildcat Lake 7.5' Quadrangle Kitsap and Mason Counties, Washington, USGS OFR 00-356", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-122.875, 47.5, -122.75, 47.625", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549341-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549341-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00356_Version%201.0", + "description": "Digital representation of geologic mapping facilitates the presentation and analysis of earth-science data. Digital maps may be displayed at any scale or projection, however, the geologic data in this coverage is not intended for use at a larger scale. This data set represents reconnaissance geologic mapping of the Wildcat Lake 7.5' Quadrangle, Kitsap and Mason Counties, Washington. It is used to create the map sheet in USGS OFR 00-356 , which shows bedrock, surficial, and structural geology of the Wildcat Lake Quadrangle. This data was hand digitized in ARC/Info from an unfolded paper 1:24,000 scale compilation map. The arcs and polygons were attributed. For the purposes of distribution, the coverage has been converted to an interchange format file using the ARC/Info export command.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_00359_Version 1.0", + "title": "Geologic Map and Digital Database of the Apache Canyon 7.5' Quadrangle, Ventura and Kern Counties, California, USGS OFR 00-359", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-119.37509, 34.749985, -119.24991, 34.875", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555330-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555330-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00359_Version%201.0", + "description": "The data set for the Apache Canyon quadrangle has been prepared by the Southern California Areal Mapping Project (SCAMP), a cooperative project sponsored jointly by the U.S. Geological Survey and the California Division of Mines and Geology, as part of an ongoing effort to utilize a Geographical Information System (GIS) format to create a regional digital geologic database for southern California. This regional database is being developed as a contribution to the National Geologic Map Data Base of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the Apache Canyon quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. The Apache Canyon database is not suitable for site-specific geologic evaluations at scales greater than 1:24,000 (1 in = 2,000 ft). This data set maps and describes the geology of the Apache Canyon 7.5' quadrangle, Ventura and Kern Counties, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage showing geologic contacts, faults and units, (2) a separate coverage layer showing structural data, (3) an additional point coverage which contains bedding data, (4) a point coverage containing sample localities, (5) a scanned topographic base at a scale of 1:24,000, and (6) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). The data base is accompanied by a readme file and this metadata file. In addition, the data set includes the following graphic and text products: (1) A jpg file (.jpg) containing a browse-graphic of the geologic map on a 1:24,000 topographic base. The map is accompanied by a marginal explanation consisting of a List of Map Units, a Correlation of Map Units, and a key to point and line symbols. (2) A .pdf file of a geologic explanation pamphlet that includes a Description of Map Units. (3) Two postScript graphic plot-files: one containing the geologic map on a 1:24,000 topographic base and the other, three accompanying structural cross sections. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. The map was created by transferring lines and point data from the aerial photographs to a 1:24,000 topographic base by using a PG-2 plotter. The map was scribed, scanned, and imported into ARC/INFO, where the database was built. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum and link it to other tables (.rel) that provide more detailed geologic information.", + "license": "proprietary" + }, { "id": "USGS_OFR_00366", "title": "Archive of Chirp Data Collected During USGS Cruise DIAN 97011 Long Island, NY Inner Shelf -- Fire Island, 5-26 May, 1997, USGS OFR 00-306", @@ -182584,6 +187368,19 @@ "description": "In November 1999, the U. S. Geological Survey, in cooperation with Coastal Carolina University, began a program to produce geologic maps of the nearshore regime off northern South Carolina, utilizing high resolution sidescan sonar, interferometric (direct phase methods) swath bathymetry, and seismic subbottom profiling systems. The study areas extends from the ~7m isobath to about 10km offshore (water depths <12m). The goals of the investigation are to determine regional scale sand resource availability needed for planned beach nourishment programs, to investigate the roles that the inner shelf morphology and geologic framework play in the evolution of this coastal region, and to provide baseline geologic maps for use in proposed biologic habitat studies. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS ATSV 99044 cruise. The coverage is the nearshore of the northern South Carolina. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed.", "license": "proprietary" }, + { + "id": "USGS_OFR_00409_Digital Version 1.0", + "title": "Digital Geologic Map of Arizona: A Digital Database Derived from the 1983 Printing of the Wilson, Moore, and Cooper 1:500,000-scale Map, USGS OFR 00-409", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-01-01", + "bbox": "-115, 31.25, -108.75, 37", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549678-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549678-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_00409_Digital%20Version%201.0", + "description": "This database was developed to provide a GIS of the geologic map of the State of Arizona for use at a scale of 1:500,000 or smaller. This GIS is intended for use in future spatial analysis by a variety of users. The geologic unit descriptions for this map may be updated to reflect more current description of structures and the geochronology of the map units. This database is not meant to be used or displayed at any scale larger than 1:500,000 (e.g., 1:100,000 or 1:24,000) The Geologic Map of Arizona was compiled at a scale of 1:500,000 by Eldred D. Wilson, Richard T. Moore and John R. Cooper, in 1969 and reprinted in 1977, 1981, and 1983. Comparison of an acetate copy of the 1983 map with existing paper copies of earlier maps shows some updating of the original by 1983. This 1983 acetate was scanned and vectorized by Optronics Specialty Co., Inc. in 1998, and put into an Arc/Info geographic information system (GIS). The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps. This GIS database consists of 4 Arc/Info data sets: one line and polygon file (azgeol) containing geologic contacts and structures (lines) and geologic map rock units (polygons), one line file (azfold) containing the folds and crater boundaries, one point file (azptfeat) containing geologic features, cinder cones and diatremes. one point file (azptdec) containing decorations, and", + "license": "proprietary" + }, { "id": "USGS_OFR_011_version 1.0", "title": "Dataset of Aggregate Producers in New Mexico, USGS Open File Report 011", @@ -182597,6 +187394,110 @@ "description": "This data set was developed as part of a larger effort by the U.S. Geological Survey to provide plottable locations of aggregate producers for National Atlas and for aggregate research. This data set contains latitudes, longitudes, and other descriptive data for aggregate producers in New Mexico that are believed to have been active during the period 1997-1999. The data in this compilation were derived from U.S. Geological Survey files, U.S. Bureau of Land Management files, contact with producers, and reports from the New Mexico Bureau of Mines and Mineral Resources, the New Mexico Bureau of Mine Inspection, and the New Mexico Mining and Mineral Division. This dataset includes 2 tables: Table 1 contains crushed stone operations and table 2 contains sand and gravel operations. This data set consists of one Excel 97 spreadsheet file (NMsandg2.xls) which contains information about Sand Gravel operations in New Mexico and one Excel 97 spreadsheet file NMcstn1.xls) which contains information about Crushed Stone operations in New Mexico. The files are also included in DIF format under the same filenames, but with the .DIF extension.", "license": "proprietary" }, + { + "id": "USGS_OFR_02-266", + "title": "Ice Core Depth-Age Relation for Vostok (delD) and Dome Fuji (del18O) Records Based on the Devils Hole Paleotemperature Chronology, USGS Open File Report 02-266", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-116.3, -78.5, 40, 36.42", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548530-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548530-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_02-266", + "description": "This report presents the data for the Vostok - Devils Hole chronology, termed V-DH chronology, for the Antarctic Vostok ice core record. This depth - age relation is based on a join between the Vostok deuterium profile (delD) and the stable oxygen isotope ratio (del18O) record of paleotemperature from a calcitic core at Devils Hole, Nevada, using the algorithm developed by Landwehr and Winograd (2001). Both the control points defining the V-DH chronology and the numeric values for the chronology are given. In addition, a plausible chronology for a deformed bottom portion of the Vostok core developed with this algorithm is presented. Landwehr and Winograd (2001) demonstrated the broader utility of their algorithm by applying it to another appropriate Antarctic paleotemperature record, the Antarctic Dome Fuji ice core del18O record. Control points for this chronology are also presented in this report but deemed preliminary because, to date, investigators have published only the visual trace and not the numeric values for the Dome Fuji del18O record. The total uncertainty that can be associated with the assigned ages is also given.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_02005", + "title": "Multibeam Mapping of the West Florida Shelf, Gulf of Mexico, USGS OFR 02-005", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-09-03", + "end_date": "2001-10-12", + "bbox": "-86.71667, 28, -84.583336, 31.083334", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554018-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554018-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_02005", + "description": "Our objective was to map the region between the 50 to 150-m isobaths south from the eastern edge of De Soto Canyon as far as Steamboat Lumps using a state-of-the-art multibeam mapping system (MBES). The cruise used a Kongsberg Simrad EM1002 MBES, the latest generation of high-resolution mapping systems. The EM1002 produces both geodetically accurate georeferenced bathymetry and coregistered, calibrated, acoustic backscatter. Acoustic backscatter is the intensity of an acoustic pulse that is backscattered off the seafloor back to the transducer. The signal can give an indication of the type of material exposed on the ocean floor (i.e. rock vs. mud). These data should prove extremely useful in relating dominant species groups (which display highly specific biotope affinities) to the geomorphology (e.g., reef flattop, forereef crest, reef wall, reef base, circum-reef talus zone, circum-reef, high-reflectivity sediment apron, etc.). These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) or other software to display bathymetry and backscatter data of the West Florida Shelf, Gulf of Mexico. This report provides multibeam bathymetry and acoustic backscatter data, along with images for parts of the sea floor. These data were obtained through a multibeam sonar survey of the West Florida Shelf, Gulf of Mexico. Data are provided in ASCII and ArcInfo GRID formats. Information for USGS Coastal and Marine Geology related activities are online at \"http://walrus.wr.usgs.gov/infobank/m/m201gm/html/m-2-01-gm.meta.html\"", + "license": "proprietary" + }, + { + "id": "USGS_OFR_02006", + "title": "Multibeam mapping of the Pinnacles region, Gulf of Mexico, USGS OFR 02-006", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-06-02", + "end_date": "2000-07-28", + "bbox": "-88.537, 29.154, -87.367, 29.629", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553636-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553636-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_02006", + "description": "Our objective was to map as large an area of the outer shelf deep reefs off Alabama-Mississippi as the project budget allowed using a state-of-the-art multibeam mapping system. The cruise used a Kongsberg Simrad EM1002, the latest generation of high-resolution multibeam mapping systems (HRMBS). The EM1002 produces both accurate georeferenced bathymetry and coregistered, calibrated, acoustic backscatter. These data should prove extremely useful in relating dominant species groups (which display highly specific biotope affinities) to the geomorphology (e.g., reef flattop, forereef crest, reef wall, reef base, circumreef talus zone, circum-reef, high-reflectivity sediment apron). The mapping is the first phase of a two-phase study of the Pinnacles area. The second year of this study (FY01) will concentrate on measuring the currents in and around the reefs as well as continued census of the fish populations. These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) or other software to display bathymetry and backscatter data of the West Florida Shelf, Gulf of Mexico. This report provides multibeam bathymetry and acoustic backscatter data, along with images for parts of the sea floor. These data were obtained through a multibeam sonar survey of the Pinnacles region, northern Gulf of Mexico. Data are provided in ASCII and ArcInfo GRID formats. Information for USGS Coastal and Marine Geology related activities are online at \"http://walrus.wr.usgs.gov/infobank/m/m201gm/html/m-2-01-gm.meta.html\"", + "license": "proprietary" + }, + { + "id": "USGS_OFR_0205", + "title": "Multibeam Mapping of the West Florida Shelf, Gulf of Mexico, USGS OFR 02-05", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-09-03", + "end_date": "2001-10-12", + "bbox": "-86.71667, 28, -84.583336, 31.083334", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550230-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550230-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_0205", + "description": "The objective was to map the region between the 50 to 150-m isobaths south from the eastern edge of De Soto Canyon as far as Steamboat Lumps using a state-of-the-art multibeam mapping system (MBES). The cruise used a Kongsberg Simrad EM1002 MBES, the latest generation of high-resolution mapping systems. The EM1002 produces both geodetically accurate georeferenced bathymetry and coregistered, calibrated, acoustic backscatter. Acoustic backscatter is the intensity of an acoustic pulse that is backscattered off the seafloor back to the transducer. The signal can give an indication of the type of material exposed on the ocean floor (i.e. rock vs. mud). These data should prove extremely useful in relating dominant species groups (which display highly specific biotope affinities) to the geomorphology (e.g., reef flattop, forereef crest, reef wall, reef base, circum-reef talus zone, circum-reef, high-reflectivity sediment apron, etc.). These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) or other software to display bathymetry and backscatter data of the West Florida Shelf, Gulf of Mexico. This report provides multibeam bathymetry and acoustic backscatter data, along with images for parts of the sea floor. These data were obtained through a multibeam sonar survey of the West Florida Shelf, Gulf of Mexico. Data are provided in ASCII and ArcInfo GRID formats. Information for USGS Coastal and Marine Geology related activities are online at \"http://walrus.wr.usgs.gov/infobank/m/m201gm/html/m-2-01-gm.meta.html\"", + "license": "proprietary" + }, + { + "id": "USGS_OFR_0206", + "title": "Multibeam mapping of the Pinnacles region, Gulf of Mexico, USGS OFR 02-06", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-06-02", + "end_date": "2000-07-28", + "bbox": "-88.537, 29.154, -87.367, 29.629", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550560-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550560-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_0206", + "description": "The objective was to map as large an area of the outer shelf deep reefs off Alabama-Mississippi as the project budget allowed using a state-of-the-art multibeam mapping system. The cruise used a Kongsberg Simrad EM1002, the latest generation of high-resolution multibeam mapping systems (HRMBS). The EM1002 produces both accurate georeferenced bathymetry and coregistered, calibrated, acoustic backscatter. These data should prove extremely useful in relating dominant species groups (which display highly specific biotope affinities) to the geomorphology (e.g., reef flattop, forereef crest, reef wall, reef base, circumreef talus zone, circum-reef, high-reflectivity sediment apron). The mapping is the first phase of a two-phase study of the Pinnacles area. The second year of this study concentrated on measuring the currents in and around the reefs as well as continued census of the fish populations. These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) or other software to display bathymetry and backscatter data of the West Florida Shelf, Gulf of Mexico. This report provides multibeam bathymetry and acoustic backscatter data, along with images for parts of the sea floor. These data were obtained through a multibeam sonar survey of the Pinnacles region, northern Gulf of Mexico. Data are provided in ASCII and ArcInfo GRID formats. Information for USGS Coastal and Marine Geology related activities are online at: \"http://walrus.wr.usgs.gov/infobank/m/m201gm/html/m-2-01-gm.meta.html\"", + "license": "proprietary" + }, + { + "id": "USGS_OFR_02110_1.0", + "title": "Mines and Mineral Occurrences of Afghanistan, USGS OFR 02-110", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-01-01", + "end_date": "2002-12-31", + "bbox": "60.45, 29.25, 75, 38.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552209-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552209-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_02110_1.0", + "description": "This data set was compiled due to interest in Afghanistan and anticipated continuing interest as post-war aid and reconstruction begin. This data set contains latitudes, longitudes, commodity, and limited geologic data for metallic and nonmetallic mines, deposits, and mineral occurrences of Afghanistan. The data in this compilation were derived from published literature and data files of members of the USGS National Industrial Minerals project. This data set consists of one table with 17 fields and over 1000 sites. This data set consists of one Excel 98 spreadsheet file, OF02110.xls. Data fields include location, deposit, commodity, and geologic data for mineral deposits, mines and occurrences.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_0221_Version 1.0", + "title": "Geologic Map of the Corona South 7.5' Quadrangle, Riverside and Orange Counties, California, USGS OFR 02-21", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-01-01", + "end_date": "2002-12-31", + "bbox": "-117.62509, 33.749985, -117.49991, 33.875", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553180-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553180-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_0221_Version%201.0", + "description": "The data set for the Corona South 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographic Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. This data set maps and describes the geology of the Corona South 7.5' quadrangle, Riverside and Orange Counties, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) a coverage containing structural data, (3) a coverage containing geologic unit annotation and leaders, and (4) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) a postscript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), and a key for point and line symbols, and (2) PDF files of the Readme (including the metadata file as an appendix), and the graphic produced by the Postscript plot file. The Corona South quadrangle is located near the northern end of the Peninsular Ranges Province. Diagonally crossing the quadrangle is the northern end of the Elsinore Fault zone, a major active right-lateral strike-slip fault zone of the San Andreas Fault system. East of the fault zone is the Perris block and to the west the Santa Ana Mountains block. Basement in the Perris block part of the quadrangle is almost entirely Cretaceous volcanic rocks and granitic rocks of the Cretaceous Peninsular Ranges batholith. Three small exposures of very low metamorphic grade siliceous rocks correlated on the basis of lithology with Mesozoic age rocks are located near the eastern edge of the quadrangle. Exposures of batholithic rocks is restricted to mostly granodiorite of the Cajalco pluton that underlies extensive areas to the east and north. There are limited amounts of undifferentiated granitic rock and one small body of gabbro. The most extensive basement rocks are volcanic shallow intrusives and extrusives of the Estelle Mountain volcanics. The volcanics, predominantly latite and rhyolite, are quarried as a source of crushed rock. West of the Elsinore Fault zone is a thick section of Bedford Canyon Formation of Jurassic age. This unit consists of incipiently metamorphosed marine sedimentary rocks consisting of argillite, slate, graywacke, impure quartzite, and small pods of limestone. Bedding and other primary sedimentary structures are commonly preserved and tight folds are common. Incipiently developed transposed layering, S1, is locally well developed. Included within the siliceous rocks are small outcrops of fossiliferous limestone than contain a fauna indicating the limestone formed in a so-called black smoker environment. Unconformably overlying and intruding the Bedford Canyon Formation is the Santiago Peak Volcanics of Cretaceous age. These volcanics consist of basaltic andesite, andesite, dacite, rhyolite, breccia and volcanoclastic rocks. Much of the unit has been hydrothermally altered; the alteration was contemporaneous with the volcanism. A minor occurrence of serpentine and associated silica-carbonate rock occurs in association with the volcanics. Sedimentary rocks of late Cretaceous and Paleogene age and a few Neogene age rocks occur within the Elsinore Fault zone. Marine sandstone of the middle Miocene Topanga Formation occurs within the fault zone southeast of Corona. Underlying the Topanga Formation is the nonmarine undivided Sespe and Vaqueros Formation that are predominantly sandstone. Sandstone, siltstone, and conglomerate of the marine and nonmarine Paleocene Silverado Formation extends essentially along the entire length of the fault zone in the quadrangle. Clay beds in the Silverado Formation have been an important source of clay. In the northwest corner of the quadrangle is a thick, faulted, sedimentary section that ranges in age from Cretaceous to early Pliocene-Miocene. Emanating from the Santa Ana Mountains is an extensive alluvial fan complex that underlies Corona and the surrounding valleys. This fan complex includes both Pleistocene and Holocene age deposits. The Elsinore Fault zone at the base of the Santa Ana Mountains splays in the northwestern part of the quadrangle; beyond the quadrangle boundary the name Elsinore Fault is generally not used. The southern splay takes a more western trend and to the west of the quadrangle is termed the Whittier Fault, a major active fault. The eastern splay continues on strike along the east side of the Chino (Puente) Hills north of the quadrangle where it is termed the Chino Fault. The Chino Fault appears to have very limited displacement. The geologic map data base contains original U.S. Geological Survey data generated by detailed field observation recorded on 1:24,000 scale aerial photographs. The map was created by transferring lines from the aerial photographs to a 1:24,000 scale topographic base. The map was digitized and lines, points, and polygons were subsequently edited using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units are polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_0222_Version 1.0", + "title": "Geologic Map of the Corona North 7.5' Quadrangle Riverside and San Bernardino Counties, California, USGS OFR 02-22", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-01-01", + "end_date": "2002-12-31", + "bbox": "-117.62509, 33.874985, -117.49991, 34", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552842-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552842-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_0222_Version%201.0", + "description": "The data set for the Corona North 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographic Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. This data set maps and describes the geology of the Corona North 7.5' quadrangle, Riverside and San Bernardino Counties, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) a coverage containing structural data, (3) a coverage containing geologic unit annotation and leaders, and (4) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) a postscript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), and a key for point and line symbols, and (2) PDF files of the Readme (including the metadata file as an appendix), and the graphic produced by the Postscript plot file. The Corona North quadrangle is located near the northern end of the Peninsular Ranges Province. All but the southeastern tip of the quadrangle is within the Perris block, a relatively stable, rectangular in plan area located between the Elsinore and San Jacinto fault zones. The southeastern tip of the quadrangle is barely within the Elsinore fault zone. The quadrangle is underlain by Cretaceous plutonic rocks that are part of the composite Peninsular Ranges batholith. These rocks are exposed in a triangular-shaped area bounded on the north by the Santa Ana River and on the south by Temescal Wash, a major tributary of the Santa Ana River. A variety of mostly silicic granitic rocks occur in the quadrangle, and are mainly of monzogranite and granodioritic composition, but range in composition from micropegmatitic granite to gabbro. Most rock units are massive and contain varying amounts of meso- and melanocratic equant-shaped inclusions. The most widespread granitic rock is monzogranite of the Cajalco pluton, a large pluton that extends some distance south of the quadrangle. North of Corona is a body of micropegmatite that appears to be unique in the batholith rocks. Diagonally bisecting the quadrangle is the Santa Ana River. North of the Santa Ana River alluvial deposits are dominated by the distal parts of alluvial fans emanating from the San Gabriel Mountains north of the quadrangle. Widespread areas of the fan deposits are covered by a thin layer of wind blown sand. Alluvial deposits in the triangular-shaped area between the Santa Ana River and Temescal Wash are quite varied, but consist principally of locally derived older alluvial fan deposits. These deposits rest on remnants of older, early Quaternary or late Tertiary age, nonmarine sedimentary deposits that were derived from both local sources and sources as far away as the San Bernardino Mountains. These deposits in part were deposited by an ancestral Santa Ana River. Older are a few scattered remnants of late Tertiary (Pliocene) marine sandstone that include some conglomerate lenses. Clasts in the conglomerate include siliceous volcanic rocks exotic to this part of southern California. This sandstone was deposited as the southeastern-most part of the Los Angeles sedimentary marine basin and was deposited along a rocky shoreline developed in the granitic rocks, much like the present day shoreline at Monterey, California. Most of the sandstone and granitic paleoshoreline features have been removed by quarrying and grading in the area of Porphyry north to Highway 91. Excellent exposures in highway road cuts still remain on the north side of Highway 91 just east of the 91-15 interchange and on the east side of U.S. 15 just north of the interchange. South of Temescal Wash is a series of both younger and older alluvial fan deposits emanating from the Santa Ana Mountains to the southeast. In the immediate southwest corner of the quadrangle is a small exposure of sandstone and pebble conglomerate of the Sycamore Canyon member of the Puente Formation of early Pliocene and Miocene age and sandstone and conglomerate of undivided Sespe and Vaqueros Formations of early Miocene, Oligocene, and late Eocene age. The geologic map data base contains original U.S. Geological Survey data generated by detailed field observation recorded on 1:24,000 scale aerial photographs. The map was created by transferring lines from the aerial photographs to a 1:24,000 scale topographic base. The map was digitized and lines, points, and polygons were subsequently edited using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units are polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum.", + "license": "proprietary" + }, { "id": "USGS_OFR_2001_0497_1.0", "title": "Databases and Simplified Geology for Mineralized Areas, Claims, Mines, and Prospects in Wyoming", @@ -182610,6 +187511,58 @@ "description": "This data release contains mineral resource data for metallic and nonmetallic mineral sites in the State of Wyoming. Along with resource data is additional data, such as mineralized areas and mining districts; mine, prospect and commodity information; claim density by section; county boundaries; quadrangles; and simplified geology. All the data are provided in both spreadsheet format (Microsoft Excel) and in formats for two commonly used Geographic Information Systems (GIS) software packages (MapInfo and ESRI's ArcView). Not only does GIS software allow the data to be shown as layers in map views that can be displayed with various geographic and geologic data, but the data can be queried and analyzed relative to data in any of the layers. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2001_164", + "title": "Earthquake Ground-Motion Amplification in Southern California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-121, 33, -117, 35", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553205-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553205-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2001_164", + "description": "The two most important factors influencing the level of earthquake ground motion at a site are the magnitude and distance of the earthquake. The map available here shows the influence of a third important factor, the site effect: conditions at a particular location can increase (amplify) or decrease the level of shaking that is otherwise expected for a given magnitude and distance. Combining information about site effects with where and how often earthquakes of various magnitudes are likely to occur should provide improved assessments of seismic hazard. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2002_002", + "title": "Geological Framework Data from Long Island Sound, 1981-1990: A Digital Data Release", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-75, 40, -70, 43", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553651-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553651-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2002_002", + "description": "Since 1980 the Coastal and Marine Geology Program of the U.S. Geological Survey and Connecticut Department of Environmental Protection have conducted a joint program of cooperative geologic research in Long Island Sound and its vicinity. As part of this program, a highly successful regional-scale study of theSeismic reflection acquisition illustration geologic framework was completed. Reconnaissance high-resolution seismic reflection data were collected and used to establish the basic stratigraphy within the Sound and to map the major geologic units (Needell and Lewis, 1984; Lewis and Needell, 1987; Needell and others, 1987); field verification of the geologic interpretations of the seismic profiles was primarily accomplished with vibratory cores (Williams, 1981; Thomas, 1985; Neff and others, 1989). These interpretations were in turn used to produce basin-wide syntheses of the late Quaternary depositional history (Lewis and Stone, 1991; Stone and others, 1998; Lewis and DiGiacomo-Cohen, 2000). Unfortunately, the original seismic records and core logs were generated only in analog form. These unique paper documents, which are still under demand for industrial applications and academic research, are fragile and have become ragged from frequent use. The purpose of this report is to preserve these data by converting the seismic profiles, core descriptions, and ancillary reports into digital form, and to organize these files into a product that can be more readily accessed and disseminated. Not all of the existing high-resolution seismic-reflection surveys, collected in Long Island Sound through cooperatives with the U.S. Geological Survey and the Connecticut Department of Environmental Protection, have been incorporated into this report. These surveys, whose records are still in need of preprocessing and annotation, generally cover smaller areas along the Connecticut coast and were originally intended to provide additional detail to the larger, more regional data sets presented herein. The digital release of the omitted data sets is planned as part of a future product.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2002_206", + "title": "Environmental Atlas of the Lake Pontchartrain Basin", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-91, 29, -89, 31", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554508-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554508-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2002_206", + "description": "The story of Lake Pontchartrain and its surrounding Basin is a fascinating saga. Created at the end of the last Ice Age, this estuary is much more than just a magnificent natural resource. It has provided humans with sources of food, as well as a means of communication, transportation and commerce. These and a host of other benefits have supported the growth of New Orleans and the surrounding communities. Today, the 1632 km2 (630 mi2) Lake Pontchartrain is the centerpiece of the 12,173 km2 (4,700 mi2) Pontchartrain drainage basin or watershed. The Basin encompasses land in 16 Louisiana parishes and 4 Mississippi counties. This vast ecological system includes lakes, rivers, bayous, forest, swamps and marshes. It is habitat for countless species of fish, birds, mammals, reptiles and plants. It is also the most densely populated portion of Louisiana with almost 1.5 million people residing immediately around the Lake. The history of the environmental quality of the Pontchartrain Basin demonstrates that no resource should be taken for granted or exploited. As the population grew in the Twentieth Century, use and, unfortunately, abuse of this nationally important estuary also grew. By the second half of the Twentieth Century, Pontchartrain's environmental quality had deteriorated to a point that many believed unrecoverable. Responsibility and stewardship are necessary for natural resource protection, restoration and preservation. Recognizing these needs, area citizens began the SAVE OUR LAKE movement that led to the creation of the Lake Pontchartrain Basin Foundation in 1989. The Foundation's mission is to coordinate the overall restoration and preservation of the entire Lake Pontchartrain Basin ecosystem. The Environmental Atlas of the Lake Pontchartrain Basin will become one of our tools to help accomplish that mission. The Environmental Atlas of the Lake Pontchartrain Basin is more than a summary of Pontchartrain's ecology. It presents information about geology, land cover, types of shorelines, biological resources, flow patterns, significant storms, growth trends and more. This Atlas is more of a directory to the Basin's environment. Hopefully, it will become an easily understandable reference for students and the public as well as a readily used source for professionals. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2003_080", + "title": "New Jersey Aeromagnetic and Gravity Maps and Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-75.69, 38.8, -73.78, 41.47", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551303-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551303-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_080", + "description": "Aeromagnetic anomalies are due to variations in the Earth's magnetic field caused by the uneven distribution of magnetic minerals (primarily magnetite) in the rocks that make up the upper part of the Earth's crust. The features and patterns of the aeromagnetic anomalies can be used to delineate details of subsurface geology including the locations of buried faults, magnetite-bearing rocks, and the thickness of surficial sedimentary rocks (which are generally non-magnetic). This information is valuable for mineral exploration, geologic mapping, and environmental studies. The New Jersey aeromagnetic map in this report is constructed from grids that combine aeromagnetic data (see data processing details) collected in eight separate aeromagnetic surveys flown between 1950 and 1979. The data from these surveys are of varying quality. The design and specifications (terrain clearance, flight line separation, flight direction, analog/digital recording, navigation, and reduction procedures) may vary between surveys depending on the purpose of the project and the technology of that time. All of the pre-1976 data are available only on hand-contoured analog maps and had to be digitized. These maps were digitized along flight-line/contour-line intersections, which is considered to be the most accurate method of recovering the original data. Digitized data are available as USGS Open File Report 99-557. All surveys have been continued to 304.8 meters (1000 feet) above ground and then blended or merged together. The merging of grids and production of images were created using a PC version of Geosoft/OASIS montaj software. An index map and data table gives an overview of the original surveys and summarizes the specifications of the surveys. The resulting grid has a data interval of 500 m and can be downloaded. A color-shaded relief image of the grid is shown on the opening page of this web report. This grid is an interim product. Considerable editing of digital flight line data was undertaken for survey 3144 to reduce leveling inconsistencies between adjacent flight lines, most notably in the southern part of the state. Anomaly resolution is only fair in the northern portion of this survey, which was flown at one-mile flight line separation, where the source rocks are at or near the surface. In these areas of this survey where the anomalies run roughly parallel to the flight lines, the gridding process produces a 'string of pearls' effect. Improved resolution can only be rectified by new surveys with more closely spaced flight lines. Heavy strike filtering in the direction of the flight lines was necessary to reduce flight line striping for two digital surveys (5004 and 6027). Where local high-resolution surveys were not available, in either digital or digitized format, we used aeromagnetic data collected by the National Uranium Resource Evaluation (NURE) program of the U.S. Department of Energy, which are available in digital format and together cover the entire state. However, because magnetic surveying was not the primary objective in the design of the NURE surveys, these data are subject to certain limitations. Although the NURE surveys were flown at elevations close to the reduction datum level, the spacing between flight lines generally ranged from 4.8 to 9.6 km (3 to 6 mile). In some areas of the U.S., detailed NURE surveys were flown with a finer line spacing, usually at a 0.4 km (0.25 mile) interval. In New Jersey, the NURE program flew the Reading Prong (5004) at this interval. This New Jersey aeromagnetic compilation is one part of a national digital compilation by the U.S. Geological Survey. Certain characteristics are common to all of the State compilations. Whereas surveys are typically flown either at a constant elevation above sea level or draped to a constant mean terrain clearance, the standard selected for this national compilation is a survey elevation of 305 m (1000 ft) above mean terrain. All of the surveys used in the New Jersey compilation were flown at either 122 m (400 ft) or 152 m (500 ft) above terrain. To conform to the national standard, the entire State grid was analytically continued upward to 305 m (1000 ft) above ground (Hildenbrand, 1983). This aeromagnetic compilation supercedes a prior report (Snyder, 1992) releasing the same data as three separate grids on 5.25\" floppies. The same data have since been reprocessed to produce better results. This project was supported by the Mineral Resource and Geologic Mapping Programs of the USGS. Thanks to USGS colleagues Pat Hill and Robert Kucks for their assistance in preparing this report. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2003_090_1.0", "title": "Databases and Simplified Geology for Mineralized Areas, Claims, Mines and Prospects in Colorado", @@ -182623,6 +187576,58 @@ "description": "This data release contains mineral resource data for metallic and nonmetallic mineral sites in the State of Colorado. Along with the resource data, there is additional data, such as mineralized areas and mining districts; mine, prospect and commodity information; claim density by section; county boundaries; quadrangles; and simplified geology. All the geographic data are provided in formats for two commonly used Geographic Information Systems (GIS) software packages (MapInfo and ESRI?s ArcView). Not only does GIS software allow the data to be shown as layers in ?map? views that can be displayed with various geographic and geologic data, but the data can be queried and analyzed relative to data in any of the layers. Free shareware, ArcExplorer, is provided with this report so users may display the data in ?map? views and query the various datasets (Appendix A) without requiring a GIS program such as Arc/Info1, ArcView1, or MapInfo1. Additional data, such as original and unedited mine and prospect files, bibliography and references, and text are provided in appropriate formats such as in spreadsheets (Microsoft Excel), or documents (text, WordPerfect, or Microsoft Word). [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2003_095_1.1", + "title": "Map and Data for Quaternary Faults and Folds in Oregon", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-126.29038, 41.47393, -116.71798, 46.417915", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551564-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551564-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_095_1.1", + "description": "The map shows faults and folds in the state of Oregon that exhibit evidence of Quaternary deformation, and includes data on timing of most recent movement, sense of movement, slip rate, and continuity of surface expression. The primary purpose of this compilation is for use in earthquake-hazard evaluations. Paleoseismic studies, which evaluate the history of surface faulting or deformation along structures with evidence of Quaternary movement, provide a long-term perspective that augments the short historic records of seismicity in many regions. Published or publicly available data are the primary sources of data used to compile this report. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2003_096_1.0", + "title": "Geologic Map of the Valjean Hills 7.5' Quadrangle, San Bernardino County, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-01-01", + "end_date": "2003-12-31", + "bbox": "-116.126366, 35.624027, -115.99844, 35.750973", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551946-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551946-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_096_1.0", + "description": "This data set maps and describes the geology of the Valjean Hills 7.5' quadrangle, San Bernardino County, California.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2003_102_1.0", + "title": "Geologic Map and Digital Database of the Romoland 7.5' Quadrangle, Riverside County, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-124.9, 32.02, -113.61, 42.51", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552936-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552936-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_102_1.0", + "description": "The Geologic Map and Digital Database of the Romoland 7.5' Quadrangle, Riverside County, California report contains a digital geologic map database of the Romoland 7.5' quadrangle, Riverside County, California that includes: 1. ARC/INFO version 7.2.1 coverages of the various elements of the geologic map. 2. A Postscript file to plot the geologic map on a topographic base, and containing a Correlation of Map Units diagram (CMU), a Description of Map Units (DMU), and an index map. 3. Portable Document Format (.pdf) files of: a. This Readme; includes in Appendix I, data contained in rom_met.txt b. The same graphic as plotted in 2 above. Test plots have not produced precise 1:24,000-scale map sheets. Adobe Acrobat page size setting influences map scale. The Correlation of Map Units and Description of Map Units is in the editorial format of USGS Geologic Investigations Series (I-series) maps but has not been edited to comply with I-map standards. Within the geologic map data package, map units are identified by standard geologic map criteria such as formationname, age, and lithology. Where known, grain size is indicated on the map by a subscripted letter or letters following the unit symbols as follows: lg, large boulders; b, boulder; g, gravel; a, arenaceous; s, silt; c, clay; e.g. Qyfa is a predominantly young alluvial fan deposit that is arenaceous. Multiple letters are used for more specific identification or for mixed units, e.g., Qfysa is a silty sand. In some cases, mixed units are indicated by a compound symbol; e.g., Qyf2sc. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2003_103_1.0", + "title": "Geologic Map and Digital Database of the Bachelor Mountain 7.5' Quadrangle, Riverside County, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-124.9, 32.02, -113.61, 42.51", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553086-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553086-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_103_1.0", + "description": "The Geologic Map and Digital Database of the Bachelor Mountain 7.5' Quadrangle, Riverside County, California contains a digital geologic map database of the Bachelor Mountain 7.5 - quadrangle, Riverside County, California that includes: 1. ARC/INFO (Environmental Systems Research Institute, http://www.esri.com) version 7.2.1 coverages of the various elements of the geologic map. 2. A Postscript file to plot the geologic map on a topographic base, and containing a Correlation of Map Units diagram (CMU), a Description of Map Units (DMU), and an index map. 3. Portable Document Format (.pdf) files of: a. This Readme; includes in Appendix I, data contained in bch_met.txt b. The same graphic as plotted in 2 above. Test plots have not produced precise 1:24,000- scale map sheets. Adobe Acrobat page size setting influences map scale. The Correlation of Map Units and Description of Map Units is in the editorial format of USGS Geologic Investigations Series (I-series) maps but has not been edited to comply with I-map standards. Within the geologic map data package, map units are identified by standard geologic map criteria such as formationname, age, and lithology. Where known, grain size is indicated on the map by a subscripted letter or letters following the unit symbols as follows: lg, large boulders; b, boulder; g, gravel; a, arenaceous; s, silt; c, clay; e.g. Qyfa is a predominantly young alluvial fan deposit that is arenaceous. Multiple letters are used for more specific identification or for mixed units, e.g., Qfysa is a silty sand. In some cases, mixed units are indicated by a compound symbol; e.g., Qyf2sc. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2003_108_1.0", "title": "Coastal Vulnerability Assessment of Gulf Islands To Sea-Level Rise", @@ -182649,6 +187654,45 @@ "description": "Bathymetry and selected perspective views of 6 reef and coastal areas in Northern Lake Michigan involves applying state of the art laser technology and derivative imagery to map the detailed morphology and of principal lake trout spawning sites on reefs in Northern Lake Michigan and to provide a geologic interpretation. One objective was to identify the presence of ideal spawning substrate: shallow, \"clean\" gravel/cobble substrate, adjacent to deeper water. This study is a pilot collaborative effort with the US Army Corps of Engineers SHOALS (Scanning Hydrographic Operational Airborne Lidar Survey) program. The high-definition maps are integrated with known and developing data on fisheries, as well as limited substrate sedimentology information and underlying Paleozoic carbonate rocks. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2003_135", + "title": "Geologic database for digital geology of California, Nevada, and Utah?An application of the North American Data Model", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-125, 36, -109, 43", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551929-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551929-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_135", + "description": "The USGS is creating an integrated national database for digital state geologic maps that includes stratigraphic, age, and lithologic information. The majority of the conterminous 48 states have digital geologic base maps available, often at scales of 1:500,000. This product is a prototype, and is intended to demonstrate the types of derivative maps that will be possible with the national integrated database. This database permits the creation of a number of types of maps via simple or sophisticated queries, maps that may be useful in a number of areas, including mineral-resource assessment, environmental assessment, and regional tectonic evolution. This database is distributed with three main parts: a Microsoft Access 2000 database containing geologic map attribute data, an Arc/Info (Environmental Systems Research Institute, Redlands, California) Export format file containing points representing designation of stratigraphic regions for the Geologic Map of Utah, and an ArcView 3.2 (Environmental Systems Research Institute, Redlands, California) project containing scripts and dialogs for performing a series of generalization and mineral resource queries. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2003_150", + "title": "Geophysical Surveys of Bear Lake, Utah-Idaho, September, 2002", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-112, 41, -111, 43", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549315-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549315-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_150", + "description": "The objectives of the September, 2002 Geophysical Surveys of Bear Lake, Utah-Idaho operations, preliminarily reported here, were (1) to compile a detailed bathymetric map of the lake using swath-mapping techniques, in order to provide baseline data for a variety of applications and studies, and (2) to complete a sidescan-sonar survey of the lake, providing a nearly complete acoustic image of the lake floor. Limited amounts of subbottom acoustic-reflection data (chirp) were also collected, along with samples of lake-floor sediments representative of different kinds of backscatter patterns. These surveys followed an earlier subbottom acoustic-reflection survey (1997), using boomer and 3.5 kHz systems (S. M. Colman, unpublished data). Past seismic-reflection work has indicated that faults secondary to the east-side master fault cut the lake floor. These faults were among the primary targets of the sidescan-sonar survey. Preliminary interpretation of the data suggests that the morphology of the fault scarps on the lake floor are too subtle to be imaged by the sidescan-sonar system. However, some segments of the East Bear Lake fault at the foot of the steep eastern margin of the lake, are visible in the sidescan-sonar images. The other main targets of the sidescan-sonar survey were possible springs discharging at the lake floor. Discharge from such springs may be necessary to explain the chemistry and mineralogy of the lake sediments. A number of structures that appear to be related to spring discharge were observed in the sidescan-sonar images, and sediments at some of these features were sampled. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2003_225_1.0", + "title": "Generalized Lithology and Lithogeochemical Character of Near-Surface Bedrock in the New England Region", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-73.73, 40.9, -66.93, 47.42", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548779-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548779-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_225_1.0", + "description": "This geographic information system (GIS) data layer shows the dominant lithology and geochemical, termed lithogeochemical, character of near-surface bedrock in the New England region covering the states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. The bedrock units in the map are generalized into groups based on their lithological composition and, for granites, geochemistry. Geologic provinces are defined as time-stratigraphic groups that share common features of age of formation, geologic setting, tectonic history, and lithology. This data set incorporates data from digital maps of two NAWQA study areas, the New England Coastal Basin (NECB) and the Connecticut, Housatonic, and Thames River Basins (CONN) areas and extends data to cover the states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. The result is a regional dataset for the lithogeochemical characterization of New England (the layer named NE_LITH). Polygons in the final coverage are attributed according to state, drainage area, geologic province, general rock type, lithogeochemical characteristics, and specific bedrock map unit. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2003_230_1.1", "title": "Digital depth horizon compilations of the Alaskan North Slope and adjacent arctic regions", @@ -182662,6 +187706,32 @@ "description": "The Digital depth horizon compilations of the Alaskan North Slope and adjacent arctic regions file report contains data that has been digitized and combined to create four detailed depth horizon grids spanning the Alaskan North Slope and adjacent offshore areas. These map horizon compilations were created to aid in petroleum system modeling and related studies. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2003_235", + "title": "High-resolution seismic-reflection surveys in the nearshore of outer Cape Cod, Massachusetts", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-73.68, 41.06, -69.75, 43.07", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549794-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549794-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_235", + "description": "The U.S. Geological Survey (USGS) Woods Hole Field Center (WHFC), in cooperation with the USGS Water Resources Division conducted high-resolution seismic-reflection surveys along the nearshore areas of outer Cape Cod, Massachusetts from Chatham to Provincetown, Massachusetts. The objectives of this investigation were to determine the stratigraphy of the nearshore in relation to the Quaternary stratigraphy of outer Cape Cod by correlating units between the nearshore and onshore and to define the geologic framework of the region. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2003_236_1.0", + "title": "National Geochronological Database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-177.1, 13.71, -61.48, 76.63", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549399-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549399-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_236_1.0", + "description": "The National Geochronological Data Base (NGDB) was established by the United States Geological Survey (USGS) to collect and organize published isotopic (also known as radiometric) ages of rocks in the United States. The NGDB (originally known as the Radioactive Age Data Base, RADB) was started in 1974. A committee appointed by the Director of the USGS was given the mission to investigate the feasibility of compiling the published radiometric ages for the United States into a computerized data bank for ready access by the user community. A successful pilot program, which was conducted in 1975 and 1976 for the State of Wyoming, led to a decision to proceed with the compilation of the entire United States. For each dated rock sample reported in published literature, a record containing information on sample location, rock description, analytical data, age, interpretation, and literature citation was constructed and included in the NGDB. The NGDB was originally constructed and maintained on a mainframe computer, and later converted to a Helix Express relational database maintained on an Apple Macintosh desktop computer. The NGDB and a program to search the data files were published and distributed on Compact Disc-Read Only Memory (CD-ROM) in standard ISO 9660 format as USGS Digital Data Series DDS-14 (Zartman and others, 1995). As of May 1994, the NGDB consisted of more than 18,000 records containing over 30,000 individual ages, which is believed to represent approximately one-half the number of ages published for the United States through 1991. Because the organizational unit responsible for maintaining the database was abolished in 1996, and because we wanted to provide the data in more usable formats, we have reformatted the data, checked and edited the information in some records, and provided this online version of the NGDB. This report describes the changes made to the data and formats, and provides instructions for the use of the database in geographic information system (GIS) applications. The data are provided in *.mdb (Microsoft Access), *.xls (Microsoft Excel), and *.txt (tab-separated value) formats. We also provide a single non-relational file that contains a subset of the data for ease of use. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2003_241_1.0", "title": "Contaminated Sediments Database for Long Island Sound and the New York Bight", @@ -182688,6 +187758,19 @@ "description": "This report consists of a compilation of twelve digital geologic maps provided in ARC/INFO interchange (e00) format for the state of Oklahoma. The source maps consisted of nine USGS 1:250,000-scale quadrangle maps and three 1:125,000 scale county maps. This publication presents a digital composite of these data intact and without modification across quadrangle boundaries to resolve geologic unit discontinuities. An ESRI ArcView shapefile formatted version and Adobe Acrobat (pdf) plot file of the compiled digital map are also provided. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2003_265", + "title": "Grand Canyon Riverbed Sediment Changes, Experimental Release of September 2000 - A Sample Data Set", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-08-28", + "end_date": "2000-09-18", + "bbox": "-112.09242, 36.08593, -111.47837, 36.93602", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552397-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552397-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_265", + "description": "An experimental water release from the Glen Canyon Dam into the Colorado River above Grand Canyon was conducted in September 2000 by the U.S. Bureau of Reclamation. The U.S. Geological Survey (USGS) conducted sidescan sonar surveys between Glen Canyon Dam (mile -15) and Diamond Creek (mile 220), Arizona (mile designations after Stevens, 1998) to determine the sediment characteristics of the Colorado River bed before and after the release. The first survey (R3-00-GC, 28 Aug to 5 Sep 2000) was conducted before the release when the river was at its Low Summer Steady Flow (LSSF) of 8,000 cfs. The second survey (R4-00-GC, 10 to 18 Sep 2000) was conducted immediately after the September 2000 experimental release when the average daily flow was as high as 30,800 cfs as measured below Glen Canyon Dam (Figure 2). Riverbed sediment properties interpreted from the sidescan sonar images include sediment type and sandwaves; overall changes in these properties between the two surveys were calculated. Sidescan sonar data from the USGS surveys were processed for segments of the Colorado River from Glen Canyon Dam (mile -15) to Phantom Ranch (mile 87.7, Figure 3). The surveys targeted pools between rapids that are part of the Grand Canyon Monitoring and Research Center (GCMRC http://www.gcmrc.gov/) physical sciences study. Maps interpreted from the sidescan sonar images show the distribution of sediment types (bedrock, boulders, pebbles or cobbles, and sand) and the extent of sandwaves for each of the pre- and post-flow surveys. The changes between the two surveys were calculated with spatial arithmetric and had properties of fining, coarsening, erosion, deposition, and the appearance or disappearance of sandwaves. This report describes GIS spatial data files for this project and provides examples of the data from the Colorado River near mile 2 below the confluence of the Paria and Colorado Rivers. The complete data set includes sidescan sonar images and interpreted map files for each of the pre- and post-flow surveys and the changes between the segments of rivers. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2003_267", "title": "Catalog of Earthquake Hypocenters at Alaskan Volcanoes: January 1 through December 31, 2002", @@ -182701,6 +187784,19 @@ "description": "The Alaska Volcano Observatory (AVO), a cooperative program of the U.S. Geological Survey, the Geophysical Institute of the University of Alaska Fairbanks, and the Alaska Division of Geological and Geophysical Surveys, has maintained seismic monitoring networks at historically active volcanoes in Alaska since 1988 (Power and others, 1993; Jolly and others, 1996; Jolly and others, 2001; Dixon and others, 2002). The primary objectives of this program are the seismic monitoring of active, potentially hazardous, Alaskan volcanoes and the investigation of seismic processes associated with active volcanism. This catalog presents the basic seismic data and changes in the seismic monitoring program for the period January 1, 2002 through December 31, 2002. Appendix G contains a list of publications pertaining to seismicity of Alaskan volcanoes based on these and previously recorded data. The AVO seismic network was used to monitor twenty-four volcanoes in real time in 2002. These include Mount Wrangell, Mount Spurr, Redoubt Volcano, Iliamna Volcano, Augustine Volcano, Katmai Volcanic Group (Snowy Mountain, Mount Griggs, Mount Katmai, Novarupta, Trident Volcano, Mount Mageik, Mount Martin), Aniakchak Crater, Mount Veniaminof, Pavlof Volcano, Mount Dutton, Isanotski Peaks, Shishaldin Volcano, Fisher Caldera, Westdahl Peak, Akutan Peak, Makushin Volcano, Great Sitkin Volcano, and Kanaga Volcano (Figure 1). Monitoring highlights in 2002 include an earthquake swarm at Great Sitkin Volcano in May-June; an earthquake swarm near Snowy Mountain in July-September; low frequency (1-3 Hz) tremor and long-period events at Mount Veniaminof in September-October and in December; and continuing volcanogenic seismic swarms at Shishaldin Volcano throughout the year. Instrumentation and data acquisition highlights in 2002 were the installation of a subnetwork on Okmok Volcano, the establishment of telemetry for the Mount Veniaminof subnetwork, and the change in the data acquisition system to an EARTHWORM detection system. AVO located 7430 earthquakes during 2002 in the vicinity of the monitored volcanoes. This catalog includes: (1) a description of instruments deployed in the field and their locations; (2) a description of earthquake detection, recording, analysis, and data archival systems; (3) a description of velocity models used for earthquake locations; (4) a summary of earthquakes located in 2002; and (5) an accompanying UNIX tar-file with a summary of earthquake origin times, hypocenters, magnitudes, and location quality statistics; daily station usage statistics; and all HYPOELLIPSE files used to determine the earthquake locations in 2002. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2003_85_1.0", + "title": "Nearshore Benthic Habitat GIS for the Channel Islands National Marine Sanctuary and Southern California State Fisheries Reserves Volume 1", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-122, 33, -119, 35", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551552-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551552-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2003_85_1.0", + "description": "The nearshore benthic habitat of the Santa Barbara coast and Channel Islands supports diverse marine life that is commercially, recreationally, and intrinsically valuable. Some of these resources are known to be endangered including a variety of rockfish and the white abalone. Agencies of the state of California and the United States have been mandated to preserve and enhance these resources. Data from sidescan sonar, bathymetry, video and dive observations, and physical samples are consolidated in a geographic information system (GIS). The GIS provides researchers and policymakers a view of the relationship among data sets to assist scienctific research and to help with economic and social policy-making decisions regarding this protected environment. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1007_1.0", "title": "Desert Landforms and Surface Processes in the Mojave National Preserve and Vicinity", @@ -182714,6 +187810,84 @@ "description": "Landscape features in the Mojave National Preserve are a product of ongoing processes involving tectonic forces, weathering, and erosion. Long-term climatic cycles (wet and dry periods) have left a decipherable record preserved as landform features and sedimentary deposits. This website provides and introduction to climate-driven desert processes influencing landscape features including stream channels, alluvial fans, playas (dry lakebeds), dunes, and mountain landscapes. Bedrock characteristics, and the geometry of past and ongoing faulting, fracturing, volcanism, and landscape uplift and subsidence influence the character of processes happening at the surface. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1008_1.0", + "title": "Geophysical Terranes of the Great Basin and Parts of Surrounding Provinces", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-170, 25, -85, 72", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552043-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552043-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1008_1.0", + "description": "This study of geophysical terranes within and surrounding the Great Basin of the western United States integrates geophysical and geologic data to provide new insights on basement composition and structure at local, intermediate, and regional scales. Potential field (gravity and magnetic) studies are particularly useful to define the location, depth, and extent of buried basement sources and fundamental structural or compositional boundaries. They especially serve in imaging the subsurface in areas of extensive Cenozoic cover or where surface outcrops may be detached from the deeper crust. Identifying buried compositional or structural boundaries has applications, for example, in tectonic and earthquake hazard studies as they may reflect unmapped or buried faults. In many places, such features act as guides or barriers to fluid or magma flow or form favorable environments for mineralization and are therefore important to mineral, groundwater, and geothermal studies. This work serves in assessing the potential for undiscovered mineral deposits and provides important long-term land-use planning information. The primary component of this report is a set of geophysical maps with anomalies that are labeled and keyed to tables containing information on the anomaly and its source. Maps and data tables are provided in a variety of formats (tab delimited text, Microsoft Excel, PDF, and ArcGIS) for readers to review and download. The PDF formatted product allows the user to easily move between features on the maps and their entries in the tables, and vice-versa. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2004_1009", + "title": "Geology of the Ugashik-Mount Peulik Volcanic Center, Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-170, 50, -130, 72", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552364-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552364-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1009", + "description": "The Ugashik-Mount Peulik volcanic center, 550 km southwest of Anchorage on the Alaska Peninsula, consists of the late Quaternary 5-km-wide Ugashik caldera and the stratovolcano Mount Peulik built on the north flank of Ugashik. The center has been the site of explosive volcanism including a caldera-forming eruption and post-caldera dome-destructive activity. Mount Peulik has been formed entirely in Holocene time and erupted in 1814 and 1845. A large lava dome occupies the summit crater, which is breached to the west. A smaller dome is perched high on the southeast flank of the cone. Pyroclastic-flow deposits form aprons below both domes. One or more sector-collapse events occurred early in the formation of Mount Peulik volcano resulting in a large area of debris-avalanche deposits on the volcano's northwest flank. The Ugashik-Mount Peulik center is a calcalkaline suite of basalt, andesite, dacite, and rhyolite, ranging in SiO2 content from 51 to 72 percent. The Ugashik-Mount Peulik magmas appear to be co-genetic in a broad sense and their compositional variation has probably resulted from a combination of fractional crystallization and magma-mixing. The most likely scenario for a future eruption is that one or more of the summit domes on Mount Peulik are destroyed as new magma rises to the surface. Debris avalanches and pyroclastic flows may then move down the west and, less likely, east flanks of the volcano for distances of 10 km or more. A new lava dome or series of domes would be expected to form either during or within some few years after the explosive disruption of the previous dome. This cycle of dome disruption, pyroclastic flow generation, and new dome formation could be repeated several times in a single eruption. The volcano poses little direct threat to human population as the area is sparsely populated. The most serious hazard is the effect of airborne volcanic ash on aircraft since Mount Peulik sits astride heavily traveled air routes connecting the U.S. and Europe to Asia. Activity of the type described could produce eruption columns to heights of 15 km and result in significant amounts of ash 250-300 km downwind. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2004_1010_1.0", + "title": "Effect of Structural Heterogeneity and Slip Distribution on Coseismic Vertical Displacement from Rupture on the Seattle Fault", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-177.1, 13.71, -61.48, 76.63", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551656-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551656-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1010_1.0", + "description": "Workshops in 2001 and 2002 were convened to determine critical issues in the development of tsunami inundation maps for the Puget Sound region. The Tsunami Inundation Mapping Effort (TIME) is conducted under the multi-agency National Tsunami Hazard Mitigation Program (NTHMP). The Puget Sound Tsunami/Landslide Workshop in 2001 focused on integrated tsunami research involving a wide range of research studies and tsunami hazard mitigation issues. The 2002 Puget Sound Tsunami Sources workshop (Gonz\u00e1lez et al., 2003) made specific recommendations for tsunami source modeling and improving our state of knowledge for sources in the Puget Sound region. One of the recommendations stated in Gonz\u00e1lez et al. (2003) is \"Develop methods to assess the sensitivity of coastal areas to tsunami inundation, based on multiple simulations that reflect the possible range of variations in the source parameters.\" Tsunami inundation models rely heavily on the imposed initial conditions which, for an earthquake source, is the coseismic vertical displacement field. For example, Koshimura et al. (2002) use the geologic uplift observations (Buknam et al., 1992) to constrain the slip distribution for the event that occurred 1100 years ago, resulting in an average slip of 3.7 m and a magnitude of 7.6. Walsh et al. (2003) develop a tsunami inundation map for Elliot Bay based on a M 7.3 earthquake and the geologic uplift observations from the 1100 y.b.p. event as in Koshimura et al. (2002), though they use a constant fault dip of 60\u00b0 rather than different dips for deep and shallow segments. The objective of this report is to examine how coseismic vertical displacement from a smaller M 6.5 Seattle Fault earthquake (as in Hartzell et al., 2002) is affected by structural heterogeneity and different slip distribution patterns. The three-dimensional crustal structure of the Puget Sound region has recently been defined using shallow seismic reflection data (Pratt et al., 1997; Johnson et al., 1999) and reflection and wide-angle recordings from the large-scale SHIPS experiments (e.g., Brocher et al., 2001; ten Brink et al., 2002). The presence of a deep sedimentary basin (Seattle Basin) adjacent to the Seattle Fault has led to the question of whether structural heterogeneity has an effect on our estimate of vertical displacement for earthquake scenarios in the region. We use a three-dimensional elastic finite-element model (Yoshioka et al., 1989) to calculate vertical displacements from rupture on a two-segment (deep and shallow) Seattle fault using a heterogeneous crustal structure. Similar studies by Geist and Yoshioka (1996) and Masterlark et al. (2001) used three-dimensional, finite-element models (FEM) to study the effect of structural heterogeneity on coseismic displacement fields. Results for the Puget Sound study are compared to calculations using a homogeneous structure as assumed with conventional elastic dislocation solutions. Effects of slip distribution patterns on vertical displacement is computed using the stochastic source model adopted for tsunami studies by Geist (2002). Finally, we examine an alternate model for shallow faulting proposed by ten Brink et al. (2002) and Brocher et al. (submitted) and its effect on the vertical displacement field. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2004_1011_1.0", + "title": "Emergency Assessment of Debris-Flow Hazards from Basins Burned by the Cedar and Paradise Fires of 2003, Southern California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-125, 32, -112, 40", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552021-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552021-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1011_1.0", + "description": "These maps present preliminary assessments of the probability of debris-flow activity and estimates of peak discharges that can potentially be generated by debris flows issuing from basins burned by the Cedar and Paradise Fires of October 2003 in southern California in response to 25-year, 10-year, and 2-year recurrence, 1-hour duration rain storms. The probability maps are based on the application of a logistic multiple regression model that describes the percent chance of debris-flow production from an individual basin as a function of burned extent, soil properties, basin gradients, and storm rainfall. The peak-discharge maps are based on application of a multiple-regression model that can be used to estimate debris-flow peak discharge at a basin outlet as a function of basin gradient, burn extent, and storm rainfall. Probabilities of debris-flow occurrence for the Cedar Fire range between 0 and 98% and estimates of debris-flow peak discharges range between 893 and 5,987 ft3/s (25 to 170 m3/s). Basins burned by the Paradise Fire show probabilities for debris-flow occurrence between 2 and 98%, and peak discharge estimates between 1,814 and 5,980 ft3/s (51 and 169 m3/s). These maps are intended to identify those basins that are most prone to the largest debris-flow events and provide critical information for the preliminary design of mitigation measures and for the planning of evacuation timing and routes. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2004_1013_1.0", + "title": "Maps Showing the Stratigraphic Framework of South Carolina's Long Bay from Little River to Winyah Bay", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-01-01", + "end_date": "", + "bbox": "-81, 32.5, -78.5, 34", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551083-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551083-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1013_1.0", + "description": "South Carolina's Grand Strand is a heavily populated coastal region that supports a large tourism industry. Like most densely developed coastal communities, the potential for property damage and lost revenues associated with coastal erosion and vulnerability to severe storms is of great concern. In response to these concerns, the U.S. Geological Survey (USGS) and the South Carolina Sea Grant Consortium have chosen to focus upon the Grand Strand (the arcuate strand of beaches between the North Carolina Border and Winyah Bay, SC) and adjacent Long Bay as a portion of Phase II of the South Carolina/Georgia Coastal Erosion Study (SC/GCES). Phase I of the SC/GCES (1994 - 1999) focused upon critical areas of erosion along the central portion of the South Carolina coastline. Research conducted during Phase I began to identify how physical processes, inlet-beach interaction, framework geology and shoreline geometry combine to control patterns of erosion along the central South Carolina coast. Phase II of SC/GCES (1999 - present) was designed to gain a further understanding of the factors affecting shoreline change within northern South Carolina and Georgia. Specific goals of the Phase II study include: 1) quantifying historic shoreline change and identifying erosional hotspots; 2) mapping geologic framework and determining its role in the area's coastal evolution; and 3) calculating a sediment budget and identifying transport mechanisms within the study area. In November 1999, to address the second goal of Phase II of the SC/GCES, the USGS, Coastal Carolina University (CCU) and Scripps Institution of Oceanography (SIO) began a program to systematically map the geologic framework within the South Carolina segment of Long Bay. Data sources used to produce these maps include high-resolution sidescan-sonar, interferometric sonar swath bathymetry and sub-bottom profiling. Surface sediment samples, vibracores and video data provide groundtruth for the geophysical data. The goals of the program include determining regional-scale sand-resource availability (needed for ongoing beach nourishment projects) and investigating the role that inner-shelf morphology and geologic framework play in the evolution of this portion of coastal South Carolina. This report presents preliminary maps generated through integrated interpretation of geophysical data, which detail the geometries of Cretaceous and Tertiary continental shelf deposits, show the location and extent of paleochannel incisions, and define a regional transgressive unconformity and overlying bodies of reworked sediment. Defining the shallow sub-surface geologic framework will provide a base for future process-oriented studies and provide insight into coastal evolution. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2004_1014", + "title": "Geophysical, Sedimentological, and Photographic Data from the John Day Reservoir, Washington and Oregon", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-128, 39, -118, 52", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550514-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550514-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1014", + "description": "Recently, concerns about declining stocks of endangered anadromous salmonids in the Columbia River basin raised the issue of restoration of riverine functions in this and other Columbia and Snake River reservoirs (ISG, 2000; Dauble and others, 2003). One option for restoration of riverine functions includes lowering water levels within selected reservoirs such as the John Day Reservoir. Questions about how much sediment has been trapped by this dam warranted a detailed study of the floor of the reservoir to assess changes that had occurred since impoundment. High-resolution geophysical mapping techniques were employed to provide, to our knowledge, the first detailed view of the floor of the reservoir since its formation. This geophysical \"road map\" in concert with bottom video images, some sediment samples, and historical data collected prior to creation of the reservoir were incorporated into a GIS. The subsequent text summarizes the techniques used in this study. It also provides a preliminary analysis of the results and a background for the GIS that accompanies this report. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1020_1.0", "title": "Coastal Vulnerability Assessment of Assateague Island National Seashore (ASIS) To Sea-Level Rise", @@ -182753,6 +187927,45 @@ "description": "This online publication portrays regional data for pH, alkalinity, and specific conductance for stream waters and a multi-element geochemical dataset for stream sediments collected in the New England states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. A series of interpolation grid maps portray the chemistry of the stream waters and sediments in relation to bedrock geology, lithology, drainage basins, and urban areas. A series of box plots portray the statistical variation of the chemical data grouped by lithology and other features. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1038", + "title": "Inventory of Significant Mineral Deposit Occurrences in the Headwaters Project Area in Idaho, Western Montana, and Extreme Eastern Oregon and Washington", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-126, 42, -110, 52", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551664-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551664-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1038", + "description": "The significant mineral deposit inventory supports the U.S. Geological Survey Headwaters project, which will provide Federal land management agencies with basic geologic and mineral resource information that can be used to manage near-term mineral resource development activity. The Headwaters study is focused on areas in Idaho lying north of the Snake River plain and in western Montana where a preponderance of the lands are managed by the U.S. Forest Service. The scope of this mineral resource inventory embraces a broader geographic area that includes all of Idaho, the western half of Montana and small portions of extreme eastern Oregon and Washington. This inventory covers only significant mineral deposits. Significant deposits are those deposits where a mineral or natural material endowment occurs in such a high concentration that it is reasonable to expect that recovery was or could, in the future, be economically viable. Minimum endowments proposed by Long (personal communication) for 46 commodities have been used in this compilation. For deposits of other commodities where minimum endowments have not been established a default deposit size minimum of one million metric tons of ore has been utilized. A significant status has also been applied to deposits where a commodity or material is of a highly unusual nature. Data collection was limited to deposit attributes that reflect directly on the endowed size and location of a deposit, and ancillary information that can be used in assessing regional mineral resource potential. The data are organized in topical information categories that include name, location, deposit classification, discovery date, production and resources, surface area, development status, and source of new information. Data were extracted from a diverse array of sources that includes scientific, technical, and trade publications of public and private institutions, organizations, and associations that follow and report on scientific, business, and environmental issues in the minerals industry; company financial reports, news releases, and technical reports available at company web sites; mineral information databases maintained by Federal and state agencies involved with monitoring and regulating mining activities and compiling mining industry statistics; and oral communications with individual mining company personnel and with staff of Federal and state regulatory agencies. Several formatting conventions are used to indicate what the relative accuracy of the numerical data is believed to be. A total of 256 significant deposit sites are identified by location and deposit-type. Production and resource figures are given in both English and metric units and the approximate surface areas associated with three aspects of deposit development are expressed in acres. Of the 256 sites, 208 have some history of past or present production, of which 23 are currently producing and mining could resume at 7 others on short notice with a rise in commodity prices. Within the 208 sites are 34 placer districts and two zeolite operations wherein mining activity on a small scale occurs intermittently. There are 166 sites where the presence of a significant resource has been recognized, of which 49 have no prior history of development. Due to the presence of a significant resource, these 166 sites are candidates for consideration when addressing issues associated with management of near-term mineral development. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2004_1039", + "title": "Location, Age, and Tectonic Significance of the Western Idaho Suture Zone", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-118, 43, -112, 47", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552012-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552012-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1039", + "description": "The Western Idaho Suture Zone (WISZ) represents the boundary between crust overlying Proterozoic North American lithosphere and Late Paleozoic and Mesozoic intraoceanic crust accreted during Cretaceous time. Highly deformed plutons constituted of both arc and sialic components intrude the WISZ and in places are thrust over the accreted terranes. Pronounced variations in Sr, Nd, and O isotope ratios and in major and trace element composition occur across the suture zone in Mesozoic plutons. The WISZ is located by an abrupt west to east increase in initial 87Sr/86Sr ratios, traceable for over 300 km from eastern Washington near Clarkston, east along the Clearwater River thorough a bend to the south of about 110\u00b0 from Orofino Creek to Harpster, and extending south-southwest to near Ola, Idaho, where Columbia River basalts conceal its extension to the south. K-Ar and 40Ar/39Ar apparent ages of hornblende and biotite from Jurassic and Early Cretaceous plutons in the accreted terranes are highly discordant within about 10 km of the WISZ, exhibiting patterns of thermal loss caused by deformation, subsequent batholith intrusion, and rapid rise of the continental margin. Major crustal movements within the WISZ commenced after about 135 Ma, but much of the displacement may have been largely vertical, during and following emplacement of batholith-scale silicic magmas. Deformation continued until at least 85 Ma and probably until 74 Ma, progressing from south to north. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2004_1049_1.0", + "title": "Geologic and Bathymetric Reconnaissance Overview of the San Pedro Shelf Region, Southern California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-01-01", + "end_date": "2002-12-31", + "bbox": "-118.33333, 33.46667, -117.83333, 33.78333", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548808-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548808-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1049_1.0", + "description": "This report presents a series of maps that describe the bathymetry and late Quaternary geology of the San Pedro shelf area as interpreted from seismic-reflection profiles and 3.5-kHz and multibeam bathymetric data. Some of the seismic-reflection profiles were collected with Uniboom and 120-kJ sparker during surveys conducted by the U.S. Geological Survey (USGS) in 1973 (K-2-73-SC), 1978 (S-2-78-SC), and 1979 (S-2a-79-SC). The remaining seismic-reflection profiles were collected in 2000 using Geopulse boomer and minisparker during USGS cruise A-1-00-SC. The report consists of seven oversized sheets: 1. Map of 1978 and 1979 uniboom seismic-reflection and 3.5-kHz tracklines used to map faults and folds on San Pedro Shelf. 2. Maps of multibeam shaded bathymetric relief with faults and folds, and bathymetric contours. 3. Isopach map of unconsolidated sediment, seismic-reflection profile across the San Pedro shelf, seismic-reflection profile across San Gabriel paleo-valley. 4. Seismic-reflection profiles across the Palos Verdes Fault Zone. 5. Geologic map and samples of Uniboom and 120-kJ sparker seismic-reflection profiles used to make the map. 6. Map showing thickness of uppermost (Holocene?) sediment layer. 7. Map of San Gabriel Canyon paleo-valley and associated drainage basins. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1054", "title": "Assessment of Hazards Associated with the Bluegill Landslide, South-Central Idaho", @@ -182818,6 +188031,19 @@ "description": "Scientific measurements at Wolverine Glacier, on the Kenai Peninsula in south-central Alaska, began in April 1966. At three long-term sites in the research basin, the measurements included snow depth, snow density, heights of the glacier surface and stratigraphic summer surfaces on stakes, and identification of the surface materials. Calculations of the mass balance of the surface strata-snow, new firn, superimposed ice, and old firn and ice mass at each site were based on these measurements. Calculations of fixed-date annual mass balances for each hydrologic year (October 1 to September 30), as well as net balances and the dates of minimum net balance measured between time-transgressive summer surfaces on the glacier, were made on the basis of the strata balances augmented by air temperature and precipitation recorded in the basin. From 1966 through 1995, the average annual balance at site A (590 meters altitude) was -4.06 meters water equivalent; at site B (1,070 meters altitude), was -0.90 meters water equivalent; and at site C (1,290 meters altitude), was +1.45 meters water equivalent. Geodetic determination of displacements of the mass balance stake, and glacier surface altitudes was added to the data set in 1975 to detect the glacier motion responses to variable climate and mass balance conditions. The average surface speed from 1975 to 1996 was 50.0 meters per year at site A, 83.7 meters per year at site B, and 37.2 meters per year at site C. The average surface altitudes were 594 meters at site A, 1,069 meters at site B, and 1,293 meters at site C; the glacier surface altitudes rose and fell over a range of 19.4 meters at site A, 14.1 meters at site B, and 13.2 meters at site C. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1074", + "title": "Flood of June 4, 2002, in the Indian Creek Basin, Linn County, Iowa", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-06-04", + "end_date": "2002-06-04", + "bbox": "-96.97, 40.05, -89.82, 43.83", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549367-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549367-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1074", + "description": "Severe flooding occurred on June 4, 2002, in the Indian Creek Basin in Linn County, Iowa, following thunderstorm activity over east-central Iowa. The rain gage at Cedar Rapids, Iowa, recorded a 24-hour rainfall of 4.76 inches at 6:00 p.m. on June 4th. Radar indications estimated as much as 6 inches of rain fell in the headwaters of the Indian Creek Basin. Peak discharges on Indian Creek of 12,500 cubic feet per second at County Home Road north of Marion, Iowa, and 24,300 cubic feet per second at East Post Road in southeast Cedar Rapids, were determined for the flood. The recurrence interval for these peak discharges both exceed the theoretical 500-year flood as computed using flood-estimation equations developed by the U.S. Geological Survey. Information about the basin and flood history, the 2002 thunderstorms and associated flooding, and a profile of high-water marks are presented for selected reaches along Indian and Dry Creeks. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1075", "title": "Bedrock Geology and Mineral Resources of the Knoxville 1 degree x 2 degree Quadrangle, Tennessee, North Carolina, and South Carolina (Digital Version)", @@ -182857,6 +188083,19 @@ "description": "The Catalog of Significant Historical Earthquakes in the Central United States use Modified Mercalli intensity assignments to estimate source locations and moment magnitude M for eighteen 19th-century and twenty early- 20th-century earthquakes in the central United States (CUS) for which estimates of M are otherwise not available. We use these estimates, and locations and M estimated elsewhere, to compile a catelog of significant historical earthquakes in the CUS. The 1811-1812 New Madrid earthquakes apparently dominated CUS seismicity in the first two decades of the 19th century. M5-6 earthquakes occurred in the New Madrid Seismic Zone in 1843 and 1878, but none have occurred since 1878. There has been persistent seismic activity in the Illinois Basin in southern Illinois and Indiana, with M > 5.0 earthquakes in 1895, 1909, 1917, 1968, and 1987. Four other M > 5.0 CUS historical earthquakes have occurred: in Kansas in 1867, in Nebraska in 1877, in Oklahoma in 1882, and in Kentucky in 1980. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1096", + "title": "Ground Magnetic Data from within the Long Valley Caldera, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-07-27", + "end_date": "2003-08-01", + "bbox": "-124.9, 32.02, -113.61, 42.51", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554402-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554402-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1096", + "description": "The past two decades have been a period of unrest for the Long Valley caldera of eastern California. The unrest began in 1978 and continued through late 1999 and included recurring swarms of moderate earthquakes, as well as uplifting of the Resurgent Dome, which has totaled approximately 80 cm. It is believed that the seismicity is accompanied by magmatic intrusion beneath both the Resurgent Dome at a depth of about 7 km; 10 km and the South Moat Seismic Zone (SMSZ) at a depth of about 15 km (Sorey and others, 2003). Seismic surveys within the caldera's topographic boundary have indicated the seismicity beneath the northwest section of the caldera is associated with fluid injection into narrow conduits and fractures (Stroujkova and Malin, 2000). Like the dominant regional structural trend, these conduits run in a northwest-southeast direction and are only expressed at the surface by a slight topographic relief of about 3 m. Merged aeromagnetic data (Roberts and Jachens, 1999) over the caldera show a magnetic low in the west and a high in the east (Figure 3). The western part has been modeled to relate to altered, low-magnetization (about 2.5 km thick) Bishop Tuff beneath the Resurgent Dome, indicating hydrothermal alteration in the west, whereas the high in the east represents the unaltered Bishop Tuff (Williams and others, 1977). The ground magnetic survey was conducted to locate magnetic lows that might indicate altered zones reflecting conduits for hydrothermal fluid flow in the northwest portion of the caldera. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1192", "title": "Deposition, Erosion, and Bathymetric Change in South San Francisco Bay: 1858-1983", @@ -182896,6 +188135,19 @@ "description": "A coastal vulnerability index (CVI) was used to map the relative vulnerability of the coast to future sea-level rise within Cumberland Island National Seashore in Georgia. The CVI ranks the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level rise, historical shoreline change rates, mean tidal range and mean significant wave height. The rankings for each input variable were combined and an index value calculated for 1-minute grid cells covering the park. The CVI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural vulnerability to the effects of sea-level rise. The CVI provides an objective technique for evaluation and long-term planning by scientists and park managers. Cumberland Island National Seashore consists of stable to washover-dominated portions of barrier beach backed by wetland, marsh, mudflat and tidal creek. The areas within Cumberland that are likely to be most vulnerable to sea-level rise are those with the lowest foredune ridge and highest rates of shoreline erosion. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1201", + "title": "Hydraulic Conductivity of Near-Surface Alluvium in the Vicinity of Cattlemans Detention Basin, South Lake Tahoe, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-121, 38, -119, 40", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550993-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550993-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1201", + "description": "Cattlemans detention basin, South Lake Tahoe, California is designed to capture and reduce urban runoff and pollutants originating from developed areas before entering Cold Creek, which is tributary to Trout Creek and to Lake Tahoe. The effectiveness of the basin in reducing sediment and nutrient loads currently is being assessed with a five-year study. Hydraulic conductivity of the alluvium near the detention basin is needed to estimate ground-water flow and subsurface nutrient transport. Hydraulic conductivity was estimated using slug tests in 27 monitoring wells that surround the detention basin. For each test, water was poured rapidly into a well, changes in water-level were monitored, and the observed changes were analyzed using the Bouwer and Rice method. Each well was tested one to four times. A total of 24 wells were tested more than once. Of the 24 wells, the differences among the tests were within 10 percent of the average. Estimated hydraulic conductivities of basin alluvium range from 0.5 to 70 feet per day with an average of 17.8 feet per day. This range is consistent with the sandy alluvial deposits observed in the area of Cattlemans detention basin. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1208", "title": "Concentrations of Polycyclic Aromatic Hydrocarbons (PAHs) and Major and Trace Elements in Simulated Rainfall Runoff From Parking Lots, Austin, Texas, 2003", @@ -182922,6 +188174,19 @@ "description": "Lead-rich sediments, containing at least 1000 ppm of lead (Pb), and derived mainly from discarded mill tailings in the Coeur d'Alene mining region, cover about 60 km2 of the 80-km2 floor of the main stem of the Coeur d'Alene River valley, in north Idaho. Although mill tailings have not been discarded directly into tributary streams since 1968, frequent floods continue to re-mobilize sediment from large secondary sources, previously deposited on the bed, banks, alluvial terraces, and natural levees of the river. Thus, lead-rich sediments (also enriched in iron, manganese, zinc, copper, arsenic, cadmium, antimony and mercury) continue to be deposited on the floodplain. This is hazardous to the health of resident and visiting human and wildlife populations, attracted by the river and its lateral lakes and wetlands. This report documents and compares depositional rates and lead concentrations of lead-rich sediments deposited on the bed, banks, natural levees, and flood basins of the main stem of the Coeur d'Alene River during several time-stratigraphic intervals. These intervals are defined by their stratigraphic positions relative to the base of the section of lead-rich sediments, the 1980 Mt. St. Helens volcanic-ash layer, and the sedimentary surface at the time of sampling. Four important intervals represent sediment deposition during the following time spans (younger to older): 1. Baseline, from 1980 to about 1993 (after tailings disposal to streams ended, but before any major removals of lead-rich sediments); 2. Early post-tailings-release, from about 1968 to 1980; 3. Historic floodplain-contamination, from about 1903 to 1968; and 4. Background, before the 1893 flood (the first major flood after large-scale mining and milling began upstream in 1886). Medians of baseline depositional rates and lead concentrations in levee sediments vary laterally, from 6.4 cm/10y and 3300 ppm Pb on riverbanks and levee fore-slopes to 2.8 cm/10y and 3800 ppm Pb on levee back-slope uplands. In lateral flood basins, baseline medians increase with water depth, from 2.2 cm/10y and 1900 ppm Pb in lateral marshes, to 2.9 cm/10y and 2100 ppm Pb in littoral margins of lateral lakes, and 4.0 cm/10y and 4400 ppm Pb on limnetic bottoms of lateral lakes. The median of lead concentrations in baseline sediments is 82 percent of the median for early post-tailings-release sediments, with a 69-percent probability that the two data sets represent statistically different populations. By contrast, the median of lead concentrations in baseline sediments is 57 percent of the corresponding median for historic-interval sediments, and these two data sets definitely represent statistically different populations. The area-weighted average of medians of lead concentrations in baseline sediments of all depositional settings is 2900 ppm Pb, which is 1.6 times the 1800 ppm Pb that can be lethal to waterfowl. It also is 2.9 times the 1000-ppm-Pb threshold for removal of contaminated soil from residential yards in the Coeur d'Alene mining region, and 111 times the 26-ppm median of background lead concentrations in pre-industrial floodplain sediments. During episodes of high discharge, lead-rich sediments will continue to be mobilized from large secondary sources on the bed, banks, and natural levees of the river, and will continue to be deposited on the floodplain during frequent floods. Floodplain deposition of lead-rich sediments will continue for centuries unless major secondary sources are removed or stabilized. It is therefore important to design, sequence, implement, and maintain remediation in ways that will limit recontamination. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1214", + "title": "Dissolved Pesticide and Organic Carbon Concentrations Detected in Surface Waters, Northern Central Valley, California, 2001-2002", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2002-06-30", + "bbox": "-128, 36, -120, 45", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550635-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550635-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1214", + "description": "Field and laboratory studies were conducted to determine the effects of pesticide mixtures on Chinook salmon under various environmental conditions in surface waters of the northern Central Valley of California. This project was a collaborative effort between the U.S. Geological Survey (USGS) and the University of California. The project focused on understanding the environmental factors that influence the toxicity of pesticides to juvenile salmon and their prey. During the periods January through March 2001 and January through May 2002, water samples were collected at eight surface water sites in the northern Central Valley of California and analyzed by the USGS for dissolved pesticide and dissolved organic carbon concentrations. Water samples were also collected by the USGS at the same sites for aquatic toxicity testing by the Aquatic Toxicity Laboratory at the University of California Davis; however, presentation of the results of these toxicity tests is beyond the scope of this report. Samples were collected to characterize dissolved pesticide and dissolved organic carbon concentrations, and aquatic toxicity, associated with winter storm runoff concurrent with winter run Chinook salmon out-migration. Sites were selected that represented the primary habitat of juvenile Chinook salmon and included major tributaries within the Sacramento and San Joaquin River Basins and the Sacramento San Joaquin Delta. Water samples were collected daily for a period of seven days during two winter storm events in each year. Additional samples were collected weekly during January through April or May in both years. Concentrations of 31 currently used pesticides were measured in filtered water samples using solid-phase extraction and gas chromatography-mass spectrometry at the U.S. Geological Survey's organic chemistry laboratory in Sacramento, California. Dissolved organic carbon concentrations were analyzed in filtered water samples using a Shimadzu TOC-5000A total organic carbon analyzer. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1220", "title": "Baseline Characteristics of Jordan Creek, Juneau, Alaska", @@ -182935,6 +188200,19 @@ "description": "Anadromous fish populations historically have found healthy habitat in Jordan Creek, Juneau, Alaska. Concern regarding potential degradation to the habitat by urban development within the Mendenhall Valley led to a cooperative study among the City and Borough of Juneau, Alaska Department of Environmental Conservation, and the U.S. Geological Survey, that assessed current hydrologic, water-quality, and physical-habitat conditions of the stream corridor. Periods of no streamflow were not uncommon at the Jordan Creek below Egan Drive near Auke Bay stream gaging station. Additional flow measurements indicate that periods of no flow are more frequent downstream of the gaging station. Although periods of no flow typically were in March and April, streamflow measurements collected prior to 1999 indicate similar periods in January, suggesting that no flow conditions may occur at any time during the winter months. This dewatering in the lower reaches likely limits fish rearing and spawning habitat as well as limiting the migration of juvenile salmon out to the ocean during some years. Dissolved-oxygen concentrations may not be suitable for fish survival during some winter periods in the Jordan Creek watershed. Dissolved-oxygen concentrations were measured as low as 2.8 mg/L at the gaging station and were measured as low as 0.85 mg/L in a tributary to Jordan Creek. Intermittent measurements of pH and dissolved-oxygen concentrations in the mid-reaches of Jordan Creek were all within acceptable limits for fish survival, however, few measurements of these parameters were made during winter-low-flow conditions. One set of water quality samples was collected at six different sites in the Jordan Creek watershed and analyzed for major ions and dissolved nutrients. Major-ion chemistry showed Jordan Creek is calcium bicarbonate type water with little variation between sampling sites. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1221", + "title": "Los Angeles and San Diego Margin High-Resolution Multibeam Bathymetry and Backscatter Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-118.85, 33.334, -117.754, 34.029", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552471-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552471-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1221", + "description": "The U.S. Geological Survey in cooperation with the University of New Hampshire and the University of New Brunswick mapped the nearshore regions off Los Angeles and San Diego, California using multibeam echosounders. Multibeam bathymetry and co-registered, corrected acoustic backscatter were collected in water depths ranging from about 3 to 900 m offshore Los Angeles and in water depths ranging from about 17 to 1230 m offshore San Diego. Continuous, 16-m spatial resolution, GIS ready format data of the entire Los Angeles Margin and San Diego Margin are available online as separate USGS Open-File Reports. For ongoing research, the USGS has processed sub-regions within these datasets at finer resolutions. The resolution of each sub-region was determined by the density of soundings within the region. This Open-File Report contains the finer resolution multibeam bathymetry and acoustic backscatter data that the USGS, Western Region, Coastal and Marine Geology Team has processed into GIS ready formats as of April 2004. The data are available in ArcInfo GRID and XYZ formats. See the Los Angeles or San Diego maps for the sub-region locations. These datasets in their present form were not originally intended for publication. The bathymetry and backscatter have data-collection and processing artifacts. These data are being made public to fulfill a Freedom of Information Act request. Care must be taken not to confuse artifacts with real seafloor morphology and acoustic backscatter. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1228", "title": "Bottom Photographs from the Pulley Ridge Deep Coral Reef", @@ -182961,6 +188239,19 @@ "description": "The Alaska Volcano Observatory (AVO), a cooperative program of the U.S. Geological Survey, the Geophysical Institute of the University of Alaska Fairbanks, and the Alaska Division of Geological and Geophysical Surveys, has maintained seismic monitoring networks at historically active volcanoes in Alaska since 1988. The primary objectives of this program are the near real time seismic monitoring of active, potentially hazardous, Alaskan volcanoes and the investigation of seismic processes associated with active volcanism. This catalog presents the calculated earthquake hypocenter and phase arrival data, and changes in the seismic monitoring program for the period January 1 through December 31, 2003. The AVO seismograph network was used to monitor the seismic activity at twenty-seven volcanoes within Alaska in 2003. These include Mount Wrangell, Mount Spurr, Redoubt Volcano, Iliamna Volcano, Augustine Volcano, Katmai volcanic cluster (Snowy Mountain, Mount Griggs, Mount Katmai, Novarupta, Trident Volcano, Mount Mageik, Mount Martin), Aniakchak Crater, Mount Veniaminof, Pavlof Volcano, Mount Dutton, Isanotski Peaks, Shishaldin Volcano, Fisher Caldera, Westdahl Peak, Akutan Peak, Makushin Volcano, Okmok Caldera, Great Sitkin Volcano, Kanaga Volcano, Tanaga Volcano, and Mount Gareloi. Monitoring highlights in 2003 include: continuing elevated seismicity at Mount Veniaminof in January-April (volcanic unrest began in August 2002), volcanogenic seismic swarms at Shishaldin Volcano throughout the year, and low-level tremor at Okmok Caldera throughout the year. Instrumentation and data acquisition highlights in 2003 were the installation of subnetworks on Tanaga and Gareloi Islands, the installation of broadband installations on Akutan Volcano and Okmok Caldera, and the establishment of telemetry for the Okmok Caldera subnetwork. AVO located 3911 earthquakes in 2003. This catalog includes: (1) a description of instruments deployed in the field and their locations; (2) a description of earthquake detection, recording, analysis, and data archival systems; (3) a description of velocity models used for earthquake locations; (4) a summary of earthquakes located in 2003; and (5) an accompanying UNIX tar-file with a summary of earthquake origin times, hypocenters, magnitudes, phase arrival times, and location quality statistics; daily station usage statistics; and all HYPOELLIPSE files used to determine the earthquake locations in 2003. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1235", + "title": "Distribution of Holocene Sediment in Chesapeake Bay", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-78, -36, -74, 41", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552442-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552442-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1235", + "description": "The distribution of sedimentary environments presents the limited domain of deposits from \"River Input\", the flood tide wedge of \"Atlantic Sediment\", and the extensive region of indigenous, recycled \"Coastal Erosion Sediment\" in the Chesapeake Bay littoral environment. Studies by Miller (1982, 1983, 1987) along selected reaches of the tidewater Potomac River showed that bluff retreat in the littoral environment could be measured and modeled at as much as 0.5 to 1.0 m/yr. During the September 18, 2003 Hurricane Isabel storm surge of nearly 3 m, as much as 8 to 10 m of coastal erosion was measured near some of Miller's sites. Storm-driven coastal erosion is the most extensive source of Holocene sediment in the modern Bay. Although massive amounts were eroded from the terraces and uplands during lowered sea level and cold climates, presently most sediment eroded and transported from terrace and upland source areas has been stored on slopes and alluvial bottoms of the Coastal Plain landscapes that surround the Chesapeake. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1249", "title": "A Forest Vegetation Database for Western Oregon", @@ -182974,6 +188265,19 @@ "description": "Data on forest vegetation in western Oregon were assembled for 2323 ecological survey plots. All data were from fixed-radius plots with the standardized design of the Current Vegetation Survey (CVS) initiated in the early 1990s. For each site, the database includes: 1) live tree density and basal area of common tree species, 2) total live tree density, basal area, estimated biomass, and estimated leaf area; 3) age of the oldest overstory tree examined, 4) geographic coordinates, 5) elevation, 6) interpolated climate variables, and 7) other site variables. The data are ideal for ecoregional analyses of existing vegetation. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1252", + "title": "Digital Files for Northeast Asia Geodynamics, Mineral Deposit Location, and Metallogenic Belt Maps, Stratigraphic Columns, Descriptions of Map Units, and Descriptions of Metallogenic Belts", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "60, 27, 170, 81", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554750-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554750-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1252", + "description": "This publication contains a a series of files for Northeast Asia geodynamics, mineral deposit location, and metallogenic belt maps descriptions of map units and metallogenic belts, and stratigraphic columns. This region includes Eastern Siberia, Russian Far East, Mongolia, Northeast China, South Korea, and Japan. The files include: (1) a geodynamics map at a scale of 1:5,000,000; (2) page-size stratigraphic columns for major terranes; (3) a generalized geodynamics map at a scale of 1:15,000,000; (4) a mineral deposit location map at a scale of 1:7,500,000; (5) metallogenic belt maps at a scale of 1:15,000,000; (6) detailed descriptions of geologic units with references; (7) detailed descriptions of metallogenic belts with references; and (8) summary mineral deposit and metallogenic belt tables. The purpose of this publication is to provide high-quality, digital graphic files for maps and figures, and Word files for explanations, descriptions, and references to customers and users. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1260", "title": "Channel-Morphology Data for the Tongue River and Selected Tributaries, Southeastern Montana, 2001-02", @@ -182987,6 +188291,32 @@ "description": "Coal-bed methane exploration and production have begun within the Tongue River watershed in southeastern Montana. The development of coal-bed methane requires production of large volumes of ground water, some of which may be discharged to streams, potentially increasing stream discharge and sediment load. Changes in stream discharge or sediment load may result in changes to channel morphology through changes in erosion and vegetation. These changes might be subtle and difficult to detect without baseline data that indicate stream-channel conditions before extensive coal-bed methane development began. In order to provide this baseline channel-morphology data, the U.S. Geological Survey, in cooperation with the Bureau of Land Management, collected channel-morphology data in 2001-02 to document baseline conditions for several reaches along the Tongue River and selected tributaries. This report presents channel-morphology data for five sites on the mainstem Tongue River and four sites on its tributaries. Bankfull, water-surface, and thalweg elevations, channel sections, and streambed-particle sizes were measured along reaches near streamflow-gaging stations. At each site, the channel was classified using methods described by Rosgen. For six sites, bankfull discharge was determined from the stage- discharge relation at the gage for the stage corresponding to the bankfull elevation. For three sites, the step-backwater computer model HEC-RAS was used to estimate bankfull discharge. Recurrence intervals for the bankfull discharge also were estimated for eight of the nine sites. Channel-morphology data for each site are presented in maps, tables, graphs, and photographs. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1265", + "title": "Hydrologic Data Summary for the St. Lucie River Estuary, Martin and St. Lucie Counties, Florida, 1998-2001", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1998-01-01", + "end_date": "2001-12-31", + "bbox": "-81, 27, -80, 28", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549907-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549907-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1265", + "description": "A hydrologic analysis was made at three canal sites and four tidal sites along the St. Lucie River Estuary in southeastern Florida from 1998 to 2001. The data included for analysis are stage, 15-minute flow, salinity, water temperature, turbidity, and suspended-solids concentration. During the period of record, the estuary experienced a drought, major storm events, and high-water discharge from Lake Okeechobee. Flow mainly occurred through the South Fork of the St. Lucie River; however, when flow increased through control structures along the C-23 and C-24 Canals, the North Fork was a larger than usual contributor of total freshwater inflow to the estuary. At one tidal site (Steele Point), the majority of flow was southward toward the St. Lucie Inlet; at a second tidal site (Indian River Bridge), the majority of flow was northward into the Indian River Lagoon. Large-volume stormwater discharge events greatly affected the St. Lucie River Estuary. Increased discharge typically was accompanied by salinity decreases that resulted in water becoming and remaining fresh throughout the estuary until the discharge events ended. Salinity in the estuary usually returned to prestorm levels within a few days after the events. Turbidity decreased and salinity began to increase almost immediately when the gates at the control structures closed. Salinity ranged from less than 1 to greater than 35 parts per thousand during the period of record (1998-2001), and typically varied by several parts per thousand during a tidal cycle. Suspended-solids concentrations were observed at one canal site (S-80) and two tidal sites (Speedy Point and Steele Point) during a discharge event in April and May 2000. Results suggest that most deposition of suspended-solids concentration occurs between S-80 and Speedy Point. The turbidity data collected also support this interpretation. The ratio of inorganic to organic suspended-solids concentration observed at S-80, Speedy Point, and Steele Point during the discharge event indicates that most flocculation of suspended-solids concentration occurs between Speedy Point and Steele Point. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2004_1269_1.0", + "title": "Liquefaction-Induced Lateral Spreading in Oceano, California, During the 2003 San Simeon Earthquake", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-124.9, 32.02, -113.61, 42.51", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553668-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553668-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1269_1.0", + "description": "The December 22, 2003, San Simeon, California, (M6.5) earthquake caused damage to houses, road surfaces, and underground utilities in Oceano, California. The community of Oceano is approximately 50 miles (80 km) from the earthquake epicenter. Damage at this distance from a M6.5 earthquake is unusual. To understand the causes of this damage, the U.S. Geological Survey conducted extensive subsurface exploration and monitoring of aftershocks in the months after the earthquake. The investigation included 37 seismic cone penetration tests, 5 soil borings, and aftershock monitoring from January 28 to March 7, 2004. The USGS investigation identified two earthquake hazards in Oceano that explain the San Simeon earthquake damage?site amplification and liquefaction. Site amplification is a phenomenon observed in many earthquakes where the strength of the shaking increases abnormally in areas where the seismic-wave velocity of shallow geologic layers is low. As a result, earthquake shaking is felt more strongly than in surrounding areas without similar geologic conditions. Site amplification in Oceano is indicated by the physical properties of the geologic layers beneath Oceano and was confirmed by monitoring aftershocks. Liquefaction, which is also commonly observed during earthquakes, is a phenomenon where saturated sands lose their strength during an earthquake and become fluid-like and mobile. As a result, the ground may undergo large permanent displacements that can damage underground utilities and well-built surface structures. The type of displacement of major concern associated with liquefaction is lateral spreading because it involves displacement of large blocks of ground down gentle slopes or towards stream channels. The USGS investigation indicates that the shallow geologic units beneath Oceano are very susceptible to liquefaction. They include young sand dunes and clean sandy artificial fill that was used to bury and convert marshes into developable lots. Most of the 2003 damage was caused by lateral spreading in two separate areas, one near Norswing Drive and the other near Juanita Avenue. The areas coincided with areas with the highest liquefaction potential found in Oceano. Areas with site amplification conditions similar to those in Oceano are particularly vulnerable to earthquakes. Site amplification may cause shaking from distant earthquakes, which normally would not cause damage, to increase locally to damaging levels. The vulnerability in Oceano is compounded by the widespread distribution of highly liquefiable soils that will reliquefy when ground shaking is amplified as it was during the San Simeon earthquake. The experience in Oceano can be expected to repeat because the region has many active faults capable of generating large earthquakes. In addition, liquefaction and lateral spreading will be more extensive for moderate-size earthquakes that are closer to Oceano than was the 2003 San Simeon earthquake. Site amplification and liquefaction can be mitigated. Shaking is typically mitigated in California by adopting and enforcing up-to-date building codes. Although not a guarantee of safety, application of these codes ensures that the best practice is used in construction. Building codes, however, do not always require the upgrading of older structures to new code requirements. Consequently, many older structures may not be as resistant to earthquake shaking as new ones. For older structures, retrofitting is required to bring them up to code. Seismic provisions in codes also generally do not apply to nonstructural elements such as drywall, heating systems, and shelving. Frequently, nonstructural damage dominates the earthquake loss. Mitigation of potential liquefaction in Oceano presently is voluntary for existing buildings, but required by San Luis Obispo County for new construction. Multiple mitigation procedures are available to individual property owners. These procedures typically involve either changing the physical state of the underlying sands so they cannot liquefy or building a foundation that can resist the permanent displacement of the ground. Lateral spreading, which is the major threat to underground utilities, is particularly challenging to mitigate because typically large areas are involved and sizeable volumes of soil must be prevented from moving. Procedures to prevent spreading commonly require subsurface barrier walls. Prevention of lateral spreading may also require community rather than individual efforts because of the scale and cost of these mitigation measures. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1287_1.0", "title": "Coastal Circulation and Sediment Dynamics Along West Maui, Hawaii", @@ -183000,6 +188330,19 @@ "description": "High-resolution measurements of currents, temperature, salinity and turbidity were made over the course of three months off West Maui in the summer and early fall of 2003 to better understand coastal dynamics in coral reef habitats. Measurements were made through the emplacement of a series of bottom-mounted instruments in water depths less than 11 m. The studies were conducted in support of the U.S. Geological Survey (USGS) Coastal and Marine Geology Program's Coral Reef Project. The purpose of these measurements was to collect hydrographic data to better constrain the variability in currents and water column properties such as water temperature, salinity and turbidity in the vicinity of nearshore coral reef systems over the course of a summer and early fall when coral larvae spawn. These measurements support the ongoing process studies being conducted under the Coral Reef Project; the ultimate goal is to better understand the transport mechanisms of sediment, larvae, pollutants and other particles in coral reef settings. This report, the third in a series of three, describes data acquisition, processing and analysis. Previous reports provided data and results on: Long-term measurements of currents, temperature, salinity and turbidity off Kahana (PART I), and The spatial structure of currents, temperature, salinity and suspended sediment along West Maui (PART II). [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1297", + "title": "Isostatic residual gravity map of The Santa Clara Valley and vicinity, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-123, 37, -121, 38", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553893-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553893-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1297", + "description": "This map has 2 mGal gravity contours over a topographic base at a scale of 1:100,000. It covers the southern portion of San Francisco Bay, most of the Santa Clara Valley, and the surrounding mountains. It is a companion to U.S. Geological Survey Open-File Report 03-360, Shaded Relief Aeromagnetic Map of the Santa Clara Valley and Vicinity, California by Carter W. Roberts and Robert C. Jachens. [Summary provided by USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1303_1.0", "title": "Bedrock Geologic Map of the Port Wing, Solon Springs, and parts of the Duluth and Sandstone 30' X 60' Quadrangles, Wisconsin", @@ -183013,6 +188356,19 @@ "description": "This Open-File Report provides digital data (shapefiles and .e00 files) for the bedrock geology in the Port Wing, Solon Springs, and parts of the Duluth and Sandstone quadrangles in Wisconsin. A Miscellaneous Investigations Series map (I map) is currently in review with analogous data in paper format. This map portrays the geology of part of the Midcontinent rift system (MRS) along the southern extension of the Lake Superior syncline in northern Wisconsin. The map area contains the St. Croix horst, a rift graben filled with Mesoproterozoic rocks of the Keweenawan Supergroup that was subsequently inverted. The horst exposes about 15 - 20 km of strata that record the opening of the Midcontinent rift, its subsequent transition to a thermal subsidence basin, and eventual inversion. About 3 km of underlying Mesoproterozoic strata, including the Gogebic iron range, and about 10 km of Neoarchean rocks, exposed in the southernmost part of the map area lie to the southeast of the horst. The nearly flat-lying continental red beds of the Oronto and Bayfield Groups, the youngest strata of the Keweenawan Supergroup, overlie the volcanic rocks. A wealth of geologic data exists for the area as a result of many individual studies over the last hundred years, but much has remained unpublished in theses, dissertations, and other reports of limited availability. This map has incorporated most of that data (see list of data sources) and includes results of our investigations conducted from 1992 to 2000. Our studies were designed to fill gaps in existing data and reconcile conflicting interpretations on some aspects of the geology of the region. The purpose of this map is to complete digital coverage of quadrangles with significant exposure of rocks of the Midcontinent rift in Wisconsin and Michigan at a scale of 1:100,000. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1322_1.0", + "title": "Digital Shaded-Relief Map of Venezuela", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-73.41939, 0.598294, -59.75341, 12.213118", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554134-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554134-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1322_1.0", + "description": "The Digital Shaded-Relief Map of Venezuela is a composite of more than 20 tiles of 90 meter (3 arc second) pixel resolution elevation data, captured during the Shuttle Radar Topography Mission (SRTM) in February 2000. The SRTM, a joint project between the National Geospatial-Intelligence Agency (NGA) and the National Aeronautics and Space Administration (NASA), provides the most accurate and comprehensive international digital elevation dataset ever assembled. The 10-day flight mission aboard the U.S. Space Shuttle Endeavour obtained elevation data for about 80% of the world's landmass at 3-5 meter pixel resolution through the use of synthetic aperture radar (SAR) technology. SAR is desirable because it acquires data along continuous swaths, maintaining data consistency across large areas, independent of cloud cover. Swaths were captured at an altitude of 230 km, and are approximately 225 km wide with varying lengths. Rendering of the shaded-relief image required editing of the raw elevation data to remove numerous holes and anomalously high and low values inherent in the dataset. Customized ArcInfo Arc Macro Language (AML) scripts were written to interpolate areas of null values and generalize irregular elevation spikes and wells. Coastlines and major water bodies used as a clipping mask were extracted from 1:500,000-scale geologic maps of Venezuela (Bellizzia and others, 1976). The shaded-relief image was rendered with an illumination azimuth of 315\u00ef\u00bf\u00bd and an altitude of 65\u00ef\u00bf\u00bd. A vertical exaggeration of 2X was applied to the image to enhance land-surface features. Image post-processing techniques were accomplished using conventional desktop imaging software. [Summary provided by the USGS.] ", + "license": "proprietary" + }, { "id": "USGS_OFR_2004_1335", "title": "Binational Digital Soils Map of the Ambos Nogales Watershed, Southern Arizona and Northern Sonora, Mexico", @@ -183026,6 +188382,71 @@ "description": "A digital map of soil parameters for the international Ambos Nogales watershed was prepared to use as input for selected soils-erosion models. The Ambos Nogales watershed in southern Arizona and northern Sonora, Mexico, contains the Nogales wash, a tributary of the Upper Santa Cruz River. The watershed covers an area of 235 km squared just under half of which is in Mexico. Preliminary investigations of potential erosion revealed a discrepancy in soils data and mapping across the United States-Mexican border due to issues including different mapping resolutions, incompatible formatting, and varying nomenclature and classification systems. To prepare a digital soils map appropriate for input to a soils-erosion model, the historical analog soils maps for Nogales, Ariz., were scanned and merged with the larger-scale digital soils data available for Nogales, Sonora, Mexico using a geographic information system. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2004_1345_1", + "title": "Modeling of the Climax Stock and Related Plutons Based on the Inversion of Magnetic data, Southwest Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-116.26802, 37.149956, -115.917786, 37.31486", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553092-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553092-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1345_1", + "description": "The raster grid, model1, represents the elevation of the surface of the Climax and Gold Meadows Stocks. The elevation was generated by inverse modeling of the pseudogravity anomaly. This raster grid was created to model depth to the granitoid body that crops out at the Climax and Gold Meadows Stocks. Because granitic bodies may have hydrologic properties different from those of rocks they intrude, knowledge of their three-dimensional distribution in the subsurface is important for analyzing the southward flow of ground water into Yucca flat. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2004_1352", + "title": "Digital Engineering Aspects of Karst Map", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1984-01-01", + "end_date": "1984-12-31", + "bbox": "-178, 10, -16, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553390-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553390-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2004_1352", + "description": "These data are digital facsimiles of the original 1984 Engineering Aspects of Karst map by Davies and others. This data set was converted from a printed map to a digital GIS coverage to provide users with a citable national scale karst data set to use for graphic and demonstration purposes until new, improved data are developed. These data may be used freely with proper citation. Because it has been converted to GIS format, these data can be easily projected, displayed and queried for multiple uses in GIS. The karst polygons of the original map were scanned from the stable base negatives of the original, vectorized, edited and then attributed with unit descriptions. All of these processes potentially introduce small errors and distortions to the geography. The original map was produced at a scale of 1:7,500,000; this coverage is not as accurate, and should be used for broad-scale purposes only. It is not intended for any site-specific studies. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1038_1.0", + "title": "Geologic Shaded Relief Map of Venezuela", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-73.81, -0.11, -58.91, 12.92", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551683-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551683-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1038_1.0", + "description": "The geologic shaded relief map of Venezuela was created by direct digitization of geologic and hydrologic data north of the Orinoco River from a 1:500,000 scale paper map set. These data were integrated with a digital geologic map of the Venezuela Guayana Shield, also derived from 1:500,000 scale paper maps. Fault type information portrayed on the map, including unlabeled fault types, are as depicted in the original data sources. Geologic polygons were attributed for age, name, and lithologic type following the Lexico Estratigrafico de Venezuela. Significant revisions to the geology of the Cordillera de la Costa were incorporated based on new, detailed (1:25,000 scale) geologic mapping. Geologic polygons and fold and fault lines were draped over a shaded relief image produced by processing 90 m (3-arc second) radar interferometric data obtained by the space shuttle radar topography mission (SRTM). Values for null-data areas inherent in the SRTM data set were filled by interpolation based on surrounding data cells. The digital elevation model data was hill-shaded using an illumination direction of 315 degrees at an angle of 65 degrees above the horizon to produce the shaded relief image. The map projection used is equidistant conic, with latitudes 4 and 9 degrees north as standard parallels, and longitude 66 degrees west as the central meridian. The data contained in this map compilation primarily was derived from 1:500,000 scale maps and arranged for presentation and use at the scale of 1:750,000. Users may zoom in to view greater detail at larger scale; however, the authors make no guarantee of the accuracy of the map representation at scales larger than 1:750,000. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1063", + "title": "Hydrologic Monitoring of Landslide-Prone Coastal Bluffs near Edmonds and Everett, Washington, 2001?2004", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2004-12-31", + "bbox": "-123, 47.3, -122, 48.3", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548776-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548776-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1063", + "description": "In 2001, a cooperative monitoring effort between the U.S. Geological Survey (USGS), the Burlington Northern Santa Fe Railway (BNSF), BNSF's geotechnical consultant, Shannon and Wilson, Inc., and the Washington Department of Transportation was begun to determine whether near-real-time monitoring of rainfall and shallow subsurface hydrologic conditions could be used to anticipate landslide activity on the bluffs. Monitoring currently occurs at two sites-one near Edmonds, Washington, and the other near Everett, Washington. During initial planning, the USGS proposed to evaluate the monitoring results at the end of 3 years. This report summarizes site conditions, methods, system reliability, data, and scientific results, and identifies possible future directions for development of monitoring and early warning of impending landslide activity. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1067_1.0", + "title": "Landslide Hazards at La Conchita, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-124.9, 32.02, -113.61, 42.51", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549807-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549807-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1067_1.0", + "description": "On January 10, 2005, a landslide struck the community of La Conchita in Ventura County, California, destroying or seriously damaging 36 houses and killing 10 people. This was not the first destructive landslide to damage this community, nor is it likely to be the last. This open file report describes the field observations and provides a description of the La Conchita area and its landslide history, a comparison of the 1995 and 2005 landslides, and a discussion. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2005_1069", "title": "Coastal Change Rates and Patterns: Kaloko-Honokohau National Historical Park, Hawaii", @@ -183039,6 +188460,58 @@ "description": "A collaborative project between the U.S. Geological Survey's Coastal and Marine Geology Program and the National Park Service (NPS) has been developed to create an inventory of geologic resources for National Park Service lands on the Big Island of Hawai?i. The NPS Geologic Resources Inventories are recognized as essential for the effective management, interpretation, and understanding of vital park resources. In general, there are three principal components of the inventories: geologic bibliographies, digital geologic maps, and geologic reports. The geologic reports are specific to each individual park and include information on the geologic features and processes that are important to the management of park resources, including ecological, cultural and recreational resources. This report summarizes a component of the geologic inventory concerned specifically with characterizing the coastal geomorphology of the beach system within Kaloko-Honokohau National Historical Park (NHP) and describes an analysis that utilizes georeferenced and orthorectified aerial photography to understand the spatial and temporal trends in shoreline change from 1950 to 2002. In addition, spatial patterns of beach change were examined and a beach stability map was developed. Both the shoreline change rates and the beach stability map are designed to help Park personnel effectively manage the valuable park resources within the context of understanding natural changes to the KAHO beach system. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2005_1070_1.0", + "title": "Molokai Benthic Habitat Mapping", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-161, 18, -154, 23", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549432-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549432-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1070_1.0", + "description": "The detailed high-resolution map layer provided here documents habitat characterization of a critical coral reef in Hawai'i. Integration of the aerial imagery, SHOALS bathymetry, and field observations made it possible to create detailed thematic maps reaching depths of 35 m (120 ft). This depth range encompasses the base of the Moloka'i forereef, and is deeper than can be mapped with standard optical remote sensing instruments. These maps can be used as stand-alone or in a GIS to provide useful information to scientists, managers and the general public. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1132_1.0", + "title": "Ground-Magnetic Studies of the Amargosa Desert Region, California and Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-124.9, 32.02, -113.61, 42.51", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555068-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555068-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1132_1.0", + "description": "High-resolution aeromagnetic surveys of the Amargosa Desert region, California and Nevada, exhibit a diverse array of magnetic anomalies reflecting a wide range of mid- and upper-crustal lithologies. In most cases, these anomalies can be interpreted in terms of exposed rocks and sedimentary deposits. More difficult to explain are linear magnetic anomalies situated over lithologies that typically have very low magnetizations. Aeromagnetic anomalies are observed, for example, over thick sections of Quaternary alluvial deposits and spring deposits associated with past or modern ground-water discharge in Ash Meadows, Pahrump Valley, and Furnace Creek Wash. Such deposits are typically considered nonmagnetic. To help determine the source of these aeromagnetic anomalies, we conducted ground-magnetic studies at five areas: near Death Valley Junction, at Point of Rocks Spring, at Devils Hole, at Fairbanks Spring, and near Travertine Springs. Depth-to-source calculations show that the sources of these anomalies lie within the Tertiary and Quaternary sedimentary section. We conclude that they are caused by discrete volcanic units lying above the pre-Tertiary basement. At Death Valley Junction and Travertine Springs, these concealed volcanic units are probably part of the Miocene Death Valley volcanic field exposed in the nearby Greenwater Range and Black Mountains. The linear nature of the aeromagnetic anomalies suggests that these concealed volcanic rocks are bounded and offset by near-surface faults. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1135_1.0", + "title": "Modified Mercalli Intensity Maps for the 1906 San Francisco Earthquake Plotted in ShakeMap Format", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1906-04-18", + "end_date": "1906-04-18", + "bbox": "-124, 34, -120, 40", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554244-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554244-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1135_1.0", + "description": "This website presents Modified Mercalli Intensity maps for the great San Francisco earthquake of April 18, 1906. These new maps combine two important developments. First, we have re-evaluated and relocated the damage and shaking reports compiled by Lawson (1908). These reports yield intensity estimates for more than 600 sites and constitute the largest set of intensities ever compiled for a single earthquake. Second, we use the recent ShakeMap methodology to map these intensities. The resulting MMI intensity maps are remarkably detailed and eloquently depict the enormous power and damage potential of this great earthquake. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1144", + "title": "Huminite Reflectance Measurements of Paleocene and Upper Cretaceous Coals from Borehole Cuttings, Zavala and Dimmit Counties, South Texas", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-107.31, 25.19, -92.85, 37.14", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553355-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553355-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1144", + "description": "The reflectance of huminite in 19 cuttings samples was determined in support of ongoing investigations into the coal bed methane potential of subsurface Paleocene and Upper Cretaceous coals of South Texas. Coal cuttings were obtained from the Core Research Center of the Bureau of Economic Geology, The University of Texas at Austin. Geophysical logs, mud-gas logs, driller's logs, completion cards, and scout tickets were used to select potentially coal-bearing sample suites and to identify specific sample depths. Reflectance measurements indicate coals of subbituminous rank are present in a wider area in South Texas than previously recognized. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2005_1148_1.0", "title": "Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004", @@ -183052,6 +188525,19 @@ "description": "Recent construction for Interstate Highway 99 (I?99) exposed pyrite and associated Zn-Pb sulfide minerals beneath a >10-m thick gossan to oxidative weathering along a 40-60-m deep roadcut through a 270-m long section of the Ordovician Bald Eagle Formation at Skytop, near State College, Centre County, Pennsylvania. Nearby Zn-Pb deposits hosted in associated sandstone and limestone in Blair and Centre Counties were prospected in the past; however, these deposits generally were not viable as commercial mines. The pyritic sandstone from the roadcut was crushed and used locally as road base and fill for adjoining segments of I?99. Within months, acidic (pH<3), metal-laden seeps and runoff from the exposed cut and crushed sandstone raised concerns about surface- and ground-water contamination and prompted a halt in road construction and the beginning of costly remediation. Mineralized sandstones from the cut contain as much as 34 wt. % Fe, 28 wt. % S, 3.5 wt. % Zn, 1% wt. Pb, 88 ppm As, and 32 ppm Cd. A composite of <2 mm material sampled from the cut face contains 8.1 wt. % total sulfide S, 0.6 wt. % sulfate S, and is net acidic by acid-base accounting (net neutralization potential ?234 kg CaCO3/t). Primary sulfide minerals include pyrite, marcasite, sphalerite (2 to 12 wt. % Fe) and traces of chalcopyrite and galena. Pyrite occurs in mm- to cm-scale veinlets and disseminated grains in sandstone, as needles, and in a locally massive pyrite-cemented breccia along a fault. Inclusions (<10 ?m) of CdS and Ni-Co-As minerals in pyrite and minor amounts of Cd in sphalerite (0.1 wt. % or less) explain the primary source of trace metals in the rock and in associated secondary minerals and seepage. Wet/dry cycles associated with intermittent rainfall promoted oxidative weathering and dissolution of primary sulfides and their oxidation products. Resulting sulfate solutions evaporated during dry periods to form intermittent ?blooms? of soluble, yellow and white efflorescent sulfate salts (copiapite, melanterite, and halotrichite) on exposed rock and other surfaces. Salts coating the cut face incorporated Fe, Al, S, and minor Zn. They readily dissolved in deionized water in the laboratory to form solutions with pH <2.5, consistent with field observations. In addition to elevated dissolved Fe and sulfate concentrations (>1,000 mg/L), seep waters at the base of the cut contain >100 mg/L dissolved Zn and >1 mg/L As, Co, Cu, and Ni. Lead is relatively immobile (<10 ?g/L in seep waters). The salts sequester metals and acidity between rainfall events. Episodic salt dissolution then contributes pulses of contamination including acid to surface runoff and ground water. The Skytop experience highlights the need to understand dynamic interactions of mineralogy and hydrology in order to avoid potentially negative environmental impacts associated with excavation in sulfidic rocks. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2005_1153_1.0", + "title": "Multibeam Bathymetry and Backscatter Data: Northeastern Channel Islands Region, Southern California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2004-08-06", + "end_date": "2004-08-15", + "bbox": "-119.72, 33.88, -119.03, 34.33", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553010-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553010-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1153_1.0", + "description": "The U.S. Geological Survey (USGS) in cooperation with the Minerals Management Service (MMS) conducted multibeam mapping in the eastern Santa Barbara Channel and northeastern Channel Islands region from August 8 to15, 2004 aboard the R/V Maurice Ewing. The survey was directed and funded by the Minerals Management Service, which is interested in maps of hard bottom habitats, particularly natural outcrops, that support reef communities in areas affected by oil and gas activity. The maps are also useful to biologists studying fish that use the platforms and the sea floor beneath them as habitat. The survey collected bathymetry and corrected, co-registered acoustic backscatter using a Kongsberg Simrad EM1002 multibeam echosounder that was mounted on the hull of the R/V Maurice Ewing. Three main regions were mapped during the survey including: (1) the Eastern Santa Barbara Channel adjacent to an area previously mapped with multibeam-sonar by the Monterey Bay Aquarium Research Institute (see the MBARI Santa Barbara Basin Multibeam Survey web page), (2) the Footprint area south of Anacapa Island, which has been studied extensively by rockfish biologists and is considered a good site for a marine protected area, and (3) part of the submarine canyons along the continental slope south of Port Hueneme. These data will be used to support a number of new and ongoing projects including, habitat mapping, shelf and slope processes, and offshore hazards and resources. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2005_1164_1.0", "title": "An Assessment of Volcanic Threat and Monitoring Capabilities in the United States: Framework for a National Volcano Early Warning System", @@ -183065,6 +188551,58 @@ "description": "A National Volcano Early Warning System NVEWS is being formulated by the Consortium of U.S. Volcano Observatories (CUSVO) to establish a proactive, fully integrated, national-scale monitoring effort that ensures the most threatening volcanoes in the United States are properly monitored in advance of the onset of unrest and at levels commensurate with the threats posed. Volcanic threat is the combination of hazards (the destructive natural phenomena produced by a volcano) and exposure (people and property at risk from the hazards). The United States has abundant volcanoes, and over the past 25 years the Nation has experienced a diverse range of the destructive phenomena that volcanoes can produce. Hazardous volcanic activity will continue to occur, and because of increasing population, increasing development, and expanding national and international air traffic over volcanic regions the exposure of human life and enterprise to volcano hazards is increasing. Fortunately, volcanoes exhibit precursory unrest that if detected and analyzed in time allows eruptions to be anticipated and communities at risk to be forewarned with reliable information in sufficient time to implement response plans and mitigation measures. In the 25 years since the cataclysmic eruption of Mount St. Helens, scientific and technological advances in volcanology have been used to develop and test models of volcanic behavior and to make reliable forecasts of expected activity a reality. Until now, these technologies and methods have been applied on an ad hoc basis to volcanoes showing signs of activity. However, waiting to deploy a robust, modern monitoring effort until a hazardous volcano awakens and an unrest crisis begins is socially and scientifically unsatisfactory because it forces scientists, civil authorities, citizens, and businesses into playing catch up with the volcano, trying to get instruments and civil-defense measures in place before the unrest escalates and the situation worsens. Inevitably, this manner of response results in our missing crucial early stages of the volcanic unrest and hampers our ability to accurately forecast events. Restless volcanoes do not always progress to eruption; nevertheless, monitoring is necessary in such cases to minimize either over-reacting, which costs money, or under-reacting, which may cost lives. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2005_1176", + "title": "Flooding of the Androscoggin River during December 18-19, 2003, in Canton, Maine", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-12-18", + "end_date": "2003-12-19", + "bbox": "-71.31, 42.85, -66.74, 47.67", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550802-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550802-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1176", + "description": "The Androscoggin River flooded the town of Canton, Maine in December 2003, resulting in damage to and (or) evacuation of 44 homes. Streamflow records at the U.S. Geological Survey (USGS) streamflow-gaging stations at Rumford (USGS station identification number 01054500) and Auburn (01059000) were used to estimate the peak streamflow for the Androscoggin in the town of Canton for this flood (December 18-19, 2003). The estimated peak flood streamflow at Canton was approximately 39,800 ft3/s, corresponding to an estimated recurrence interval of 4.4 years; however, an ice jam downstream from Canton Point on December 18-19 obstructed river flow resulting in a high-water elevation commensurate with an open-water flood approximately equal to a 15-year event. The high water-surface elevations attained during the December 18-19 flood event in Canton were higher than the expected open-water flood water-surface elevations; this verified the assumption that the water-surface elevation was augmented due to the downstream ice jam. The change in slope of the riverbed from upstream of Canton to the impoundment at the downstream corporate limits, and the river bend near Stevens Island are principal factors in ice-jam formation near Canton. The U.S. Army Corps of Engineers Ice Jam Database indicates five ice-jam-related floods (including December 2003) for the town of Canton: March 13, 1936; January 1978; March 12, 1987; January 29, 1996; and December 18-19, 2003. There have been more ice-jam-related flood events in Canton than these five documented events, but the exact number and nature of ice jams in Canton cannot be determined without further research.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1201", + "title": "Estimated Water Use in Puerto Rico, 2000", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-67.08, 17.88, -65.45, 18.64", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551011-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551011-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1201", + "description": "Water-use data were compiled for the 78 municipios of the Commonwealth of Puerto Rico for 2000. Five offstream categories were considered: public-supply water withdrawals, domestic self-supplied water use, industrial self-supplied withdrawals, crop irrigation water use, and thermoelectric power fresh water use. Two additional categories also were considered: power generation instream use and public wastewater treatment return-flows. Fresh water withdrawals for offstream use from surface- and ground-water sources in Puerto Rico were estimated at 617 million gallons per day. The largest amount of fresh water withdrawn was by public-supply water facilities and was estimated at 540 million gallons per day. Fresh surface- and ground-water withdrawals by domestic self-supplied users was estimated at 2 million gallons per day and the industrial self-supplied withdrawals were estimated at 9.5 million gallons per day. Withdrawals for crop irrigation purposes were estimated at 64 million gallons per day, or approximately 10 percent of all offstream fresh water withdrawals. Saline instream surface-water withdrawals for cooling purposes by thermoelectric power facilities was estimated at 2,191 million gallons per day, and instream fresh water withdrawals by hydroelectric facilities at 171 million gallons per day. Total discharge from public wastewater treatment facilities was estimated at 211 million gallons per day. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1203_1.0", + "title": "Magnetic Properties of Sediments in Cores BL96-1, -2, and -3 from Bear Lake, Utah and Idaho", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-117.59, 36.74, -108.79, 49.35", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550298-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550298-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1203_1.0", + "description": "As part of an ongoing study to derive records of past environmental change from lake sediments in the western United States, a set of three cores was collected from Bear Lake, Utah, in 1996. The three cores, BL96-1, -2, and -3, form an east-west profile and are located in about 50, 40 , and 30 m of water, respectively. The cores range in length from 4 m to 5 m, but because sediments thin markedly to the west (Colman, 2005) the maximum age of sediments penetrated increases from east to west. Together the cores provide a record from the last glacial period through the Holocene. This report presents magnetic property data acquired from these cores. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1253_1.0", + "title": "Major- and Trace-Element Concentrations in Soils from Two Continental-Scale Transects of the United States and Canada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-140, 23, -50, 62", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554514-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554514-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1253_1.0", + "description": "This report contains major- and trace-element concentration data for soil samples collected from 265 sites along two continental-scale transects in North America. One of the transects extends from northern Manitoba to the United States-Mexico border near El Paso, Tex. and consists of 105 sites. The other transect approximately follows the 38th parallel from the Pacific coast of the United States near San Francisco, Calif., to the Atlantic coast along the Maryland shore and consists of 160 sites. Sampling sites were defined by first dividing each transect into approximately 40-km segments. For each segment, a 1-km-wide latitudinal strip was randomly selected; within each strip, a potential sample site was selected from the most representative landscape within the most common soil type. At one in four sites, duplicate samples were collected 10 meters apart to estimate local spatial variability. At each site, up to four separate soil samples were collected as follows: (1) material from 0-5 cm depth; (2) O horizon, if present; (3) a composite of the A horizon; and (4) C horizon. Each sample collected was analyzed for total major- and trace-element composition by the following methods: (1) inductively coupled plasma-mass spectrometry (ICP-MS) and inductively coupled plasma-atomic emission spectrometry (ICPAES) for aluminum, antimony, arsenic, barium, beryllium, bismuth, cadmium, calcium, cerium, cesium, chromium, cobalt, copper, gallium, indium, iron, lanthanum, lead, lithium, magnesium, manganese, molybdenum, nickel, niobium, phosphorus, potassium, rubidium, scandium, silver, sodium, strontium, sulfur, tellurium, thallium, thorium, tin, titanium, tungsten, uranium, vanadium, yttrium, and zinc; (2) cold vapor- atomic absorption spectrometry for mercury; (3) hydride generation-atomic absorption spectrometry for antimony and selenium; (4) coulometric titration for carbonate carbon; and (5) combustion for total carbon and total sulfur. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2005_1307", "title": "Contribution of Atmospheric Deposition to Pesticide Loads in Surface Water Runoff", @@ -183078,6 +188616,19 @@ "description": "A 3.5-year study was conducted to determine the signifcance of atmospheric deposition to the pesticide concentrations in runoff. Both wet and dry atmospheric depostion were collected at six sites in the central San Joaquin Valley, California. Wet deposition samples were collected during individual rain events and dry deposition samples were collected for periods ranging from three weeks to four months. Each sample was analyzed for 41 currently used pesticides and 23 transformation products, including the oxygen analogs of nine organophosphorus (OP) insecticides. Ten compounds in rainfall and 19 in dry deposition were detected in at least 50% of the samples. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2005_1315", + "title": "Hawaiian Volcano Observatory Seismic Data, January to December 2004", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2004-01-01", + "end_date": "2004-12-31", + "bbox": "-157, 18, -154, 21", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548689-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548689-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1315", + "description": "The Hawaiian Volcano Observatory (HVO) summary presents seismic data gathered during the year. The seismic summary is offered without interpretation as a source of preliminary data. It is complete in the sense that most data for events of Me1.5 routinely gathered by the Observatory are included. The HVO summaries have been published in various forms since 1956. Summaries prior to 1974 were issued quarterly, but cost, convenience of preparation and distribution, and the large quantities of data dictated an annual publication beginning with Summary 74 for the year 1974. Summary 86 (the introduction of CUSP at HVO) includes a description of the seismic instrumentation, calibration, and processing used in recent years. Beginning with 2004, summaries will simply be identified by the year, rather than Summary number. The present summary includes background information on the seismic network and processing to allow use of the data and to provide an understanding of how they were gathered. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2005_1317", "title": "Compressional and Shear Wave Velocity Versus Depth in the San Francisco Bay Area, California: Rules for USGS Bay Area Velocity Model 05.0.0", @@ -183104,6 +188655,175 @@ "description": "The overall goal of our research on Bear Lake is to create records of past climate change for the region, including changes in precipitation (rain and snow) patterns during the last 10,000 years and longer. As part of the project, we are attempting to determine how the size of Bear Lake has varied in the past in order to assess the possibility of future flooding and drought. We also seek to understand human influences on sediment deposition, chemistry, and life in the lake. Evidence of past conditions comes from sediments deposited in the lake, so reconstructions of past conditions require accurate dating of the sediments. The study includes the upper Bear River watershed as well as Bear Lake. The Bear River is the largest river in the Great Basin and the source of the majority of water flowing into the Great Salt Lake. In this region, wet periods may produce flooding along the course of the Bear River and around Great Salt Lake, while dry periods, or droughts, may affect water availability for ecosystems, as well as for agricultural, industrial, and residential use. Diatoms are one of the most sensitive indicators of environments in many lakes. In addition to species compositions and abundances (Moser and Kimball, 2005), total diatom productivity commonly varies considerably with changes in limnological conditions. Biogenic silica preserved in sediments is an index of total diatom productivity and, thus, is an indirect proxy for paleolimnology (for example, Colman and others, 1995; Johnson and others, 2001). In this paper, we present the results of biogenic silica analyses of two cores taken in Bear Lake, Utah, and discuss preliminary paleolimnologic conclusions based on these data. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2005_1329", + "title": "Ground-Water Reconnaissance of the Bijou Creek Watershed, South Lake Tahoe, California, June-October 2003", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-06-01", + "end_date": "2003-10-31", + "bbox": "-124.9, 32.02, -113.61, 42.51", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411614-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411614-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1329", + "description": "A ground-water reconnaissance study of the Bijou Creek watershed in South Lake Tahoe, California was done during the summer and early fall of 2003. This study provides basic hydrologic data for a region in the Lake Tahoe Basin in which a continuing loss of lake clarity is occurring in the nearshore zone of Lake Tahoe. Wells, springs, and a surface-water site were located and basic hydrologic data were collected. Water levels were measured and water samples were collected and analyzed for nutrients. Measurements of water temperature, specific conductance, and pH were made at all ground-water sites where possible and at one surface-water site. Organic nitrogen plus ammonia, ammonia, and biologically-available iron concentrations generally were greater in the ground water in the Bijou Creek watershed than those observed in ground water elsewhere in the Lake Tahoe Basin. Nitrate concentrations were similar in the two groups. Phosphorus and orthophosphate concentrations generally were lower in the ground water of the Bijou Creek watershed compared to ground water from elsewhere in the Lake Tahoe Basin. Specific conductance and pH of ground water were similar between the Bijou Creek watershed and the Lake Tahoe Basin, but the temperature of ground water was generally greater in the Bijou Creek watershed. Nitrate concentrations appeared to increase over time at one of two long-term ground-water sites. Orthophosphate concentration decreased while specific conductance increased at one of the two sites, but no trend was detected at the other site for either parameter. No trends were detected for phosphorus, biologically-available iron, water temperature, or pH at either of the long-term sites. Trends in ammonia and organic nitrogen plus ammonia concentrations were not evaluated because a majority of the values were below the method detection limits. There were no obvious spatial distribution patterns for nutrient concentrations or field parameters in the Bijou Creek watershed. The altitude of the ground-water table above sea level generally increased with increasing distance from Lake Tahoe. The altitude of the ground-water table was greater than the altitude of the surface of Lake Tahoe except at one ground-water site which is influenced by a cone of depression around a nearby production well. Ground water in the Bijou Creek watershed discharges to Lake Tahoe and may contribute to the higher than normal turbidity in the area. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1333", + "title": "Estimates of Ground-Water Recharge Based on Streamflow-Hydrograph Methods: Pennsylvania", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1885-01-01", + "end_date": "2001-12-31", + "bbox": "-80.82, 39.43, -74.41, 42.56", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554566-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554566-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1333", + "description": "This study, completed by the U.S. Geological Survey (USGS) in cooperation with the Pennsylvania Department of Conservation and Natural Resources, Bureau of Topographic and Geologic Survey (T&GS), provides estimates of ground-water recharge for watersheds throughout Pennsylvania computed by use of two automated streamflow-hydrograph-analysis methods--PART and RORA. The PART computer program uses a hydrograph-separation technique to divide the streamflow hydrograph into components of direct runoff and base flow. Base flow can be a useful approximation of recharge if losses and interbasin transfers of ground water are minimal. The RORA computer program uses a recession-curve displacement technique to estimate ground-water recharge from each storm period indicated on the streamflow hydrograph. Recharge estimates were made using streamflow records collected during 1885-2001 from 197 active and inactive streamflow-gaging stations in Pennsylvania where streamflow is relatively unaffected by regulation. Estimates of mean-annual recharge in Pennsylvania computed by the use of PART ranged from 5.8 to 26.6 inches; estimates from RORA ranged from 7.7 to 29.3 inches. Estimates from the RORA program were about 2 inches greater than those derived from the PART program. Mean-monthly recharge was computed from the RORA program and was reported as a percentage of mean-annual recharge. On the basis of this analysis, the major ground-water recharge period in Pennsylvania typically is November through May; the greatest monthly recharge typically occurs in March. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1339_1.0", + "title": "Gravity Studies of Cave, Dry Lake, and Delamar Valleys, East-Central Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-120.35, 34.65, -113.69, 42.34", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549975-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549975-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1339_1.0", + "description": "Analysis of gravity anomalies in Cave, Dry Lake, and Delamar valleys in east-central Nevada defines the overall shape of their basins, provides estimates of the depth to pre-Cenozoic basement rocks, and identifies buried faults beneath the sedimentary cover. In all cases, the basins are asymmetric in their cross section and in their placement beneath the valley, reflecting the extensional tectonism that initiated during Miocene time in this area. Absolute values of basin depths are estimated using a density-depth profile calibrated by deep oil and gas wells that encountered basement rocks in Cave Valley. The basin beneath southern Cave Valley extends down to -6.0 km, that of Dry Lake Valley extends to -8.2 km, and that of Delamar Valley extends to -6.4 km. The ranges surrounding Dry Lake and Delamar valleys are dominated by volcanic units that may produce lower-density basin infill, which in turn, would make the maximum depth estimates somewhat less. Dry Lake Valley is characterized by a slot-like graben in its center, whereas the deep portions of Cave and Delamar valleys are more bowl-shaped. Significant portions of the basins are shallow (<1 km deep), as are the transitions between each of these valleys. A seismic reflection image across southern Cave and Muleshoe valleys confirms the basin shapes inferred from gravity analysis. The architecture of these basins inferred from gravity will aid in interpreting the hydrogeologic framework of Cave, Dry Lake, and Delamar valleys by placing estimates on the volume and connectivity of potential unconsolidated alluvial aquifers and by identifying faults buried beneath basin deposits. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1402", + "title": "Interpreted Regional Seismic Reflection Lines, National Petroleum Reserve in Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-162, 67, -148, 76", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551534-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551534-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1402", + "description": "Interpretation of reprocessed data from a regional grid of 25 public-domain 2-D seismic profiles in the National Petroleum Reserve in Alaska has enabled an analysis of subsurface geologic relations throughout that region. Notable results include interpretations of the geometry of the Mississippian Umiat and Meade basins, and depositional patterns in the thick succession of younger strata that were influenced by major structural features such as the Barrow arch and the Brooks Range. Pre-Mississippian low-grade metamorphic rocks and subordinate granites of the Franklinian sequence are the basement rocks of the region. The top of the Franklinian is imaged as one of the highest amplitude, most continuous reflections. The sedimentary succession includes (1) the Mississippian to Triassic Ellesmerian sequence (consisting of the Endicott, Lisburne and Sadlerochit groups, and the Shublik Formation and Sag River Sandstone; (2) the Beaufortian sequence, comprising the Jurassic to Lower Cretaceous Kingak Shale and the overlying Lower Cretaceous pebble shale unit; and (3) the Cretaceous to Paleocene Brookian sequence, which includes the Hue Shale and the Torok, Nanushuk, Seabee, Tuluvak, Schrader Bluff, and Prince Creek formations. Stratigraphic horizons that were mapped seismically include the tops of the Franklinian basement, the Endicott, Lisburne, and Sadlerochit groups, the Shublik Formation, the Sag River Sandstone, the Lower Cretaceous unconformity (LCU), and the gamma-ray zone of the Hue Shale. Distinguishing criteria were established for the seismic-reflection characteristics for each of these horizons, and the results were used in the correlation of units across the basins and onto the bordering margins. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1405", + "title": "Landslide Susceptibility Estimated From Mapping Using Light Detection and Ranging (LIDAR) Imagery and Historical Landslide Records, Seattle, Washington", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-123, 46, -121, 48", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550356-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550356-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1405", + "description": "Landforms in Seattle, Washington, that were created primarily by landsliding were mapped using LIDAR-derived imagery. These landforms include landslides (primarily landslide complexes), headscarps, and denuded slopes. Over 93 percent of about 1,300 reported historical landslides are located within the LIDAR-mapped landform boundaries. The spatial densities of reported historical landslides within the LIDAR-mapped landforms provide the relative susceptibilities of the landforms to past landslide activity. Because the landforms were primarily created by prehistoric landslides, the spatial densities also provide reasonable estimates of future landslide susceptibility. The mapped landforms and susceptibilities provide useful tools for landslide hazard reduction in Seattle. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1407", + "title": "Fracture Trace Map and Single-Well Aquifer Test Results in a Carbonate Aquifer in Jefferson County, West Virginia", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-82.89, 36.96, -77.48, 40.88", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550959-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550959-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1407", + "description": "These data contain information on the results of single-well aquifer tests, lineament analysis, and a bedrock geologic map compilation for Jefferson County, West Virginia. Efforts have been initiated by management agencies of Jefferson County in cooperation with the U.S. Geological Survey to further the understanding of the spatial distribution of fractures in the carbonate regions and their correlation with aquifer properties. This report presents transmissivity values from 181 single-well aquifer tests and a map of fracture-traces determined from aerial photos and field investigations. Transmissivity values were compared to geologic factors possibly affecting their magnitude. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2005_1450", + "title": "Geochemical Analyses of Geologic Materials from Areas of Critical Environmental Concern, Clark and Nye Counties, Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-117, 34, -113, 38", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554141-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554141-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2005_1450", + "description": "An assessment by the U.S. Geological Survey (USGS), Nevada Bureau of Mines and Geology (NBMG), and University of Nevada, Las Vegas (UNLV) is in progress of known and undiscovered mineral resources of selected areas administered by the Bureau of Land Management (BLM) in Clark and Nye Counties, Nevada. The purpose of this work is to provide the BLM with information for use in their long-term planning process in southern Nevada so that they can make better-informed decisions. Existing information about the areas, including geology, geophysics, geochemistry, and mineral-deposit information is being compiled, and field examinations of selected areas and mineral occurrences have been conducted. This information will be used to determine the geologic setting, metallogenic characteristics, and mineral potential of the areas. Twenty-five Areas of Critical Environmental Concern (ACECs) have been identified by BLM as the object of this study. They range from tiny (less than one square km) to large (more than 1,000 square km). This report includes geochemical data for rock samples collected by the USGS and NBMG in these ACECs and nearby areas. Samples have been analyzed from the Big Dune, Ash Meadows, Arden, Desert Tortoise Conservation Center, Coyote Springs Valley, Mormon Mesa, Virgin Mountains, Gold Butte A and B, Whitney Pockets, Rainbow Gardens, River Mountains, and Piute-Eldorado Valley ACECs. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2006_1032", + "title": "Estimating Landslide Losses - Preliminary Results of a Seven-State Pilot Project", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-124.96, 32.02, -74.41, 46.68", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548978-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548978-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1032", + "description": "In 2001, the U.S. Geological Survey Landslide Hazards Program provided funding for seven State geological surveys to report on the status of landslide investigation strategies in each of their States, and to suggest improved ways to approach the tracking of landslides, their effects, losses associated with the landslides, and hazard mitigation strategies. Each State was to provide a draft report suggesting innovative ways to track landslides, and to participate in subsequent workshops. A workshop was convened in June 2003 in Lincoln, Neb., to discuss the results and future strategies on how best to incorporate the seven pilot projects into one methodology that all of the 50 States could adopt. The seven individual reports produced by the State surveys are published here to put forth a forum for discussion of the varying methods of tracking landslides. This pilot study, conducted by seven State geological surveys, examines the feasibility of collecting accurate and reliable information on economic losses associated with landslides. Each State survey examined the availability, distribution, and inherent uncertainties of economic loss data in their study areas. Their results provide the basis for identifying the most fruitful methods of collecting landslide loss data nationally, using methods that are consistent and provide common goals. These results can enhance and establish the future directions of scientific investigation priorities by convincingly documenting landslide risks and consequences that are universal throughout the 50 States. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2006_1038", + "title": "Geologic and Mineral Resource Map of Afghanistan", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "59.9, 28.66, 75.65, 39.11", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553654-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553654-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1038", + "description": "This map shows the physical characteristics, geology and mineral resources for Afghanistan.", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2006_1042", + "title": "Gravity and Magnetic Data in the Vicinity of Virgin Valley, Southern Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-120.35, 34.65, -113.69, 42.34", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551992-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551992-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1042", + "description": "Gravity and magnetic data were collected in the vicinity of Virgin Valley to help better characterize the buried sedimentary Mesquite and Mormon basins. Detailed gravity measurements were made over the buried saddle between the Mesquite and Mormon basins, discovered by earlier gravity studies, in order to calculate the depth to pre-Cenozoic basement. The purpose of this study was to provide estimates of sedimentary fill in this area prior to drilling a water well on Mormon Mesa. The calculated depth-to-basement results in an estimate of about 1.5 km of alluvial fill in this area. Additional gravity data were collected to help better define the shape and magnitude of the anomaly associated with the Mesquite Basin. Testing of an experimental towed magnetometer was also carried out, which showed very good correlation with an existing aeromagnetic survey. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2006_1051", + "title": "Isotopic Ages of Rocks in the Northern Front Range, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-106, 39, -105, 41", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550579-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550579-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1051", + "description": "These maps, and the tables that accompany them, are a compilation of isotopic age determinations of rocks and minerals in four 1:100,000-scale quadrangles in the northern and central Front Range, Colorado. Phanerozoic (primarily Tertiary and Cretaceous) age data are shown on one map; Proterozoic data are on the other (sheet 1). A sample location map (sheet 2) is included for ease of matching specific localities and data in the tables to the maps. Several records in the tables were not included in the maps because either there were ambiguous dates or lack of location precluded accurate plotting. To illustrate the geological setting for the samples, the plutonic rocks are shown on the maps. The boundaries of the plutons are from the Geologic Map of Colorado with a few modifications. For ease of reference, we labeled each of the larger (and some of the smaller) plutons with a generally accepted name from the literature. As a convenience in using the data, we have informally named some plutons based on geographic features on or near those plutons. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2006_1070", + "title": "Major- and Trace-Element Concentrations in Rock Samples Collected in 2004 from the Taylor Mountains 1:250,000-scale Quadrangle, Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "156, 60, 159, 61", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552409-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552409-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1070", + "description": "The Kuskokwim mineral belt of Bundtzen and Miller (1997) forms an important metallogenic region in southwestern Alaska that has yielded more than 3.22 million ounces of gold and 400,000 ounces of silver. Precious-metal and related deposits in this region associated with Late Cretaceous to early Tertiary igneous complexes extend into the Taylor Mountains 1:250,000-scale quadrangle. The U.S. Geological Survey is conducting geologic mapping and a mineral resource assessment of this area that will provide a better understanding of the geologic framework, regional geochemistry, and may provide targets for mineral exploration and development. During the 2004 field season 137 rock samples were collected for a variety of purposes. All samples were analyzed for a suite of 42 trace-elements to provide data for use in geochemical exploration as well as some baseline data. Selected samples were analyzed by additional methods; 104 targeted geochemical exploration samples were analyzed for gold, arsenic, and mercury; 21 of these samples were also analyzed to obtain concentrations of 10 loosely bound metals; 33 rock samples were analyzed for major element oxides to support the regional mapping program, of which 28 sedimentary rock samples were also analyzed for total carbon, and carbonate carbon. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2006_1081", + "title": "Geologic Characteristics of Benthic Habitats in Glacier Bay, Southeast Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-144, 50, -130, 63", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552010-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552010-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1081", + "description": "In April 2004, more than 40 hours of georeferenced submarine digital video was collected in water depths of 15-370 m in Glacier Bay to (1) ground-truth existing geophysical data (bathymetry and acoustic reflectance), (2) examine and record geologic characteristics of the sea floor, and (3) investigate the relation between substrate types and benthic communities, and (4) construct predictive maps of sea floor geomorphology and habitat distribution. Common substrates observed include rock, boulders, cobbles, rippled sand, bioturbated mud, and extensive beds of living horse mussels and scallops. Four principal sea-floor geomorphic types are distinguished by using video observations. Their distribution in lower and central Glacier Bay is predicted using a supervised, hierarchical decision-tree statistical classification of geophysical data. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2006_1085", "title": "Coastal Circulation and Sediment Dynamics in Hanalei Bay, Kauai. PART I: Measurements of waves, currents, temperature, salinity and turbidity: June-August, 2005", @@ -183143,6 +188863,19 @@ "description": "The primary purpose of the USGS National Assessment of Coastal Change Project is to provide accurate representations of pre-storm ground conditions for areas that are designated high priority because they have dense populations or valuable resources that are at risk from storm waves. A secondary purpose of the project is to develop a geomorphic (land feature) coastal classification that, with only minor modification, can be applied to most coastal regions in the United States. A Coastal Classification Map describing local geomorphic features is the first step toward determining the hazard vulnerability of an area. The Coastal Classification Maps of the National Assessment of Coastal Change Project present ground conditions such as beach width, dune elevations, overwash potential, and density of development. In order to complete a hazard-vulnerability assessment, that information must be integrated with other information, such as prior storm impacts and beach stability. The Coastal Classification Maps provide much of the basic information for such an assessment and represent a critical component of a storm-impact forecasting capability. The map above shows the areas covered by this web site. Click on any of the location names or outlines to view the Coastal Classification Map for that area. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2006_1110", + "title": "Geophysical Studies of the Crump Geyser Known Geothermal Resource Area, Oregon, in 1975", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-130, 42, -122, 52", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552525-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552525-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1110", + "description": "The U.S. Geological Survey (USGS) conducted geophysical studies in support of the resource appraisal of the Crump Geyser Known Geothermal Resource Area (KGRA). This area was designated as a KGRA by the USGS, and this designation became effective on December 24, 1970. The land classification standards for a KGRA were established by the Geothermal Steam Act of 1970 (Public Law 91-581). Federal lands so classified required competitive leasing for the development of geothermal resources. The author presented an administrative report of USGS geophysical studies entitled \"Geophysical background of the Crump Geyser area, Oregon, KGRA\" to a USGS resource committee on June 17, 1975. This report, which essentially was a description of geophysical data and a preliminary interpretation without discussion of resource appraisal, is in Appendix 1. Reduction of sheets or plates in the original administrative report to page-size figures, which are listed and appended to the back of the text in Appendix 1, did not seem to significantly degrade legibility. Bold print in the text indicates where minor changes were made. A colored page-size index and tectonic map, which also show regional geology not shown in figure 2, was substituted for original figure 1. Detailed descriptions for the geologic units referenced in the text and shown on figures 1 and 2 were separately defined by Walker and Repenning (1965) and presumably were discussed in other reports to the committee. Heavy dashed lines on figures 1 and 2 indicate the approximate KGRA boundary. One of the principal results of the geophysical studies was to obtain a gravity map (Appendix 1, fig. 10; Plouff, and Conradi, 1975, pl. 9), which reflects the fault-bounded steepness of the west edge of sediments and locates the maximum thickness of valley sediments at about 10 kilometers south of Crump Geyser. Based on the indicated regional-gravity profile and density-contrast assumptions for the two-dimensional profile, the maximum sediment thickness was estimated at 820 meters. A three-dimensional gravity model would have yielded a greater thickness. Audiomagnotelluric measurements were not made as far south as the location of the gravity low, as determined in the field, due to a lack of communication at that time. A boat was borrowed to collect gravity measurements along the edge of Crump Lake, but the attempt was curtailed by harsh, snowy weather on May 21, 1975, which shortly followed days of hot temperature. Most of the geophysical data and illustrations in Appendix 1 have been published (Gregory and Martinez, 1975; Plouff, 1975; and Plouff and Conradi, 1975), and Donald Plouff (1986) discussed a gravity interpretation of Warner Valley at the Fall 1986 American Geophysical Union meeting in San Francisco. Further interpretation of possible subsurface geologic sources of geophysical anomalies was not discussed in Appendix 1. For example, how were apparent resistivity lows (Appendix 1, figs. 3-6) centered near Crump Geyser affected by a well and other manmade electrically conductive or magnetic objects? What is the geologic significance of the 15-milligal eastward decrease across Warner Valley? The explanation that the two-dimensional gravity model (Appendix 1, fig. 14) was based on an inverse iterative method suggested by Bott (1960) was not included. Inasmuch as there was no local subsurface rock density distribution information to further constrain the gravity model, the three-dimensional methodology suggested by Plouff (1976) was not attempted. Inasmuch as the associated publication by Plouff (1975), which released the gravity data, is difficult to obtain and not in digital format, that report is reproduced in Appendix 2. Two figures of the publication are appended to the back of the text. A later formula for the theoretical value of gravity for the given latitudes at sea level (International Association of Geodesy, 1971) should be used to re-compute gravity anomalies. To merge the observed-gravity values printed in that report with later measurements, an empirically determined constant gravity datum shift should be applied. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2006_1129_WIPP_NM_1.0 Online", "title": "Aquifer-Test Data for Wells H-1, H-2A, H-2B, H-2C, and H-3 at the Waste Isolation Pilot Plant, Southeastern New Mexico", @@ -183169,6 +188902,97 @@ "description": "This is a USGS Open-File-Report for the preliminary release of aeromagnetic data collected in the Dillingham Area of Southwest Alaska and associated contractor reports. An airborne high-resolution magnetic survey was completed over the Dillingham and Nushagak Bay and Naknek area in southwestern Alaska. The flying was undertaken by McPhar Geosurveys Ltd. on behalf of the United States Geological Survey (USGS). First tests and calibration flights were completed by August 26th, 2005 and data acquisition was initiated on September 1st, 2005. The final data acquisition flight was completed on October 22nd, 2005. A total of 8,630 line-miles of data were acquired during the survey. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2006_1247", + "title": "High-resolution chirp seismic reflection data acquired from the Cap de Creus shelf and canyon area, Gulf of Lions, Spain in 2004", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-09-25", + "end_date": "2003-10-01", + "bbox": "3.1808, 42.1763, 3.4586, 42.4418", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550660-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550660-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1247", + "description": "This report consists of high-resolution chirp seismic reflection profiledata from the northern Gulf of Lions, Spain. These data were acquired in2004 using the Research Vessel Oceanus (USGS Cruise ID: O-1-04-MS). Thedata are available in binary and JPEG image formats. Binary data arein Society of Exploration Geologists (SEG) SEG-Y format and may bedownloaded for further processing or display. Reference maps andJPEG images of the profiles may be viewed with your Web browser. Marine seismic reflection data are used to image and mapsedimentary and structural features of the seafloor and subsurface.These data were acquired across the shelf and canyon area of the Gulfof Lions, Spain as part of a multinational effort to characterize thegeologic framework and sedimentary environment of the region.The specific objective of this seismic survey is to provide seismicreflection images of the depositional geometry of the upper 50 meters ofsubbottom stratigraphy in order to better understand the mechanisms ofsediment transport and deposition. These chirp seismic profiles providehigh-quality images with approximately 20 cm of verticalresolution and up to 80 m of subbottom penetration. Chirp seismic reflection profiles are acquired by means of anacoustic source and a hydrophone array, both contained in a single unittowed in the water behind a survey vessel. The sound source emits ashort (30 ms) swept-frequency (500 to 7200 Hz)acoustic pulse,which propagates through the water and sediment columns. The acousticenergy is reflected at density boundaries (such as the seafloor orsediment layers beneath the seafloor), and detected by the hydrophonearray, and digitally recorded by the onboard PC-based acquisition system.As the vessel moves, this process is repeated multiple times per second,producing a two-dimensional image of the shallow geologic structurebeneath the ship track. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2006_1274", + "title": "Land Area Changes in Coastal Louisiana After the 2005 Hurricanes: A Series of Three Maps", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1956-01-01", + "end_date": "2005-12-31", + "bbox": "-96, 30, -88, 32", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553246-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553246-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1274", + "description": "This report includes three posters with analyses of net land area changes in coastal Louisiana after the 2005 hurricanes (Katrina and Rita). The first poster presents a basic analysis of net changes from 2004 to 2005; the second presents net changes within marsh communities from 2004 to 2005; and the third presents net changes from 2004 to 2005 within the historical perspective of change in coastal Louisiana from 1956 to 2004. The purpose of this analysis was to provide preliminary information on land area changes shortly after Hurricanes Katrina and Rita and to serve as a regional baseline for monitoring wetland recovery following the 2005 hurricane season. Estimation of permanent losses cannot be made until several growing seasons have passed and the transitory impacts of the hurricanes are minimized, but this preliminary analysis indicates an approximate 217-mi2 (562.03-km2) decrease in land/increase in water across coastal Louisiana. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2006_1280", + "title": "Metallogeny of the Great Basin: Crustal Evolution, Fluid Flow, and Ore Deposits", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-126, 29, -116, 45", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551576-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551576-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1280", + "description": "The Great Basin physiographic province in the Western United States contains a diverse assortment of world-class ore deposits. It currently (2006) is the world's second leading producer of gold, contains large silver and base metal (Cu, Zn, Pb, Mo, W) deposits, a variety of other important metallic (Fe, Ni, Be, REE's, Hg, PGE) and industrial mineral (diatomite, barite, perlite, kaolinite, gallium) resources, as well as petroleum and geothermal energy resources. Ore deposits are most numerous and largest in size in linear mineral belts with complex geology. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2006_1299_1.0", + "title": "Inversion of Gravity Data to Define the Pre-Cenozoic Surface and Regional Structures Possibly Influencing Groundwater Flow in the Rainier Mesa Region, Nye County, Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-117, 36, -116, 38", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552439-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552439-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1299_1.0", + "description": "A three-dimensional inversion of gravity data from the Rainier Mesa area and surrounding regions reveals a topographically complex pre-Cenozoic basement surface. This model of the depth to pre-Cenozoic basement rocks is intended for use in a 3D hydrogeologic model being constructed for the Rainier Mesa area. Prior to this study, our knowledge of the depth to pre-Cenozoic basement rocks was based on a regional model, applicable to general studies of the greater Nevada Test Site area but inappropriate for higher resolution modeling of ground-water flow across the Rainier Mesa area. The new model incorporates several changes that lead to significant improvements over the previous regional view. First, the addition of constraining wells, encountering old volcanic rocks lying above but near pre-Cenozoic basement, prevents modeled basement from being too shallow. Second, an extensive literature and well data search has led to an increased understanding of the change of rock density with depth in the vicinity of Rainier Mesa. The third, and most important change, relates to the application of several depth-density relationships in the study area instead of a single generalized relationship, thereby improving the overall model fit. In general, the pre-Cenozoic basement surface deepens in the western part of the study area, delineating collapses within the Silent Canyon and Timber Mountain caldera complexes, and shallows in the east in the Eleana Range and Yucca Flat regions, where basement crops out. In the Rainier Mesa study area, basement is generally shallow (< 1 km). The new model identifies previously unrecognized structures within the pre-Cenozoic basement that may influence ground-water flow, such as a shallow basement ridge related to an inferred fault extending northward from Rainier Mesa into Kawich Valley. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2006_1396_1.0", + "title": "Geophysical Studies Based on Gravity and Seismic Data of Tule Desert, Meadow Valley Wash, and California Wash Basins, Southern Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-115, 36.1, -114, 37.2", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549521-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549521-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1396_1.0", + "description": "Gravity and seismic data from Tule Desert, Meadow Valley Wash, and California Wash, Nevada, provide insight into the subsurface geometry of these three basins that lie adjacent to rapidly developing areas of Clark County, Nevada. Each of the basins is the product of Tertiary extension accommodated with the general form of north-south oriented, asymmetrically-faulted half-grabens. Geophysical inversion of gravity observations indicates that Tule Desert and Meadow Valley Wash basins are segmented into subbasins by shallow, buried basement highs. In this study, basement refers to pre-Cenozoic bedrock units that underlie basins filled with Cenozoic sedimentary and volcanic units. In Tule Desert, a small, buried basement high inferred from gravity data appears to be a horst whose placement is consistent with seismic reflection and magnetotelluric observations. Meadow Valley Wash consists of three subbasins separated by basement highs at structural zones that accommodated different styles of extension of the adjacent subbasins, an interpretation consistent with geologic mapping of fault traces oblique to the predominant north-south fault orientation of Tertiary extension in this area. California Wash is a single structural basin. The three seismic reflection lines analyzed in this study image the sedimentary basin fill, and they allow identification of faults that offset basin deposits and underlying basement. The degree of faulting and folding of the basin-fill deposits increases with depth. Pre-Cenozoic units are observed in some of the seismic reflection lines, but their reflections are generally of poor quality or are absent. Factors that degrade seismic reflector quality in this area are rough land topography due to erosion, deformed sedimentary units at the land surface, rock layers that dip out of the plane of the seismic profile, and the presence of volcanic units that obscure underlying reflectors. Geophysical methods illustrate that basin geometry is more complicated than would be inferred from extrapolation of surface topography and geology, and these methods aid in defining a three-dimensional framework to understand groundwater storage and flow in southern Nevada. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2006_1397_1.0.1", + "title": "Map showing Features and Displacements of the Scenic Drive Landslide, La Honda, California, During the Period March 31, 2005 - November 5, 2006", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2005-03-31", + "end_date": "2006-11-05", + "bbox": "-122.269485, 37.317654, -122.26557, 37.3204", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549626-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549626-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2006_1397_1.0.1", + "description": "The Scenic Drive landslide in La Honda, San Mateo County, California began movement during the El Ni\u00f1o winter of 1997-98. Recurrent motion occurred during the mild El Nino winter of 2004-2005 and again during the winter of 2005-06. This report documents the changing geometry and motion of the Scenic Drive landslide in 2005-2006, and it documents changes and persistent features that we interpret to reflect underlying structural control of the landslide. We have also compared the displacement history to near-real time rainfall history at a continuously recording gauge for the period October 2004-November 2006. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1006", + "title": "Mapping Phyllic and Argillic-Altered Rocks in Southeastern Afghanistan using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "62, 28, 69, 34", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554413-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554413-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1006", + "description": "ASTER data and logical operators were successfully used to map phyllic and argillic-altered rocks in the southeastern part of Afghanistan. Hyperion data were used to correct ASTER band 5 and ASTER data were georegistered to orthorectified Landsat TM data. Logical operator algorithms produced argillic and phyllic byte ASTER images that were converted to vector data and overlain on ASTER and Landsat TM images. Alteration and fault patterns indicated that two areas, the Argandab igneous complex, and the Katawaz basin may contain potential polymetallic vein and porphyry copper deposits. ASTER alteration mapping in the Chagai Hills indicates less extensive phyllic and argillic-altered rocks than mapped in the Argandab igneous complex and the Katawaz basin and patterns of alteration are inconclusive to predict potential deposit types. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2007_1011_1.0", "title": "Circulation and Physical Processes within the San Gabriel River Estuary During Summer 2005", @@ -183182,6 +189006,19 @@ "description": "The Southern California Coastal Water Research Project (SCCWRP) is developing a hydrodynamic model of the SGR estuary, which is part of the comprehensive water-quality model of the SGR estuary and watershed investigated by SCCWRP and other local agencies. The hydrodynamic model will help understanding of 1) the exchange processes between the estuary and coastal ocean; 2) the circulation patterns in the estuary; 3) upstream natural runoff and the cooling discharge from PGS. Like all models, the SGR hydrodynamic model is only useful after it is fully calibrated and validated. In May 2005, SCCWRP requested the assistance of the U.S. geological Survey (USGS) Coastal and Marine Geology team (CMG) in collecting data on the hydrodynamic conditions in the estuary during the summer dry season. The summer was chosen for field data collection as this was assumed to be the season with the greatest potential for chronic degraded water quality due to low river flow and high thermal stratification within the estuary (due to both higher average air temperature and PGS output). Water quality can be degraded in winter as well, when higher river discharge events bring large volumes of water from the Los Angeles basin into the estuary. The objectives of this project were to 1) collect hydrodynamic data along the SGR estuary; 2) study exchange processes within the estuary through analysis of the hydrodynamic data; and 3) provide field data for model calibration and validation. As the data only exist for the summer season, the results herein only apply to summer conditions.", "license": "proprietary" }, + { + "id": "USGS_OFR_2007_1029_1.0", + "title": "Landsat ETM+ False-Color Image Mosaics of Afghanistan", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "59.9, 28.66, 75.65, 39.11", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554838-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554838-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1029_1.0", + "description": "In 2005, the U.S. Agency for International Development and the U.S. Trade and Development Agency contracted with the U.S. Geological Survey to perform assessments of the natural resources within Afghanistan. The assessments concentrate on the resources that are related to the economic development of that country. Therefore, assessments were initiated in oil and gas, coal, mineral resources, water resources, and earthquake hazards. All of these assessments require geologic, structural, and topographic information throughout the country at a finer scale and better accuracy than that provided by the existing maps, which were published in the 1970's by the Russians and Germans. The very rugged terrain in Afghanistan, the large scale of these assessments, and the terrorist threat in Afghanistan indicated that the best approach to provide the preliminary assessments was to use remotely sensed, satellite image data, although this may also apply to subsequent phases of the assessments. Therefore, the first step in the assessment process was to produce satellite image mosaics of Afghanistan that would be useful for these assessments. This report discusses the production of the Landsat false-color image database produced for these assessments, which was produced from the calibrated Landsat ETM+ image mosaics described by Davis (2006). [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2007_1054", "title": "Assessment and Management of Dead-Wood Habitat", @@ -183195,6 +189032,45 @@ "description": "Dead wood has become an increasingly important conservation issue in managed forests, as awareness of its function in providing wildlife habitat and in basic ecological processes has dramatically increased over the last several decades. The Decayed Wood Advisor (DecAID) is the most comprehensive tool currently available to inform dead-wood management. This report highlights the advantages of using DecAID to evaluate and manage dead-wood resources. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2007_1055", + "title": "Geochemical Data from Produced Water Contamination Investigations: Osage-Skiatook Petroleum Environmental Research (OSPER) Sites, Osage County, Oklahoma", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-02-01", + "end_date": "", + "bbox": "-97, 36, -96, 37", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411629-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411629-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1055", + "description": "The USGS reports chemical and isotopic analyses of 345 water samples collected from the Osage-Skiatook Petroleum Environmental Research (OSPER) project. Water samples were collected as part of an ongoing multi-year USGS investigation to study the transport, fate, natural attenuation, and ecosystem impacts of inorganic salts and organic compounds present in produced water releases at two oil and gas production sites from an aging petroleum field located in Osage County, in northeast Oklahoma. The water samples were collected primarily from monitoring wells and surface waters at the two research sites, OSPER A (legacy site) and OSPER B (active site), during the period March, 2001 to February, 2005. The data include produced water samples taken from seven active oil wells, one coal-bed methane well and two domestic groundwater wells in the vicinity of the OSPER sites. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1073_1.0", + "title": "Hawaiian Volcano Observatory Seismic Data, January to December 2006", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2006-01-01", + "end_date": "2006-12-31", + "bbox": "-157, 18, -154, 21", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552064-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552064-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1073_1.0", + "description": "The Hawaiian Volcano Observatory (HVO) summary presents seismic data gathered during the year. The seismic summary is offered without interpretation as a source of preliminary data. It is complete in the sense that most data for events of M≥1.5 routinely gathered by the Observatory are included. The HVO summaries have been published in various forms since 1956. Summaries prior to 1974 were issued quarterly, but cost, convenience of preparation and distribution, and the large quantities of data dictated an annual publication beginning with Summary 74 for the year 1974. Summary 86 (the introduction of CUSP at HVO) includes a description of the seismic instrumentation, calibration, and processing used in recent years. Beginning with 2004, summaries are simply identified by the year, rather than Summary number. The present summary includes background information on the seismic network and processing to allow use of the data and to provide an understanding of how they were gathered. A report by Klein and Koyanagi (1980) tabulates instrumentation, calibration, and recording history of each seismic station in the network. It is designed as a reference for users of seismograms and phase data and includes and augments the information in the station table in this summary. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1084", + "title": "Heavy Oil and Natural Bitumen Resources in Geological Basins of the World", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550538-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550538-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1084", + "description": "Heavy oil and natural bitumen are oils set apart by their high viscosity (resistance to flow) and high density (low API gravity). These attributes reflect the invariable presence of up to 50 weight percent asphaltenes, very high molecular weight hydrocarbon molecules incorporating many heteroatoms in their lattices. Almost all heavy oil and natural bitumen are alteration products of conventional oil. Total resources of heavy oil in known accumulations are 3,396 billion barrels of original oil in place, of which 30 billion barrels are included as prospective additional oil. The total natural bitumen resource in known accumulations amounts to 5,505 billion barrels of oil originally in place, which includes 993 billion barrels as prospective additional oil. This resource is distributed in 192 basins containing heavy oil and 89 basins with natural bitumen. Of the nine basic Klemme basin types, some with subdivisions, the most prolific by far for known heavy oil and natural bitumen volumes are continental multicyclic basins, either basins on the craton margin or closed basins along convergent plate margins. The former includes 47 percent of the natural bitumen, the latter 47 percent of the heavy oil and 46 percent of the natural bitumen. Little if any heavy oil occurs in fore-arc basins, and natural bitumen does not occur in either fore-arc or delta basins. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2007_1108", "title": "Debris Flows and Record Floods from Extreme Mesoscale Convective Thunderstorms over the Santa Catalina Mountains, Arizona", @@ -183208,6 +189084,97 @@ "description": "Ample geologic evidence indicates early Holocene and Pleistocene debris flows from the south side of the Santa Catalina Mountains north of Tucson, Arizona, but few records document historical events. On July 31, 2006, an unusual set of atmospheric conditions aligned to produce record floods and an unprecedented number of debris flows in the Santa Catalinas. During the week prior to the event, an upper-level area of low pressure centered near Albuquerque, New Mexico generated widespread heavy rainfall in southern Arizona. After midnight on July 31, a strong complex of thunderstorms developed over central Arizona in a deformation zone that formed on the back side of the upper-level low. High atmospheric moisture (2.00\" of precipitable water) coupled with cooling aloft spawned a mesoscale thunderstorm complex that moved southeast into the Tucson basin. A 15-20 knot low-level southwesterly wind developed with a significant upslope component over the south face of the Santa Catalina Mountains advecting moist and unstable air into the merging storms. National Weather Service radar indicated that a swath of 3-6\" of rainfall occurred over the lower and middle elevations of the southern Santa Catalina Mountains. This intense rain falling on saturated soil triggered over 250 hill slope failures and debris flows throughout the mountain range. Sabino Canyon, a heavily used recreation area administered by the U.S. Forest Service, was the epicenter of mass wasting, where at least 18 debris flows removed structures, destroyed the roadway in multiple locations, and closed public access for months. The debris flows were followed by stream flow floods which eclipsed the record discharge in the 75-year gaging record of Sabino Creek. In five canyons adjacent to Sabino Canyon, debris flows approached or excited the mountain front, compromising flow conveyance structures and flooding some homes. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2007_1115_1.0", + "title": "Major crustal fault zone trends and their relation to mineral belts in the north-central Great Basin, Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-120, 34, -112, 44", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552969-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552969-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1115_1.0", + "description": "The Great Basin physiographic province covers a large part of the western United States and contains one of the world's leading gold-producing areas, the Carlin Trend. In the Great Basin, many sedimentary-rock-hosted disseminated gold deposits occur along such linear mineral-occurrence trends. The distribution and genesis of these deposits is not fully understood, but most models indicate that regional tectonic structures play an important role in their spatial distribution. Over 100 magnetotelluric (MT) soundings were acquired between 1994 and 2001 by the U.S. Geological Survey to investigate crustal structures that may underlie the linear trends in north-central Nevada. MT sounding data were used to map changes in electrical resistivity as a function of depth that are related to subsurface lithologic and structural variations. Two-dimensional (2-D) resistivity modeling of the MT data reveals primarily northerly and northeasterly trending narrow 2-D conductors (1 to 30 ohm-m) extending to mid-crustal depths (5-20 km) that are interpreted to be major crustal fault zones. There are also a few westerly and northwesterly trending 2-D conductors. However, the great majority of the inferred crustal fault zones mapped using MT are perpendicular or oblique to the generally accepted trends. The correlation of strike of three crustal fault zones with the strike of the Carlin and Getchell trends and the Alligator Ridge district suggests they may have been the root fluid flow pathways that fed faults and fracture networks at shallower levels where gold precipitated in favorable host rocks. The abundant northeasterly crustal structures that do not correlate with the major trends may be structures that are open to fluid flow at the present time. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1122", + "title": "Flood of May 2006 in New Hampshire", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2006-05-13", + "end_date": "2006-05-17", + "bbox": "-72.68, 42.57, -70.58, 45.43", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551865-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551865-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1122", + "description": "From May 13-17, 2006, central and southern New Hampshire experienced severe flooding caused by as much as 14 inches of rainfall in the region. As a result of the flood damage, a presidential disaster declaration was made on May 25, 2006, for seven counties-Rockingham, Hillsborough, Strafford, Merrimack, Belknap, Carroll, and Grafton. Following the flooding, the U.S. Geological Survey, in a cooperative investigation with the Federal Emergency Management Agency, determined the peak stages, peak discharges, and recurrence-interval estimates of the May 2006 flood at 65 streamgages in the counties where the disaster declaration was made. Data from flood-insurance studies published by the Federal Emergency Management Agency also were compiled for each streamgage location for comparison purposes. The peak discharges during the May 2006 flood were the largest ever recorded at 14 long-term (more than 10 years of record) streamgages in New Hampshire. In addition, peak discharges equaled or exceeded a 100-year recurrence interval at 14 streamgages and equaled or exceeded a 50-year recurrence interval at 22 streamgages. The most severe flooding occurred in Rockingham, Strafford, Merrimack, and eastern and northern Hillsborough Counties. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1133", + "title": "National Assessment of Shoreline Change Part 4: Historical Coastal Cliff Retreat along the California Coast", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-128, 32, -118, 48", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550419-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550419-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1133", + "description": "Coastal cliff retreat, the landward migration of the cliff face, is a chronic problem along many rocky coastlines in the United States. As coastal populations continue to grow and community infrastructures are threatened by erosion, there is increased demand for accurate information regarding trends and rates of coastal cliff retreat. There is also a need for a comprehensive analysis of cliff retreat that is consistent from one coastal region to another. To meet these national needs, the U.S. Geological Survey is conducting an analysis of historical coastal cliff retreat along open-ocean rocky coastlines of the conterminous United States and parts of Hawaii, Alaska, and the Great Lakes. One purpose of this work is to develop standard repeatable methods for mapping and analyzing coastal cliff retreat so that periodic updates of coastal erosion can be made nationally that are systematic and internally consistent. This report on the California Coast is an accompaniment to a report on long-term sandy shoreline change for California. This report summarizes the methods of analysis, interprets the results, and provides explanations regarding long-term rates of cliff retreat. Neither detailed background information on the National Assessment of Shoreline Change Project nor detailed descriptions of the geology and geomorphology of the California coastline are presented in this report. The reader is referred to the shoreline change report (Hapke et al., 2006) for this type of background information. Cliff retreat evaluations are based on comparing one historical cliff edge digitized from maps, with a recent cliff edge interpreted from lidar (Light Detection and Ranging) topographic surveys. The historical cliff edges are from a period ranging from 1920-1930, whereas the lidar cliff edges are from either 1998 or 2002. Long-term (~70-year) rates of retreat are calculated using the two cliff edges. The rates of retreat presented in this report represent conditions from the 1930s to 1998, and are not intended for predicting future cliff edge positions or rates of retreat. Due to the geomorphology of much of California's rocky coast (high-relief, steep slopes with no defined cliff edge) as well as to gaps in both the historical maps and lidar data, we were able to derive two cliff edges and therefore calculate cliff retreat rates for a total of 353 km. The average rate of coastal cliff retreat for the State of California was -0.3\u00b10.2 m/yr, based on rates averaged from 17,653 individual transects measured throughout all areas of California's rocky coastline. The average amount of cliff retreat was 17.7 m over the 70-year time period of our analysis. Retreat rates were generally lowest in Southern California where coastal engineering projects have greatly altered the natural coastal system. California permits shoreline stabilization structures where homes, buildings or other community infrastructure are imminently threatened by erosion. While seawalls and/or riprap revetments have been constructed in all three sections of California, a larger proportion of the Southern California coast has been protected by engineering works, due, in part, to the larger population pressures in this area. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1146", + "title": "Estimated Magnitudes and Recurrence Intervals of Peak Flows on the Mousam and Little Ossipee Rivers for the Flood of April 2007 in Southern Maine", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2007-04-15", + "end_date": "2007-04-16", + "bbox": "-71, 43.2, -70.3, 44", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549127-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549127-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1146", + "description": "Large amounts of rain fell on southern Maine from the afternoon of April 15, 2007, to the afternoon of April 16, 2007, causing substantial damage to houses, roads, and culverts. This report provides an estimate of the peak flows on two rivers in southern Maine - the Mousam River and the Little Ossipee River because of their severe flooding. The April 2007 estimated peak flow of 9,230 ft per second at the Mousam River near West Kennebunk had a recurrence interval between 100 and 500 years; 95-percent confidence limits for this flow ranged from 25 years to greater than 500 years. The April 2007 estimated peak flow of 8,220 ft per second at the Little Ossipee River near South Limington had a recurrence interval between 100 and 500 years; 95-percent confidence limits for this flow ranged from 50 years to greater than 500 years. [Summary provided by the USGS.] ", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1152", + "title": "High-Resolution Seismic Imaging Investigations in Salt Lake and Utah Valleys for Earthquake Hazards", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-09-01", + "end_date": "2005-09-30", + "bbox": "-113, 40, -111.5, 41", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549137-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549137-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1152", + "description": "In support of earthquake hazards and ground motion studies by researchers at the Utah Geological Survey, University of Utah, Utah State University, Brigham Young University, and San Diego State University, the U.S. Geological Survey Geologic Hazards Team Intermountain West Project conducted three high-resolution seismic imaging investigations along the Wasatch Front between September 2003 and September 2005. These three investigations include: (1) a proof-of-concept P-wave minivib reflection imaging profile in south-central Salt Lake Valley, (2) a series of seven deep (as deep as 400 m) S-wave reflection/refraction soundings using an S-wave minivib in both Salt Lake and Utah Valleys, and (3) an S-wave (and P-wave) investigation to 30 m at four sites in Utah Valley and at two previously investigated S-wave (Vs) minivib sites. In addition, we present results from a previously unpublished downhole S-wave investigation conducted at four sites in Utah Valley. The locations for each of these investigations are shown in figure 1. Coordinates for the investigation sites are listed in Table 1. With the exception of the P-wave common mid-point (CMP) reflection profile, whose end points are listed, these coordinates are for the midpoint of each velocity sounding. Vs30 and Vs100, also shown in Table 1, are defined as the average shear-wave velocities to depths of 30 and 100 m, respectively, and details of their calculation can be found in Stephenson and others (2005). The information from these studies will be incorporated into components of the urban hazards maps along the Wasatch Front being developed by the U.S. Geological Survey, Utah Geological Survey, and numerous collaborating research institutions. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1159_2007-1159", + "title": "Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions in a Subbasin of the Red River of the North", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-106, 37, -84, 49", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553843-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553843-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1159_2007-1159", + "description": "Concern over flooding along rivers in the Prairie Pothole Region has stimulated interest in developing spatially distributed hydrologic models to simulate the effects of wetland water storage on peak river flows. Such models require spatial data on the storage volume and interception area of existing and restorable wetlands in the watershed of interest. In most cases, information on these model inputs is lacking because resolution of existing topographic maps is inadequate to estimate volume and areas of existing and restorable wetlands. Consequently, most studies have relied on wetland area to volume or interception area relationships to estimate wetland basin storage characteristics by using available surface area data obtained as a product from remotely sensed data (e.g., National Wetlands Inventory). Though application of areal input data to estimate volume and interception areas is widely used, a drawback is that there is little information available to provide guidance regarding the application, limitations, and biases associated with such approaches. Another limitation of previous modeling efforts is that water stored by wetlands within a watershed is treated as a simple lump storage component that is filled prior to routing overflow to a pour point or gaging station. This approach does not account for dynamic wetland processes that influence water stored in prairie wetlands. Further, most models have not considered the influence of human-induced hydrologic changes, such as land use, that greatly influence quantity of surface water inputs and, ultimately, the rate that a wetland basin fills and spills. The goals of this study were to (1) develop and improve methodologies for estimating and spatially depicting wetland storage volumes and interceptions areas and (2) develop models and approaches for estimating/simulating the water storage capacity of potentially restorable and existing wetlands under various restoration, land use, and climatic scenarios. To address these goals, we developed models and approaches to spatially represent storage volumes and interception areas of existing and potentially restorable wetlands in the upper Mustinka subbasin within Grant County, Minn. We then developed and applied a model to simulate wetland water storage increases that would result from restoring 25 and 50 percent of the farmed and drained wetlands in the upper Mustinka subbasin. The model simulations were performed during the growing season (May October) for relatively wet (1993; 0.67 m of precipitation) and dry (1987; 0.32 m of precipitation) years. Results from the simulations indicated that the 25 percent restoration scenario would increase water storage by 2732 percent and that a 50 percent scenario would increase storage by 5363 percent. Additionally, we estimated that wetlands in the subbasin have potential to store 11.5720.98 percent of the total precipitation that fell over the entire subbasin area (52,758 ha). Our simulation results indicated that there is considerable potential to enhance water storage in the subbasin; however, evaluation and calibration of the model is necessary before simulation results can be applied to management and planning decisions. In this report we present guidance for the development and application of models (e.g., surface area-volume predictive models, hydrology simulation model) to simulate wetland water storage to provide a basis from which to understand and predict the effects of natural or human-induced hydrologic alterations. In developing these approaches, we tried to use simple and widely available input data to simulate wetland hydrology and predict wetland water storage for a specific precipitation event or a series of events. Further, the hydrology simulation model accounted for land use and soil type, which influence surface water inputs to wetlands. Although information presented in this report is specific to the Mustinka subbasin, the approaches and methods developed should be applicable to other regions in the Prairie Pothole Region. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1161", + "title": "Historical Changes in the Mississippi-Alabama Barrier Islands and the Roles of Extreme Storms, Sea Level, and Human Activities", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-94, 30, -86, 32", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555148-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555148-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1161", + "description": "An historical analysis of images and documents shows that the Mississippi-Alabama (MS-AL) barrier islands are undergoing rapid land loss and translocation. The barrier island chain formed and grew at a time when there was a surplus of sand in the alongshore sediment transport system, a condition that no longer prevails. The islands, except Cat, display alternating wide and narrow segments. Wide segments generally were products of low rates of inlet migration and spit elongation that resulted in well-defined ridges and swales formed by wave refraction along the inlet margins. In contrast, rapid rates of inlet migration and spit elongation under conditions of surplus sand produced low, narrow, straight barrier segments. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2007_1169", "title": "2005 Hydrographic Survey of South San Francisco Bay, California", @@ -183221,6 +189188,45 @@ "description": "An acoustic hydrographic survey of South San Francisco Bay (South Bay) was conducted in 2005. Over 20 million soundings were collected within an area of approximately 250 sq km (97 sq mi) of the bay extending south of Coyote Point on the west shore, to the San Leandro marina on the east, including Coyote Creek and Ravenswood, Alviso, Artesian, and Mud Sloughs. This is the first survey of this scale that has been conducted in South Bay since the National Oceanic and Atmospheric Administration National Ocean Service (NOS) last surveyed the region in the early 1980s. Data from this survey will provide insight to changes in bay floor topography from the 1980s to 2005 and will also serve as essential baseline data for tracking changes that will occur as restoration of the South San Francisco Bay salt ponds progress. This report provides documentation on how the survey was conducted, an assessment of accuracy of the data, and distributes the sounding data with Federal Geographic Data Committee (FGDC) compliant metadata. Reports from NOS and Sea Surveyor, Inc., containing additional survey details are attached as appendices. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2007_1190", + "title": "Geophysical Data from Spring Valley to Delamar Valley, East-Central Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-115, 37, -113, 40", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549251-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549251-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1190", + "description": "Cenozoic basins in eastern Nevada and western Utah constitute major ground-water recharge areas in the eastern part of the Great Basin and these were investigated to characterize the geologic framework of the region. Prior to these investigations, regional gravity coverage was variable over the region, adequate in some areas and very sparse in others. Cooperative studies described herein have established 1,447 new gravity stations in the region, providing a detailed description of density variations in the middle to upper crust. All previously available gravity data for the study area were evaluated to determine their reliability, prior to combining with our recent results and calculating an up-to-date isostatic residual gravity map of the area. A gravity inversion method was used to calculate depths to pre-Cenozoic basement rock and estimates of maximum alluvial/volcanic fill in the major valleys of the study area. The enhanced gravity coverage and the incorporation of lithologic information from several deep oil and gas wells yields a much improved view of subsurface shapes of these basins and provides insights useful for the development of hydrogeologic models for the region. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1202", + "title": "Geochemistry of Selected Coal Samples from Sumatra, Kalimantan, Sulawesi, and Papua, Indonesia", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "90, -20, 140, 20", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555267-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555267-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1202", + "description": "Indonesia is an archipelago of more than 17,000 islands that stretches astride the equator for about 5,200 km in southeast Asia (figure 1) and includes major Cenozoic volcano-plutonic arcs, active volcanoes, and various related onshore and offshore basins. These magmatic arcs have extensive Cu and Au mineralization that has generated much exploration and mining in the last 50 years. Although Au and Ag have been mined in Indonesia for over 1000 years (van Leeuwen, 1994), it was not until the middle of the nineteenth century that the Dutch explored and developed major Sn and minor Au, Ag, Ni, bauxite, and coal resources. The metallogeny of Indonesia includes Au-rich porphyry Cu, porphyry Mo, skarn Cu-Au, sedimentary-rock hosted Au, epithermal Au, laterite Ni, and diamond deposits. For example, the Grasberg deposit in Papua has the world's largest gold reserves and the third-largest copper reserves (Sillitoe, 1994). Coal mining in Indonesia also has had a long history beginning with the initial production in 1849 in the Mahakam coal field near Pengaron, East Kalimantan; in 1891 in the Ombilin area, Sumatra, (van Leeuwen, 1994); and in South Sumatra in 1919 at the Bukit Asam mine (Soehandojo, 1989). Total production from deposits in Sumatra and Kalimantan, from the 19thth century to World War II, amounted to 40 million metric tons (Mt). After World War II, production declined due to various factors including politics and a boom in the world-wide oil economy. Active exploration and increased mining began again in the 1980's mainly through a change in Indonesian government policy of collaboration with foreign companies and the global oil crises (Prijono, 1989). This recent coal revival (van Leeuwen, 1994) has lead Indonesia to become the largest exporter of thermal (steam) coal and the second largest combined thermal and metallurgical (coking) coal exporter in the world market (Fairhead and others, 2006). The exported coal is desirable as it is low sulfur and ash (generally <1 and < 10 wt.%, respectively). Coal mining for both local use and for export has a very strong future in Indonesia although, at present, there are concerns about the strong need for a major revision in mining laws and foreign investment policies (Wahju, 2004; United States Embassy Jakarta, 2004). The World Coal Quality Inventory (WoCQI) program of the U.S. Geological Survey (Tewalt and others, 2005) is a cooperative project with about 50 countries (out of 70 coal-producing countries world-wide). The WoCQI initiative has collected and published extensive coal quality data from the world's largest coal producers and consumers. The important aspects of the WoCQI program are; (1) samples from active mines are collected, (2) the data have a high degree of internal consistency with a broad array of coal quality parameters, and (3) the data are linked to GIS and available through the world-wide-web. The coal quality parameters include proximate and ultimate analysis, sulfur forms, major-, minor-, and trace-element concentrations and various technological tests. This report contains geochemical data from a selected group of Indonesian coal samples from a range of coal types, localities, and ages collected for the WoCQI program. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1208", + "title": "Geophysical Characterization of Pre-Cenozoic Basement for Hydrocarbon Assessment, Yukon Flats, Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-170, 52, -132, 79", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549660-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549660-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1208", + "description": "The Cenozoic basins of interior Alaska are poorly understood, but may host undiscovered hydrocarbon resources in sufficient quantities to serve remote villages and for possible export. Purported oil seeps and the regional occurrence of potential hydrocarbon source and reservoir rocks fuel an exploration interest in the 46,000 km2 Yukon Flats basin. Whether hydrocarbon source rocks are present in the pre-Cenozoic basement beneath Yukon Flats is difficult to determine because vegetation and surficial deposits obscure the bedrock geology, only limited seismic data are available, and no deep boreholes have been drilled. Analysis of regional potential field data (aeromagnetics and gravity) is valuable, therefore, for preliminary characterization of basement lithology and structure. We present our analysis as a red-green-blue composite spectral map consisting of: (1) reduced-to-the-pole magnetics (red), (2) magnetic potential (green), and (3) basement gravity (blue). The color and texture patterns on this composite map highlight domains with common geophysical characteristics and, by inference, lithology. The observed patterns yield the primary conclusion that much of the basin is underlain by Devonian to Jurassic oceanic rocks related to the Angayucham and Tozitna terranes (JDat). These rocks are part of a lithologically diverse assemblage of brittlely deformed, generally low-grade metamorphic rocks of oceanic affinity; such rocks probably have little or no potential for hydrocarbon generation. The JDat geophysical signature extends from the Tintina fault system northward to the Brooks Range. Along the eastern edge of the basin, JDat appears to overlie moderately dense and non-magnetic Proterozoic(?) and Paleozoic continental margin rocks. The western edge of the JDat in subsurface is difficult to distinguish due to the presence of magnetic granites similar to those exposed in the Ruby geanticline. In the southern portion of the basin, geophysical patterns indicate the possibility of overthrusting of Cenozoic sediments and underlying JDat by Paleozoic and Proterozoic rocks of the Schwatka sequence. These structural hypotheses provide the basis for an overthrust play within the Cenozoic section just south of the basin. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2007_1217", "title": "Coastal Processes Study at Ocean Beach, San Francisco, CA: Summary of Data Collection 2004-2006", @@ -183234,6 +189240,19 @@ "description": "Ocean Beach in San Francisco, California, contains a persistent erosional section in the shadow of the San Francisco ebb tidal delta and south of Sloat Boulevard that threatens valuable public infrastructure as well as the safe recreational use of the beach. Coastal managers have been discussing potential mediation measures for over a decade, with little scientific research available to aid in decision making. The United States Geological Survey (USGS) initiated the Ocean Beach Coastal Processes Study in April 2004 to provide the scientific knowledge necessary for coastal managers to make informed management decisions. This study integrates a wide range of field data collection and numerical modeling techniques to document nearshore sediment transport processes at the mouth of San Francisco Bay, with emphasis on how these processes relate to erosion at Ocean Beach. The Ocean Beach Coastal Processes Study is the first comprehensive study of coastal processes at the mouth of San Francisco Bay. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2007_1238", + "title": "Estimates of Monthly Ground-Water Recharge to the Yakima River Basin Aquifer System, Washington, 1960-2001, for Current Land-Use and Land-Cover Conditions", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2001-12-31", + "bbox": "-128, 45, -120, 53", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554323-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554323-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1238", + "description": "Monthly values of ground-water recharge, for current land-use and land-cover conditions, to the Yakima River Basin aquifer system, Washington, during water years 1960-2001 were previously estimated. Monthly estimates are spatially related to a Geographic Information System raster dataset with a grid cell size of 500 ft on a side. These estimates of monthly recharge are provided in 42 ASCII files, 1 file for each water year. The grid with its metadata and 42 files provide potential users easy access to the information. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2007_1248", "title": "Digital Data From the Taos West Aeromagnetic Survey in Taos County, New Mexico", @@ -183247,6 +189266,45 @@ "description": "This report contains digital data, image files, and text files describing data formats and survey procedures for aeromagnetic data collected during a survey covering the southwestern portion of Taos County west of the Town of Taos, New Mexico, in October, 2006. Several derivative products from these data are also presented as grids and images, including reduced-to-pole data and data continued to a reference surface. Images are presented in various formats and are intended to be used as input to geographic information systems, standard graphics software, or map plotting packages. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2007_1264", + "title": "Lava Flow Hazard Assessment, as of August 2007, for Kilauea East Rift Zone Eruptions, Hawai'i Island", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, 14, -146, 33", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552308-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552308-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1264", + "description": "The most recent episode in the ongoing Pu OO-Kupaianaha eruption of Kilauea Volcano is currently producing lava flows north of the east rift zone. Although they pose no immediate threat to communities, changes in flow behavior could conceivably cause future flows to advance downrift and impact communities thus far unaffected. This report reviews lava flow hazards in the Puna District and discusses the potential hazards posed by the recent change in activity. Members of the public are advised to increase their general awareness of these hazards and stay up-to-date on current conditions. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1269", + "title": "Modeling the Spatial and Temporal Variation of Monthly and Seasonal Precipitation on the Nevada Test Site and Vicinity, 1960-2006", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2006-12-31", + "bbox": "-117, 36, -115, 38", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551716-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551716-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1269", + "description": "The Nevada Test Site (NTS), located in the climatic transition zone between the Mojave and Great Basin Deserts, has a network of precipitation gages that is unusually dense for this region. This network measures monthly and seasonal variation in a landscape with diverse topography. Precipitation data from 125 climate stations on or near the NTS were used to spatially interpolate precipitation for each month during the period of 1960 through 2006 at high spatial resolution (30 m). The data were collected at climate stations using manual and/or automated techniques. The spatial interpolation method, applied to monthly accumulations of precipitation, is based on a distance-weighted multivariate regression between the amount of precipitation and the station location and elevation. This report summarizes the temporal and spatial characteristics of the available precipitation records for the period 1960 to 2006, examines the temporal and spatial variability of precipitation during the period of record, and discusses some extremes in seasonal precipitation on the NTS. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1270", + "title": "High-Resolution Topographic, Bathymetric, and Oceanographic Data for the Pleasure Point Area, Santa Cruz County, California: 2005-2007", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-126, 33, -120, 40", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552295-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552295-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1270", + "description": "The County of Santa Cruz Department of Public Works and the County of Santa Cruz Redevelopment Agency requested the U.S. Geological Survey (USGS) Western Coastal and Marine Geology Team (WCMG) to provide baseline geologic and oceanographic information on the coast and inner shelf at Pleasure Point, Santa Cruz County, California. The rationale for this proposed work is a need to better understand the environmental consequences of a proposed bluff stabilization project on the beach, the near shore and the surf at Pleasure Point, Santa Cruz County, California. To meet these information needs, the USGS-WCMG Team collected baseline scientific information on the morphology and waves at Pleasure Point. This study provided high-resolution topography of the coastal bluffs and bathymetry of the inner shelf off East Cliff Drive between 32nd Avenue and 41st Avenue. The spatial and temporal variation in waves and their breaking patterns at the study site were documented. Although this project did not actively investigate the impacts of the proposed bluff stabilization project, these data provide the baseline information required for future studies directed toward predicting the impacts of stabilization on the sea cliffs, beach and near shore sediment profiles, natural rock reef structures, and offshore habitats and resources. They also provide a basis for calculating potential changes to wave transformations into the shore at Pleasure Point. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2007_1305", "title": "Bathymetry, Substrate and Circulation in Westcott Bay, San Juan Islands, Washington", @@ -183260,6 +189318,19 @@ "description": "Nearshore bathymetry, substrate type, and circulation patterns in Westcott Bay, San Juan Islands, Washington, were mapped using two acoustic sonar systems, video and direct sampling of seafloor sediments. The goal of the project was to characterize nearshore habitat and conditions influencing eelgrass (Z. marina) where extensive loss has occurred since 1995. A principal hypothesis for the loss of eelgrass is a recent decrease in light availability for eelgrass growth due to increase in turbidity associated with either an increase in fine sedimentation or biological productivity within the bay. To explore sources for this fine sediment and turbidity, a dual-frequency Biosonics sonar operating at 200 and 430 kHz was used to map seafloor depth, morphology and vegetation along 69 linear kilometers of the bay. The higher frequency 430 kHz system also provided information on particulate concentrations in the water column. A boat-mounted 600 kHz RDI Acoustic Doppler Current Profiler (ADCP) was used to map current velocity and direction and water column backscatter intensity along another 29 km, with select measurements made to characterize variations in circulation with tides. An underwater video camera was deployed to ground-truth acoustic data. Seventy one sediment samples were collected to quantify sediment grain size distributions across Westcott Bay. Sediment samples were analyzed for grain size at the Western Coastal and Marine Geology Team sediment laboratory in Menlo Park, Calif. These data reveal that the seafloor near the entrance to Westcott Bay is rocky with a complex morphology and covered with dense and diverse benthic vegetation. Current velocities were also measured to be highest at the entrance and along a deep channel extending 1 km into the bay. The substrate is increasingly comprised of finer sediments with distance into Westcott Bay where current velocities are lower. This report describes the data collected and preliminary findings of USGS Cruise B-6-07-PS conducted between May 31, 2007 and June 5, 2007. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2007_1306_1.0", + "title": "Gravity Data from Newark Valley, White Pine County, Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-116, 39, -115, 41", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552498-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552498-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1306_1.0", + "description": "The Newark Valley area, eastern Nevada is one of thirteen major ground-water basins investigated by the BARCAS (Basin and Range Carbonate Aquifer Study) Project. Gravity data are being used to help characterize the geophysical framework of the region. Although gravity coverage was extensive over parts of the BARCAS study area, data were sparse for a number of the valleys, including the northern part of Newark Valley. We addressed this lack of data by establishing seventy new gravity stations in and around Newark Valley. All available gravity data were then evaluated to determine their reliability, prior to calculating an isostatic residual gravity map to be used for subsequent analyses. A gravity inversion method was used to calculate depths to pre-Cenozoic basement rock and estimates of maximum alluvial/volcanic fill. The enhanced gravity coverage and the incorporation of lithologic information from several deep oil and gas wells yields a view of subsurface shape of the basin and will provide information useful for the development of hydrogeologic models for the region. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2007_1308", "title": "Development of an Impervious-Surface Database for the Little Blackwater River Watershed, Dorchester County, Maryland", @@ -183312,6 +189383,32 @@ "description": "Water-quality and streamflow data from 34 sites in nontidal parts of the Chesapeake Bay watershed are presented to document annual nutrient and sediment loads and trends for 1985 through 2006, as part of an annual evaluation of water-quality conditions by the U.S. EPA Chesapeake Bay Program. This study presents the results of trends analysis for streamflow, loads, and concentrations. Annual mean flow to the bay for 2006 (78,650 cubic feet per second) was approximately 1 percent above the long-term annual mean flow from 1937 to 2005. Total freshwater flow entering the bay for the summer season (July-August-September) was the only season classified as 'wet' in 2006. For the period 1985 through 2006, streamflow was significantly increasing at two of the 34 sites. Observed (bias-corrected) concentration summaries indicate higher ranges in concentrations of total nitrogen in the northern major river basins (Pennsylvania, Maryland, and northern Virginia) than in the southern basins in Virginia. Results indicate almost half of the monitoring sites in the northern basins exhibited significant downward bias-corrected concentration trends in total nitrogen over time; results were similar for total phosphorus and sediment. Generally, loads for all constituents at the nine River Input Monitoring Program (RIM) sites, which comprise 78 percent of the streamflow entering the bay, were lower in 2006 than in 2005. The loads for total nitrogen are below the long-term average loads at eight of the nine RIM sites and total phosphorus and sediment loads are also below the long-term average at seven RIM sites. Combined annual mean total nitrogen flow-weighted concentrations from the nine RIM sites indicated an upward tendency in 2006; in contrast, total phosphorus and sediment indicated a downward tendency. From 1990 to 2006 for the 9 RIM sites, the mean concentrations of total nitrogen, total phosphorus, and sediment were 3.49, 0.195, and 116 milligrams per liter, respectively. Flow-weighted concentrations for phosphorus and sediment were lowest in the Susquehanna River at Conowingo, Md., most likely because of the trapping efficiency of three large reservoirs upstream from the sampling point. For all 34 sites and all constituents, trends in concentrations (not adjusted for flow) showed 12 statistically significant upward trends and 59 statistically significant downward trends for the period 1985 through 2006. When trends in concentrations are adjusted for flow, they can be used as indicators of human activity and effectiveness of management actions. The flow-adjusted trends indicated significant downward trends at approximately 74, 68, and 32 percent of the sites for total nitrogen, total phosphorus, and sediment, respectively. This may indicate that management actions are having some effect in reducing nutrients and sediments. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2007_1392", + "title": "Long-term and Storm-related Shoreline Change Trends in the Florida Gulf Islands National Seashore", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-90, 28, -84, 32", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549325-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549325-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1392", + "description": "Coastal erosion on Northern Gulf of Mexico barrier islands is an ongoing issue that was exacerbated by the storm seasons of 2004 and 2005 when several hurricanes made landfall in the Gulf of Mexico. Two units of the Gulf Islands National Seashore (GUIS), located on Santa Rosa Island, a barrier island off the Panhandle coast of Florida, were highly impacted during the hurricanes of 2004 (Ivan) and 2005 (Cindy, Dennis, Katrina and Rita). In addition to the loss of or damage to natural and cultural resources within the park, damage to park infrastructure, including park access roads and utilities, occurred in areas experiencing rapid shoreline retreat. The main park road was located as close as 50 m to the pre-storm (2001) shoreline and was still under repair from damage incurred during Hurricane Ivan when the 2005 hurricanes struck. A new General Management Plan is under development for the Gulf Islands National Seashore. This plan, like the existing General Management Plan, strives to incorporate natural barrier island processes, and will guide future efforts to provide access to units of Gulf Islands National Seashore on Santa Rosa Island. To assess changes in island geomorphology and provide data for park management, the National Park Service and the U.S. Geological Survey are currently analyzing shoreline change to better understand long-term (100+ years) shoreline change trends as well as short-term shoreline impact and recovery to severe storm events. Results show that over an ~140-year period from the late 1800s to May 2004, the average shoreline erosion rates in the Fort Pickens and Santa Rosa units of GUIS were -0.7m/yr and -0.1 m/yr, respectively. Areas of historic erosion, reaching a maximum rate of -1.3 m/yr, correspond to areas that experienced overwash and road damage during the 2004 hurricane season.. The shoreline eroded as much as ~60 m during Hurricane Ivan, and as much as ~88 m over the course of the 2005 storm season. The shoreline erosion rates in the areas where the park road was heavily damaged were as high as -70.2 m/yr over the 2004-2005 time period. Additional post-storm monitoring of these sections of the island, to assess whether erosion rates stabilize, will help to parks to determine the best long-term management strategy for the park infrastructure. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2007_1405", + "title": "Magnetotelluric Data, San Luis Valley, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-107, 35, -104, 38", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555045-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555045-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2007_1405", + "description": "The San Luis Valley region population is growing. Water shortfalls could have serious consequences. Future growth and land management in the region depend on accurate assessment and protection of the region's ground-water resources. An important issue in managing the ground-water resources is a better understanding of the hydrogeology of the Santa Fe Group and the nature of the sedimentary deposits that fill the Rio Grande rift, which contain the principal ground-water aquifers. The shallow unconfined aquifer and the deeper confined Santa Fe Group aquifer in the San Luis Basin are the main sources of municipal water for the region. The U.S. Geological Survey (USGS) is conducting a series of multidisciplinary studies of the San Luis Basin located in southern Colorado. Detailed geologic mapping, high-resolution airborne magnetic surveys, gravity surveys, an electromagnetic survey (called magnetotellurics, or MT), and hydrologic and lithologic data are being used to better understand the aquifers. The MT survey primary goal is to map changes in electrical resistively with depth that are related to differences in rock types. These various rock types help control the properties of aquifers. This report does not include any data interpretation. Its purpose is to release the MT data acquired at 24 stations. Two of the stations were collected near Santa Fe, New Mexico, near deep wildcat wells. Well logs from those wells will help tie future interpretations of this data with geologic units from the Santa Fe Group sediments to Precambrian basement. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2007_1410", "title": "Climate Variation at Flagstaff, Arizona 1950 to 2007", @@ -183338,6 +189435,84 @@ "description": "This report summarizes the findings of a study conducted as a pilot for part of a park-wide monitoring program being developed for the Ozark National Scenic Riverways (ONSR) of southeastern Missouri. The objective was to evaluate using crayfish (Orconectes spp.) and Asian clam (Corbicula fluminea) for monitoring concentrations of metals associated with lead-zinc mining. Lead-zinc mining presently (2007) occurs near the ONSR and additional mining has been proposed. Three composite samples of each type (crayfish and Asian clam), each comprising ten animals of approximately the same size, were collected during late summer and early fall of 2005 from five sites on the Current River and Jacks Fork within the ONSR and from one site on the Eleven Point River and the Big River, which are outside the ONSR. The Big River has been contaminated by mine tailings from historical lead zinc mining. Samples were analyzed by inductively coupled plasma mass spectrometry for lead, zinc, cadmium, cobalt, and nickel concentrations. All five metals were detected in all samples; concentrations were greatest in samples of both types from the Big River, and lowest in samples from sites within the ONSR. Concentrations of zinc and cadmium typically were greater in Asian clams than in crayfish, but differences were less evident for the other metals. In addition, differences among sites were small for cobalt in Asian clams and for zinc in crayfish, indicating that these metals are internally regulated to some extent. Consequently, both sample types are recommended for monitoring. Concentrations of metals in crayfish and Asian clams were consistent with those reported by other studies and programs that sampled streams in southeast Missouri. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2008_1005", + "title": "Geomorphic Map of Worcester County, Maryland, Interpreted from a LIDAR-Based, Digital Elevation Model", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-78, 38, -74, 41", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548668-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548668-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1005", + "description": "A recently compiled mosaic of a LIDAR-based digital elevation model (DEM) is presented with geomorphic analysis of new macro-topographic details. The geologic framework of the surficial and near surface late Cenozoic deposits of the central uplands, Pocomoke River valley, and the Atlantic Coast includes Cenozoic to recent sediments from fluvial, estuarine, and littoral depositional environments. Extensive Pleistocene (cold climate) sandy dune fields are deposited over much of the terraced landscape. The macro details from the LIDAR image reveal 2 meter-scale resolution of details of the shapes of individual dunes, and fields of translocated sand sheets. Most terrace surfaces are overprinted with circular to elliptical rimmed basins that represent complex histories of ephemeral ponds that were formed, drained, and overprinted by younger basins. The terrains of composite ephemeral ponds and the dune fields are inter-shingled at their margins indicating contemporaneous erosion, deposition, and re-arrangement and possible internal deformation of the surficial deposits. The aggregate of these landform details and their deposits are interpreted as the products of arid, cold climate processes that were common to the mid-Atlantic region during the Last Glacial Maximum. In the Pocomoke valley and its larger tributaries, erosional remnants of sandy flood plains with anastomosing channels indicate the dynamics of former hydrology and sediment load of the watershed that prevailed at the end of the Pleistocene. As the climate warmed and precipitation increased during the transition from late Pleistocene to Holocene, dune fields were stabilized by vegetation, and the stream discharge increased. The increased discharge and greater local relief of streams graded to lower sea levels stimulated down cutting and created the deeply incised valleys out onto the continental shelf. These incised valleys have been filling with fluvial to intertidal deposits that record the rising sea level and warmer, more humid climate in the mid-Atlantic region throughout the Holocene. Thus, the geomorphic details provided by the new LIDAR DEM actually record the response of the landscape to abrupt climate change. Holocene trends and land-use patterns from Colonial to modern times can also be interpreted from the local macro- scale details of the landscape. Beyond the obvious utility of these data for land-use planning and assessments of resources and hazards, the new map presents new details on the impact of climate changes on a mid-latitude, outer Coastal plain landscape. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2008_1086", + "title": "Ground-Water Quality in the Mohawk River Basin, New York, 2006", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-76, 42, -73, 44", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411634-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411634-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1086", + "description": "Water samples were collected from 27 wells from August through November 2006 to characterize ground-water quality in the Mohawk River Basin. The Mohawk River Basin covers 3,500 square miles in central New York; most of the basin is underlain by sedimentary bedrock, including shale, sandstone, and carbonates. Sand and gravel form the most productive aquifers in the basin. Samples were collected from 13 sand and gravel wells and 14 bedrock wells, including production and domestic wells. The samples were collected and processed through standard U.S. Geological Survey procedures and were analyzed for 226 physical properties and constituents, including physical properties, major ions, nutrients, trace elements, radon-222, pesticides, volatile organic compounds, and bacteria. Many constituents were not detected in any sample, but concentrations of some constituents exceeded current or proposed Federal or New York State drinking-water quality standards, including color (1 sample), pH (2 samples), sodium (11 samples), chloride (2 samples), fluoride (1 sample), sulfate (1 sample), aluminum (2 samples), arsenic (2 samples), iron (10 samples), manganese (10 samples), radon-222 (12 samples), and bacteria (6 samples). Dissolved oxygen concentrations were greater in samples from sand and gravel wells (median 5.6 milligrams per liter [mg/L]) than from bedrock wells (median 0.2 mg/L). The pH was typically neutral or slightly basic (median 7.3); the median water temperature was 11°C. The ions with the highest concentrations were bicarbonate (median 276 mg/L), calcium (median 58.9 mg/L), and sodium (median 41.9 mg/L). Ground water in the basin is generally very hard (180 mg/L as CaCO3 or greater), especially in the Mohawk Valley and areas with carbonate bedrock. Nitrate-plus-nitrite concentrations were generally higher samples from sand and gravel wells (median concentration 0.28 mg/L as N) than in samples from bedrock wells (median < 0.06 mg/L as N), although no concentrations exceeded established State or Federal drinking-water standards of 10 mg/L as N for nitrate and 1 mg/L as N for nitrite. Ammonia concentrations were higher in samples from bedrock wells (median 0.349 mg/L as N) than in those from samples from sand and gravel wells (median 0.006 mg/L as N). The trace elements with the highest concentrations were strontium (median 549 micrograms per liter [¼g/L]), iron (median 143 ¼g/L), boron (median 35 ¼g/L), and manganese (median 31.1 ¼g/L). Concentrations of several trace elements, including boron, copper, iron, manganese, and strontium, were higher in samples from bedrock wells than those from sand and gravel wells. The highest radon-222 activities were in samples from bedrock wells (maximum 1,360 pCi/L); 44 percent of all samples exceeded a proposed U.S. Environmental Protection Agency drinking water standard of 300 pCi/L. Nine pesticides and pesticide degradates were detected in six samples at concentrations of 0.42 ¼g/L or less; all were herbicides or their degradates, and most were degradates of alachlor, atrazine, and metolachlor. Six volatile organic compounds were detected in four samples at concentrations of 0.8 ¼g/L or less, including four trihalomethanes, tetrachloroethene, and toluene; most detections were in sand and gravel wells and none of the concentrations exceeded drinking water standards. Coliform bacteria were detected in six samples but fecal coliform bacteria, including Escherichia coli, were not detected in any sample. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2008_1088", + "title": "Interior River Lowland Ecoregion Summary Report", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-92, 37, -86, 42", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551934-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551934-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1088", + "description": "The Interior River Lowlands ecoregion encompasses 93,200 square kilometers (km2) across southern and western Illinois, southwest Indiana, east-central Missouri, and fractions of northwest Kentucky and southeast Iowa. The ecoregion includes the confluence areas of the Mississippi, Missouri, Ohio, Illinois, and Wabash Rivers, and their tributaries. This ecoregion was formed in non-resident, non-calcareous sedimentary rock (U.S. Environmental Protection Agency, 2006). The unstratified soil deposits present north of the White River in Indiana are evidence that pre-Wisconsinan ice once covered much of the Interior River Lowlands. The geomorphic characteristics of this area also include terraced valleys filled with alluvium as well as outwash, acolian, and lacustrine deposits. Historically, agricultural land use has been a vital economic resource for this region. The drained alluvial soils are farmed for feed grains and soybeans, whereas the valley uplands also are used for forage crops, pasture, woodlots, mixed farming, and livestock (USEPA, 2006). This ecoregion provides a key component of national energy resources as it contains the second largest coal reserve in the United States, and the largest reserve of bituminous coal (Varanka and Shaver, 2007). One of the primary reasons for change in the ecoregion is urbanization. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2008_1100", + "title": "Modeling Soil Moisture in the Mojave Desert", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-122, 32, -112, 40", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551546-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551546-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1100", + "description": "This publication describes soil moisture modeling in the Mojave Desert. It provides a general background on the process of pedogenesis, or soil development, which is a major factor affecting soil moisture properties. Soil texture changes with pedogenesis, which, in turn, affects soil moisture. Soil moisture is vital to plant survival, and therefore to the survival of all desert organisms associated with plants. Developing soil moisture models provides valuable information that can be used in predicting the impacts of disturbance, an area's ability to recover from disturbance, and in making land management decisions. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2008_1119", + "title": "Geochemistry of Rock Samples Collected from the Iron Hill Carbonatite Complex, Gunnison County, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-108, 37, -107, 39", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552479-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552479-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1119", + "description": "A study conducted in 2006 by the U.S. Geological Survey collected 57 surface rock samples from nine types of intrusive rock in the Iron Hill carbonatite complex. This intrusive complex, located in Gunnison County of southwestern Colorado, is known for its classic carbonatite-alkaline igneous geology and petrology. The Iron Hill complex is also noteworthy for its diverse mineral resources, including enrichments in titanium, rare earth elements, thorium, niobium (columbium), and vanadium. This study was performed to reexamine the chemistry and metallic content of the major rock units of the Iron Hill complex by using modern analytical techniques, while providing a broader suite of elements than the earlier published studies. The report contains the geochemical analyses of the samples in tabular and digital spreadsheet format, providing the analytical results for 55 major and trace elements. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2008_1121_1.0", + "title": "Modified Mercalli Intensity Maps for the 1868 Hayward Earthquake Plotted in ShakeMap Format", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-123, 37, -121, 39", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553098-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553098-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1121_1.0", + "description": "This data set provides Modified Mercalli Intensity maps for the Hayward earthquake of October 21, 1868. To construct the Modified Mercalli Intensity (MMI) ShakeMap for the 1868 Hayward earthquake, we started with two sets of damage descriptions and felt reports. The first set of 100 sites was compiled by A.A. Bullock in the Lawson (1908) report on the 1906 San Francisco earthquake. The second set of 45 sites was compiled by Toppozada et al. (1981) from an extensive search of newspaper archives. We supplemented these two sets of reports with new observations from 30 sites using surveys of cemetery damage, reports of damage to historic adobe structures, pioneer narratives, and reports from newspapers that Toppozada et al. (1981) did not retrieve. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2008_1130_1.0", "title": "Catalog of Mount St. Helens 2004-2007 Dome Samples with Major- and Trace-element Chemistry", @@ -183364,6 +189539,45 @@ "description": "This open-file report presents a catalog of information about 135 ash samples along with geochemical analyses of bulk ash, glass and individual mineral grains from tephra deposited as a result of volcanic activity at Mount St. Helens, Washington, from October 1, 2004 until August 15, 2005. This data, in conjunction with that in a companion report on 2004-2007 Mount St. Helens dome samples by Thornber and others (2008a) are presented in support of the contents of the U.S. Geological Survey Professional Paper 1750 (Sherrod and others, ed., 2008). Readers are referred to appropriate chapters in USGS Professional Paper 1750 for detailed narratives of eruptive activity during this time period and for interpretations of sample characteristics and geochemical data presented here. All ash samples reported herein are currently archived at the David A. Johnston Cascades Volcano Observatory in Vancouver, Washington. The Mount St. Helens 2004-2005 Tephra Sample Catalogue along with bulk, glass and mineral geochemistry are tabulated in 6 worksheets of the accompanying Microsoft Excel file, of2008-1131.xls. Samples in all tables are organized by collection date. Table 1 is a detailed catalog of sample information for tephra deposited downwind of Mount St. Helens between October 1, 2004 and August 18, 2005. Table 2 provides major- and trace-element analyses of 8 bulk tephra samples collected throughout that interval. Major-element compositions of 82 groundmass glass fragments, 420 feldspar grains, and 213 mafic (clinopyroxene, amphibole, hypersthene, and olivine) mineral grains from 12 ash samples collected between October 1, 2004 and March 8, 2005 are presented in tables 3 through 5. In addition, trace-element abundances of 198 feldspars from 11 ash samples (same samples as major-element analyses) are provided in table 6. Additional mineral and bulk ash analyses from 2004 and 2005 ash samples are published in chapters 30 (oxide thermometry; Pallister and others, 2008), 32 (amphibole major elements; Thornber and others, 2008b) and 37 (210Pb; 210Pb/226Pa; Reagan and others, 2008) of U.S. Geological Survey Professional Paper 1750 (Sherrod and others, 2008). A brief overview of sample collection methods is given below as an aid to deciphering the tephra sample catalog. This is followed by an explanation of the categories of sample information (column headers) in table 1. A summary of the analytical methods used to obtain the geochemical data in this report introduces the presentation of major and trace-element geochemistry of Mount St. Helens 2004-2005 tephra samples in tables 2-6. Rhyolite glass standard analyses are reported (Appendix 1) to demonstrate the accuracy and precision of similar glass analyses presented herein. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2008_1132", + "title": "Geochemical Data for Samples Collected in 2007 Near the Concealed Pebble Porphyry Cu-Au-Mo Deposit, Southwest Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-157, 59, -149, 62", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552099-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552099-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1132", + "description": "In the summer of 2007, the U.S. Geological Survey (USGS) began an exploration geochemical research study over the Pebble porphyry copper-gold-molydenum (Cu-Au-Mo) deposit in southwest Alaska. The Pebble deposit is extremely large and is almost entirely concealed by tundra, glacial deposits, and post-Cretaceous volcanic and volcaniclastic rocks. The deposit is presently being explored by Northern Dynasty Minerals, Ltd., and Anglo-American LLC. The USGS undertakes unbiased, broad-scale mineral resource assessments of government lands to provide Congress and citizens with information on national mineral endowment. Research on known deposits is also done to refine and better constrain methods and deposit models for the mineral resource assessments. The Pebble deposit was chosen for this study because it is concealed by surficial cover rocks, it is relatively undisturbed (except for exploration company drill holes), it is a large mineral system, and it is fairly well constrained at depth by the drill hole geology and geochemistry. The goals of the USGS study are (1) to determine whether the concealed deposit can be detected with surface samples, (2) to better understand the processes of metal migration from the deposit to the surface, and (3) to test and develop methods for assessing mineral resources in similar concealed terrains. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2008_1169_1.0", + "title": "Digital Elevation Models of the Pre-Eruption 2000 Crater and 2004\u201307 Dome-Building Eruption at Mount St. Helens, Washington, USA", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2004-01-01", + "end_date": "2007-12-31", + "bbox": "-122.12, 46.12, -122.12, 46.12", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555276-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555276-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1169_1.0", + "description": "Presented in this report are 27 digital elevation model (DEM) datasets for the crater area of Mount St. Helens. These datasets include pre-eruption baseline data collected in 2000, incremental model subsets collected during the 2004\u201307 dome building eruption, and associated shaded-relief image datasets. Each dataset was collected photogrammetrically with digital softcopy methods employing a combination of manual collection and iterative compilation of x,y,z coordinate triplets utilizing autocorrelation techniques. DEM data points collected using autocorrelation methods were rigorously edited in stereo and manually corrected to ensure conformity with the ground surface. Data were first collected as a triangulated irregular network (TIN) then interpolated to a grid format. DEM data are based on aerotriangulated photogrammetric solutions for aerial photograph strips flown at a nominal scale of 1:12,000 using a combination of surveyed ground control and photograph-identified control points. The 2000 DEM is based on aerotriangulation of four strips totaling 31 photographs. Subsequent DEMs collected during the course of the eruption are based on aerotriangulation of single aerial photograph strips consisting of between three and seven 1:12,000-scale photographs (two to six stereo pairs). Most datasets were based on three or four stereo pairs. Photogrammetric errors associated with each dataset are presented along with ground control used in the photogrammetric aerotriangulation. The temporal increase in area of deformation in the crater as a result of dome growth, deformation, and translation of glacial ice resulted in continual adoption of new ground control points and abandonment of others during the course of the eruption. Additionally, seasonal snow cover precluded the consistent use of some ground control points. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2008_1270_1.0", + "title": "Liquefaction Hazard Maps for Three Earthquake Scenarios", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2008-09-12", + "end_date": "", + "bbox": "-122.206, 37.246723, -121.746, 37.48794", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550425-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550425-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1270_1.0", + "description": "Maps showing the probability of surface manifestations of liquefaction in the northern Santa Clara Valley were prepared with liquefaction probability curves. The area includes the communities of San Jose, Campbell, Cupertino, Los Altos, Los Gatos Milpitas, Mountain View, Palo Alto, Santa Clara, Saratoga, and Sunnyvale. The probability curves were based on complementary cumulative frequency distributions of the liquefaction potential index (LPI) for surficial geologic units in the study area. LPI values were computed with extensive cone penetration test soundings. Maps were developed for three earthquake scenarios, an M7.8 on the San Andreas Fault comparable to the 1906 event, an M6.7 on the Hayward Fault comparable to the 1868 event, and an M6.9 on the Calaveras Fault. Ground motions were estimated with the Boore and Atkinson (2008) attenuation relation. Liquefaction is predicted for all three events in young Holocene levee deposits along the major creeks. Liquefaction probabilities are highest for the M7.8 earthquake, ranging from 0.33 to 0.37 if a 1.5-m deep water table is assumed, and 0.10 to 0.14 if a 5-m deep water table is assumed. Liquefaction probabilities of the other surficial geologic units are less than 0.05. Probabilities for the scenario earthquakes are generally consistent with observations during historical earthquakes. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2008_1274_1.0", "title": "Debris Flows and Floods in Southeastern Arizona from Extreme Precipitation in July 2006-Magnitude, Frequency, and Sediment Delivery", @@ -183390,6 +189604,32 @@ "description": "High-resolution measurements of waves, currents, water levels, temperature, salinity and turbidity were made in Hanalei Bay, northern Kaua\u2018i, Hawai\u2018i, during the summer of 2006 to better understand coastal circulation, sediment dynamics, and the potential impact of a river flood in a coral reef-lined embayment during quiescent summer conditions. A series of bottommounted instrument packages were deployed in water depths of 10 m or less to collect long-term, high-resolution measurements of waves, currents, water levels, temperature, salinity, and turbidity. These data were supplemented with a series of profiles through the water column to characterize the vertical and spatial variability in water column properties within the bay. These measurements support the ongoing process studies being conducted as part of the U.S. Geological Survey (USGS) Coastal and Marine Geology Program\u2019s Pacific Coral Reef Project; the ultimate goal is to better understand the transport mechanisms of sediment, larvae, pollutants, and other particles in coral reef settings. Information regarding the USGS study conducted in Hanalei Bay during the 2005 summer is available in Storlazzi and others (2006), Draut and others (2006) and Carr and others (2006). This report, the last part in a series, describes data acquisition, processing, and analysis for the 2006 summer data set. [Summary provided by the U.S. Geological Survey.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2008_1299", + "title": "Gravity Data from Dry Lake and Delamar Valleys, east-central Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-116, 37, -113, 39", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550809-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550809-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1299", + "description": "Cenozoic basins in eastern Nevada and western Utah constitute major ground-water recharge areas in the eastern part of the Great Basin, and our continuing studies are intended to characterize the geologic framework of the region. Prior to these investigations, regional gravity coverage was variable over the region, adequate in some areas and very sparse in others. The current study in Nevada provides additional high-resolution gravity along transects in Dry Lake and Delamar Valleys to supplement data we established previously in Cave and Muleshoe Valleys. We combine all previously available gravity data and calculate an up-to-date isostatic residual gravity map of the study area. Major density contrasts are identified, indicating zones where Cenozoic tectonic activity could have been accommodated. A gravity inversion method is used to calculate depths to pre-Cenozoic basement rock and to estimate maximum alluvial/volcanic fill in the valleys. Average depths of basin fill in the deeper parts of Cave, Muleshoe, Dry Lake, and Delamar Valleys are approximately 4 km, 2 km, 5 km, and 3 km, respectively. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2008_1306_1.0", + "title": "Major- and Trace-Element Concentrations in Soils from Northern California: Results from the Geochemical Landscapes Project Pilot Study", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-128, 33, -118, 42", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550587-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550587-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2008_1306_1.0", + "description": "In 2004, the U.S. Geological Survey (USGS), the Geological Survey of Canada (GSC), and the Mexican Geological Survey (Servicio Geologico Mexicano, or SGM) initiated pilot studies in preparation for a soil geochemical survey of North America called the Geochemical Landscapes Project. The purpose of this project is to provide a better understanding of the variability in chemical composition of soils in North America. The data produced by this survey will be used to construct baseline geochemical maps for regions within the continent. Two initial pilot studies were conducted: (1) a continental-scale study involving a north-south and east-west transect across North America and (2) a regional-scale study. The pilot studies were intended to test and refine sample design, sampling protocols, and field logistics for the full continental soils geochemical survey. Smith and others (2005) reported the results from the continental-scale pilot study. The regional-scale California study was designed to represent more detailed, higher resolution geochemical investigations in a region of particular interest that was identified from the low-sample-density continental-scale survey. A 20,000-km area of northern California (fig. 1), representing a wide variety of topography, climate, and ecoregions, was chosen for the regional-scale pilot study. This study area also contains diverse geology and soil types and supports a wide range of land uses including agriculture in the Sacramento Valley, forested areas in portions of the Sierra Nevada, and urban/suburban centers such as Sacramento, Davis, and Stockton. Also of interest are potential effects on soil geochemistry from historical hard rock and placer gold mining in the foothills of the Sierra Nevada, historical mercury mining in the Coast Range, and mining of base-metal sulfide deposits in the Klamath Mountains to the north. This report presents the major- and trace-element concentrations from the regional-scale soil geochemical survey in northern California. [Summary provided by the U.S. Geological Survey.]", + "license": "proprietary" + }, { "id": "USGS_OFR_2010_1172", "title": "Database of Recent Tsunami Deposits", @@ -183403,6 +189643,71 @@ "description": "This report describes a database of sedimentary characteristics of tsunami deposits derived from published accounts of tsunami deposit investigations conducted shortly after the occurrence of a tsunami. The database contains 228 entries, each entry containing data from up to 71 categories. It includes data from 51 publications covering 15 tsunamis distributed between 16 countries. The database encompasses a wide range of depositional settings including tropical islands, beaches, coastal plains, river banks, agricultural fields, and urban environments. It includes data from both local tsunamis and teletsunamis. The data are valuable for interpreting prehistorical, historical, and modern tsunami deposits, and for the development of criteria to identify tsunami deposits in the geologic record. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_2010_1190_1.0", + "title": "Floods of May 30 to June 15, 2008, in the Iowa River and Cedar River Basins, Eastern Iowa", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2008-05-30", + "end_date": "2008-06-15", + "bbox": "-94, 41, -92, 44", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550194-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550194-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2010_1190_1.0", + "description": "As a result of prolonged and intense periods of rainfall in late May and early June, 2008, along with heavier than normal snowpack the previous winter, record flooding occurred in Iowa in the Iowa River and Cedar River Basins. The storms were part of an exceptionally wet period from May 29 through June 12, when an Iowa statewide average of 9.03 inches of rain fell; the normal statewide average for the same period is 2.45 inches. From May 29 to June 13, the 16-day rainfall totals recorded at rain gages in Iowa Falls and Clutier were 14.00 and 13.83 inches, respectively. Within the Iowa River Basin, peak discharges of 51,000 cubic feet per second (flood-probability estimate of 0.2 to 1 percent) at the 05453100 Iowa River at Marengo, Iowa streamflow-gaging station (streamgage) on June 12, and of 39,900 cubic feet per second (flood-probability estimate of 0.2 to 1 percent) at the 05453520 Iowa River below Coralville Dam near Coralville, Iowa streamgage on June 15 are the largest floods on record for those sites. A peak discharge of 41,100 cubic feet per second (flood-probability estimate of 0.2 to 1 percent) on June 15 at the 05454500 Iowa River at Iowa City, Iowa streamgage is the fourth highest on record, but is the largest flood since regulation by the Coralville Dam began in 1958. Within the Cedar River Basin, the May 30 to June 15, 2008, flood is the largest on record at all six streamgages in Iowa located on the mainstem of the Cedar River and at five streamgages located on the major tributaries. Flood-probability estimates for 10 of these 11 streamgages are less than 1 percent. Peak discharges of 112,000 cubic feet per second (flood-probability estimate of 0.2 to 1 percent) at the 05464000 Cedar River at Waterloo, Iowa streamgage on June 11 and of 140,000 cubic feet per second (flood-probability estimate of less than 0.2 percent) at the 05464500 Cedar River at Cedar Rapids, Iowa streamgage on June 13 are the largest floods on record for those sites. Downstream from the confluence of the Iowa and Cedar Rivers, the peak discharge of 188,000 cubic feet per second (flood-probability estimate of less than 0.2 percent) at the 05465500 Iowa River at Wapello, Iowa streamgage on June 14, 2008, is the largest flood on record in the Iowa River and Cedar River Basins since 1903. High-water marks were measured at 88 locations along the Iowa River between State Highway 99 near Oakville and U.S. Highway 69 in Belmond, a distance of 319 river miles. High-water marks were measured at 127 locations along the Cedar River between Fredonia near the mouth (confluence with the Iowa River) and Riverview Drive north of Charles City, a distance of 236 river miles. The high-water marks were used to develop flood profiles for the Iowa and Cedar River. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2010_1198", + "title": "Land-Cover Change in the Ozark Highlands, 1973\u20132000", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1973-01-01", + "end_date": "2000-12-31", + "bbox": "-95, 35, -90, 40", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554813-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554813-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2010_1198", + "description": "Led by the Geographic Analysis and Monitoring Program of the U.S. Geological Survey (USGS) in collaboration with the U.S. Environmental Protection Agency (EPA) and the National Aeronautics and Space Administration (NASA), the Land-Cover Trends Project was initiated in 1999 and aims to document the types, geographic distributions, and rates of land-cover change on a region by region basis for the conterminous United States, and to determine some of the key drivers and consequences of the change (Loveland and others, 2002). For 1973, 1980, 1986, 1992, and 2000 land-cover maps derived from the Landsat series are classified by visual interpretation, inspection of historical aerial photography and ground survey, into 11 land-cover classes. The classes are defined to capture land cover that is discernable in Landsat data. A stratified probability-based sampling methodology undertaken within the 84 Omernik Level III Ecoregions (Omernik, 1987) was used to locate the blocks, with 9 to 48 blocks per ecoregion. The sampling was designed to enable a statistically robust \"scaling up\" of the sample-classification data to estimate areal land-cover change within each ecoregion (Loveland and others, 2002; Stehman and others, 2005). At the time of writing, approximately 90 percent of the 84 conterminous United States ecoregions have been processed by the Land-Cover Trends Project. Results from these completed ecoregions illustrate that across the conterminous United States there is no single profile of land-cover/land-use change, rather, there are varying pulses affected by clusters of change agents (Loveland and others, 2002). Land-Cover Trends Project results for the conterminous United States to-date are being used for collaborative environmental change research with partners such as; the National Science Foundation, the National Oceanic and Atmospheric Administration, and the U.S. Fish and Wildlife Service. The strategy has also been adapted for use in a NASA global deforestation initiative, and elements of the project design are being used in the North American Carbon Program's assessment of forest disturbance. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2010_1223", + "title": "Estimates for Self-Supplied Domestic Withdrawals and Population Served, for Selected Principal Aquifers, Calendar Year 2005", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2005-01-01", + "end_date": "2005-12-31", + "bbox": "-130, 24, -60, 51", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549313-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549313-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2010_1223", + "description": "The National Water-Quality Assessment Program of the U.S. Geological Survey has groundwater studies that focus on water-quality conditions in principal aquifers of the United States. The Program specifically focuses on aquifers that are important to public supply, domestic, and other major uses. Estimates for self-supplied domestic withdrawals and the population served for 20 aquifers in the United States for calendar year 2005 are provided in this report. These estimates are based on county-level data for self-supplied domestic groundwater withdrawals and the population served by those withdrawals, as compiled by the National Water Use Information Program, for areas within the extent of the 20 aquifers. In 2005, the total groundwater withdrawals for self-supplied domestic use from the 20 aquifers represented about 63 percent of the total self-supplied domestic groundwater withdrawals in the United States; the population served by the withdrawals represented about 61 percent of the total self-supplied domestic population in the United States. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2010_1330", + "title": "Geomorphology and Depositional Subenvironments of Gulf Islands National Seashore, Perdido Key and Santa Rosa Island, Florida", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2010-01-01", + "end_date": "2010-12-31", + "bbox": "-87.42046, 30.29735, -86.510414, 30.406206", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551388-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551388-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2010_1330", + "description": "The U.S. Geological Survey (USGS) is studying coastal hazards and coastal change to improve our understanding of coastal ecosystems and to develop better capabilities of predicting future coastal change. One approach to understanding the dynamics of coastal systems is to monitor changes in barrier-island subenvironments through time. This involves examining morphologic and topographic change at temporal scales ranging from millennia to years and spatial scales ranging from tens of kilometers to meters. Of particular interest are the processes that produce those changes and the determination of whether or not those processes are likely to persist into the future. In these analyses of hazards and change, both natural and anthropogenic influences are considered. Quantifying past magnitudes and rates of coastal change and knowing the principal factors that govern those changes are critical to predicting what changes are likely to occur under different scenarios, such as short-term impacts of extreme storms or long-term impacts of sea-level rise. Gulf Islands National Seashore was selected for detailed mapping of barrier-island morphology and topography because the islands offer a diversity of depositional subenvironments and because island areas and positions have changed substantially in historical time. The geomorphologic and subenvironmental maps emphasize the processes that formed the surficial features and also serve as a basis for documenting which subenvironments are relatively stable, such as the vegetated barrier core, and those which are highly dynamic, such as the beach and inactive overwash zones. The primary mapping procedures were supervised functions within a Geographic Information System (GIS) that were applied to delineate and classify depositional subenvironments and features, collectively referred to as map units. The delineated boundaries of the map units were exported to create one shapefile, and are differentiated by the field \"Type\" in the associated attribute table. Map units were delineated and classified based on differences in tonal patterns of features in contrast to adjacent features observed on orthophotography. Land elevations from recent lidar surveys served as supplementary data to assist in delineating the map unit boundaries. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_OFR_2013_1305_1.0", + "title": "Global Surface Displacement Data for Assessing Variability of Displacement at a Point on a Fault", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549015-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549015-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_2013_1305_1.0", + "description": "This report presents a global dataset of site-specific surface-displacement data on faults. We have compiled estimates of successive displacements attributed to individual earthquakes, mainly paleoearthquakes, at sites where two or more events have been documented, as a basis for analyzing inter-event variability in surface displacement on continental faults. An earlier version of this composite dataset was used in a recent study relating the variability of surface displacement at a point to the magnitude-frequency distribution of earthquakes on faults, and to hazard from fault rupture (Hecker and others, 2013). The purpose of this follow-on report is to provide potential data users with an updated comprehensive dataset, largely complete through 2010 for studies in English-language publications, as well as in some unpublished reports and abstract volumes. [Summary provided by the U.S. Geological Survey.] ", + "license": "proprietary" + }, { "id": "USGS_OFR_2014-1094_SantaCatalinaBackscatter", "title": "Backscatter \u2013 Outer Mainland Shelf and Slope, Gulf of Santa Catalina, southern California, U.S. Geological Survey, 2010-2011", @@ -183442,6 +189747,32 @@ "description": "In Florida\u2019s karst terrain, where groundwater and surface waters interact, a mapping time series of the potentiometric surface in the Upper Floridan aquifer offers a versatile metric for assessing the hydrologic condition of both the aquifer and overlying streams and wetlands. Long-term groundwater monitoring data were used to generate a monthly time series of potentiometric surfaces in the Upper Floridan aquifer over a 573-square-mile area of west-central Florida between January 2000 and December 2009. Recorded groundwater elevations were collated for 260 groundwater monitoring wells in the Northern Tampa Bay area, and a continuous time series of daily observations was created for 197 of the wells by estimating missing daily values through regression relations with other monitoring wells. Kriging was used to interpolate the monthly average potentiometric-surface elevation in the Upper Floridan aquifer over a decade. The mapping time series gives spatial and temporal coherence to groundwater monitoring data collected continuously over the decade by three different organizations, but at various frequencies. Further, the mapping time series describes the potentiometric surface beneath parts of six regionally important stream watersheds and 11 municipal well fields that collectively withdraw about 90 million gallons per day from the Upper Floridan aquifer. Monthly semivariogram models were developed using monthly average groundwater levels at wells. Kriging was used to interpolate the monthly average potentiometric-surface elevations and to quantify the uncertainty in the interpolated elevations. Drawdown of the potentiometric surface within well fields was likely the cause of a characteristic decrease and then increase in the observed semivariance with increasing lag distance. This characteristic made use of the hole effect model appropriate for describing the monthly semivariograms and the interpolated surfaces. Spatial variance reflected in the monthly semivariograms decreased markedly between 2002 and 2003, timing that coincided with decreases in well-field pumping. Cross-validation results suggest that the kriging interpolation may smooth over the drawdown of the potentiometric surface near production wells. The groundwater monitoring network of 197 wells yielded an average kriging error in the potentiometric-surface elevations of 2 feet or less over approximately 70 percent of the map area. Additional data collection within the existing monitoring network of 260 wells and near selected well fields could reduce the error in individual months. Reducing the kriging error in other areas would require adding new monitoring wells. Potentiometric-surface elevations fluctuated by as much as 30 feet over the study period, and the spatially averaged elevation for the entire surface rose by about 2 feet over the decade. Monthly potentiometric-surface elevations describe the lateral groundwater flow patterns in the aquifer and are usable at a variety of spatial scales to describe vertical groundwater recharge and discharge conditions for overlying surface-water features. ", "license": "proprietary" }, + { + "id": "USGS_OFR_94-710", + "title": "Homestead Valley, California, Aftershocks Recorded on Portable Seismographs, SCEC", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-121, 32, -114, 38", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549623-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549623-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_94-710", + "description": "Information on USGS OFR 94-710 is available on-line via the World Wide Web: \"http://www.data.scec.org/ftp/ca.earthquakes/homestead/\" \"http://www.data.scec.org/fault_index/homeval.html\" The following information on the Homestead Valley earthquake aftershock data set was extracted from the Southern California Earthquake Center Data Center WWW site (\"http://www.data.scec.org/\"): On March 15, 1979, four moderate earthquakes (ML 4.9, 5.3, 4.5, 4.8) occurred in the Homestead Valley area of the Mojave Desert. Recently, phase data from portable instruments deployed by the U. S. Geological Survey on March 17 - 18, 1979 have been merged with those recorded by the Southern California Seismic Network (SCSN). The results of this study have been published in a U.S.Geological Survey-Open File Report. -homestead.hyp relocated hypocenters with portable data -homestead.phase phase data from portable instruments (hypoinverse format) -hvnetandport.dat SCSN and portable data -lnv8z0.mod velocity model used in relocations -homestead.sta portable instrument locations", + "license": "proprietary" + }, + { + "id": "USGS_OFR_97_745_E", + "title": "Map of Debris-Flow Source Areas in the San Francisco Bay Region, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-124, 29, -121, 37", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551873-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551873-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_97_745_E", + "description": "This report is a digital database package containing both plotfiles and Geographic Information Systems (GIS) databases of maps of potential debris flow sources, as well as locations of historic debris flow sources, in the San Francisco Bay Region. The data are provided for both the entire region and each county within the region, in two formats. The data are provided as ARC/INFO (Environmental Systems Research Institute, Redlands, CA) coverages and grids for use in GIS packages, and as PostScript plotfiles of formatted maps similar to traditional U.S. Geological Survey map products. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_99-11", "title": "Color Shaded Relief Map of the Conterminous United States", @@ -183455,6 +189786,19 @@ "description": "The color shaded relief map of the conterminous U.S. was created from 15 arc-second digital elevation model (DEM) data. The data set traces its origins back to the early 1960's when .01 inch scans of 1:250,000 USGS topographic sheets were produced by the Defense Mapping Agency and converted to 3 second data by the USGS National Cartographic Information Center. The 15 second grid cell data (Michael Webring, written communication) used in this report dates from the mid-1980's with occasional local and regional updates. The 3 second grid nodes were averaged with a 6x6 operator and decimated to 15 second grid cells which is about the resolution of the original .01 inch data set. The 3 second data is available as 950 separate 1x1 degree quadrangles from the USGS EROS Data Center. Additional information available at \"http://pubs.usgs.gov/of/of99-011/1readme.html\" [Summary provided by the USGS.] .", "license": "proprietary" }, + { + "id": "USGS_OFR_99-422_1.0", + "title": "Geographic Information Systems (GIS) Compilation of Geophysical, Geologic, and Tectonic Data for the Circum-North Pacific", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-115, 40, 120, 80", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550558-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550558-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_99-422_1.0", + "description": "The accompanying directory structure contains a Geographic Information Systems (GIS) compilation of geophysical, geological, and tectonic data for the Circum-North Pacific. This area includes the Russian Far East, Alaska, the Canadian Cordillera, linking continental shelves, and adjacent oceans. This GIS compilation extends from 120\u00b0E to 115\u00b0W, and from 40\u00b0N to 80\u00b0N. This area encompasses: (1) to the south, the modern Pacific plate boundary of the Japan-Kuril and Aleutian subduction zones, the Queen Charlotte transform fault, and the Cascadia subduction zone; (2) to the north, the continent-ocean transition from the Eurasian and North American continents to the Arctic Ocean; (3) to the west, the diffuse Eurasian-North American plate boundary, including the probable Okhotsk plate; and (4) to the east, the Alaskan-Canadian Cordilleran fold belt. This compilation should be useful for: (1) studying the Mesozoic and Cenozoic collisional and accretionary tectonics that assembled this continental crust of this region; (2) studying the neotectonics of active and passive plate margins in this region; and (3) constructing and interpreting geophysical, geologic, and tectonic models of the region. Geographic Information Systems (GIS) programs provide powerful tools for managing and analyzing spatial databases. Geological applications include regional tectonics, geophysics, mineral and petroleum exploration, resource management, and land-use planning. This CD-ROM contains thematic layers of spatial data-sets for geology, gravity field, magnetic field, oceanic plates, overlap assemblages, seismology (earthquakes), tectonostratigraphic terranes, topography, and volcanoes. The GIS compilation can be viewed, manipulated, and plotted with commercial software (ArcView and ArcInfo) or through a freeware program (ArcExplorer) that can be downloaded from http://www.esri.com for both Unix and Windows computers using the button below. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_99-463_1.0", "title": "Digital Data Sets of Depth-Duration Frequency of Precipitation For Oklahoma", @@ -183494,6 +189838,19 @@ "description": "This report contains digital data sets describing water use, toxic chemical releases, metropolitan areas, and population density of the conterminous United States. The data source for the water-use data is from the U.S. Geological Survey (USGS) (U.S. Geological Survey, 1990, 1998b; Lanfear, 1984). The toxic chemical release information is from the U.S. Environmental Protection Agency (1997, 1998), and the metropolitan area and population density data sets were derived from data provided by the U.S. Bureau of the Census (1995) and the Consortium for International Earth Science Information Network (1995). Because most of the source materials do not cover Alaska and Hawaii, only the conterminous 48 states are included in these data sets. Compilation of the data sets was supported by the National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey. The objectives of the NAWQA Program are to: (1) describe current water-quality conditions for a large part of the Nation's freshwater streams, rivers, and aquifers, (2) describe how water quality is changing over time, and (3) improve the understanding of the primary natural and anthropogenic factors that affect water-quality conditions. National analysis of data, based on aggregation of comparable information obtained from across the United States, is a major component of the NAWQA Program. The data sets included in this report were created for NAWQA national data analysis activities.", "license": "proprietary" }, + { + "id": "USGS_OFR_99_414_1.0", + "title": "Geologic Datasets for Weights-of-Evidence Analysis in Northeast Washington--3. Minerals-Related Permits on National Forests, 1967 to 1998", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1998-01-01", + "end_date": "1998-12-31", + "bbox": "-121.25, 47.25, -117, 49", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554622-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554622-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_99_414_1.0", + "description": "This dataset was developed to provide mineral resource data for the region of northeast WA for use in future spatial analysis by a variety of users. This database is not meant to be used or displayed at any scale larger than 1:24,000. Permits to explore for and (or) develop mineral resources on Federal lands can be used to indicate locations and types of mineral-related activities on national forests. Permits for these activities require filing of a Notice of Intent to conduct mineral exploration activities and (or) a Plan of Operation, if significant land disturbance results. This compilation of notices and plans for the Colville, Kaniksu, Okanogan, and Wenatchee national forests between 1967 and 1998 is intended for use in combination with geologic and economic information to predict future mineral-related activities in the region. This dataset consists of one Excel 97 spreadsheet file (of99-414.xls) which contains information about permits on national forest lands in northeast Washington State. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_99_436", "title": "Archive of Sparker Subbottom Data Collected During USGS Cruise ALPH 98013, New York, 10-22 September, 1997", @@ -183507,6 +189864,19 @@ "description": "This project will generate reconnaissance maps of the sea floor offshore of the New York - New Jersey metropolitan area -- the most heavily populated, and one of the most impacted coastal regions of the United States. The surveys will provide an overall synthesis of the sea floor environment, including seabed texture and bed forms, Quaternary strata geometry, and anthropogenic impact (e.g., ocean dumping, trawling, channel dredging). The goal of this project is to survey the offshore area, the harbor, and the southern shore of Long Island, providing a regional synthesis to support a wide range of management decisions and a basis for further process-oriented investigations. The project is conducted cooperatively with the U.S. Army Corps of Engineer (USACE). This CD-ROM contains digital high resolution seismic reflection data collected during the USGS ALPH 98013 cruise. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_OFR_99_438_1.0", + "title": "Digital geologic map of part of the Thompson Falls 1:100,000 quadrangle, Idaho", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-01-01", + "end_date": "1999-12-31", + "bbox": "-116, 47.5, -115, 48", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554236-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554236-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_OFR_99_438_1.0", + "description": "This data set was developed to provide geologic map GIS of the Idaho portion of the Thompson Falls 1:100,000 quadrangle for use in future spatial analysis by a variety of users. This database is not meant to be used or displayed at any scale larger than 1:100,000 (e.g., 1:62,500 or 1:24,000). The geology of the Thompson Falls 1:100,000 quadrangle, Idaho was compiled by Reed S. Lewis in 1997 onto a 1:100,000-scale topographic base map for input into an Arc/Info geographic information system (GIS). The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps. This GIS consists of two major Arc/Info data sets: one line and polygon file (tf100k) containing geologic contacts and structures (lines) and geologic map rock units (polygons), and one point file (tfpnt) containing structural data. [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_OFR_Acid_Deposition", "title": "Acid Deposition Sensitivity of the Southern Appalachian Assessment Area", @@ -183572,6 +189942,32 @@ "description": "This atlas explores the continental-scale relations between the geographic ranges of woody plant species and climate in North America. A 25-km equal-area grid of modern climatic and bioclimatic parameters was constructed from instrumental weather records. The geographic distributions of selected tree and shrub species were digitized, and the presence or absence of each species was determined for each cell on the 25-km grid, thus providing a basis for comparing climatic data and species' distributions. The relations between climate and plant distributions are explored in graphical and tabular form. The results of this effort are primarily intended for use in biogeographic, paleoclimatic, and global-change research. These web pages provide access to the text, digital representations of figures, and supplemental data files from USGS Professional Paper 1650, chapters A and B. A printed set of these volumes can be ordered from the USGS at a cost of US$63.00. To order, please call or write: USGS Information Services Box 25286 Denver Federal Center Denver, CO 80225 Tel: 303-202-4700; Fax: 303-202-4693 [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_PA_DIGIT_1.0", + "title": "Digital drainage basin boundaries of named streams in Pennsylvania", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-76.4304, 39.7151, -74.6865, 42.0007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548560-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548560-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_PA_DIGIT_1.0", + "description": "In 1989, the Pennsylvania Department of Environmental Resources (PaDER), in cooperation with the U.S. Geological Survey, Water Resources Division (USGS published the Pennsylvania (PA) Gazetteer of Streams. This publication contains information related to named streams in Pennsylvania. Drainage basin boundaries are delineated on the 7.5-minute series topographic paper quadrangle maps for PA and parts of the bordering states of New York, Maryland, Ohio, West Virginia, and Delaware. These boundaries enclose catchment areas for named streams officially recognized by the Board on Geographic Names and other unofficially named streams that flow through named hollows, using the hollow name, e.g. \"Smith Hollow\". This was done in an effort to name as many of the 64,000 streams as possible. In 1991, work began by USGS to put these drainage basin boundaries into digital form for use in a geographic information system (GIS). Digitizing started with USGS in Lemoyne, PA., but expanded with assistance by PaDER and the Natural Resource Conservation Service (NRCS), formerly the U.S Department of Agriculture, Soil Conservation Service (SCS). USGS performed all editing, attributing, and edgematching. There are 878, 7.5-minute quadrangle maps in PA. This documentation applies to only those maps in the Delaware River basin (164). Parts of the Delaware River drainage originate outside the PA border. At this time, no effort is being made by USGS to include those named stream basins. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_PONTCHARTRAIN", + "title": "Geologic Framework and Processes of the Lake Pontchartrain Basin", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-91, 29, -89, 31", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549642-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549642-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_PONTCHARTRAIN", + "description": "Lake Pontchartrain and adjacent lakes in Louisiana form one of the larger estuaries in the Gulf Coast region. The estuary drains the Pontchartrain Basin (at right), an area of over 12,000 km2 situated on the eastern side of the Mississippi River delta plain. In Louisiana, nearly one-third of the State population lives within the 14 parishes of the basin. Over the past 60 years, rapid growth and development within the basin, along with natural processes, have resulted in significant environmental degradation and loss of critical habitat in and around Lake Pontchartrain. Human activities associated with pollutant discharge and surface drainage have greatly affected the water quality in the lake. This change is evident in the bottom sediments, which record the historic health of the lake. Also, land-altering activities such as logging, dredging, and flood control in and around the lake, lead to shoreline erosion and loss of wetlands.The effects of pollution, shoreline erosion and wetland loss on the lake and surrounding areas have become a major public concern. To better understand the basin's origin and the processes driving its development and degradation requires a wide-ranging study involving many organizations and personnel. When the U.S. Geological Survey began the study of Lake Pontchartrain in 1994, information on four topics was needed: -Geologic Framework, or how the various sedimentary layers that make up the basin are put together -Sediment Characterization, that is, what are the sediments made of, where did they come from, and what kinds of pollutants do they contain -Shoreline and Wetland Change over time -what are the processes that control Water Circulation [Summary provided by the USGS.]", + "license": "proprietary" + }, { "id": "USGS_PWRC_BioEco", "title": "Biological and Ecological Characteristics of Terrestrial Vertebrate Species Residing in Estuaries", @@ -183598,6 +189994,32 @@ "description": "The USGS Chesapeake Bay River Input Monitoring (RIM) Program was established to quantify loads and long-term trends in concentrations of nutrients and suspended material entering the tidal part of the Chesapeake Bay Basin from its nine major tributaries. These nine rivers account for approximately 93% of the stream flow entering Chesapeake Bay from the non-tidal part of its watershed. Results of the RIM program are being used by resource managers, policy makers, and concerned citizens to help evaluate the effectiveness of strategies aimed at reducing nutrients and sediment entering Chesapeake Bay from its tributaries. Water samples are collected upstream of the transition area between the tidal and non-tidal regions of the nine rivers (Figure 1). This transition zone historically has been referred to as the \"Fall Line\" for the many sets of falls and rapids that are found at this point on the rivers. Below the Fall Line in the tidal areas of these rivers, tides can transport water, nutrients, and suspended material from downstream, making it difficult to determine the cause of any observed changes. Because water-quality samples are collected above the influence of tides, any observed changes in nutrients or suspended material can be attributed to upstream causes. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_RITA_COASTAL_IMPACT", + "title": "Hurricane Rita Impact Studies", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2005-09-24", + "end_date": "", + "bbox": "-98, 27, -84, 36", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552111-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552111-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_RITA_COASTAL_IMPACT", + "description": "Hurricane Rita made landfall on September 24, 2005 near the TX-LA border. The U.S. Geological Survey (USGS), NASA, the U.S. Army Corps of Engineers, the University of New Orleans, Louisiana State University and the Texas Bureau of Economic Geology are cooperating in a research project investigating coastal change that is expected as a result of Hurricane Rita. Aerial video, still photography, and laser altimetry surveys of post-storm beach conditions will be collected for comparison with earlier data as soon as weather allows. The comparisons will show the nature, magnitude, and spatial variability of coastal changes such as beach erosion, overwash deposition, and island breaching. These data will also be used to further refine predictive models of coastal impacts from severe storms. The data will be made available to local, state, and federal agencies for purposes of disaster recovery and erosion mitigation. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_Report_MF-2332_1.0", + "title": "Geologic map and map database of the Palo Alto 30' X 60' quadrangle, California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-123, 36.934082, -121.99254, 37.504234", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554105-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554105-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Report_MF-2332_1.0", + "description": "This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:100,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (pamf.ps, pamf.pdf, pamf.txt), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:62,500 or smaller. The attached text file mf2332.rev contains current revision numbers for all parts of this product. This report consists of a set of geologic map database files (Arc/ Info coverages) and supporting text and plotfiles. In addition, the report includes two sets of plotfiles (PostScript and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described in the explanatory pamphlets (pamf.ps, pamf.pdf, pamf.txt). The base map layer used in the preparation of the geologic map plotfiles was scanned from a scale-stable version of the USGS 1:100,000 topographic map at a resolution of 300 dpi as a monochrome TIFF image. The raster data was converted to a GRID in Arc/Info, and combined with geologic polygon data to produce the final map image. The base map coverages included with the database publication are vectorized versions of scans of scale-stable seperates. These coverages contain no database information other than position, and are included for reference only. In both cases the map digitized was the Palo Alto (1982 version) 1: 100,000 quadrangle, which has a 50-meter contour interval.", + "license": "proprietary" + }, { "id": "USGS_SESC_CrayfishStatus", "title": "American Fisheries Society Crayfish of the United States and Canada", @@ -183611,6 +190033,19 @@ "description": "About: This website presents the 2007 American Fisheries Society Endangered Species Committee list of freshwater crayfishes of United States and Canada. The committee evaluated the conservation status and determined the major threats impacting these taxa. Summary: This is the second compilation of crayfishes of the United States and Canada prepared by the American Fisheries Society's Endangered Species Committee. Since the last revision in 1996, the number of crayfish taxa in need of conservation attention changed little. This list includes 363 taxa representing 12 genera and 2 families. Approximately 48% of species or subspecies in the study area are imperiled. There are 54 vulnerable, 52 threatened, and 66 endangered extant taxa; 2 taxa are possibly extinct. For some crayfishes, limited natural range (e.g., one locality or one drainage system) precipitates recognition as Endangered or Threatened; but for many others, status assignments continue to be hampered by a paucity of recent distributional information. While progress has been made in this arena, basic ecological and current distributional information are lacking for 60% of the U.S. and Canadian crayfish fauna. Threats highlighted in Taylor et al. (1996) such as habitat loss and introduction of nonindigenous crayfishes continue to persist and are greatly magnified by the limited distribution of many species. Recognition of the potential for rapid decimation of crayfish species, especially those with limited ranges, should provide impetus for proactive efforts toward conservation. Maps: Each taxon on the list was assigned to one or more states or provinces that circumscribe its native distribution. Mapped distributions indicate where taxa naturally occur or occurred in the past. States or provinces with parentheses in text and tables are locations where taxa are known or suspected to be introduced. A variety of sources were used to obtain distributional information, most notably Taylor et al. (1996) and multiple state-specific literature and websites. ", "license": "proprietary" }, + { + "id": "USGS_SESC_ExtinctFish", + "title": "Extinct North American Freshwater Fishes", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553139-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553139-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SESC_ExtinctFish", + "description": "Extinction is a natural process in nature and is the opposite of speciation\u2014the evolution of new life forms. Importantly, 90%\u201396% of all species that became extinct over geological time disappeared during the normal give and take of speciation and extinction1. There is widespread evidence that modern rates of extinction in many plants and animals significantly exceed background rates in the fossil record. From 1900 to 2010, 57 species and subspecies of North American freshwater fishes became extinct, and since 1898, three distinct populations of valued fishes were extirpated from the continent2. Intuitively, this number of extinctions seems unnaturally high. Since the first tally of extinct North American fishes in 19893, the number of extinct fishes increased by 25%2,4. From the end of the 19th century to the present, modern extinctions varied by decade but significantly increased after 1950. The post-1950s increase in extinction rates likely corresponds to substantial economic, demographic, and land-use changes that occurred in North America after WWII2. A meaningful way to evaluate modern extinctions is to compare modern rates of extinction to background rates using data from the fossil record. The mean background extinction rate (from origination to extinction) for freshwater fish species is estimated to be one extinction/3 million years. The modern extinction rate in North American freshwater fishes is conservatively estimated to be 877 times greater than the background rate\u2014for the interval 1900 to 2010. Calculation of modern to background extinction rate (M:BER) is similar to extinctions per million species years (E/MSY) but differs in that actual background extinction rates are used in lieu of one extinction/million years.) M:BER ratios fluctuate by year because total North American fishes increases each year due to descriptions of new species and because extinctions are intermittent (the last one occurred in 2006). The M:BER value for North American freshwater fishes in 2012=863 and will continue to decline annually until the next extinction occurs. During the 20th century, freshwater fishes had the highest extinction rate among all vertebrates worldwide. Low numbers of fish extinctions documented from other continents suggests that extinctions are under-reported in Africa, Eurasia, and South America at this time. It is estimated that future extinctions in North America will increase from 39 currently extinct fish species to between 53 and 86 species by 2050. ", + "license": "proprietary" + }, { "id": "USGS_SESC_ImperiledFish", "title": "American Fisheries Society Imperiled Freshwater and Diadromous Fishes of North America", @@ -183624,6 +190059,19 @@ "description": "About: This website presents the 2008 American Fisheries Society Endangered Species Committee list of imperiled North American freshwater and diadromous fishes. The committee considered continental fishes native to Canada, Mexico, and the United States, evaluated their conservation status and determined the major threats impacting these taxa. We use the terms taxon (singular) or taxa (plural) to include named species, named subspecies, undescribed forms, and distinct populations as characterized by unique morphological, genetic, ecological, or other attributes warranting taxonomic recognition. Undescribed taxa are included, based on the above diagnostic criteria in combination with known geographic distributions and documentation deemed of scientific merit, as evidenced from publication in peer-reviewed literature, conference abstracts, unpublished theses or dissertations, or information provided by recognized taxonomic experts. Although we did not independently evaluate the taxonomic validity of undescribed taxa, the committee adopted a conservative approach to recognize them on the basis of prevailing evidence which suggests that these forms are sufficiently distinct to warrant conservation and management actions. Summary: This is the third compilation of imperiled (i.e., endangered, threatened, vulnerable) plus extinct freshwater and diadromous fishes of North America prepared by the American Fisheries Society's Endangered Species Committee. Since the last revision in 1989, imperilment of inland fishes has increased substantially. This list includes 700 extant taxa representing 133 genera and 36 families, a 92% increase over the 364 listed in 1989. The increase reflects the addition of distinct populations, previously non-imperiled fishes, and recently described or discovered taxa. Approximately 39% of described fish species of the continent are imperiled. There are 230 vulnerable, 190 threatened, and 280 endangered extant taxa; 61 taxa are presumed extinct or extirpated from nature. Of those that were imperiled in 1989, most (89%) are the same or worse in conservation status; only 6% have improved in status, and 5% were delisted for various reasons. Habitat degradation and nonindigenous species are the main threats to at-risk fishes, many of which are restricted to small ranges. Documenting the diversity and status of rare fishes is a critical step in identifying and implementing appropriate actions necessary for their protection and management. Maps: In collaboration with the World Wildlife Fund, the committee developed a map of freshwater ecoregions that combines spatial and faunistic information derived from Maxwell and others (1995), Abell and others (2000; 2008), U.S. Geological Survey Hydrologic Unit Code maps (Watermolen 2002), Atlas of Canada (2003), and Commission for Environmental Cooperation (2007). Eighty ecoregions were identified based on physiography and faunal assemblages of the Atlantic, Arctic, and Pacific basins. Each taxon on the list was assigned to one or more ecoregions that circumscribes its native distribution. A variety of sources were used to obtain distributional information, most notably Lee and others (1980), Hocutt and Wiley (1986), Page and Burr (1991), Behnke (2002), Miller and others (2005), numerous state and provincial fish books for the United States and Canada, and the primary literature, including original taxonomic descriptions. Taxa were also associated with the states or provinces where they naturally occur or occurred in the past.", "license": "proprietary" }, + { + "id": "USGS_SESC_ImperiledFreshwaterOrganisms", + "title": "Imperiled Freshwater Organisms of North America", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549663-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549663-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SESC_ImperiledFreshwaterOrganisms", + "description": "This website provides access to maps and lists of imperiled freshwater organisms of North America as determined by the American Fisheries Society (AFS) Endangered Species Committee (ESC). At this website, one can view lists of animals by freshwater ecoregion, by state or province boundary, and plot distributions of these same creatures by ecoregions or political boundaries. Both the AFS and U.S. Geological Survey (USGS) have a long standing commitment to the advancement of aquatic sciences and sharing that information with the public. Since 1972, the ESC has been tracking the status of imperiled fishes and aquatic invertebrates in North America. Recently, the fish (2008) and crayfish (2007) subcommittees provided revised status lists of at-risk taxa, and the mussel and snail subcommittees are in the process of completing similar revisions. Historically, the revised AFS lists of imperiled fauna have been published in Fisheries. With rapid advances in technology and information transfer, there is a growing need to provide to stakeholders immediate and dynamic data on imperiled resources. The USGS is a leader in aquatic resource research that effectively disseminates results from those studies to the public through print and internet media. A Memorandum Of Understanding formally establishes an agreement between the AFS and USGS to create this website that will serve as a conduit for information exchange about imperiled aquatic organisms of North America.", + "license": "proprietary" + }, { "id": "USGS_SESC_SnailStatus", "title": "American Fisheries Society List of Freshwater Snails from Canada and the United States", @@ -183676,6 +190124,19 @@ "description": "The objectives of this study are to develop and continue a program of surface water flow monitoring across I-75 and SR 29 in the I-75 corridor from Snake Road west to SR 29 and SR 29 from I-75 south to USGS site 02291000 Barron River near Everglades, Florida. Quarterly discharge measurements will be made along both reaches to assess hydrologic flow patterns and evaluate the feasibility of creating a stage-discharge/index-velocity relationship for this area. Data collected in this project will provide baseline information about a major current barrier to sheetflow, I-75. The data are expected to support the research on the existing linkages among geologic, hydrologic, chemical, climatological, and biological processes that currently shape the Everglades and will provide insight into the predrainage Everglades. The baseline flow will contribute to the Southwest Florida Feasibility Study addressing the health of upland and aquatic ecosystems in the 4,300 square mile area.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_75_29_hydro_data", + "title": "Hydrologic Data Collected along I-75/SR29 corridor in Big Cypress National Preserve", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2005-11-01", + "end_date": "2009-09-30", + "bbox": "-81.325, 25.75, -80.75, 26.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549847-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549847-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_75_29_hydro_data", + "description": "The location of each site is shown on a Google Map. Data are available as a Google Map with links to Station Information and Data for each site. Data are available for 58 sites along I-75 and for 28 sites along State Road 29 in Big Cypress National Preserve. Data collected in this project will provide baseline information about a major current barrier to sheetflow, I-75. The data are expected to support the research on the existing linkages among geologic, hydrologic, chemical, climatological, and biological processes that currently shape the Everglades and will provide insight into the predrainage Everglades. The baseline flow will contribute to the Southwest Florida Feasibility Study addressing the health of upland and aquatic ecosystems in the 4,300 square mile area.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_ACME_DB", "title": "Aquatic Cycling of Mercury in the Everglades Project Database", @@ -183728,6 +190189,32 @@ "description": "ABSTRACT: The map is a composite image of spectral bands 3 (630-690 nanometers, red), 4 (775-900 nanometers, near-infrared), and 5 (1,550-1750 nanometers, middle-infrared) and the new panchromatic band (520-900, green to near-infrared) acquired by the Landsat 7 enhanced thematic mapper (ETM) sensor on January 27, 2000.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_Caloos_Franklin_Locks_flow", + "title": "Flow Monitoring Along the Tidal Caloosahatchee River and Tributaries West of Franklin Locks", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2007-01-01", + "end_date": "2011-12-31", + "bbox": "-82.04, 26.4, -81.6, 26.8", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552489-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552489-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Caloos_Franklin_Locks_flow", + "description": "Monitoring stations established thru this project are designed as part of a larger network needed for the Caloosahatchee River and tributaries that should remain in place long-term (~10 years). Data from monitoring stations included in this project will be evaluated during the third year of data collection in order to assess viability and need for changes . The objective of this study is to quantify freshwater flows into the tidal reach of the Caloosahatchee River, west of Franklin Locks.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_Caloosahatchee_water_quality", + "title": "Near-Surface Water-Quality Surveys of the Caloosahatchee River and Downstream Estuaries, Florida, USA", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2011-09-30", + "end_date": "2014-08-19", + "bbox": "-82.25, 26.33, -81.76, 26.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554663-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554663-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Caloosahatchee_water_quality", + "description": "Beginning in September 2011, water-quality surveys were conducted a minimum of six times per year in the tidal Caloosahatchee River and surrounding estuaries. Geo-referenced measurements were made at 5 second intervals during moving boat surveys in order to create high resolution water-quality maps of the study area. Water-quality characteristics measured and recorded include salinity, temperature, dissolved oxygen, pH, and turbidity", + "license": "proprietary" + }, { "id": "USGS_SOFIA_CarbonFlux", "title": "Carbon Flux and Greenhouse Gasses of Restored and Degraded Greater Everglades Wetlands: Flux Tower Measurements of Water, Energy and Carbon Cycling in the Big Cypress National Preserve - USGS_SOFIA_CarbonFlux", @@ -183741,6 +190228,45 @@ "description": "Greenhouse gas emissions, specifically carbon dioxide (CO2), are commonly linked with increasing global temperatures and rising sea-level. Of particular concern are rates of sea-level rise and carbon cycling including CO2 emissions or \"footprints\" of urban areas and the capacity of plant communities to absorb and release CO2. Defining and predicting ecosystem response to regional (e.g., freshwater discharge) and global (e.g., sea level rise) environmental change will require empirical baseline data on the functioning of water, energy and carbon cycles within Greater Everglades ecosystems. However, measurements of carbon and surface-energy cycling are sparse over plant communities within Department of Interior (DOI) managed lands in south Florida. Specifically, the quantity of CO2 absorbed or released annually within subtropical forests and wetlands as well as carbon and energy cycling in response to changes in hydrology, salinity, forest-fires and/or other factors are poorly known. To reduce these uncertainties, eddy-covariance flux stations were constructed by the U.S. Geological Survey and South Florida Water Management District in the Big Cypress National Preserve in 2006. Water, energy and carbon fluxes are empirically measured at these stations. The goals of the project are to (1) quantify key variables of interest to researchers and policy makers such as latent heat flux (the energy equivalent of evapotranspiration), sensible heat flux, incoming solar radiation, net radiation, changes in stored heat energy, albedos, Bowen ratios, net ecosystem production (NEP), gross ecosystem production (GEP), ecosystem respiration; (2) understand variability and linkages within water, energy and carbon-cycles imposed by both natural processes and regional / global stresses; and (3) publish project results in USGS reports and peer-reviewed journal papers. Defining and predicting ecosystem response to regional (e.g., freshwater discharge) and global (e.g., sea level rise) environmental change will require empirical baseline data on the state and functioning of water, energy and carbon cycles within DOI lands. However, measurements of carbon and surface-energy cycling are sparse over plant communities within DOI managed lands in south Florida. This project intends to measure water and surface energy fluxes within the BCNP. We propose to begin carbon cycling measurements in 2012 and 2013, as time and funding permits. Plant communities selected for study included Pine Upland, Marsh, Cypress Swamp, and Dwarf Cypress. These plant communities are spatially extensive within DOI lands and resources", "license": "proprietary" }, + { + "id": "USGS_SOFIA_Ding_Darling_baseline", + "title": "Ding Darling National Wildlife Refuge - Greater Everglades Baseline Information and Response to CERP", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2009-10-01", + "end_date": "2014-09-30", + "bbox": "-82.5, 26.3, -81.6, 27", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549274-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549274-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Ding_Darling_baseline", + "description": "The greater Everglades Restoration program includes a management plan for the C-43 Canal, or Caloosahatchee River. This plan affects the quantity, quality, and timing of freshwater releases at control structure S-79 at Franklin Locks. Freshwater contributions are from Lake Okeechobee, and farming runoff along the canal from Lake Okeechobee to the town of Alva. This study will provide basic information on the effects on the quality of water entering J. N. Ding Darling National Wildlife Refuge as the result of freshwater releases at control structure S-79", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_EDEN_grid_shapefile_v02", + "title": "EDEN Grid Shapefile", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-81.51, 24.7, -79.9, 27.2", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549862-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549862-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_EDEN_grid_shapefile_v02", + "description": "This shapefile serves as a net (fishnet or grid) to be placed over the South Florida study area to allow for sampling within the 400 meter cells (grid cells or polygons). The origin and extent of the Everglades Depth Estimation Network (EDEN) grid were selected to cover not only existing Airborne Height Finder (AHF) data and current regions of interest for Everglades restoration, but to cover a rectangular area that includes all landscape units (USACE, 2004) and conservation areas in place at the time of its development. This will allow for future expansion of analyses throughout the Greater Everglades region should resources allow and scientific or management questions require it. Combined with the chosen extent, the 400m cell resolution produces a grid that is 675 rows and 375 columns.. The shapefile contains the 253125 grid cells described above. Some characteristics of this grid, such as the size of its cells, its origin, the area of Florida it is designed to represent, and individual grid cell identifiers, could not be changed once the grid database was developed. These characteristics were selected to design as robust a grid as possible and to ensure the grid\u2019s long-term utility.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_EDEN_proj", + "title": "Everglades Depth Estimation Network (EDEN)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-01-01", + "end_date": "2008-10-28", + "bbox": "-81.3, 25, -80.16, 26.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550596-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550596-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_EDEN_proj", + "description": "The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring, ground elevation modeling, and water-surface modeling that provides scientists and managers with current (1999-present), on-line water-depth information for the entire freshwater portion of the Greater Everglades. Presented on a 400-square-meter grid spacing, EDEN offers a consistent and documented dataset that can be used by scientists and managers to:1) guide large-scale field operations, 2) integrate hydrologic and ecological responses, and 3) support biological and ecological assessments that measure ecosystem responses to the implementation of the comprehensive Everglades Restoration plan (CERP) from the U.S. Army Corps of Engineers in 1999. Research has shown that relatively high-resolution data are needed to explicitly represent variations in the Everglades topography and vegetation that are important for landscape analysis and modeling. The EDEN project will provide a better representation of water depths if elevation variation within each 400-meter grid cell can be taken into account. The EDEN network provides a framework to integrate data collected by other agencies in a common quality-assured database. In addition to real-time network, collaboration among agencies will provide the EDEN project with valuable historic vegetation and water-depth data. This is the first time these data have been compiled and analyzed as a collective set.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_Eco_hist_db_2008_present_2", "title": "2008 - Present Ecosystem History of South Florida's Estuaries Database version 2", @@ -183754,6 +190280,240 @@ "description": "The 2008 - Present Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), and modern monitoring site survey information (water chemistry, floral and faunal data, etc.). Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - location information. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain basic location information.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_Ever_hydr_FB_dynam", + "title": "Interrelationships of Everglades Hydrology and Florida Bay Dynamics", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1850-01-01", + "end_date": "2004-12-31", + "bbox": "-80.89015, 25.1004, -80.39827, 25.471722", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554284-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554284-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Ever_hydr_FB_dynam", + "description": "This interdisciplinary synthesis project is designed to identify and document the interrelation of Everglades\u2019 hydrology and tidal dynamics of Florida Bay on ecosystem response to past environmental changes, both natural and human imposed. The project focuses on integrating historical, hydrological, and ecological findings of scientific investigations within the Southern Inland and Coastal System (SICS), which encompasses the transition zone between the wetlands of Taylor Slough and C-111 canal and nearshore embayments of Florida Bay. In the ecological component, hindcast simulations of historical flow events are being developed for ecological analyses. The Across Trophic Level System Simulation (ATLSS) ecological modeling team is collaborating with the SICS hydrologic modeling team to develop the necessary hydrologic inputs for refined indicator species models. The interconnected freshwater wetland and coastal marine ecosystems of south Florida have undergone numerous human disturbances, including the introduction of exotic species and the alteration of wetland hydroperiods, landscape characteristics, and drainage patterns through implementation of the extensive canal and road system and the expansion of agricultural activity. In this project, collaborative efforts are focused on documenting the impact of past hydrological and ecological changes along the southern Everglades interface with Florida Bay by reconstructing past hydroperiods and investigating the correlation of human-imposed and natural impacts on hydrological changes with shifts in biotic species. The primary objectives are to identify the historical effects of past management practices, to integrate refined hydrological and ecological modeling efforts at indicator species levels to identify cause-and-effect relationships, and to produce a report that documents findings that link hydrological and ecological changes to management practices, wherever evident.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_Fbbslmap", + "title": "Florida Bay Bottom Salinity Maps", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-11-01", + "end_date": "1996-12-31", + "bbox": "-81.167, 24.83, -80.33, 25.33", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549334-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549334-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Fbbslmap", + "description": "The maps show the bottom salinity for Florida Bay at 5ppt salinity intervals approximately every other month beginning in November 1994 through December 1996. Recent algal blooms and seagrass mortality have raised concerns about the water quality of Florida Bay, particularly its nutrient content (nitrogen and phosphorous), hypersalinity, and turbidity. Water quality is closely tied to sediment transport processes because resuspension of sediments increases turbidity, releases stored nutrients, and facilitates sediment export to the reef tract. The objective of this research is to provide a better understanding of how and when sediments within Florida Bay are resuspended and deposited, to define the spatial distribution of the potential for resuspension, to delineate patterns of potential bathymetric change, and to predict the impacts of storms or seagrass die-off on bathymetry and circulation within the bay. By combining these results with the findings of other research being conducted in Florida Bay, we hope to quantify sediment export from the bay, better define the nutrient input during resuspension events, and assist in modeling circulation and water quality. Results will enable long-term sediment deposition and erosion in various regions of the bay to be integrated with data on the anticipated sea-level rise to predict future water depths and volumes. Results from this project, together with established sediment production rates, will provide the basis for a sediment budget for Florida Bay.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_Fbbtypes", + "title": "Florida Bay Bottom Types map - USGS_SOFIA_Fbbtypes", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "1997-01-31", + "bbox": "-81.25, 24.75, -80.25, 25.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553376-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553376-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Fbbtypes", + "description": "The map shows the bottom types for Florida Bay that resulted from site surveys and boat transects (summer 1996-January 1997) compared with aerial photographs (December 1994-January 1995) and SPOT satellite imagery (1987). The purpose of this map is to describe the bottom types found within Florida Bay for use in 1) assessing bottom friction associated with sediment and benthic communities and 2) providing a very general description for other research needs. For these purposes, two descriptors were considered particularly important: density of seagrass cover and sediment texture. Seagrass estimates are visual estimates of the amount of seagrass cover including both number of plants and leaf length. Therefore, seagrass cover may be greater in areas with long leaves than in areas with short blades, even though the number of shoots may be the same. Seagrass cover is a different measure than density (Zieman et al., 1989 or Durako et al., 1996). It is used here to more accurately reflect hydrodynamic influence than the standing crop of seagrass. The use and definitions of dense, intermediate and sparse seagrass cover are similar to those used by Scoffin (1970). This map and associated descriptions are not meant to assess ecologic communities or detail sedimentological facies. The resolution of the map has been selected in an effort to define broad regions for use in modeling efforts. For these purposes, small-scale changes in bottom type (e.g. small seagrass patches) are not delineated.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_Fbsaldat", + "title": "Florida Bay Salinity Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-11-01", + "end_date": "2001-11-30", + "bbox": "-81.167, 24.83, -80.33, 25.33", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554208-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554208-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Fbsaldat", + "description": "The raw data files contain a point ID, date of collection, salinity values in ppt, and longitude and latitude. For some dates water temperature, time of data collection, and conductivity in millisiemens were recorded. Surface salinity values for Florida Bay are available beginning in November 1994 through November 2001 and bottom salinity values from November 1994 through December 1996. The data are in comma-separated ASCII text files. Recent algal blooms and seagrass mortality have raised concerns about the water quality of Florida Bay, particularly its nutrient content (nitrogen and phosphorous), hypersalinity, and turbidity. Water quality is closely tied to sediment transport processes because resuspension of sediments increases turbidity, releases stored nutrients, and facilitates sediment export to the reef tract. The objective of this research is to provide a better understanding of how and when sediments within Florida Bay are resuspended and deposited, to define the spatial distribution of the potential for resuspension, to delineate patterns of potential bathymetric change, and to predict the impacts of storms or seagrass die-off on bathymetry and circulation within the bay. By combining these results with the findings of other research being conducted in Florida Bay, we hope to quantify sediment export from the bay, better define the nutrient input during resuspension events, and assist in modeling circulation and water quality. Results will enable long-term sediment deposition and erosion in various regions of the bay to be integrated with data on the anticipated sea-level rise to predict future water depths and volumes. Results from this project, together with established sediment production rates, will provide the basis for a sediment budget for Florida Bay.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_FireHydroSoils", + "title": "Fire, Hydrology, and Soils along the Mangrove Ecotone within the Greater Everglades Ecosystem", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2010-01-01", + "end_date": "2013-12-31", + "bbox": "-81.6, 25, -80.1, 27.6", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554228-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554228-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_FireHydroSoils", + "description": "Fire in the south Florida landscape has historically been influential in shaping the ecosystem. The link between hydrology, soil formation, and fire is a critical complex component in the persistence of the biotic components of the Everglades (Smith et al 2003, Beckage 2005). As a result, Everglades National Park has been at the forefront of NPS fire policy development since the inception of the park. It was the first to allow prescribed burns and one of the first to develop a fire management plan (Taylor 1981). The occurrence of invasive exotic plants has confounded the fire regime in Everglades National Park by changing the dynamics of how the vegetation burns. This phenomenon has been observed in mangrove forests especially along ecotones with upland vegetation communities. By examining the association between fire, soil, water and vegetation we can begin to understand the ecology and dynamics of these areas.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_HAED_WCA_Everglades", + "title": "High Accuracy Elevation Data - Water Conservation Areas and Greater Everglades Region", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "2007-12-31", + "bbox": "-81.5, 25.125, -80.125, 26.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550369-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550369-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_HAED_WCA_Everglades", + "description": "The High Accuracy Elevation Data Project collected elevation data (meters) on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters to define the topography in South Florida. The data are referenced to the horizontal datum North American Datum 1983 (NAD 83) and the vertical datum North American Vertical Datum 1988 (NAVD 88). In some areas, the surveying was accomplished using airboats. Because access was a logistical problem with airboats, the USGS developed a helicopter-based instrument known as the Airborne Height Finder (AHF). All subsequent data collection used the AHF. Data were collected from the Loxahatchee National Wildlife Refuge, south through the Water Conservation Areas (1A, 2A, 2B, 3A, and 3B), Big Cypress National Park, the Everglades National Park, to the Florida Bay. The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html . The work was performed for Everglades ecosystem restoration purposes. The data are from regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that are being developed for ecosystem restoration activities. Surveying services were also rendered to provide vertical reference points for numerous water level gauges. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) were collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_HAED_okee", + "title": "High Accuracy Elevation Data - Lake Okeechobee Littoral Zone", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2006-07-24", + "end_date": "2006-10-12", + "bbox": "-81.25, 26.625, -80.625, 27.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552794-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552794-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_HAED_okee", + "description": "The U.S. Geological Survey (USGS) coordinated the acquisition of high accuracy elevation data (meters) for the Lake Okeechobee Littoral Zone collected on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters. The elevations are referenced to the horizontal North American Datum of 1983 (NAD83) and vertical North American Vertical Datum of 1988 (NAVD88). The topographic surveys were performed using differential GPS technology and a USGS developed helicopter-based instrument known as the Airborne Height Finder (AHF). The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html This project performed regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that are being developed for ecosystem restoration activities. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists have determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) are being collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_HAED_truck", + "title": "High Accuracy Elevation Data - Truck", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "-80.625, 25.375, -80.25, 25.625", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548984-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548984-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_HAED_truck", + "description": "The High Accuracy Elevation Data Project collected elevation data (meters) on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters to define the topography in South Florida. The data are referenced to the horizontal datum North American Datum 1983 (NAD 83) and the vertical datum North American Vertical Datum 1988 (NAVD 88). The High Accuracy Elevation Data Project began with a pilot study in FY 1995 to determine if the then state-of-the-art GPS technology could be used to perform a topographic survey that would meet the vertical accuracy requirements of the hydrologic modeling community. The initial testing platform was from a truck and met the accuracy requirements. Data were collected in areas near Homestead, Florida. The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html These data are from topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that were being developed for ecosystem restoration activities. Surveying services were also rendered to provide vertical reference points for numerous water level gauges. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists have determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) were collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_Hg_DOC_fy04", + "title": "Interactions of Mercury with Dissolved Organic Carbon in the Florida Everglades", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-03-01", + "end_date": "2006-12-31", + "bbox": "-80.89114, 25.597273, -80.10298, 26.78571", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551732-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551732-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Hg_DOC_fy04", + "description": "This project is designed to more clearly define the factors that control the occurrence, nature, and reactivity of dissolved organic matter (DOM) in the Florida Everglades, especially with regard to the biological transformation and accumulation of mercury (Hg). The primary objectives of our research are (1) to more clearly define the factors that control the occurrence, nature and reactivity of dissolved organic matter (DOM) in the Florida Everglades, and (2) to quantify the effects of DOM on the transport and reactivity of Hg, especially with regard to the biological transformation and accumulation of mercury (Hg) in the Everglades. To meet these objectives, we have adopted a combined field/ laboratory approach. In conjunction with other research projects our field efforts are designed (1) to characterize DOM at a variety of field locations chosen to provide information about the influences of hydrology, seasonal factors (wetting and drying events) and source materials (e.g. vegetation, periphyton, peat) on the nature and amount of DOM in the system, and (2) to elucidate the roles of DOM in controlling the reactivity and bioavailability of Hg in the Everglades. This research is relevant because of the high natural production of organic carbon in the peat soils and wetlands, the relatively high carbon content of shallow ground water systems in the region, the interactions of organic matter with other chemical species, such as trace metals, divalent cations, mercury, and anthropogenic compounds, the accumulation of organic carbon in corals and carbonate precipitates, and the potential changes in the quality and reactivity of dissolved organic carbon (DOC) resulting from land use and water management practices. Proposed attempts to return the Everglades to more natural flow conditions will result in changes to the current transport of organic matter from the Everglades Agricultural Area and the northern conservation areas to Florida Bay. In addition, the presence of dissolved organic matter is important in the production of drinking water, contributes to pollutant transport, and will influence ASR performance. Finally, interactions of mercury (Hg) with organic matter play important roles in controlling the reactivity, bioavailability and transport of Hg in the Everglades.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_Hi_res_bathy_FB", + "title": "High-Resolution Bathymetry of Florida Bay", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1889-01-01", + "end_date": "1999-12-31", + "bbox": "-81.11667, 24.733334, -80.36667, 25.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552903-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552903-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_Hi_res_bathy_FB", + "description": "The objective of this research was to collect new bathymetry for all of Florida Bay, digitize the historical shoreline and bathymetric data, compare previous data to modern data, and produce maps and digital grids of historical and modern bathymetry. Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay had not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. An accurate, modern bathymetric survey provides a baseline for assessing future sedimentation rates in the Bay, and a foundation for developing a sediment budget. Due to the complexity of the Bay and age of existing data, a current bathymetric grid (digitally derived from the survey) is critical for numerical models. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Application of these models is complicated due to the complex seafloor topography (basin/mudbank morphology) of the Bay. The only complete topography data set of the Bay is 100 years old. Consequently, an accurate, modern seafloor bathymetry map of the Bay is critical for numerical modeling research. A modern bathymetry data set will also permit a comparison to historical data in order to help access sedimentation rates within the Bay.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_IMMAGE", + "title": "IMMAGE -Internet-based Modelling, Mapping, and Analysis for the Greater Everglades", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "2015-12-31", + "bbox": "-81.65, 25, -80, 26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551660-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551660-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_IMMAGE", + "description": "IMMAGE will develop a coupled GIS-enabled web-based decision support (DS) framework to provide interactive model-based scenarios to evaluate the potential impact of sea level rise on water supply, inland flooding, storm surge, and habitat management in South Florida. The DS framework will be developed to allow scientists, local planners and resource managers to evaluate the impact of sea level rise on: 1. salt water intrusion into coastal water well fields, 2. the optimal use of canals to impede the inland movement of saline groundwater, 3. urban flooding, 4. the risk to populated areas and natural habitat from catastrophic storm surge, 5. wetland inundation periods and depths, 6. habitat suitability, 7. magnitude and distribution of future population growth, and 8. the impact of forecasted population growth on water demand and protected areas. The IMMAGE project will address the need to run the model with changing input parameters by developing a framework of online GIS-based interfaces to four selected models, thereby enhancing their usability and making them available to a broader user community.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_L-31NSeep_Pilot", + "title": "L-31N Seepage Management Pilot", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-01-01", + "end_date": "2004-12-31", + "bbox": "-80.5, 25.6, -80.4, 25.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554708-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554708-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_L-31NSeep_Pilot", + "description": "The objectives of this data acquisition project were to provide the on-site geologic expertise while the four coreholes and the deepest well in each cluster are drilled and constructed, and to complete the downhole geophysical logging including video and flow meter logging of these boreholes. The USGS on-site geologist provided technical guidance to the drill crew, described the lithology of the core and unconsolidated sediments, and stored the cores and sediment samples for the duration of the project. USGS staff provided and ran gamma ray, fluid conductivity and temperature, EM-induction, 3-arm caliper, full wave form sonic tools, a heat-pulse flow meter, an OBI-40 Mark III digital optical tool, and the Laval video tool. The goal of the L-31N Seepage Management Pilot Project is to reduce levee seepage that moves from Everglades National Park to the east. As participants in this pilot project, the South Florida Water Management District, the United States Army Corps of Engineers, and the United States Geological Survey worked together to provide a hydrogeologic characterization of the Surficial aquifer underlying the L-31N Levee in Miami-Dade County, Florida. Because there is little detailed hydrogeologic data of the Surficial aquifer (to depth) in this area, the L-31N Seepage Management Project Delivery Team (PDT) installed two clusters of monitor wells and four additional coreholes along the levee to provide the necessary detailed hydrogeologic data to the PDT. The L-31N project was spilt into two seperate projects, a data acquisition project and an interpretive project. The objectives of the data acquisition project was to provide the on-site geologic expertise while the four coreholes and the deepest well in each cluster were drilled and constructed, and to complete the downhole geophysical logging including video and flow meter logging of these boreholes. The objective of the data interpretation project was to provide the PDT with detailed hydrogeologic information in order to understand the movement of water in the Surficial aquifer along the L-31N levee and delineate the lithology and hydrostratigraphy of the rocks and sediments underlying the levee.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_L-31N_wells_data", + "title": "L-31N Lithological and Geophysical Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-01-01", + "end_date": "2004-12-31", + "bbox": "-80.5, 25.6, -80.4, 25.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554608-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554608-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_L-31N_wells_data", + "description": "The objectives of this data acquisition project were to provide the on-site geologic expertise while the four coreholes and the deepest well in each cluster were drilled and constructed, and to complete the downhole geophysical logging including video and flow meter logging of these boreholes. The USGS on-site geologist provided technical guidance to the drill crew, described the lithology of the core and unconsolidated sediments, and stored the cores and sediment samples for the duration of the project. USGS staff provided and ran gamma ray, fluid conductivity and temperature, EM-induction, 3-arm caliper, full wave form sonic tools, a heat-pulse flow meter, an OBI-40 Mark III digital optical tool, and the Laval video tool. The goal of the L-31N Seepage Management Pilot Project is to reduce levee seepage that moves from Everglades National Park to the east. As participants in this pilot project, the South Florida Water Management District, the United States Army Corps of Engineers, and the United States Geological Survey worked together to provide a hydrogeologic characterization of the Surficial aquifer underlying the L-31N Levee in Miami-Dade County, Florida. Because there is little detailed hydrogeologic data of the Surficial aquifer (to depth) in this area, the L-31N Seepage Management Project Delivery Team (PDT) installed two clusters of monitor wells and four additional coreholes along the levee to provide the necessary detailed hydrogeologic data to the PDT. The L-31N project was spilt into two seperate projects, a data acquisition project and an interpretive project. The objectives of the data acquisition project was to provide the on-site geologic expertise while the four coreholes and the deepest well in each cluster were drilled and constructed, and to complete the downhole geophysical logging including video and flow meter logging of these boreholes. The objective of the data interpretation project was to provide the PDT with detailed hydrogeologic information in order to understand the movement of water in the Surficial aquifer along the L-31N levee and delineate the lithology and hydrostratigraphy of the rocks and sediments underlying the levee.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_LOX_NWR_data", + "title": "Loxahatchee National Wildlife Refuge Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1974-06-01", + "end_date": "2002-10-31", + "bbox": "-81.55, 25.11, -80.125, 26.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552557-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552557-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_LOX_NWR_data", + "description": "The Loxahatchee National Wildlife Refuge (LOX) is a water-dominated ecosystem that is susceptible to water-quality impacts. A comprehensive analysis of historical water-quality and ancillary data is needed to direct the restoration of the Everglades and the adoption of water-quality standards in LOX because of its designation as Outstanding Florida Waters. Loxahatchee National Wildlife Refuge (LOX) maintains a separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout the units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data, and dependence on surface water depth and season. Collection and analysis of water-quality samples at LOX was done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. Water-quality data have been collected at 14 internal marsh sites in LOX by the U.S. Fish and Wildlife Service for over 10 years. These samples have been analyzed by SFWMD laboratory. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. The study area was extended into LOX in 2003.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_LinkingLandAirManagement", + "title": "Linking Land, Air, and Water Management in the Southern Everglades and Coastal Zone to Water Quality and Ecosystem Restoration", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-01-01", + "end_date": "2009-12-31", + "bbox": "-82, 24.4, -80, 28", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411684-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411684-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_LinkingLandAirManagement", + "description": "The approaches used will be extensions of previous efforts by the lead investigators, whereby we will enhance our abilities to address land management and ecosystem restoration questions. Major changes implemented in this project will include the use of environmental chambers (controlled enclosures or mesocosums) and isotopic tracers to provide a more definitive means addressing specific management questions, such as \"What reductions in toxicity (methylation and bioaccumulation) would be realized if atmospheric mercury emissions were reduced by 75%?\" or, \"Over what time scales could we expect to see improvements to the ecosystem if nutrient and sulfur loading were reduced by implementation of agricultural best management practices and the storm water treatment areas (STA)?\" Results of these geochemical investigations will provide critical elements for building ecosystem models and screening-level risk assessment for contaminants in the ecosystem", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_LinkingLandAirManagement_Task1", + "title": "Linking Land, Air, and Water Management in the Southern Everglades and Coastal Zone to Water Quality and Ecosystem restoration: Task 1, Mercury Cycling, Fate and Bioaccumulation", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2007-12-31", + "bbox": "-81.33137, 24.67165, -80.22201, 25.890877", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548635-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548635-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_LinkingLandAirManagement_Task1", + "description": "This proposal identifies work elements that are logical extensions, and which build off, our previous work. Our overall scientific objective is to provide a complete understanding of the external factors (such as atmospheric mercury and sulfate runoff loads) and internal factors (such as hydroperiod maintenance and water chemistry) that result in the formation and bioaccumulation of MeHg in south Florida ecosystems, and to conduct this research is such a way that it will be directly useable by land and water resource managers. More specifically, we will seek to achieve the following subobjectives (1) Extend our mesocosms studies to provide a more omprehensive examination of the newly discovered 'new versus old' mercury effect by conducting studies under differing hydrologic conditions and sub-ecosystem settings so that our experimental results will be more generally applicable to the greater south Florida ecosystem including the STA\u2019s that have been recently constructed and are yielding very high levels of methylmercury but the cause is currently unknown; (2) Seek to further identify the mechanisms that result in extremely high levels of MeHg after natural drying and rewetting cycles in the Everglades and which have major implications for the Restoration Plan; (3) Further our studies on the production of methylmercury in south Florida estuaries and tidal marshes by conducting mass-balance studies of tidal marshes; (4) Begin to partner with wildlife toxicologists funded by the State of Florida to unravel the complexities surrounding methylmercury exposure and effects to higher order wildlife in south Florida; and , (5) Continue to participate with mercury ecosystem modelers who are funded by the State of Florida and the USEPA to evaluate the overall ecological effects of reducing mercury emissions and the risks associated with methylmercury exposure.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_LinkingLandAirManagement_Task2", + "title": "Linking Land, Air and Water Management in the Southern Everglades and Coastal Zone to Water Quality and Ecosystem Restoration: Task 2, Sulfur and Nutrient Contamination, Biogeochemical Cycling, and Effects", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2007-09-30", + "bbox": "-82, 24.4, -80.1, 28", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549234-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549234-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_LinkingLandAirManagement_Task2", + "description": "The scientific focus of this project is to examine the complex interactions (synergistic and antagonistic) of contaminants (externally derived nutrients, mercury, sulfur, pesticides, herbicides, polycyclic aromatic and aliphatic hydrocarbons, and other metals), ecosystem responses to variations in contaminant loading (time and space dimensions), and how imminent ecosystem restoration steps may affect existing contaminant pools. The major objectives of this project are to use an integrated biogeochemical approach to examine: (1) anthropogenic-induced changes in the water chemistry of the Everglades ecosystem, (2) biogeochemical processes within the ecosystem affecting water chemistry, and (3) the predicted impacts of restoration efforts on water chemistry. The project uses a combination of field investigations, experimental approaches (mesocosm experiments in the ecosystem, and controlled laboratory experiments), and modeling to achieve these objectives. Contaminants of concern will include nutrients, sulfur, mercury, organic compounds, and other metals. Protocols for the collection of samples and chemical analyses developed during earlier studies will be employed in these efforts. Integration of the individual tasks within the project is achieved by colocation of field sampling sites, and cooperative planning and execution of laboratory and mesocosm experiments. Data available for this project include dissolved sulfate and solid sulfur geochemistry and surface and pore water chemistry.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_LinkingLandAirManagement_Task3", + "title": "Linking Land, Air and Water Management in the Southern Everglades and Coastal Zone to Water Quality and Ecosystem Restoration: Task 3, Natural Organic Matter-Mercury Interactions", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2006-01-01", + "end_date": "2010-12-31", + "bbox": "-81.5, 25, -80, 27", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549144-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549144-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_LinkingLandAirManagement_Task3", + "description": "This Task (Task 3 of the overall study) focuses on the factors that control the occurrence, nature and reactivity of dissolved organic matter (DOM) in the Florida Everglades, especially with regard to the biological transformation and accumulation of mercury (Hg). Our goal is to provide fundamental information on the nature and reactivity of DOM in the Everglades and to elucidate the mechanisms and pathways by which the DOM influences the chemistry of Hg throughout the system.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_Mangrove_Sawfish", "title": "Characterizing Past and Present Mangrove Shorelines to Aid Conservation of the Smalltooth Sawfish, Pristis pectinata, Along the Southwest Coast of Florida", @@ -183767,6 +190527,19 @@ "description": "This pilot project has several related goals concerning a specific type of habitat thought to be important for juvenile sawfish habitat: mangrove shorelines. First, we will delineate and classify historic mangrove shorelines. Second, we will map and classify current mangrove shorelines. Third, we will determine amounts of shoreline change. Lastly, we will conduct an analysis to compare sawfish sightings and / or captures with the type of shoreline where those sightings-captures occurred. This will allow us to answer the question: Are juvenile sawfish selecting for a specific type of mangrove shoreline, and if so, what type of mangrove shoreline is it?", "license": "proprietary" }, + { + "id": "USGS_SOFIA_MeHg_degrad_rates", + "title": "Methylmercury Degradation Rates", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-06-01", + "end_date": "1998-06-01", + "bbox": "-81, 25, -80, 26.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551413-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551413-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_MeHg_degrad_rates", + "description": "The spreadsheet contains the data for 12 sites for sediment methylmercury degradation potential rate measurements. High concentrations of methyl-mercury (CH3Hg+), a toxic substance to both animals and humans, recently have been measured in a number of top predators (including panthers and game fish) native to the Florida Everglades. The objective of this research was to provide ecosystem managers with CH3Hg+ degradation rate data from a number of study sites that represent a diversity of hydrologic and nutrient regimes common to the Everglades. The focus was on better understanding the microbial and geochemical controls regulating CH3Hg+ degradation. At the time of the study, little was known about the specific factors influencing this process in natural systems.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_SF_CIR_DOQs", "title": "Color Infrared Digital Orthophoto Quadrangles for the South Florida Ecosystem Area", @@ -183897,6 +190670,19 @@ "description": "The objectives of this data acquisition project were to complete the downhole geophysical logging including video and flowmeter logging of two core holes (9A and 11A), which are the deepest wells at monitor well sites 0009AB and 0011AB. The goal of the Comprehensive Everglades Restoration Plan Biscayne Bay Coastal Wetlands Project (BBCWP) is to rehydrate wetlands and reduce point-source discharge to Biscayne Bay. The BBCWP will replace lost overland flow and partially compensate for the reduction in ground-water seepage by redistributing, through a spreader system, available surface water entering the area from regional canals. The proposed redistribution of freshwater flow across a broad front is expected to restore or enhance freshwater wetlands, tidal wetlands, and near shore bay habitat. A critical component of the BBCWP is the development of a realistic representation of ground-water flow within the karst Biscayne aquifer. Mapping these ground-water flow units is key to the development of models that simulate ground-water flow from the Everglades and urban areas through the coastal wetlands to Biscayne Bay. Because there is little detailed hydrogeologic data of the Surficial aquifer (to depth) in this area, the Biscayne Bay Coastal Wetlands Project Delivery Team installed two monitor-well sites and collected the necessary detailed hydrogeologic data. The L-31 North Canal Seepage Management Pilot Project is intended to curtail easterly seepage emanating from within Everglades National Park (ENP). The pilot project is examining various seepage management technologies as well as operational changes that could be implemented to reduce the water losses from ENP. This project is in close proximity to Biscayne Bay so an effort has been made to combine ongoing work efforts at the two project areas. The distribution of seepage into the L-31 North Canal and beneath it is not known with any degree of certainty today. A canal draw down experiment was conducted to provide additional field data that will be utilized to refine seepage estimates in the study area as well as determine aquifer parameters in the study area. This project was funded by the USGS Florida Integrated Science Center and the South Florida Water Management District (SFWMD).", "license": "proprietary" }, + { + "id": "USGS_SOFIA_bcunits_pts_point", + "title": "Hydrogeologic unit depth information sites in Broward County and northern Dade County, WRIR 92-4061 figure 3.4.3-1", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1987-01-01", + "end_date": "2002-12-31", + "bbox": "-80.87278, 25.84111, -80.07235, 26.240347", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554686-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554686-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_bcunits_pts_point", + "description": "Hydrogeologic unit depths at 321 selected points, determined from published cross sections and contour maps, were entered into a point data layer. Generalized land-surface elevations were also entered for each point. Geographic information systems (GIS) have become an important tool in assessing and planning for the protection of natural resources. Most Federal and State natural resource agencies and many County environmental agencies in Florida are currently using GIS to assist in mathematical modeling, resource mapping, and risk assessments. The U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District (SFWMD) and the Broward County Office of Natural Resource Protection (BCONRP), developed a digital spatial data base for Broward County consisting of layers of data that can be used in water-resources investigations. These data layers include manmade features such as municipal boundaries and roads, topographic features, hydrologic features such as canals and lakes, and hydrogeologic features such as aquifer thickness. Computer programs were written for use in developing additional layers of data from existing data bases such as the Florida Department of Environmental Regulation (FDER) underground storage tank data base. This report describes the digital spatial data base that was developed and the five computer programs that can be used to create additional data layers from existing data files or to document existing layers. Most of the data layers cover Broward County east of the conservation areas. Some data layers cover all of Broward and may include parts of Miami-Dade County.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_bicy_fish_inventory", "title": "Big Cypress National Preserve Fish Inventory and Monitoring Data", @@ -183923,6 +190709,58 @@ "description": "The approximate western and northern limits of the Biscayne aquifer are shown in this map. The limit is drawn where the thickness of very highly permeable limestone or calcareous sandstone is estimated to decrease to less than 10 feet. The sediments in the excluded area are predominantly muddy sands and shell or limestone that are generally not highly permeable. Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_brwd_config_base_biscayne_arc", + "title": "Generalized Configuration of the Base of the Biscayne Aquifer in Broward County, USGS WRIR 87-4034, figure 37", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1939-01-01", + "end_date": "1984-12-31", + "bbox": "-80.73605, 25.953321, -80.094734, 26.334244", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550736-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550736-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_brwd_config_base_biscayne_arc", + "description": "The base of the Biscayne aquifer are shown in this map. The base is drawn on the bottom of highly permeable limestone or sandstone in the Tamiami Formation that is virtually contiguous with overlying rocks of very high permeability in the Fort Thompson Formation, Anastasia Formation, or Tamiami Formation. In general, the Biscayne aquifer is shallow, and the base deepens gradually in west and central Broward county. However, the aquifer thickens, and the base deepens very rapidly in the coastal area to more than 300 feet below sea level. Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_brwd_config_base_surficial_arc", + "title": "Generalized Configuration of the Base of the Surficial Aquifer System in Broward County, USGS WRIR 87-4034, figure 35", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1939-01-01", + "end_date": "1984-12-31", + "bbox": "-80.86949, 25.952961, -80.103165, 26.336115", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549571-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549571-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_brwd_config_base_surficial_arc", + "description": "This map shows the altitude of the base of the surficial aquifer system below sea level. In addition to the test holes drilled in this study, eight others from Parker and others (1955) or from the U.S. Geological Survey files were used to select the base. The contour interval is 20 feet. Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_brwd_glime_altbase_arc", + "title": "Generalized Configuration of the Base of the Gray Limestone Aquifer in Broward County, USGS WRIR 87-4034, figure 36", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1939-01-01", + "end_date": "1984-12-31", + "bbox": "-80.89983, 25.975756, -80.35531, 26.336508", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555340-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555340-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_brwd_glime_altbase_arc", + "description": "This map contains contours of the base of the highly permeable gray limestone aquifer in the Tamiami Formation. The contour interval is 10 feet. Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_brwd_glime_alttop_arc", + "title": "Generalized Configuration of the Top of the Gray Limestone Aquifer in Broward County, USGS WRIR 87-4034, figure 36", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1939-01-01", + "end_date": "1984-12-31", + "bbox": "-80.883705, 25.956867, -80.351875, 26.336231", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548959-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548959-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_brwd_glime_alttop_arc", + "description": "This is map contains contours of the top of the highly permeable gray limestone aquifer in the Tamiami Formation. The contour interval is 10 feet. Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_ccsoil", "title": "Collier County FL soil map", @@ -184079,6 +190917,58 @@ "description": "Contours of the elevation of the top of the highly permeable gray limestone aquifer in the Tamiami Formation are shown in this map. The aquifer, as mapped, includes all intervals of the gray limestone that are at least 10 ft. thick and have an estimated hydraulic conductivity of at least 100ft/d. Also included are highly permeable beds of coarse, shelly sands (sometimes with sandstone) that are contiguous with limestone above or below or are likely to connect laterally with the limestone. The contour interval is 10 feet. Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_dawmet", + "title": "Ecosystem History: Terrestrial and Fresh-Water Ecosystems of southern Florida", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-01-01", + "end_date": "2007-12-31", + "bbox": "-81.83, 25, -80.3, 26.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553647-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553647-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_dawmet", + "description": "Analysis of 209 pollen assemblages from surface samples in ten vegetation types in the Florida Everglades form the basis to identify wetland sub-environments from the pollen record. This calibration dataset makes it possible to infer past trends in hydrology and disturbance regime based on pollen assemblages preserved in sediment cores. Pollen assemblages from sediment cores collected in different vegetation types throughout the Everglades provide evidence on wetland response to natural fluctuations in climate as well as impacts of human alteration of Everglades hydrology. Sediment cores were located primarily in sawgrass marshes, cattail marshes, tree islands, sawgrass ridges, sloughs, marl prairies, and mangroves. The datasets contain raw data on pollen abundance as well as pollen concentration (pollen grains per gram dry sediment). This project is designed to document the terrestrial ecosystem history of south Florida and is collaborating with other projects at the USGS and other agencies on Florida Bay, Biscayne Bay, and the Buttonwood Embankment. The specific goals of the project are 1) document the patterns of floral and faunal change at sites throughout southern Florida over the last 150 years; 2) determine whether changes occurred throughout the entire region or whether they were localized; 3) examine the floral and faunal history of the region over the last few millennia; 4) determine the baseline level of variability in the communities prior to significant human activity in the region; and 5) determine whether the fire frequency, extent, and influence can be quantified, and if so, document the fire history for sites in the region. Data generated from this project will be integrated with data from other projects to provide biotic reconstructions for the area at selected time slices and will be useful in testing ecological models designed to predict floral and faunal response to changes in environmental parameters.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_discharge_tamiami_canal", + "title": "Discharge Data (Tamiami Canal)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1986-01-01", + "end_date": "2001-12-31", + "bbox": "-81.5, 25.75, -80.5, 26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553115-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553115-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_discharge_tamiami_canal", + "description": "The data are from the following four stations: Station 02288800 - Tamiami Canal Outlets, Monroe to Carnestown; Station 02288900 - Tamiami Canal Outlets, 40-Mile Bend to Monroe, near Miami, FL; Station 02289040 - Tamiami Canal Outlets, Levee 67A to 40-Mile Bend, near Miami, FL; Station 02289060 - Tamiami Canal Outlets, Levee 30 to Levee 67A, near Miami, FL. The data were compiled from records from 1986 to 1999 in the USGS Ft. Lauderdale, FL office of the Water Resources Discipline in 2000. Each station has numerous individual flow measurements at gages that were used in the calculation of the mean flow for each station. The data were collected by USGS personnel and the gages are maintained and operated by USGS Ft. Lauderdale office personnel. Canals are a major water-delivery component of the south Florida ecosystem. They interact with surrounding flow systems and waterbodies, either directly through structure discharges and levee overflows or indirectly through levee seepage and leakage, and thereby quantitatively affect wetland hydroperiods as well as estuarine salinities. Knowledge of this flow interaction, as well as timing, extent, and duration of inundation that it contributes to, is needed to identify and eliminate any potential adverse effects of altered flow conditions and transported constituents on vegetation and biota. Comprehensive analytical tools and methods are needed to assess the effects of nutrient and contaminant loads from agricultural and urban run-off entering canals and thereby conveyed into connected wetlands and other adjoining coastal ecosystems. These data from the individual gages were transferred to electronic form to provide a better understanding of the distribution of flow from north to south under the Tamiami Trail to aid in decisions about future changes to flow along the Trail.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_dk_merc_cycl_bio", + "title": "Mercury Cycling and Bioaccumulation", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-10-01", + "end_date": "2006-12-31", + "bbox": "-81.33137, 24.67165, -80.22201, 25.890877", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550667-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550667-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_dk_merc_cycl_bio", + "description": "This proposal identifies work elements that are logical extensions, and which build off, our previous work. Our overall scientific objective is to provide a complete understanding of the external factors (such as atmospheric mercury and sulfate runoff loads) and internal factors (such as hydroperiod maintenance and water chemistry) that result in the formation and bioaccumulation of MeHg in south Florida ecosystems, and to conduct this research is such a way that it will be directly useable by land and water resource managers. More specifically, we will seek to achieve the following subobjectives (1) Extend our mesocosms studies to provide a more omprehensive examination of the newly discovered 'new versus old' mercury effect by conducting studies under differing hydrologic conditions and sub-ecosystem settings so that our experimental results will be more generally applicable to the greater south Florida ecosystem including the STA\u0092s that have been recently constructed and are yielding very high levels of methylmercury but the cause is currently unknown; (2) Seek to further identify the mechanisms that result in extremely high levels of MeHg after natural drying and rewetting cycles in the Everglades and which have major implications for the Restoration Plan; (3) Further our studies on the production of methylmercury in south Florida estuaries and tidal marshes by conducting mass-balance studies of tidal marshes; (4) Begin to partner with wildlife toxicologists funded by the State of Florida to unravel the complexities surrounding methylmercury exposure and effects to higher order wildlife in south Florida; and , (5) Continue to participate with mercury ecosystem modelers who are funded by the State of Florida and the USEPA to evaluate the overall ecological effects of reducing mercury emissions and the risks associated with methylmercury exposure. Although ecological impacts from phosphorous contamination have become synonymous with water quality in south Florida, especially for Everglades restoration, there are several other contaminants presently entering the Everglades that may be of equal or greater impact, including: pesticides, herbicides, polycyclic aromatic hydrocarbons, and trace metals. This project focuses on mercury, a sparingly soluble trace metal that is principally derived from atmospheric sources and affects the entire south Florida ecosystem. Mercury interacts with another south Florida contaminant, sulfur, that is derived from agricultural runoff, and results in a problem with potentially serious toxicological impacts for all the aquatic food webs (marine and freshwater) in the south Florida ecosystem. The scientific focus of this project is to examine the complex interactions of these contaminants (synergistic and antagonistic), ecosystem responses to variations in contaminant loading (time and space dimensions), and how imminent ecosystem restoration steps may affect existing contaminant pools. The Everglades restoration program is prescribing ecosystem-wide changes to some of the physical, hydrological and chemical components of this ecosystem. However, it remains uncertain what overall effects will occur as these components react to the perturbations (especially the biological and chemical components) and toward what type of 'new ecosystem' the Everglades will evolve. The approaches used by this study have been purposefully chosen to yield results that should be directly useable by land management and restoration decision makers. Presently, we are addressing several major questions surrounding the mercury research field, and the Everglades Restoration program: (l) What, if any, ecological benefit to the Everglades would be realized if mercury emissions reductions would be enacted, and over what time scales (years or tens of years) would improvements be realized? (2) What is the role of old mercury (previously deposited and residing in soils and sediment) versus new mercury (recent deposition) in fueling the mercury problem? (3) In the present condition, is controlling sulfur or mercury inputs more important for reducing the mercury problem in the Everglades? (4) Does sulfur loading have any additional ecological impacts that have not been realized previously (e.g., toxicity to plant and animals) that may be contributing to an overall decreased ecological health? (5) Commercial fisheries in the Florida Bay are contaminated with mercury, is this mercury derived from Everglades runoff or atmospheric runoff? (6) What is the precise role of carbon (the third member of the 'methylmercury axis of evil', along with sulfur and mercury), and do we have to be concerned with high levels of natural carbon mobilization from agricultural runoff as well? (7) Hundreds of millions of dollars are being, or have been spent, on STA construction to reduce phosphorus loading to the Everglades, however, recently constructed STAs have yielded the highest known concentration of toxic methylmercury; can STA operations be altered to reduce methylmercury production and maintain a high level of phosphorus retention over extended periods of time? The centerpiece of our research continues to be the use of environmental chambers (enclosures or mesocosms), inside which we conduct dosing experiments using sulfate, dissolved organic carbon and mercury isotopic tracers. The goal of the mesocosm experiments is to quantify the in situ ecological response to our chemical dosing, and to also determine the ecosystem recovery time to the doses.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_eco_assess_risk_toxics", + "title": "Ecological Risk Assessment of Toxic Substances in the Greater Everglades Ecosystem: Wildlife Effects and Exposure Assessment", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-10-01", + "end_date": "2004-09-30", + "bbox": "-81.125, 25.125, -80.125, 26.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551985-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551985-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eco_assess_risk_toxics", + "description": "This project will be carried out in several locations throughout those areas critical to the South Florida Restoration Initiative. These areas include: 1) Water Conservation Areas 1, 2, and 3 of the Central Everglades, 2) Everglades National Park, 3) Loxahatchee National Wildlife Refuge, 4) Big Cypress National Preserve, 5) multiple Miami Metropolitan area canals and drainages, and 6) restoration related STA\u0092s (STA\u0092s 1-6) adjacent to the Everglades. Specific site selections will be based upon consideration of USACE restoration plans and upon discussions with other place-based and CESI approved projects. The overall objectives are characterize the exposure of wildlife to contaminants within the aquatic ecosystems of South Florida, through a multi-stage process: a) screening of biota to identify hazards/contaminants posing risk, and b) evaluation of the potential effects of those contaminants on appropriate animal/wildlife receptors. This project will focus upon each of these stages/needs, with an emphasis on understanding the effects of contaminants on alligators, fishes, birds, amphibians and macroinvertebrates. Historically, little consideration has been given to environmental chemical stressors/contaminants within the ecosystem restoration efforts for the Greater Everglades Ecosystem. The restoration is primarily guided by determining and restoring the historical relationships between ecosystem function and hydrology. The restoration plan was formulated to restore the natural hydrology and therefore, the resultant landscape patterns, bio-diversity and wildlife abundance. However, additional efforts need to consider the role that chemical contaminants such as pesticides and other inorganic/organic contaminants play in the structure and function of the resultant South Florida ecosystems. Indeed, the current level of agriculture and expanding urbanization and development necessitate that more emphasis be placed on chemical contaminants within this sensitive ecosystem and the current restoration efforts. The primary goal of the proposed project, therefore, is to develop an improved understanding of the exposure/fate (i.e. degradation, metabolism, dissipation, accumulation and transport) and potential ecological effects produced as a result of chemical stressors and their interactions in South Florida freshwater and wetland ecosystems. The overall objectives are to evaluate the risk posed by contaminants to biota within the aquatic ecosystems of South Florida, through a multi-stage process: a) screening of biota to identify hazards/contaminants posing risk and b) evaluation of the potential effects of those contaminants on appropriate animal/wildlife receptors. This project will focus upon each of these stages/needs, with an emphasis on understanding the effects of contaminants on alligators, fishes, birds, amphibians and macroinvertebrates. The specific objectives of this project are to: 1. Assess current exposure and potential adverse effects for appropriate receptors/species within the South Florida ecosystems with some emphasis on DOI trust species. These efforts will determine whether natural populations are significantly exposed to a variety of chemical stressors/contaminants, such as mercury, chlorinated hydrocarbon pesticides, historic and/or current use agricultural chemicals, and/or mixtures, as well as document lethal and non-lethal adverse effects in multiple health, physiologic and/or endocrine endpoints. 2. Assess exposure and potential adverse effects for appropriate species within South Florida as a function of restoration implementation.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_eco_hist_db1995-2007_version 7", "title": "1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7", @@ -184092,6 +190982,110 @@ "description": "The 1995 - 2007 Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), modern monitoring site survey information (water chemistry, floral and faunal data, etc.), and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain either percent abundance data or actual counts of the distribution of mollusks, ostracodes, forams, and pollen within the cores collected in the estuaries. For some cores dinocyst or diatom data may be available.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_eco_hist_db_version 3", + "title": "Ecosystem History of South Florida Estuaries Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-02-24", + "end_date": "2008-03-20", + "bbox": "-81.83, 24.75, -80, 26.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552167-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552167-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eco_hist_db_version%203", + "description": "The Ecosystem History Access Database contains listings of all sites (modern and core), modern monitoring site survey information, and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Scientists over the past few decades have noticed that the South Florida ecosystem has become increasingly stressed. The purposes of the ecosystem history projects (started in 1995) are to determine what south Florida's estuaries have looked like over time, how they have changed, and what is the rate and frequency of change. To accomplish this, shallow sediment cores are collected within the bays, and the faunal and floral remains, sediment geochemistry, and shell biochemistry are analyzed. Modern field data are collected from the same region as the cores and serve as proxies to allow accurate interpretation of past depositional environments. The USGS South Florida Ecosystem History Project is designed to integrate studies from a number of researchers compiling data from terrestrial, marine, and freshwater ecosystems within south Florida. The project is divided into 3 regions: Biscayne Bay and the Southeast coast, Florida Bay and the Southwest coast, and Terrestrial and Freshwater Ecosystems of Southern Florida. The purpose of the projects is to provide information about the ecosystem's recent history based on analyses of paleontology, geochemistry, hydrology, and sedimentology of cores taken from the south Florida region. Data generated from the South Florida Ecosystem History project will be integrated to provide biotic reconstructions for the area at selected time slices and will be useful in testing ecological models designed to predict floral and faunal response to changes in environmental parameters. Biscayne Bay is of interest to scientists because of the rapid urbanization that has occurred in the Miami area and includes Biscayne National Park. Dredging, propeller scars, and changes in freshwater input have altered parts of Biscayne Bay. Currently, the main freshwater input to Biscayne Bay is through the canal system, but many scientists believe subsurface springs used to introduce fresh groundwater into the Bay ecosystem. Study of the modern environment and core sediments from Biscayne Bay will provide important information on past salinity and seagrass coverage which will be useful for predicting future change within the Bay. Plant and animal communities in the South Florida ecosystem have undergone striking changes over the past few decades. In particular, Florida Bay has been plagued by seagrass die-offs, algal blooms, and declining sponge and shellfish populations. These alterations in the ecosystem have traditionally been attributed to human activities and development in the region. Scientists at the U.S. Geological Survey (USGS) are studying the paleoecological changes taking place in Florida Bay in hopes of understanding the physical environment to aid in the restoration process. As in Biscayne Bay, scientists must first determine which changes are part of the natural variation in Florida Bay and which resulted from human activities. To answer this question, scientists are studying both modern samples and piston cores that reveal changes over the past 150-600 years. These two types of data complement each other by providing information about the current state of the Bay, changes that occurred over time, and patterns of change. Terrestrial ecosystems of South Florida have undergone numerous human disturbances, ranging from alteration of the hydroperiod, fire history, and drainage patterns through implementation of the canal system to expansion of the agricultural activity to the introduction of exotic species such as Melalueca, Australian pine, and the Pepper Tree. Over historical time, dramatic changes in the ecosystem have been documented and these changes attributed to various human activities. However, cause-and-effect relationships between specific biotic and environmental changes have not been established scientifically. One part of the South Florida Ecosystem History group of project is designed to document changes in the terrestrial ecosystem quantitatively, to date any changes and determine whether they resulted from documented human activities, and to establish the baseline level of variability in the South Florida ecosystem to estimate whether the observed changes are greater than what would occur naturally. Specific goals of this part of the project are to 1) document the patterns of floral and faunal changes at sites throughout southern Florida over the last 150 years, 2) determine whether the changes occurred throughout the region or whether they were localized, 3) examine the floral and faunal history of the region over the last few millennia, 4) determine the baseline level of variability in the communities prior to significant human activity in the region, and 5) determine whether the fire frequency, extent, and influence can be quantified, and if so, document the fire history for sites in the region.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_eco_hist_swcoast_srs_04", + "title": "Ecosystem History of the Southwest Coast-Shark River Slough Outflow Area", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-10-01", + "end_date": "2008-09-30", + "bbox": "-81.75, 25, -80.83, 26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554376-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554376-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eco_hist_swcoast_srs_04", + "description": "The objectives of this project are to document impacts of changes in salinity, water quality, coastal plant and animal communities and other critical ecosystem parameters on a subdecadal-centennial scale in the southwest coastal region (from Whitewater Bay, north to the 10,000 Islands), and to correlate these changes with natural events and resource management practices. Emphasis will be placed on 1) determining the amount, timing and sources of freshwater influx (groundwater vs. runoff) into the coastal ecosystem prior to and since significant anthropogenic alteration of flow; and 2) determining whether the rate of mangrove and brackish marsh migration inland has increased since 20th century water diversion and what role sealevel rise might play in the migration. First, the environmental preferences and distributions of modern fauna and flora are established through analyses of modern samples in south Florida estuaries and coastal systems. Much of these data have already been obtained through project work conducted in Florida Bay and the terrestrial Everglades starting in 1995. These modern data are used as proxies for interpreting the historical data from Pb-210 and C-14 dated sediment cores based on assemblage analysis. On the basis of USGS data obtained from cores in Florida Bay and Biscayne Bay, the temporal span of the cores should be at a minimum the last 150 years; this is in agreement with University of Miami data showing sedimentation rates in Whitewater Bay to be approximately 1cm/year. For the estuarine/coastal ecosystems, a multidisciplinary, multiproxy approach will be utilized on cores from a transect from Whitewater Bay north to 10,000 Islands. Biochemical analyses of shells and chemical analyses of sediments will be used to refine data on salinity and nutrient supply, and isotopic analyses of shells will determine sources of water influx into the system. Nutrient analyses will be conducted to determine historical patterns of nutrient influx. To examine the inland migration of the mangrove/coastal marsh ecotone, transects from the mouth of the Shark and Harney Rivers inland into Shark River slough will be taken. These cores will be evaluated for floral remains, nutrients, charcoal, and if present, faunal remains. This project will provide 1) baseline data for restoration managers and hydrologic modelers on the amount and sources of freshwater influx into the southwest coastal zone and the quality of the water, 2) the relative position of the coastal marsh-mangrove ecotone at different periods in the past, and 3) data to test probabilities of system response to restoration changes. One of the primary goals of the Central Everglades Restoration Plan (CERP) is to restore the natural flow of water through the terrestrial Everglades and into the coastal zones. Historically, Shark River Slough, which flows through the central portion of the Everglades southwestward, was the primary flow path through the Everglades Ecosystem. However, this flow has been dramatically reduced over the last century as construction of canals, water conservation areas and the Tamiami Trail either retained or diverted flow from Shark River Slough. The reduction in flow and changes in water quality through Shark River have had a profound effect on the freshwater marshes and the associated coastal ecosystems. Additionally, the flow reduction may have shifted the balance of fresh to salt-water inflow along coastal zones, resulting in an acceleration of the rate of inland migration of mangroves into the freshwater marshes. For successful restoration to occur, it is critical to understand how CERP and the natural patterns of freshwater flow, precipitation, and sea level rise will affect the future maintenance of the mangrove-freshwater marsh ecotone and the coastal environment.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_eden_dem_cm_nov07_nc", + "title": "Everglades Depth Estimation Network (EDEN) November 2007 Digital Elevation Model for use with EDENapps", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "2007-12-31", + "bbox": "-81.36353, 25.229605, -80.22176, 26.683613", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551925-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551925-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eden_dem_cm_nov07_nc", + "description": "This is the 1st release of the third version of an Everglades Depth Estimation Network (EDEN) digital elevation model (DEM) generated from certified airborne height finder (AHF) and airboat collected ground surface elevations for the Greater Everglades Region. This version includes all data collected and certified by the USGS prior to the conclusion of the AHF collection process. It differs from the previous elevation model (EDEN_EM_JAN07) in that the modeled area of WCA3N (all the WCA3A area north of I-75) is increased while the modeled area of the Big Cypress National Preserve (BNCP) has been both refined and reduced to the region where standard error of cross-validation points falls below 0.16 meters. EDEN offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to Comprehensive Everglades Restoration Plan. To produce historic and near-real time maps of water depths, the EDEN requires a system-wide DEM of the ground surface. This file is a modification of the eden dem released in October of 2007 (i.e., eden_em_oct07) in which the elevation values have been converted from meters (m) to centimeters(cm) for use by EDEN applications software. This file is intended specifically for use in the EDEN applications software. Aside from this difference in horizontal units, the following documentation applies. These data were specifically created for the development of water depth information using interpolated water surfaces from the EDEN stage data network.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_eden_em_oct07_400m", + "title": "Everglades Depth Estimation Network (EDEN) October 2007 Digital Elevation Model", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "2007-12-31", + "bbox": "-81.36353, 25.229605, -80.22176, 26.683613", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548584-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548584-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eden_em_oct07_400m", + "description": "This is the 1st release of the third version of an Everglades Depth Estimation Network (EDEN) digital elevation model (DEM) generated from certified airborne height finder (AHF) and airboat collected ground surface elevations for the Greater Everglades Region. This version includes all data collected and certified by the USGS prior to the conclusion of the AHF collection process. It differs from the previous elevation model (EDEN_EM_JAN07) in that the modeled area of WCA3N (all the WCA3A area north of I-75) is increased while the modeled area of the Big Cypress National Preserve (BNCP) has been both refined and reduced to the region where standard error of cross-validation points falls below 0.16 meters. EDEN offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to Comprehensive Everglades Restoration Plan. To produce historic and near-real time maps of water depths, the EDEN requires a system-wide DEM of the ground surface. These data were specifically created for the development of water depth information using interpolated water surfaces from the EDEN stage data network.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_eden_water_surfs", + "title": "Everglades Depth Estimation Network (EDEN) Water Surfaces Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "", + "bbox": "-81.3, 25, -80.16, 26.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549934-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549934-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eden_water_surfs", + "description": "Spatially continuous interpolation of water surface across the greater Everglades is generated for daily mean values of the water level gages for the EDEN network beginning January 1, 2000. Surfaces are recorded as elevations in centimeters relative to the North American Vertical Datum of 1988 (NAVD 88). These surfaces are served on the web as GIS data layers. Spatially explicit hydrologic information can be critical in understanding and predicting changes in biotic communities in wetland ecosystems. Repeated field measurements, the traditional method of collecting water surface information, is labor intensive and doesn't produce spatially continuous data across large areas. For this reason the EDEN project was started to collect data from over 200 real time stage monitoring gages that automatically record and radio-transmit data. The project integrates existing and new telemetered water level gages into a single network. Combined with a high resolution ground elevation model it generates a daily continuous water surface and water depth for the freshwater greater Everglades.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_estero_bay_ap_data", + "title": "Estero Bay Aquatic Preserve hydrological data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-10-01", + "end_date": "2005-09-30", + "bbox": "-81.96, 26.33, -81.83, 26.47", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552405-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552405-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_estero_bay_ap_data", + "description": "The data for each of the collection sites are available for fiscal years 2002-2005. The files are available in several formats. Salinity and temperature were collected for all stations. Stage, discharge, and wind speed and direction were also collected at some of the stations. Estero Bay is a shallow estuary, across which salinity gradients from freshwater to saltwater occur over short land-sea distances. Such gradient compressions can result in a highly variable salinity environment and affect a diverse range of estuarine flora and fauna when even a small change in watershed runoff occurs. Rapid development within the bay's watershed has a changing effect on the amount, timing, and quality of runoff into the bay. Currently there is no information available to assess the effect that these alterations of runoff may have on the bay and its biota, nor to define watershed runoff and loading limits that provide desirable ranges in salinity and water quality at historical, current, and potential locations for seagrass, oysters, and other species of concern. To manage and preserve the Estero Bay ecosystem, it is necessary to: (1) understand the salinity patterns of the bay in relation to freshwater inflow and water exchange with the Gulf of Mexico; (2) describe the mixing and freshwater residence times within the bay; and (3) study the effects on light penetration from increased Total Suspended-Solids (TSS) load and re-suspension. Results from this study will facilitate management decisions geared toward defining flow and sediment loading limits that provide desirable ranges in salinity and water quality by providing necessary hydrological information. To carry out the objectives of the study, a network of monitoring stations will be established and will include: (1) the monitoring of flow, water level, salinity, temperature, Acoustic Backscatter Strength (ABS), and turbidity near the mouth of three of four tributaries flowing into Estero Bay; (2) monitoring of water level, salinity, temperature, turbidity, wind speed and direction, and barometric pressure at one location inside the bay; (3) monitoring of water level, flow, salinity, temperature, and ABS at three of four tidal exchange points with the Gulf of Mexico along the barrier islands; (4) monitoring of water level (depth), salinity and temperature at one open-water location in the Gulf of Mexico.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_ever_hydro_wq_data", + "title": "Everglades Hydrology and Water Quality Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-06-01", + "end_date": "1998-12-21", + "bbox": "-80.67, 25.9, -80.33, 26.7", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550405-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550405-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_ever_hydro_wq_data", + "description": "At present there are few reliable estimates of hydrologic fluxes between groundwater and surface water in the Everglades. This gap in hydrological investigations not only leaves the water budget of the Everglades uncertain, it also hampers progress in understanding the processes that determine mobility and transformation of contaminants, such as mercury, sulfate and nutrients. The objective of this project is to quantify hydrologic exchange fluxes between groundwater and surface water and its effects on transport of contaminants in the Everglades. The research furthermore relates surface water and ground water interactions to past, present, and proposed management of surface-water levels and flows in the Everglades. The principal research sites are the Everglades Nutrient Removal Project (ENR), Water Conservation Area 2A (WCA-2A), and the freshwater wetlands of Everglades National Park. Results are being used to quantify ground-water exchange with surface flow, and to quantify the enhancement of chemical transformations of contaminants during transport across the interface between surface water and ground water. Two hydrology and water quality datasets are available for this project. The Northern Everglades Research Site and Sample Information dataset contains a summary of the site locations, data types, and measurement periods in ENR, WCA2A, and WCA2B. The Seepage Meters Site and Sample Information dataset contains vertical fluxes across wetland peat surface measured by seepage meters at research sites in ENR, WCA2A, WCA2B, and WCA3A. Additional data can be found in the appendices of the Open-File Reports 00-168 and 00-483. For restoration of the Everglades to succeed there must be comprehensive knowledge about physical, chemical, and biological processes throughout the system. A key measure of success in the Everglades is the improvement or protection of water quality under changing hydrologic conditions. Although there is a basic understanding of how interactions between groundwater and surface water will affect water budgets under restoration, there is only a rudimentary understanding of how interactions between groundwater and surface water will affect water quality. Only field-oriented research and modeling can determine whether interactions between groundwater and surface water are currently storing pollutants in groundwater, how long those pollutants are likely to be stored in the aquifer, and under what changing management conditions associated with restoration will those pollutants be returned into the surface water system.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_ever_isotope_data", + "title": "Everglades Isotope Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-03-01", + "end_date": "1999-10-31", + "bbox": "-81.0202, 25.2475, -80.3069, 26.6712", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548535-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548535-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_ever_isotope_data", + "description": "Data are available for the following sites: Cell 4, ENR-OUT, L7, Cell 3, LOX, North Holeyland, E0, F1, U3/Glory Hole, L35B, 2BS, L67, 3A-15, 3A-TH, Lostmans Creek, North Prong Creek, TS-7, and TS-9 for the plants and animals found at each site. A first step of the Everglades restoration efforts is \"getting the water right\". However, the underlying goal is actually to re-establish, as much as possible, the \"pre-development\" spatial and temporal distribution of ecosystems throughout the Everglades. Stable isotope compositions of dissolved nutrients, biota, and sediments provide critical information about current and historic ecosystem conditions in the Everglades, including temporal and spatial variations in contaminant sources, biogeochemical reactions in the water column and shallow subsurface, and trophic relations. Hence, the scientific focus of this project is to use stable isotope techniques to examine ecosystem responses (especially variations in foodweb base and trophic structure) to temporal and spatial variations in hydroperiod and contaminant loading for the entire freshwater Everglades. The major \"long-term\" objectives of this project have been to: (1) determine the stable C, N, and S isotopic compositions of Everglades biota, (2) use bulk and compound-specific isotopic ratios to determine relative trophic positions for major organisms, (3) examine the spatial and temporal changes in foodweb structures across the ecosystem, especially with respect to the effect of anthropogenically derived nutrients and contaminants from agricultural land uses on foodwebs, (4) evaluate the effectiveness of isotopic techniques vs. gut content analysis for determining trophic relations in the Everglades, (5) evaluate the role of algae vs. detritus/microbial materials in foodwebs for the entire freshwater marsh part of the Everglades, and (6) work with modelers to correctly incorporate food web and MeHg bioaccumulation information into predictive models. We have generally completed the sample analysis parts of objectives #1-5, and are writing interpretative reports on topics #1-5. More recent and specific objectives include: (1) link our data on seasonal and temporal differences in foodweb bases and trophic levels with SFWMD, FGFFC, and USGS Hg datasets (first for large fish and, more recently, for lower trophic levels), (2) investigate the effects of seasonal/spatial changes in nutrients, water levels, and reactions on the isotopic compositions at the base of the foodweb (that affect our interpretation of relative trophic positions of organisms), and (3) continue our efforts to link our foodweb isotope data from samples collected at USGS-ACME and EPA-REMAP sites with the spatial environmental patterns observed by the REMAP program. This work started as part of the Aquatic Cycling of Mercury in the E verglades (ACME) project in 1996 and was made a separate project in 2000.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_exist_core", "title": "Analysis of Existing Core in the Floridan Aquifer", @@ -184118,6 +191112,84 @@ "description": "The strategy of this study was to use artificial tracers to determine rate and direction of flow. Tracers were injected into well clusters, existing sewage treatment facilities, and sewage disposal wells. In addition to tracer studies groundwaters were collected for contamination analysis so as to provide a baseline against which the effects of population increase and success of future wastewater treatment facilities can be evaluated. Treated sewage is injected into the limestone under the Florida Keys via on-site disposal systems (OSDs). There are 25,000 septic tank systems, approximately 5,000 cesspools, and approximately 1000 class 5 injection wells. Depths of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases and both marine grass and sponge mortality is perceived by the local population, NOAA, and EPA to be caused by sewage nutrients leaking from groundwater on both sides of the Florida Keys. Determining the rate and direction of saline groundwater movement beneath the Key, and the Florida Bay was considered critical to understanding the fate and effects of subsurface waste water disposal n the Florida Keys. The objective of this research was to determine the rate, direction of flow, and contamination levels of saline groundwater in the Florida Keys and Florida Bay. Contamination studies are necessary to determine if nutrient and other contaminant levels are rising and to provide a baseline of data for future decision making.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_fb_1890-1990_data_version 1", + "title": "Florida Bay 1890 and 1990 data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1889-01-01", + "end_date": "1999-12-31", + "bbox": "-81.11667, 24.733334, -80.36667, 25.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554102-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554102-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_fb_1890-1990_data_version%201", + "description": "The maps of the tracklines are linked to the corresponding data sets which contain values for X (easting), Y (northing), Z (elevation), and the Root Mean Square (RMS) error. Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay has not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Digitizing the historical shoreline and bathymetric data for comparison with modern data provides information on sedimentation rates within the Bay.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_fb_bb_pollen_data", + "title": "Florida Bay and Biscayne Bay pollen data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-02-26", + "end_date": "1996-11-01", + "bbox": "-80.62, 25, -80, 25.26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550479-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550479-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_fb_bb_pollen_data", + "description": "This project developed, refined, and utilized a variety of proxies to provide estimates of seasonal, interannual, and decadal salinity history of Florida Bay and Biscayne Bay based on strategically placed sediment cores that aided in the validation and sensitivity testing of hydrologic models and decision making in water management. The datasets contain the pollen information at various depths in the cores. Terrestrial ecosystems of south Florida have undergone numerous human disturbances, ranging from alteration of hydroperiod, fire history, and drainage patterns from the introduction of the canal system to expansion of agricultural activity to the introduction of exotic species, Over historical time, dramatic changes in the ecosystem have been documented and these changes have been attributed to various human activities. However, the natural variability of the ecosystem was unknown and needed to be determined to assess the true impact of human activity on the modern ecosystem. The project was designed to document historical changes in the terrestrial ecosystem quantitatively, to date any changes and determine whether they resulted form documented human activities, and to establish the baseline level of variability on the south Florida ecosystem to estimate whether the observed changes are greater than would occur naturally.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_field_data_bicy", + "title": "Field measurement, major ion, and nutrient data for water from the Big Cypress National Preserve", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1988-01-01", + "end_date": "2000-12-31", + "bbox": "-81.4, 25.2, -80.5, 26.3", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554990-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554990-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_field_data_bicy", + "description": "The data catagories include site name, date, time, station ID, record #, agency analyzing sample, agency collecting sample, discharge (daily mean), gage height, lab spec condition, field spec condition, total dissolved solids, water temp, pH (lab and field), and amounts of oxygen, nitrogen and nitrogen compounds, phosphorus, magnesium, sodium, potassium, chloride, sulfate, calcium, and silica. Big Cypress National Preserve (BICY) and Everglades National Park (EVER) maintain separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout their units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data (bi-monthly for BICY and monthly for EVER), and dependence on surface water depth and season. Collection and analysis of water-quality samples at BICY and EVER are done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. EVER has been sampling water quality on a monthly basis at 9 \"internal marsh\" stations since 1984 as part of this program. BICY has been sampling water quality on a monthly basis at 10 \"internal\" stations since 1995 as part of this agreement, with water quality data at these sites extending as far back to 1988 (but not as part of the agreement). Water-quality data collected at the BICY and EVER stations has been archived and reported for short-time intervals (yearly and bi-yearly), but an analysis that covers all sampled parameters, extends over the full period of record, and provides comparisons between the two units has yet to be performed. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. One purpose is to look for long-term trends and possibly relate the trends to human or natural influences on water quality such as agriculture, drought, hurricanes, changes in water management, etc. Another purpose is to interpret data from the most remote and unimpacted areas to discern, if possible, what the natural background concentrations are for water-quality constituents that have sufficient data. An attempt will be made to find correlations between available water-quality, physical, and meteorological parameters. Such analyses of water-quality and ancillary data may assist in establishing water-quality standards appropriate for the designation as Outstanding Florida Waters in both the Everglades National Park and the Big Cypress National Preserve. Ancillary data such as precipitation, water-level, water flow, dates of major storms, and beginning and ending dates of water-control effects will be studied to relate their timing to any noticeable changes in water quality. CURRENTNESS REFERENCE: ground condition SPATIAL DATA ORGANIZATION INFORMATION Indirect Spatial Reference: Big Cypress National Preserve Direct Spatial Reference: Point SDTS Point and Vector Object Type: Point Point and Vector Object Count: 5 SPATIAL REFERENCE INFORMATION - GEODETIC MODEL Horizontal Datum Name: North American Datum of 1983 Ellipsoid Name: Geodetic Reference System 80 Semi-major Axis: 6378137 Denominator of Flattening Ratio: 298.257 NATIVE: Data are provided as Excel spreadsheets.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_field_data_br105", + "title": "Field measurements, major ions, nutrient, and carbon data for Bridge-105 and the 40-Mile Bend to Monroe reach in the Big Cypress National Preserve", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1967-01-01", + "end_date": "1999-12-31", + "bbox": "-81.4, 25.2, -80.5, 26.3", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551160-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551160-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_field_data_br105", + "description": "The data catagories include site name, date, time, station ID, medium, record #, agency analyzing sample, agency collecting sample, discharge (daily mean and instantaneous), gage height, lab spec condition, field spec condition, total dissolved solids, air and water temp, pH (lab and field), and amounts of oxygen, nitrogen and nitrogen compounds, phosphorus, carbonates, magnesium, sodium, potassium, chloride, sulfate, calcium, silica, and carbon. Big Cypress National Preserve (BICY) and Everglades National Park (EVER) maintain separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout their units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data (bi-monthly for BICY and monthly for EVER), and dependence on surface water depth and season. Collection and analysis of water-quality samples at BICY and EVER are done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. EVER has been sampling water quality on a monthly basis at 9 \"internal marsh\" stations since 1984 as part of this program. BICY has been sampling water quality on a monthly basis at 10 \"internal\" stations since 1995 as part of this agreement, with water quality data at these sites extending as far back to 1988 (but not as part of the agreement). Water-quality data collected at the BICY and EVER stations has been archived and reported for short-time intervals (yearly and bi-yearly), but an analysis that covers all sampled parameters, extends over the full period of record, and provides comparisons between the two units has yet to be performed. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. One purpose is to look for long-term trends and possibly relate the trends to human or natural influences on water quality such as agriculture, drought, hurricanes, changes in water management, etc. Another purpose is to interpret data from the most remote and unimpacted areas to discern, if possible, what the natural background concentrations are for water-quality constituents that have sufficient data. An attempt will be made to find correlations between available water-quality, physical, and meteorological parameters. Such analyses of water-quality and ancillary data may assist in establishing water-quality standards appropriate for the designation as Outstanding Florida Waters in both the Everglades National Park and the Big Cypress National Preserve. Ancillary data such as precipitation, water-level, water flow, dates of major storms, and beginning and ending dates of water-control effects will be studied to relate their timing to any noticeable changes in water quality.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_field_data_interEver", + "title": "Field measurements, major ions, nutrient, and carbon data for sites in the interior of the Everglades National Park", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1959-12-16", + "end_date": "2000-12-31", + "bbox": "-80.9, 25.2, -80.5, 25.7", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552386-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552386-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_field_data_interEver", + "description": "The data catagories include site name, date, time, station ID, medium, record #, agency analyzing sample, agency collecting sample, discharge (daily mean and instantaneous), gage height, lab spec condition, field spec condition, total dissolved solids, air and water temp, pH (lab and field), and amounts of oxygen, nitrogen and nitrogen compounds, phosphorus, carbonates, magnesium, sodium, potassium, chloride, sulfate, calcium, floride, silica, and carbon. Big Cypress National Preserve (BICY) and Everglades National Park (EVER) maintain separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout their units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data (bi-monthly for BICY and monthly for EVER), and dependence on surface water depth and season. Collection and analysis of water-quality samples at BICY and EVER are done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. EVER has been sampling water quality on a monthly basis at 9 \"internal marsh\" stations since 1984 as part of this program. BICY has been sampling water quality on a monthly basis at 10 \"internal\" stations since 1995 as part of this agreement, with water quality data at these sites extending as far back to 1988 (but not as part of the agreement). Water-quality data collected at the BICY and EVER stations has been archived and reported for short-time intervals (yearly and bi-yearly), but an analysis that covers all sampled parameters, extends over the full period of record, and provides comparisons between the two units has yet to be performed. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. One purpose is to look for long-term trends and possibly relate the trends to human or natural influences on water quality such as agriculture, drought, hurricanes, changes in water management, etc. Another purpose is to interpret data from the most remote and unimpacted areas to discern, if possible, what the natural background concentrations are for water-quality constituents that have sufficient data. An attempt will be made to find correlations between available water-quality, physical, and meteorological parameters. Such analyses of water-quality and ancillary data may assist in establishing water-quality standards appropriate for the designation as Outstanding Florida Waters in both the Everglades National Park and the Big Cypress National Preserve. Ancillary data such as precipitation, water-level, water flow, dates of major storms, and beginning and ending dates of water-control effects will be studied to relate their timing to any noticeable changes in water quality.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_fire_ecol_sfl_04", + "title": "Fire Ecology of South Florida Wetlands", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "2004-09-30", + "bbox": "-81.5, 24.65, -80.75, 26.48", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552149-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552149-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_fire_ecol_sfl_04", + "description": "The project objective is to determine the importance that season of burning has on the response of vegetation to fire. We have addressed this through the use of experimental prescribed fires at different times of the year. In Big Cypress National Preserve we have established a long-term study of season and frequency of burning in the unlogged hydric pinelands of the Raccoon Point area. This study includes three seasonal treatments: winter (dry season), spring (early wet season) and summer (mid wet season). A shorter study comparing the response to winter and summer burns was carried out in the pine rocklands on Big Pine Key. We are also studying the effect of season of burning on muhly grass (Muhlenbergia filipes), a component of hydric pinelands and often a dominant in short-hydroperiod wetlands known as muhly or marl prairies. We are conducting field and nursery studies to determine how the season of burning effects the rate of recovery of muhly and its ability to tolerate flooding. Prescribed fire constitutes one of the most pervasive management actions influencing the restoration and maintenance of the Greater Everglades Ecosystem. It is generally assumed that lightning-ignited fires were common at the beginning of the rainy season, but there have probably been human-caused fires at other times for the last several thousand years. Since lighting-ignited fire cannot be allowed to operate naturally in South Florida, prescribed (or management-ignited) fire must be used to maintain these habitats. The seasonal occurrence of fire can have an important influence on ecological responses. We have conducted a set of experimental studies to determine the response of vegetation to different seasons of burning. The results of this work will influence the fire management of the publicly owned lands in the Greater Everglades ecosystem.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_fish_sample", "title": "Development of Integrated Sampling of Fishes in Forested Wetlands in South Florida with Emphasis on Food Web Structure", @@ -184131,6 +191203,136 @@ "description": "This study seeks to refine sampling methodology in the forested wetlands, to collect baseline data for aquatic animals to enable comparisons between Comprehensive Everglades Restoration Plan (CERP) and non-CERP impacted wetlands, and to begin studies of food-web structure in cypress and mangrove wetlands. Forested wetlands, mainly comprised by mangrove and cypress swamps in south Florida, and contiguous marshes formerly functioned as critical feeding and nesting sites for wading birds, populations of which have declined precipitously in coincidence with changes to the hydrology of the region. Human-induced changes have affected the natural variability of environmental conditions through the construction of canals and levees that can either act to drain or flood the wetlands. These changes are hypothesized to have negatively affected the production and availability of fish prey for the birds. A major target of restoration is the reestablishment of the natural hydrological conditions in the wetlands. Another alteration to these systems has been the introduction of more than 10 species of non-native fishes. The Big Cypress Swamp and mangrove ecosystems have been affected by these anthropogenic activities, yet the effects are unclear because of the lack of study. In both ecosystems, there is little quantitative information on the community composition, size-structure, and biomass of fishes and macro-invertebrates because few studies have been carried out there, This is especially true in the forested habitats of those ecosystems. Reasons for lack of study include logistical problems such as access to study areas and difficulties in devising appropriate sampling methods and feasible designs. However, because of the scope of anthropogenic changes in the drainage basins, there can be little doubt that the standing stocks of aquatic animals and habitat use have been affected negatively.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_fk_gw_seep", + "title": "Groundwater Seepage data (Florida Keys)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-07-07", + "end_date": "1996-08-20", + "bbox": "-80.9, 24.8, -80.2, 25.7", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549211-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549211-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_fk_gw_seep", + "description": "The dataset contains information and data collected during the seepage meter (groundwater seepage) experiments along the Florida Keys on both the Florida Bay and Atlantic Ocean sides. Treated sewage is injected into the limestone under the Florida Keys via on-site disposal systems (OSDs). There are 25,000 septic tank systems, approximately 5,000 cesspools, and approximately 1000 class 5 injection wells. Depths of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases and both marine grass and sponge mortality is perceived by the local population, NOAA, and EPA to be caused by sewage nutrients leaking from groundwater on both sides of the Florida Keys. Determining the rate and direction of saline groundwater movement beneath the Key, and the Florida Bay was considered critical to understanding the fate and effects of subsurface waste water disposal n the Florida Keys. The objective of this research was to determine the rate, direction of flow, and contamination levels of saline groundwater in the Florida Keys and Florida Bay. Contamination studies are necessary to determine if nutrient and other contaminant levels are rising and to provide a baseline of data for future decision making.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_fl_coop_map", + "title": "Florida Cooperative Geologic Mapping Project", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-10-01", + "end_date": "1999-09-30", + "bbox": "-82, 24.5, -80.28, 25.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549816-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549816-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_fl_coop_map", + "description": "This project was designed to provide the framework for understanding (1) ecosystem variability and change prior to and during human development of South Florida (i.e., the detailed ecosystem history over the last 200 years, differentiating natural variability from man-made change) and (2) the resource distribution (primarily water and phosphate) in the subsurface of Florida (i.e., the detailed geology of constraining and resource units). The overall strategy is is to: 1. Sample modern environments throughout the Greater Everglades Ecosystem to understand the present ecosystem and locate undisturbed shallow sediment cores to analyze ecosystem variability and change over the last few hundred years. 2. Analyze deep cores for sedimentology, diagenesis, biostratigraphy, paleoecology, and chemostratigraphy in transects across the southern Florida Peninsula to better understand the factors controlling ground water movement and to define aquifer characteristics. In order to understand the role of facies relationships and genetic depositional units in determining groundwater flow, the distribution and abundance of micro mollusks, foraminifers, dinocysts, ostracodes, pollen and spores, and charcoal will be analyzed, and strontium isotopes will be used for geochronology. A multitude of water-related societal issues face southern Florida in the 1990's. These issues include the increasing demands for water for agriculture; business, and the rapidly growing population in the Naples and Miami area (Miami showing the fourth fastest growth rate in the U.S. in the 1980's), the recently mandated restoration of natural sheet flow through the Everglades ecosystem, the effects of runoff from agricultural and urban areas, and the vitality of the important fisheries of Florida Bay and Biscayne Bay. This project provides baselines for ecosystem variability and tracks the change in ecosystems through the last several hundred years to provide critical information for reasonable restoration targets to land planners and managers in southern Florida. In addition, it provides the geologic framework for the aquifers that supply water to the area.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_flow_murray_solis", + "title": "Flow Data (Murray/Solis)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-01-01", + "end_date": "2004-12-31", + "bbox": "-80.96, 25.13, -80.81, 26.26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554681-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554681-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_flow_murray_solis", + "description": "Proposed modified water deliveries to Indian Tribal Lands, Big Cypress National Preserve, and Water Conservation Area 3A require that flow and nutrient loads at critical points in the interior surface water network be measured. Defining the foundation for water levels, flows, and nutrient loads has become an important baseline for Storm Treatment Area 5 and 6 development, recent C-139 Basin flow re-diversions, and future L-28 Interceptor Canal de-compartmentalization including flow rerouting into the Big Cypress Preserve. Flow monitoring for the two primary flow routes for both L-28 Interceptor Canal and L-28 is key to developing this network. Data are available for L-28 Interceptor Canal below S-190, L-28 Canal above S-140, and L-28 Interceptor South. The accurate determination of flow through the interior canal networks south of Lake Okeechobee and the C-139 basin remains critical for water budgets and regional model calibrations as defined by the Everglades Forever Act of 1994 and due to the Comprehensive Everglades Restoration Plan (CERP) initiative to reroute Big Cypress Preserve flows. The implementation of strategically located stream flow gaging points and associated data collection for nutrients has helped define future surface-water flow requirements and has provided valuable baseline flow data prior to the establishment of the recently constructed northern Storm Treatment Areas (STA\u0092s 5 and 6) and the Rotenberger Wildlife Management Area. Generating continuous flow data at selected impact points for interior basins has complemented the existing eastern coastal canal discharge network, and has allowed for more accurately timed surface-water releases while providing flow and nutrient monitoring after recent STA implementation. A unique multi-agency experiment was conducted with much success with the focus on cooperation and development of new instrumentation and acoustic flow-weight auto-sampler protocols. The original data collection and processing was provided by three separate entities at each site with responsibilities originally allocated between the U.S. Geological Survey (USGS), the Seminole Tribe of Florida, and SFWMD. USGS provides calibration, analysis and processing of acoustic velocity meters (AVM\u0092s) and side-looking Doppler systems and stage shaft encoders, SFWMD provides data loggers with real-time flow-weighted algorithms, and radio frequency (RF) telemetry instrumentation. The Seminole Tribe provides auto-sampler service and funds nutrient load analysis through the USGS Ocala Lab. This project was initiated by Mitch Murray in October 1995.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_flow_velocity", + "title": "Flow Velocity and Water Level Transects", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-07-01", + "end_date": "", + "bbox": "-80.4, 25.5, -80.2, 25.7", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553114-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553114-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_flow_velocity", + "description": "The sheet flow over the Buttonwood Embankment during periods of high flow is an unknown element of the water budget for the Everglades. An ongoing project to estimate the flows over the embankment through modeling will require water-level and water velocity data measured at the embankment to accurately estimate simulated flows over this physical land feature. The actual measurement of water velocities and depths at the embankment would greatly improve the model calibration. Although it is virtually impossible to conventionally measure flow over the entire embankment, water depths and velocities at known points along the embankment, combined with the detailed topography of the embankment being developed in another ongoing project, should allow a much better estimate of the total flow than presently available. The objective of the project is to make the best possible estimate of the flow velocities and water depths across Buttonwood Embankment during high flow for use as model input for an ongoing project being conducted in the USGS-WRD office in Miami. Estimates will be used to provide management alternatives and will create salinity concentrations suitable for ecologic integrity. This project has been integrated into the TIME project. The project was started by Marvin Franklin.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_flow_velocity_data", + "title": "Flow and Velocity Data - USGS_SOFIA_flow_velocity_data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-07-01", + "end_date": "1998-07-31", + "bbox": "-80.9, 25.25, -80.5, 25.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551525-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551525-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_flow_velocity_data", + "description": "Velocity data are being provided in an Access database and Excel spreadsheets. The database summarizes the velocity data, site location and description, vegetative characteristics, and water quality parameters. The spreadsheet filters and averages the complete raw velocity data set per measurement. These data were collected at two minute intervals for 10 minutes. The file naming convention is as follows: (example) E07T31197.xls Column 1: E or W East or west from Taylor Slough Airboat Trail Column 2-3: Sequential numbers from the center of the transect Column 4-5: Sequential transect number Column 6-9: Month and Year data was collected. The objective of the project is to make the best possible estimate of the flow velocities and water depths across Buttonwood Embankment during high flow for use as model input for an ongoing project being conducted in the USGS-WRD office in Miami. Estimates will be used to provide management alternatives and will create salinity concentrations suitable for ecologic integrity.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_freshwater_east_coast", + "title": "Freshwater Flows to the East Coast", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-11-01", + "end_date": "1996-09-30", + "bbox": "-80.45, 25.28, -80.05, 26.65", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552319-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552319-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_freshwater_east_coast", + "description": "Discharges through 10 selected coastal control structures in Broward and Palm Beach Counties and the 16 coastal structures in Miami-Dade County, Fla., Florida, are presently computed using the theoretical discharge-coefficient ratings developed from scale modeling, theoretical discharge coefficients, and some field calibrations whose accuracies for specific sites are unknown. To achieve more accurate discharge-coefficient ratings for the coastal control structures, field discharge measurements were taken with an Acoustic Doppler Current Profiler at each coastal control structure under a variety of flow conditions. These measurements were used to determine computed discharge-coefficient ratings for the coastal control structures under different flow regimes: submerged orifice flow, submerged weir flow, free orifice flow, and free weir flow. Theoretical and computed discharge-coefficient ratings for submerged orifice and weir flows were determined at the coastal control structures, and discharge ratings for free orifice and weir flows were determined at three coastal control structures. The difference between the theoretical and computed discharge-coefficient ratings varied from structure to structure. A system of canals and levees has been constructed over the last century for the purpose of drainage, flood control, and aquifer discharge. Strategically placed control structures allow the water management officials to move water from inland areas during high-rainfall periods and retain water in dry periods. Freshwater discharged to tide through coastal structures not only affects the amount of water available for water supply in the lower east coast and the Everglades, but it also affects the biota in the Intracoastal Waterway and Biscayne Bay. Therefore, it is imperative that there be accurate ratings for these structures to predict the effects of various water restoration alternatives. Although these coastal structures are a pivotal part of the man-made system, the discharge through most of them is computed only from theoretical ratings. Actual field measurements are needed in order to determine if the theoretical ratings are adequate, and to develop more accurate ratings. Stage measurements are made by the South Florida Water Management District (SFWMD) or the USGS at the east coast structures. The flows through the coastal structures in Miami-Dade, Broward, and Palm Beach counties can be computed by developing stage-discharge ratings from field measurements of flow, stage, and structure operations. Although theoretical ratings exist for the structures, no check as to the accuracy of these ratings has been made. In order to develop ratings from field measurements, discharge measurements must be made at the structure simultaneously with water level and structure operation measurements. Difficulties in making accurate discharge measurements arise from the slow flows and non-standard velocity profiles in south Florida canals. The Acoustic Doppler Current Profiler (ADCP), which uses the Doppler shift in acoustic signals to determine water velocity and compute discharge, is ideal for measurements in slow and spatially varying velocity fields. Statistical techniques were used to determine the best-fit ratings for the structures and error analysis of the ratings. The objective of this study was to determine discharge ratings for 10 coastal hydraulic control structures (7 in eastern Broward and 3 in southeastern Palm Beach counties as well as for 16 coastal hydraulic control structures in eastern Miami-Dade county.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_freshwtr_flow", + "title": "Freshwater Flows to Northeastern Florida Bay", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-10-01", + "end_date": "", + "bbox": "-80.8, 24.9, -80.3, 25.3", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548619-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548619-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_freshwtr_flow", + "description": "In 1995, the U.S. Geological Survey (USGS) began a study to gage several major creeks that discharge freshwater into northeastern Florida Bay. This study provides flow, salinity, and water-level data for model development and calibration and also provides baseline data for other physical, biological and chemical studies being conducted in the area. The monitoring network provides coastal discharge data for the majority of estuarine creeks in northeastern Florida Bay. The timing and distribution of freshwater deliveries to northeastern Florida Bay have been documented since 1996. In 2003 the USGS coastal and estuarine unit also began calculating nutrient loads at selected sites in northeastern Florida Bay and along the southwestern Everglades coast. The larger network has provided discharge information to researchers to develop nutrient budgets and loading (Rudnick, 1999; Sutula and others, 2003; Davis, 2004; and Levesque, 2004). In South Florida, changes in water-management practices to accommodate a large and rapidly growing urban population along the Atlantic coast, as well as intensive agricultural activities, have resulted in a highly managed hydrologic system. This managed system altered the natural hydrology of the Everglades ecosystem, including Florida Bay. During the last few decades, Florida Bay has experienced seagrass die-offs and algal blooms. Both are signals of ecological deterioration that has been attributed to increases in salinity and nutrient content of bay waters. With plans to restore water levels in the Everglades to more natural conditions, changes also are expected in the amount and timing of freshwater discharge through the major creeks into Florida Bay. Flow through the estuarine creeks through the Buttonwood Embankment and into Florida Bay is naturally controlled by the water level in the Everglades; regional wind patterns; and to a lesser extent, tides. Florida Bay restoration requires an understanding of the linkage between the amount of freshwater flowing into the bay and the salinity and quality of the bay environment. Historically, there has been no accurate quantification of the amount of freshwater being discharged into Florida Bay from the mainland due to the difficulties of accurately gaging flows in shallow, bi-directional, and vertically stratified streams. The project objectives are to determine the quantity, timing and distribution of freshwater flow into Florida Bay and adjacent estuaries, determine baseline hydrologic conditions and provide information on hydrologic change during the restoration process. This project helps determine how freshwater flow affects the health of Florida Bay, a critical component of the CERP, and how changes in water-management practices upstream (Taylor Slough and C-111 basins) directly influence flow and salinity conditions in the estuary. The project managers for this study include Eduardo Patino (1995-2000), Clinton Hittle (2001-2003), and Mark Zucker (2003 -present).", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_frnkflow", + "title": "Flow and Velocity Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-07-01", + "end_date": "1998-07-31", + "bbox": "-80.9, 25.25, -80.5, 25.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552937-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552937-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_frnkflow", + "description": "Velocity data are being provided in an Access database and Excel spreadsheets. The database summarizes the velocity data, site location and description, vegetative characteristics, and water quality parameters. The spreadsheet filters and averages the complete raw velocity data set per measurement. This data was collected at two minute intervals for 10 minutes. The file naming convention is as follows: (example) E07T31197.xls Column 1: E or W East or west from Taylor Slough Airboat Trail Column 2-3: Sequential numbers from the center of the transect Column 4-5: Sequential transect number Column 6-9: Month and Year data was collected. The objective of the project is to make the best possible estimate of the flow velocities and water depths across Buttonwood Embankment during high flow for use as model input for an ongoing project being conducted in the USGS-WRD office in Miami. Estimates will be used to provide management alternatives and will create salinity concentrations suitable for ecologic integrity.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_gachemca", + "title": "Everglades Water chemistry - Cations and Anions", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-03-01", + "end_date": "1995-03-31", + "bbox": "-80.9, 25.59, -80.1, 26.79", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554630-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554630-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gachemca", + "description": "This data set contains the following parameters: Lab ID, site ID, lab pH, lab alkalinity, Cl, SO4, Ca, Mg, Na, K, and ion balance for 27 samples collected from 10 sites. It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylation and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiagency research project.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_gachmdoc", + "title": "Everglades Water chemistry - DOC and other parameters", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-03-01", + "end_date": "1995-03-31", + "bbox": "-80.9, 25.59, -80.1, 26.79", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549042-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549042-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gachmdoc", + "description": "This data set contains the following parameters: Lab ID, site ID, DOC, specific UV, B, Ba, Fe, H4SiO4, Li, Mn, Sr, and Zn for 27 samples collected from 10 sites. It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylation and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiagency research project.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_gaines_04", "title": "Computer Simulation Modeling of Intermediate Trophic Levels for ATLSS of the Everglades/Big Cypress Region", @@ -184144,6 +191346,136 @@ "description": "This project includes models for primary food bases; the functional group of small fishes, upon which many of the wading birds depend, and the main reptile and amphibian functional groups, which constitute much of the diet of the American alligator. In addition, population models for several important species have been developed. These include a model for the snail kite population of Florida, models for the key wading bird species, and a model of the American crocodile population, all focusing on the effects of hydrology. This project has the goal of developing models for key components of the Everglades landscape as part of the overall Across Trophic Level System Simulation (ATLSS) program. The proposed work has four major objectives: 1. Provide rapid support for CERP by producing output and interpretation of requested runs of ATLSS models. 2. Complete an ATLSS model for the American crocodile that is in the final stage of work. 3. Validate models of the snail kite and the Cape Sable seaside sparrow. 4. Providing field work and habitat quality indices for effects of hydrology on selected small mammal and amphibian species.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_gaqwfp", + "title": "Everglades Water Quality -Field Parameters", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-03-01", + "end_date": "1995-03-31", + "bbox": "-80.9, 25.59, -80.1, 26.79", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553178-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553178-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gaqwfp", + "description": "This data set contains the following parameters: Lab ID, site ID, collection date and time, field pH, field specific conductivity, and water temperature at 10 locations. It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylation and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiagency research project.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_gaqwssi", + "title": "Everglades Water Quality - Site and Sample Information", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-03-01", + "end_date": "1995-03-31", + "bbox": "-80.9, 25.59, -80.1, 26.79", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555346-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555346-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gaqwssi", + "description": "This data set contains the following parameters: Lab ID, site ID, site name, latitude/longitude, sampling depth, sample type, subsample type, and method for 27 samples from 10 locations. It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylation and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiagency research project.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_gawlik_wading_birds", + "title": "Effects of Hydrology on Wading Bird Foraging Parameters", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1998-12-01", + "end_date": "2003-08-02", + "bbox": "-80.625, 26.3, -80.125, 26.875", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551167-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551167-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gawlik_wading_birds", + "description": "The conceptual model for this study is based on the idea that hydroperiod is a long-term process that primarily influences the abundance, body size, and species composition of the prey community whereas water depth has immediate effects on individual birds by influencing their ability to capture prey. This study seeks to determine through field experiments, the proximate effects of water depth, prey density, prey size, and prey species on wading bird foraging parameters. The species of wading birds examined in this study are those in the ATLSS wading bird model: the Wood Stork, White Ibis, Great Egret, and Great Blue Heron. The recovery of wading bird populations has been identified as a key component of successful Everglades restoration. Proposed causes for the decline in wading bird numbers have in common the notion that current hydropatterns have altered the availability of prey. Indeed, food availability may be the single most important factor limiting populations of wading birds in the Everglades. In the face of conflicting management scenarios, knowing the relative importance of each component of food availability is a precursor to understanding the effects of specific water management regimes on wading birds. Ongoing modeling efforts in south Florida such as the ATLSS program, integrate such information and provide predictive power for future management decisions. Currently, the biggest information gap limiting the wading bird model of ATLSS is foraging success as a function of prey availability. The South Florida Water Management District (SFWMD) is currently conducting a series of experiments aimed at determining the effects of water management on the use of foraging sites by wading birds. Site-use data are available immediately after each experiment and thus allow for quick analyses and write-up. However, also as part of those experiments, we recorded on film, foraging behavior of wading birds at feeding sites with known prey availabilities.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_geochem_asr_lo", + "title": "Geochemical Parameters to Evaluate Aquifer Storage and Recovery Reactions with Native Water and Aquifer Materials", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-08-01", + "end_date": "2000-05-31", + "bbox": "-81.08, 26.35, -80.28, 27.2", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550988-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550988-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_geochem_asr_lo", + "description": "The objective of this project was to determine geochemically significant water-quality characteristics of possible aquifer storage and recovery (ASR) source and receiving waters north of Lake Okeechobee and at a site along the Hillsboro Canal. The data from this study will be combined with similar information on the detailed composition of aquifer materials in ASR receiving zones to develop geochemical models. Such models are needed to evaluate the possible chemical reactions that may change the physical properties of the aquifer matrix and/or the quality of injected water prior to recovery. To meet water-supply needs of natural systems as well as existing and future urban and agricultural water demands in South Florida, the U.S. Army Corps of Engineers (Corps) has identified ASR near Lake Okeechobee and in other areas as a critical component needed to provide adequate water storage functions for successful Everglades restoration. Several ASR pilot studies have demonstrated the feasibility of storing and recovering potable water from the brackish Floridan aquifer system on a local scale in south Florida (Muniz and Ziegler, 1995; Pyne, 1995). However, to demonstrate the viability of ASR on a greatly expanded regional scale, as proposed by the Corps, considerably more water-quality information is needed to provide assurance that recovered water is suitable for intended uses. At present, little or no information exists to address the following questions: 1. Will interactions between injected water, aquifer material, and native ground water result in elevated levels of radionuclides or trace elements that would be of concern to human or environmental health? 2. What is the fate of nutrients (C, N, P) from injected surface water that could be stored in the aquifer for prolonged time periods? 3.Would chemically aggressive waters injected into target aquifers cause chemical reactions that would result in clogging, biological fouling, or extensive dissolution of aquifer material? 4. If disinfection of surface water is needed prior to injection, what is the fate of resultant disinfection byproducts in water stored in the aquifer? Geochemical models are used to answer these questions and to evaluate other geochemical processes that may affect water quality during ASR operations. These models require knowledge of the chemical composition of the injected (source) water, the native aquifer (receiving) water, and the aquifer materials. This study will provide the characterization of potential source and receiving water in areas of proposed ASR development that are needed for geochemical modeling. Characterization of aquifer materials will be done as part of a Federally funded study following exploratory drilling and recovery of core material from target zones in the Floridan aquifer system. The results of this study will also determine if seasonal changes in water chemistry will require the removal of undesirable constituents prior to injection.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_geochem_mon_restore_fy04", + "title": "Geochemical Monitoring of Restoration Progress", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-10-01", + "end_date": "2004-09-30", + "bbox": "-81.21, 24.72, -80.3, 25.27", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549034-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549034-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_geochem_mon_restore_fy04", + "description": "Continued geochemical monitoring efforts will provide a measure of the progress and effects of restoration on environmental health and water quality, and complement biological monitoring of indicator species. This information is essential for identifying when successful restoration has been accomplished. Additionally, this geochemical monitoring program will serve as a model for developing similar programs for monitoring other coastal and lacustrine environments targeted in future projects. Products include a productivity database for Florida Bay and bimonthly salinity, dissolved oxygen, pH, carbon speciation, and air:sea CO2 gas flux maps of Florida Bay. The flow of fresh water from the Everglades to Florida Bay and the interaction of Bay water with the Gulf of Mexico and Atlantic Ocean are critical processes that have defined the Florida Bay Ecosystem. Reconstruction of historical changes in the Florida Bay Ecosystem using paleoecological and geochemical data from cores and historical databases indicates that significant changes in water quality and circulation (McIvor et al., 1994; Rudnick et al., 1999; Boyer et al., 1999; Halley and Roulier, 1999; Swart et al., 1999), and biological species composition and ecology (Brewster-Wingard and Ishman, 1999; Fourqurean and Robblee, 1999; Hall et al., 1999; Zieman et al., 1999) have been coincident with alteration of drainage patterns in the Everglades and construction of bridges linking the Keys. Paleoecological data from cores also indicates that changes in the abundance of seagrass and algae in the Bay have been coincident with salinity changes and that significant loss of seagrass on mud banks and basins has occurred over the last several years. Stable isotope data from sediment cores indicate decreased circulation in the Bay coincident with railroad building and early drainage in South Florida. Water management practices in South Florida are already being altered in an effort to restore the Everglades and Florida Bay. Resulting changes in water chemistry will first affect biogeochemical processes, and may, subsequently, result in changes in species distributions (such as seagrass, algae, etc.) in the Bay. An extensive water quality monitoring program for Florida Bay has been in operation for several years. Primary participants include ENP - fixed water quality monitoring stations, NOAA -salinity, chlorophyll, and transmittance bimonthly surveys, SFWMD - northeast Bay and north coast monitoring, and Florida International University (FIU) - nutrient monitoring. These programs have provided detailed information on concentrations of water quality parameters in the Bay. However, in situ monitoring of key biogeochemical processes resulting directly from biological activity has not been undertaken. Monitoring changes in biogeochemical processes is critical to early identification of ecological response to restoration and predicting changes in species distribution within the Bay. Additionally, these processes may directly impact water quality. Calcification, photosynthesis, and respiration directly affect dissolved oxygen, pH, dissolved inorganic carbon and a number of other chemical characteristics of the water column. This information will enable managers to evaluate the progress and success of South Florida restoration efforts.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_geophys_mon_fy04", + "title": "Geophysical Monitoring of the Southwest Florida Coast", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-12-01", + "end_date": "", + "bbox": "-81.23795, 25.051413, -80.30868, 26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552919-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552919-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_geophys_mon_fy04", + "description": "Water management decisions that impact Everglades restoration efforts require high quality data and reliable hydrologic models. Traditionally these data for hydrologic models have been obtained through observation wells. In the Everglades, this approach is limited by the difficult access due to water which covers most of the area and to the limited number of roads. Airborne geophysical techniques provide a means of accessing large parcels of land and developing three-dimensional resistivity models of the area. The overall objective of this project is the collection of geophysical data that can be used to develop ground-water flow models of the area capable of modeling saltwater intrusion. This objective includes mapping of subsurface electrical properties of the aquifer and correlation of lateral variation in these properties to aspects of aquifer geometry and water quality that are pertinent to hydrologic model development. Completion of combined ground and airborne geophysical surveys in Everglades National Park and Big Cypress National Preserve has shown the utility of these methods to map saltwater intrusion and provide geological information needed to develop ground-water flow models. The strategy that has been used is to interpret the HEM data as layered-earth resistivity models that slowly vary from place to place. Surface geophysical measurements (time-domain electromagnetic soundings) have been used to assist in this interpretation and provide an independent check on the HEM data. Borehole data in the form of formation resistivities and water quality sampling have allowed us to develop relationships for converting the interpreted resistivity-depth models into estimated water quality given as specific conductance (SC) or chloride concentration. This information is of great value to hydrologic modelers. These data will be used to develop a ground-water flow model which is bounded on the north by the Tamiami Trail, on the south by Florida Bay, on the east by the Atlantic coastal ridge, and on the west by the Gulf of Mexico. Completion of a combined ground and airborne geophysical study in the southern portion of Everglades National Park has shown the utility of these methods to map the extent of saltwater intrusion and provide geological information needed to develop ground-water flow models. The same approach should prove equally useful in the development of hydrologic models in the region to the west where little subsurface information exists. The approach requires three components: ground-based, airborne, and borehole electrical geophysical measurements. In combination these measurements can provide detailed information on the location of geologic and hydrologic boundaries essential for ground-water model development. The mapping of saltwater intrusion in coastal aquifers has traditionally relied upon observation wells and collection of water samples. This approach may miss important hydrologic features related to saltwater intrusion in areas where access is difficult and wells are widely spaced, such as the Everglades. To map saltwater intrusion in Everglades National Park, a different approach has been used. We have relied heavily on helicopter electromagnetic (HEM) measurements to map lateral variations of electrical resistivity, which are directly related to water quality. The HEM data are inverted to provide a three-dimensional resistivity model of the subsurface. Borehole geophysical and water quality measurements made in a selected set of observations wells are used to determine the relation between formation resistivity and specific conductance of pore water. Applying this relation to the 3-D HEM resistivity model produces an estimated water-quality model. This model provides constraints for variable density, ground-water models of the area. Time-domain electromagnetic (TEM) soundings have also be used to map saltwater intrusion. Because of the high density of HEM sampling (a measurement point every 10 meters along flight lines) models with a cell size of 100 meters on a side are possible, revealing features which could not be recognized from either the TEM or the observation wells alone. The very detailed resistivity maps show the extent of saltwater intrusion and the effect of former and present canals and roadbeds. The TEM survey provides a means of quickly obtaining a synoptic picture of saltwater intrusion, which also serves as a baseline for monitoring the effects of Everglades restoration activities.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_german_et_04", + "title": "Evapotranspiration Measuring & Modeling in the Everglades", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-10-01", + "end_date": "2003-09-30", + "bbox": "-80.83, 25.29, -80.26, 26.76", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554463-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554463-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_german_et_04", + "description": "The overall objective is to develop a process-oriented appraisal of evapotranspiration within the Everglades drainage unit, excluding agricultural and brackish environments. Specific objectives include: 1) Field measurement of evapotranspiration at a variety of sites encompassing a regionally representative range of environmental factors.; 2) Integration of evapotranspiration estimates into a process-oriented model; 3) Verification and refinement of model using ET measurements at additional sites. Everglades restoration efforts will rely heavily upon development of hydrologic flow models that will be used to help guide restoration and management decisions. Any hydrologic model requires an assessment of the water budget, including the amount of water removed from the system by evapotranspiration (ET). ET is a major part of the water budget in the Everglades, being similar in magnitude to rainfall. The Everglades ET project provides the necessary ET data, and methods of estimating ET throughout the Everglades system, that are required by all flow models.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_german_et_data", + "title": "Evapotranspiration Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "2003-12-31", + "bbox": "-81.02, 25.33, -80.33, 26.65", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552655-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552655-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_german_et_data", + "description": "A regional evaluation of evapotranspiration (Et) in the Florida Everglades began in 1996 with operation of 9 sites at locations selected to represent the sawgrass or cattail marshes, wet prairie, and open-water areas that constitute most of the natural Everglades system. The Bowen-ratio energy-budget method was used to measure Et at 30-minute intervals. Site models were developed to determine Et for intervals when a Bowen ratio could not be accurately determined. Regional models were then developed for determining 30-minute Et at any location as a function of solar intensity and water depth using data from the 9 sites for 1996-97. Five of the original 9 sites continued in operation after 1997 for various periods. Two of these sites were operated continuously until September 2003. Three new sites were installed in the western part of Shark Valley in November 2001 for the purpose of testing regional model transferability. Additionally, an evaporation pan was installed at one site in April 2001 for comparing actual Et determined by the Bowen-ratio site with potential pan evaporation. All data collection ended in September 2003. The dataset contains the meteorological and evapotranspiration data. Additionally, tables listing model coefficients and goodness-of-fit statistics for site models for the period 1998-2003 are included, and tables listing a comparison for measured Et and Et estimated from the regional models. Data is available by year for each of the collection sites. The a_read_me file in the Data summary and data files for Everglades Et sites, 1996-2003 describes the format of data files of meteorological and evapotranspiration data. Additionally, tables listing model coefficients and goodness-of-fit statistics for site models for the period 1998-2003 are included, and tables listing a comparison for measured Et and Et estimated from the regional models. This latest data release is different in format from the original release for all data from 1998 on. No changes were made in the 1996-97 data. One change made in reporting format is that Et data from 1998 on are not smoothed by averaging over one or more measurement intervals. With this release data are provided at the measurement interval so that users may use whatever smoothing technique that is appropriate for the intended use. Another change in format for data from 1998 on is that Et sums are provided for \"raw\" and \"edited\" 30-minute periods. The \"raw\" data refer to Et sums that have not been edited from computed results, although the Et sum may be an actual measurement that has passed all input-data screening tests (see WRI 00-4217), or may be a \"gap-filled\" value computed from the Priestley-Taylor site mode that was developed using only data that passed all screening tests. Data in the \"edited\" column have been edited graphically by comparing each value to the pattern of Et defined by the entire set of data during part of a day. The final change in format for data from 1998 on is that a flag indicator is provided to show which 30-minute Et data are measured and which are model derived because the input data did not pass screening criteria. Everglades restoration efforts will rely heavily upon development of hydrologic flow models that will be used to help guide restoration and management decisions. Any hydrologic model requires an assessment of the water budget, including the amount of water removed from the system by evapotranspiration (ET). ET is a major part of the water budget in the Everglades, being similar in magnitude to rainfall. The overall objective was to develop a process-oriented appraisal of evapotranspiration within the Everglades drainage unit, excluding agricultural and brackish environments. Specific objectives included: 1) Field measurement of evapotranspiration at a variety of sites encompassing a regionally representative range of environmental factors and 2) Verification and refinement of model using ET measurements at additional sites.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_gfr_bay", + "title": "Groundwater Flow Rates at the Bayside Well Cluster study site", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-08-20", + "end_date": "1997-04-17", + "bbox": "-80.469, 25.071, -80.469, 25.071", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550408-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550408-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gfr_bay", + "description": "The dataset contains the values for the dyes from the tracer study on the bayside of Key Largo. Treated sewage is injected into the limestone under the Florida Keys via on-site disposal systems (OSDs). There are 25,000 septic tank systems, approximately 5,000 cesspools, and approximately 1000 class 5 injection wells. Depths of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases and both marine grass and sponge mortality is perceived by the local population, NOAA, and EPA to be caused by sewage nutrients leaking from groundwater on both sides of the Florida Keys. Determining the rate and direction of saline groundwater movement beneath the Key, and the Florida Bay was considered critical to understanding the fate and effects of subsurface waste water disposal n the Florida Keys. The objective of this research was to determine the rate, direction of flow, and contamination levels of saline groundwater in the Florida Keys and Florida Bay. Contamination studies are necessary to determine if nutrient and other contaminant levels are rising and to provide a baseline of data for future decision making.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_gfr_ocean", + "title": "Groundwater Flow Rates at the Oceanside Well Cluster study site", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-06-18", + "end_date": "1997-04-17", + "bbox": "-80.466, 25.067, -80.466, 25.067", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550135-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550135-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gfr_ocean", + "description": "The dataset contains the values for the dyes from the tracer study on the oceanside of Key Largo. Treated sewage is injected into the limestone under the Florida Keys via on-site disposal systems (OSDs). There are 25,000 septic tank systems, approximately 5,000 cesspools, and approximately 1000 class 5 injection wells. Depths of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases and both marine grass and sponge mortality is perceived by the local population, NOAA, and EPA to be caused by sewage nutrients leaking from groundwater on both sides of the Florida Keys. Determining the rate and direction of saline groundwater movement beneath the Key, and the Florida Bay was considered critical to understanding the fate and effects of subsurface waste water disposal n the Florida Keys. The objective of this research was to determine the rate, direction of flow, and contamination levels of saline groundwater in the Florida Keys and Florida Bay. Contamination studies are necessary to determine if nutrient and other contaminant levels are rising and to provide a baseline of data for future decision making.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_gis_tool", "title": "A GIS-based decision-support tool to evaluate land management policies in south Florida", @@ -184196,6 +191528,45 @@ "description": "The top of the gray limestone aquifer is shallowest in Collier and Hendry Counties and slopes to the southeast and east. The altitude of the top of the gray limestone aquifer generally ranges between sea level and 100 ft below sea level in the study area. The map shows the altitude of the top of the gray limestone aquifer in feet bwelow sea level. The contour interval is 50 feet. The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_glime_ext_aq_polygon", + "title": "Extent of gray limestone aquifer (interpreted to be unconfined), southwestern Florida, USGS WRIR 99-4213, figure 29", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1964-01-01", + "end_date": "1999-03-31", + "bbox": "-81.374565, 25.59151, -80.85008, 26.547632", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549505-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549505-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_glime_ext_aq_polygon", + "description": "Leakance, which is the vertical hydraulic conductivity of the confining unit divided by its thickness, can be used to provide an indication of the degree of confinement of the aquifer. For purposes of this discussion, an aquifer is considered to be well confined, or have 'good confinement', if leakance was less than 1.0 x 10-3 1/d. Sites where leakance was determined by aquifer testing to be less than 1.0 x 10-3 1/d or the behavior of the aquifer was described as confined or well confined (tables 8 and 9) are shown in figure 29. These sites are located in southern Hendry County, western Broward County, and central Miami-Dade County and are in areas where the thickness of the confining unit approaches or is more than 50 ft. However, confining bed thickness did not necessarily prove to be a determinant of confinement. The map shows the extent of the gray limestone aquifer in southwestern Florida. Results from 35 new test coreholes and aquifer-test, water-level, and water quality data were combined with existing hydrogeologic data to define the extent, thickness, hydraulic properties, and degree of confinement of the gray limestone aquifer in Southern Florida. The western boundary is not mapped and is set to the western boundary of the study area. The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_glime_lim_ucu_arc", + "title": "Limit of the Upper Confining Unit of the Gray Limestone Aquifer, Southwestern Florida, USGS WRIR 99-4213, fig. 21", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-10-01", + "end_date": "1999-03-31", + "bbox": "-81.37569, 25.567564, -80.854866, 26.342693", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551436-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551436-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_glime_lim_ucu_arc", + "description": "The upper confining unit of the gray limestone aqifer in southwestern Florida ranges from 20 to 60 ft in thickness in most of the study area, but is absent to the west and southwest in much of Collier County and most of Monroe County. The upper confining unit exists east of the line in the data set, with two small circular areas depicting areas where the unit is absent (to the north) and present (to the south). The unit is also present to the southwest of the short line in the southwest part of the area. The map shows the limit of the upper confining unit of the gray limestone aquifer in Collier, Hendry, Miami-Dade, and Monroe counties. The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_glime_limit_arc", + "title": "Limit of Gray Limestone Aquifer, Southeastern Florida, USGS WRIR 99-4213", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-10-01", + "end_date": "1999-09-30", + "bbox": "-81.37501, 25.364754, -80.25426, 26.54827", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549111-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549111-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_glime_limit_arc", + "description": "The maps shows the limit of the gray limestone aquifer in southern Florida. The lower Tamiami aquifer is mapped as being present in most of western and northeastern Hendry County, which are outside of the study area. However, the limestones of the Tamiami Formation, which are included in the lower Tamiami aquifer, thin to the north, and sand and sandstone layers make up most of the thickness of the formation in central Hendry County. The easternmost extent of the gray limestone aquifer corresponds closely to the limits previously delineated by Fish (1988) and Fish and Stewart (1991). In northeastern Broward County, the eastern edge of the aquifer occurs at the transition from highly permeable limestone or contiguous shell sand to a significantly less permeable facies composed of sandy, clayey limestone and quartz sand and sandstone. In northeastern Miami-Dade County, the eastern limit of the aquifer is mapped where the aquifer merges with the Biscayne aquifer and the intervening semiconfining unit wedges out. South of the Tamiami Trail, the eastern boundary occurs at a transition to less-permeable siliciclastic sediments. The northern and western extents of the gray limestone aquifer were not defined in this study. The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_glime_limit_brwd_east_arc", "title": "Approximate Eastern Limit of the Gray Limestone Aquifer in Broward County, USGS WRIR 87-4034, figure 36", @@ -184223,62 +191594,608 @@ "license": "proprietary" }, { - "id": "USGS_SOFIA_kendall_stable_isotopes", - "title": "Application of Stable Isotope Techniques to Identifying Foodweb Structure, Contaminant Sources, and Biogeochemical Reactions in the Everglades", + "id": "USGS_SOFIA_grndwtr_seepage", + "title": "Groundwater Seepage in the Florida Keys", "catalog": "CEOS_EXTRA STAC Catalog", - "state_date": "1995-03-01", - "end_date": "1999-10-31", - "bbox": "-81.0202, 25.2475, -80.3069, 26.6712", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553952-CEOS_EXTRA.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553952-CEOS_EXTRA.html", - "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_kendall_stable_isotopes", - "description": "This is the largest isotope foodweb study ever attempted in a marsh ecosystem, and combines detailed, long-term, trophic and biogeochemical studies at selected well-monitored USGS/SFWMD/FGFFC sites with limited synoptic foodweb data from over 300 sites sampled during 1996 and 1999 by a collaboration with the EPA-REMAP program. The preliminary synthesis of the biota isotopes at USGS and 1996 REMAP sites provides a mechanism for extrapolating the detailed foodwebs developed at the intensive USGS sites to the entire marsh system sampled by REMAP. Furthermore, this unique study strongly suggests that biota isotopes provide a simple means for monitoring how future ecosystem changes affect the role of periphyton (vs. macrophyte-dominated detritus) in local foodchains, and for predictive models for foodweb structure and MeHg bioaccumulation under different proposed land-management changes. Data are available for the following sites: Cell 4, ENR-OUT, L7, Cell 3, LOX, North Holeyland, E0, F1, U3/Glory Hole, L35B, 2BS, L67, 3A-15, 3A-TH, Lostmans Creek, North Prong Creek, TS-7, and TS-9 for the plants and animals found at each site. A first step of the Everglades restoration efforts is \"getting the water right\". However, the underlying goal is actually to re-establish, as much as possible, the \"pre-development\" spatial and temporal distribution of ecosystems throughout the Everglades. Stable isotope compositions of dissolved nutrients, biota, and sediments provide critical information about current and historic ecosystem conditions in the Everglades, including temporal and spatial variations in contaminant sources, biogeochemical reactions in the water column and shallow subsurface, and trophic relations. Hence, the scientific focus of this project is to use stable isotope techniques to examine ecosystem responses (especially variations in foodweb base and trophic structure) to temporal and spatial variations in hydroperiod and contaminant loading for the entire freshwater Everglades. The major \"long-term\" objectives of this project have been to: (1) determine the stable C, N, and S isotopic compositions of Everglades biota, (2) use bulk and compound-specific isotopic ratios to determine relative trophic positions for major organisms, (3) examine the spatial and temporal changes in foodweb structures across the ecosystem, especially with respect to the effect of anthropogenically derived nutrients and contaminants from agricultural land uses on foodwebs, (4) evaluate the effectiveness of isotopic techniques vs. gut content analysis for determining trophic relations in the Everglades, (5) evaluate the role of algae vs. detritus/microbial materials in foodwebs for the entire freshwater marsh part of the Everglades, and (6) work with modelers to correctly incorporate food web and MeHg bioaccumulation information into predictive models. More recent and specific objectives include: (1) link our data on seasonal and temporal differences in foodweb bases and trophic levels with SFWMD, FGFFC, and USGS Hg datasets (first for large fish and, more recently, for lower trophic levels), (2) investigate the effects of seasonal/spatial changes in nutrients, water levels, and reactions on the isotopic compositions at the base of the foodweb (that affect our interpretation of relative trophic positions of organisms), and (3) continue our efforts to link our foodweb isotope data from samples collected at USGS-ACME and EPA-REMAP sites with the spatial environmental patterns observed by the REMAP program. This work started as part of the Aquatic Cycling of Mercury in the Everglades (ACME) project in 1996 and was made a separate project in 2000.", + "state_date": "1995-07-17", + "end_date": "1996-08-20", + "bbox": "-80.9, 24.8, -80.3, 25.2", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549417-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549417-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_grndwtr_seepage", + "description": "This project installed seepage meters to measure the volume of groundwater seepage into the overlying marine environment. The water will be analyzed for major nutrient levels. The data from this project include the site and values of seepage flux. The Florida Keys contain 25,000 septic tank systems, approximately 5000 cesspools, and 1000 class 5 injection wells. Depth of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases, and both marine grass and sponge lortality is perceived to be caused by sewage nutrients leaking from roundwater on both sides of the Florida Keys. Determining the volume and composition of groundwaters seeping into the marine environment from teh sea floor is vital to management decisions on the area. The objective of this study was to determine the volume and composition of groundwaters seeping upward through the rock water interface into Florida Bay and the coral reef tract. Submarine groundwater input into Florida Bay has been ignored by modelers and results show current models are likely to be erroneous. An additional major product will be an improved seepage meter design.", "license": "proprietary" }, { - "id": "USGS_SOFIA_la_florida", - "title": "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from \"Down-Scaled\" AOGCM Climate Scenarios in Combination with Ecological Modeling", + "id": "USGS_SOFIA_gw-sw_wq_everglades", + "title": "Groundwater-Surface Water Interactions and Relation to Water Quality in the Everglades", "catalog": "CEOS_EXTRA STAC Catalog", - "state_date": "1970-01-01", - "end_date": "2000-12-31", - "bbox": "-92, 23, -75, 38.24", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554072-CEOS_EXTRA.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554072-CEOS_EXTRA.html", - "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_la_florida", - "description": "The objectives of this project are to develop the knowledge necessary to make accurate predictions of the response of species and their ecosystems to climate change. We propose to down-scale predictions from a suite of coupled Atmosphere-Ocean General Circulation Models (AOGCMs) to make regional scale predictions for the southeastern United States. For the time being the hydrologic and biologic models are confined to Florida. Climate outputs will then be used as inputs to a suite of species / habitat / ecosystem models that are currently being used in two key areas: the Greater Everglades and Suwannee River-Big Bend as a proof of concept that down-scaled climate results can work in ecological forecast models. We will run three scenarios of Land Use/Land Cover (LULC): past (circa 1900), present, and future (2041-2070). Additional climate model runs will address the contribution of green house gasses to climate variability and change over the Florida peninsula. Model perturbation experiments will be performed to address sources of variability and their contribution to the output regional climate change scenarios. We will develop scenarios that specifically address potential changes in temperature (land and near sea surface) and rainfall fields over the peninsula. We will then provide these scenarios and modeling results to resource management groups (NGOs, state and federal) via workshops in which the scenarios will be used to predict responses of additional selected species, habitats and ecosystems. Our approach is to develop regional climate predictions and subsequent ecological predictions for two 30-year long time periods as well as for the present. The first 30-year period is the recent past, spanning the period from 1971-2000. This will be used as a control, with copious observations of both climate variables (e.g. rainfall, ET) and species (e.g. densities, ranges) to verify both climate and ecology model outputs and to serve as a baseline to systematically judge the impacts of an altered climate. The second 30-year time period will begin 30 years in the future and extend for the thirty years from 2041-2070. This is a time horizon that is immediately relevant to habitat management.", + "state_date": "1995-01-01", + "end_date": "1998-12-31", + "bbox": "-80.87469, 25.094265, -80.03598, 26.577616", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550617-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550617-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gw-sw_wq_everglades", + "description": "At present there are few reliable estimates of hydrologic fluxes between groundwater and surface water in the Everglades. This gap in hydrological investigations not only leaves the water budget of the Everglades uncertain, it also hampers progress in understanding the processes that determine mobility and transformation of contaminants, such as mercury, sulfate and nutrients. The objective of this project is to quantify hydrologic exchange fluxes between groundwater and surface water and its effects on transport of contaminants in the Everglades. The research furthermore relates surface water and ground water interactions to past, present, and proposed management of surface-water levels and flows in the Everglades. The principal research sites are the Everglades Nutrient Removal Project (ENR), Water Conservation Area 2A (WCA-2A), and the freshwater wetlands of Everglades National Park. Results are being used to quantify ground-water exchange with surface flow, and to quantify the enhancement of chemical transformations of contaminants during transport across the interface between surface water and ground water. Two data sets are available for this project. The Northern Everglades Research Site and Sample Information data set contains a summary of the site locations, data types, and measurement periods in ENR, WCA2A, and WCA2B. The Seepage Meters Site and Sample Information data set contains vertical fluxes across wetland peat surface measured by seepage meters at research sites in ENR, WCA2A, WCA2B, and WCA3A. Additional data can be found in the appendices of the Open-File Reports 00-168 and 00-483. For restoration of the Everglades to succeed there must be comprehensive knowledge about physical, chemical, and biological processes throughout the system. A key measure of success in the Everglades is the improvement or protection of water quality under changing hydrologic conditions. Although there is a basic understanding of how interactions between groundwater and surface water will affect water budgets under restoration, there is only a rudimentary understanding of how interactions between groundwater and surface water will affect water quality. Only field-oriented research and modeling can determine whether interactions between groundwater and surface water are currently storing pollutants in groundwater, how long those pollutants are likely to be stored in the aquifer, and under what changing management conditions associated with restoration will those pollutants be returned into the surface water system.", "license": "proprietary" }, { - "id": "USGS_SOFIA_metlietz", - "title": "Determination of Nutrient Loads to East Coast Canals", + "id": "USGS_SOFIA_gw_flow_trans_TIME", + "title": "Ground Water Flow and Transport for the SICS and TIME Models", "catalog": "CEOS_EXTRA STAC Catalog", - "state_date": "1996-05-01", - "end_date": "1997-10-31", - "bbox": "-80.39, 25.35, -80.15, 25.94", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552186-CEOS_EXTRA.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552186-CEOS_EXTRA.html", - "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metlietz", - "description": "The objectives of this project were threefold: 1) to determine if historical water-quality data collected as grab samples at 0.5 and c1.0 m below the surface near the centroid of flow adequately represent stream cross-sectional chemistry, 2) to develop reliable estimates of nitrogen and phosphorus loads for east coast canals based on statistical models developed from utilizing the techniques of ordinary least squares regression, and 3) to summarize water-quality data and determine temporal trends for water-quality constituents at two sites that are strategic to Biscayne Bay and the south Florida ecosystem. During phase 1 of the project an intensive field sampling and data collection effort was undertaken. Depth-integrated samples were collected by the equal-width-increment method as well as grab samples at each canal. During Phase 2 data analysis was done. Nutrient data were collected upstream of 15 coastal control structures in Miami-Dade County. Samples were collected over a typical hydrologic period during various flow conditions. Sampling began at 5 sites in May 1996 and at 10 sites in October 1996. Constituents collected included ammonia, nitrite, nitrate, orthophosphate, and total phosphorus. Of major concern in many coastal areas around the Nation is the ecological health of bays and estuaries. A common problem in many of these areas is increased nutrient loads as a result of agricultural, commercial, industrial, and urban processes. Biscayne Bay is a shallow subtropical estuary along the southern coast of Florida. The Biscayne Bay ecosystem provides an aquatic environment that is a habitat to a diverse array of plant and animal communities. Nutrients are essential compounds for the growth and maintenance of all organisms and especially for the productivity of aquatic environments. Nitrogen and phosphorus compounds are especially important to seagrass, macroalgae, and phytoplankton. However, heavy nutrient loads to bays and estuaries can result in conditions conducive to eutrophication and the attendant problems of algal blooms and high phytoplankton productivity. Additionally, reduced light penetration in the water column because of phytoplankton blooms can adversely affect seagrasses, which many commercial and sport fish rely on for their habitat. Providing reliable estimates of nonpoint source nutrient loads to Biscayne Bay is important to the development of nutrient budgets as well as input to eutrophication models. Understanding the effects of these nutrient loads is a necessary initial step in planning restoration of the ecological health of Biscayne Bay. Nutrient data have been collected from the east coast canals for many years by various government agencies. Much of the data collected have been from grab samples at 0.5 or 1.0 meter below the stream surface near the centroid of flow. The degree to which these samples adequately represent nitrogen and phosphorus concentrations within the water column of the canals of south Florida is presently unknown and limits confidence in loading estimates. Furthermore, the relation between discharge and nutrient concentration that occurs in natural uncontrolled streams in other parts of the Nation may not apply to the artificially controlled canals of south Florida. Both of these issues need to be addressed to develop nutrient budgets and to plan effective restoration strategy now and in the future.", + "state_date": "2000-10-01", + "end_date": "2006-09-30", + "bbox": "-81.55504, 25.026571, -80.30481, 25.976713", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552234-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552234-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_gw_flow_trans_TIME", + "description": "The objective of this project is to develop a numerical groundwater flow model that can be used with the TIME surface water model to quantify and predict flows and salinities in the coastal wetlands of the southern Everglades. Field data will be collected to help formulate the hydrogeologic conceptual model and for calibration of the model to flows, water levels, and salinities. Data collection will consist of monitoring well installation, seepage measurements, spatial characterization of peat thickness, and continuous monitoring of water levels and salinities at selected locations. The SICS model encompasses Taylor Slough and uses a 300-m grid resolution. The larger TIME model encompasses Shark and Taylor Sloughs and uses a 500-m grid resolution. A groundwater model has already been developed and linked with the SICS surface water model. This integrated SICS model simulates flows, stages, and salinities for the 5-year period from 1995 to 2000. Plans for the SICS model are to extend the simulation period through 2002 and complete a linkage to the South Florida Water Management District\u0092s model, called the \"2x2\" model. The SICS model will then be capable of performing detailed restoration scenarios for the Taylor Slough area. A preliminary groundwater model has also been developed for the TIME area, but this groundwater model has not yet been linked with a surface water model. Ray Schaffranek is currently finalizing a 3-month simulation with the TIME surface water model. As part of this project, the groundwater model will be linked with the TIME surface water model, and the simulation period will be extended to cover 2 years. A related CERP (Comprehensive Everglades Restoration Plan) project will extend this simulation period to 7 years and link with the 2x2 to perform Everglade restoration scenarios. This project also involves quantifying surface water and groundwater interactions by using nested monitoring wells and seepage meters. Data from the field studies are used to calibrate and verify the SICS and TIME models. The interaction between surface water and groundwater can be a potentially significant component of the hydrologic water budget in the Everglades. Recent research has shown that surface water and groundwater interactions also can affect salinities in coastal wetlands. As Everglades restoration is largely dependent upon \"getting the water right\", the U.S. Geological Survey is developing the TIME (Tides and Inflows in the Mangroves of the Everglades) and SICS (Southern Inland and Coastal Systems) models, hydrodynamic surface water models of the southern Everglades. The purpose of the TIME and SICS models is to accurately simulate flows and salinities in the coastal wetlands of the southern Everglades. Once calibrated, these models will be used to evaluate proposed restoration scenarios by feeding hydrologic information into the ATLSS biological models. These biological models are highly sensitive to hydrologic inputs such as flows, stages, and salinities; thus, the TIME and SICS models are expected to play an important role in linking the hydrologic component of the Everglades to the biologic component. In recent years, this project focused on developing a groundwater component for the SICS model, an integrated model of Taylor Slough and northern Florida Bay. The SICS model is now calibrated, operational, and providing important insight into the flow and salinity patterns of the southern coastal Everglades. Hydrologic output from the SICS model is being used in development of ATLSS fish models. The next step with this groundwater project is to extend the methodologies developed as part of the SICS modeling effort to the much larger TIME model. This project is now part of the SICS and TIME model linkages and development in support of Everglades Restoration project.", "license": "proprietary" }, { - "id": "USGS_SOFIA_metschaf", - "title": "Canal and Wetland Flow/Transport Interaction", + "id": "USGS_SOFIA_hansen_1890_trackline_map_version 1", + "title": "Florida Bay 1890 trackline map", "catalog": "CEOS_EXTRA STAC Catalog", - "state_date": "1997-09-23", - "end_date": "", - "bbox": "-80.6, 25.25, -80.4, 25.35", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554720-CEOS_EXTRA.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554720-CEOS_EXTRA.html", - "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metschaf", - "description": "Significant canal and wetland flow exchanges can potentially occur along the southwest overbank area of canal C-111 between hydraulic control structures S-18C and S-197. This coupled flow system is of particular concern to restoration efforts in that it provides a pathway for fresh water to nearshore embayments in Florida Bay. New construction modifications and operational strategies proposed for C-111 under the Central and Southern Florida \"Restudy\" Project are intended to enhance sheet flow to these subtidal embayments. The objectives of the canal and wetland flow/transport interaction project were to (1) develop numerical techniques and algorithms to facilitate the coupling of existing generic models for improved simulation of canal and wetland interactions, (2) translate recent findings of ongoing process studies within the South Florida Ecosystem Program (SFEP) into new mathematical formulations, empirical expressions, and numerical approximations to enhance generic simulation model capabilities for the south Florida ecosystem, (3) investigate new instrument capabilities and field deployment approaches to collect the refined data needed to identify and quantify the important flow-controlling forces and landscape features for model implementation, (4) integrate process-study findings and the results of physiographic mapping and remote sensing efforts specific to the C-111 basin into a numerical simulation model of the interconnected canal and wetland flow system, and (5) use the resultant model and data to study, evaluate, and demonstrate the significance of driving forces relative to controlling flow exchanges between canal C-111 and its bordering wetlands. Discharge data for Tamiami Canal are also available for water years 1986-1999, 2000, and 2001. A complex network of canals, levees, and control structures, designed to control flooding and provide a continuous supply of fresh water for household and agricultural use, has altered naturally occurring flow patterns through the Everglades and into Florida Bay. Quantification of dynamic flow conditions within the south Florida ecosystem is vital to assessing implications of the residence time of water, potentially nutrient-enriched (with nitrates or phosphates) or contaminant-laden (with metals or pesticides), that can alter plant life and affect biological communities. Improved numerical techniques are needed not only to more accurately evaluate discrete forces governing flow in the canals and wetlands but also to analyze their complex interaction in order to facilitate coupled representation of transport processes. Flow and transport processes are integrally linked meaning that precise quantification of the fluid dynamics is required to accurately evaluate the transport of waterborne constituents. Robust models that employ highly accurate numerical methods to invoke coupled solution of the most appropriately formulated and representative equations governing flow and transport processes are needed. Through strategic use of a model, cause-and-effect relations between discharge sources, flow magnitudes, transport processes, and changes in vegetation and biota can be systematically investigated. The effects of driving forces on nutrient cycling and contaminant transport can then be quantified, evaluated, and more effectively factored into the development of remedial management plans. A well-developed model can be used to evaluate newly devised plans to improve freshwater deliveries to Florida Bay prior to implementation. This project ended in 1999. Related work can be found at http://time.er.usgs.gov/. For additional information about this project contact either: Eric Swain, edswain@usgs.gov, 954 377-5925 or Chris Langevin, langevin@usgs.gov, 954 377-5917", + "state_date": "1889-01-01", + "end_date": "1890-12-31", + "bbox": "-81.11667, 24.733334, -80.36667, 25.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549703-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549703-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hansen_1890_trackline_map_version%201", + "description": "The map shows the tracklines for historical bathymetric data for Florida Bay. The areas on the map are linked to the corresponding data sets which contain values for X (easting), Y (northing), Z (elevation), and the RMS set to 0.0. Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay has not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Digitizing the historical shoreline and bathymetric data for comparison with modern data provides information on sedimentation rates within the Bay.", "license": "proprietary" }, { - "id": "USGS_SOFIA_mmarvin", - "title": "Bacterial demethylation of methylmercury in the South Florida Ecosystem", + "id": "USGS_SOFIA_hansen_1990_trackline_map_version 1", + "title": "Florida Bay 1990 trackline map", "catalog": "CEOS_EXTRA STAC Catalog", - "state_date": "1996-06-01", + "state_date": "1995-01-01", + "end_date": "1999-12-31", + "bbox": "-81.11667, 24.733334, -80.36667, 25.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549559-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549559-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hansen_1990_trackline_map_version%201", + "description": "The map shows the tracklines for bathymetric data collected between 1995 and 1999 for Florida Bay. The areas on the map are linked to the corresponding data sets which contain values for X (easting), Y (northing), Z (elevation), and the RMS computed from Ashtech PNAV software. The data set is labeled 1990 for easy comparison. The project duration was a decade. Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay has not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. An accurate, modern bathymetric survey provides a baseline for assessing future sedimentation rates in the Bay, and a foundation for developing a sediment budget. Due to the complexity of the Bay and age of existing data, a current bathymetric grid (digitally derived from the survey) is critical for numerical models. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Application of these models is complicated due to the complex seafloor topography (basin/mudbank morphology) of the Bay. The only complete topography data set of the Bay is 100 years old. Consequently, an accurate, modern seafloor bathymetry map of the Bay is critical for numerical modeling research. A modern bathymetry data set will also permit a comparison to historical data in order to help access sedimentation rates within the Bay. The objective of this research was to collect new bathymetry for all of Florida Bay, digitize the historical shoreline and bathymetric data, compare previous data to modern data, and produce maps and digital grids of historical and modern bathymetry.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hardness_swp_lnwr", + "title": "Effects of water hardness on slough-wet prairie plant communities of the A. R. M. Loxahatchee National Wildlife Refuge", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2006-01-01", + "end_date": "", + "bbox": "-80.5, 26.3, -80.25, 26.7", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411613-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411613-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hardness_swp_lnwr", + "description": "Alterations to ground-water and surface-water hydrology and water chemistry in South Florida have contributed to increased flows of mineral-rich (hard water) canal water into historically rain-fall driven (soft water) areas of the Everglades. The interior of the A. R. M. Loxahatchee National Wildlife Refuge largely has retained its historic low conductivity or soft water condition due to its relative isolation from canal flows. However, recent sampling by USGS and the Refuge has shown that canal influences on water quality extend several kilometers into the Refuge in some areas, and Refuge managers and scientists are concerned that these influences may increase depending on future changes in water management operations. A survey across existing mineral gradients will be performed to document patterns of vegetation change and their relationship to changes in water hardness and other environmental factors. Laboratory and field experiments will test these correlative relationships to determine the relative importance of increasing water hardness as a cause of observed vegetation changes across canal gradients. Intrusion of canal waters into the Refuge increases the availability of Phosphorus (P), the primary limiting plant nutrient in the Everglades, as well as concentrations of major mineral ions such as Ca 2+, Mg 2+ and SO4 2-. While the ecological effects of P enrichment on the Everglades is fairly well understood, potential impacts caused by increased mineral concentrations in this soft-water wetland are largely unknown. Understanding the types and magnitude of these impacts is particularly important given that the area of the Refuge exposed to mineral enrichment is much greater than that exposed to P enrichment. The objective of this project is to determine the effects of increased flows of mineral-rich water on the aquatic plant community of the Refuge interior. Slough-wet prairie (SWP) habitats area a major landscape feature in the Refuge and several SWP plant species may be adapted to the soft-water conditions in the Refuge interior. Increased mineral loads to the Refuge may result in a shift towards a more species-poor and spatially homogeneous community, In addition, there is a small amount of evidence to suggest that mineral enrichment may favor the growth and expansion of sawgrass and a consequent decline in the coverage of the SWP habitats.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_helio_mag_data", + "title": "Helicopter Magnetic Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-12-09", + "end_date": "2994-12-14", + "bbox": "-81.125, 25.125, -80.375, 25.6", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553766-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553766-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_helio_mag_data", + "description": "These helicopter electromagnetic data were flown over a portion of Everglades National Park and surrounding areas in south Florida 9-14 December 1994. Two versions of the data are provided: the original dataset and the corrected dataset. This project addressed the question of determining the location of the fresh-water/salt-water interface (FWSWI) in the coastal regions of southern Miami-Dade and Monroe counties, synoptic monitoring of changes in water quality associated with changes in water management practices, and looking for geophysical evidence of subsurface discharges to Florida Bay. This project provided basic information needed to create ground-water models and test various restoration strategies and their impact on ground-water quality.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hi_accuracy_elev_collection_04", + "title": "High Accuracy Elevation Data Collection Project", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "2007-12-31", + "bbox": "-81.625, 25, -80.125, 27.33", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551560-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551560-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hi_accuracy_elev_collection_04", + "description": "The High Accuracy Elevation Data Project collected elevation data (meters) on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters to define the topography in South Florida. The data are referenced to the horizontal datum North American Datum 1983 (NAD 83) and the vertical datum North American Vertical Datum 1988 (NAVD 88). The High Accuracy Elevation Data Project began with a pilot study in FY 1995 to determine if the then state-of-the-art GPS technology could be used to perform a topographic survey that would meet the vertical accuracy requirements of the hydrologic modeling community. The initial testing platform was from a truck and met the accuracy requirements. In some areas, the surveying was accomplished using airboats. Because access was a logistical problem with airboats, the USGS developed a helicopter-based instrument known as the Airborne Height Finder (AHF). All subsequent data collection used the AHF. Data were collected from the Loxahatchee National Wildlife Refuge, south through the Water Conservation Areas (1A, 2A, 2B, 3A, and 3B), Big Cypress National Park, the Everglades National Park, to the Florida Bay. Data were also collected in the Lake Okeechobee littoral zone. The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html. The work was performed for Everglades ecosystem restoration purposes. The project started in 1995 and concluded in 2007. This project performed regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that were being developed for ecosystem restoration activities. Surveying services were also rendered to provide vertical reference points for numerous water level gauges. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) were collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_high_acc_elev_data", + "title": "High Accuracy Elevation Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "", + "bbox": "-81.375, 25.125, -80.125, 26.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549649-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549649-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_high_acc_elev_data", + "description": "The U.S. Geological Survey (USGS) is coordinating the aquisition of high accuracy elevation data. Three formats of the data are available for each data set: .cor files which contain complete lists of Global Positioning System point files, .asc files which are the same as the .cor files but have been reformatted to process into ARC/INFO coverages, and .e00 files which are the ARC/INFO coverages. The files are available in the same 7.5- by 7.5-minute coverages as USGS quadrangles. The elevation data is collected on a 400 by 400 meter grid. The elevations are referenced to the horizontal North American Datum of 1983 (NAD83) and vertical North American Vertical Datum of 1988 (NAVD88). This project is performing regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that are being developed for ecosystem restoration activities. Surveying services are also being rendered to provide vertical reference points for numerous water level gauges. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists have determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) are being collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_highres_bathy_sfl_est-coast_sys", + "title": "High Resolution Bathymetric Mapping of South Florida Estuarine and Coastal Systems", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-10-01", + "end_date": "2005-09-30", + "bbox": "-82.125, 25.75, -81.625, 26.875", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554886-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554886-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_highres_bathy_sfl_est-coast_sys", + "description": "The plan to acquire bathymetric data for the Caloosahatchee Estuary and Estero Bay areas is to employ two methods which have been developed by the U. S. Geological Survey (USGS) and National Aeronautical and Space Administration (NASA). The USGS method is an acoustic based system named System for Accurate Nearshore Depth Surveys (SANDS), and the NASA method is an airborne LIDAR system named Experimental Advanced Airborne Research Lidar (EAARL). The plan is to use the EAARL system to map shallow (less than 1.5 secchi depth) and non-turbid areas in Estero Bay and nearshore areas. The SANDS system will be used in deeper areas and those which are turbid which includes the Caloosahatchee River. High resolution, GPS based bathymetric surveying is a proven method to map river, lake, and ocean floor elevations. Of primary interest to the South Florida Water Management District (SFWMD) is the quantification of the present day bathymetry of Caloosahatchee Estuary and Estero Bay regions. This information can be used by water management decision-makers to develop of Minimum Flows and Levels (MFL) and better preserve fragile habitats. The areas in and around the Caloosahatchee Estuary and Estero Bay Watershed have undergone dramatic increases in the rate of residential and commercial development as well as population growth during the past 15 years. As a result, a series of initiatives have been proposed to balance development and environmental interests in the region. Several recent initiatives including the development MFL and the Southwest Florida Feasibility Study (SWFFS) necessitate the development of hydrodynamic models of coastal waters in the Caloosahatchee Estuary and Estero Bay areas. One of the important data requirements for these models is the bathymetry. The information available at this time is dated (the last complete bathymetric survey is over 100 years old) and needs to be upgraded with a new survey. In addition, recommendations of the Estero Bay and Watershed Assessment completed in November of 1999 recommended the development of a Bay hydrodynamic and water quality model. Updated river, bay, and coastal bathymetry is required for these efforts. The area for bathymetry collection and interpretation includes Estero Bay, Charlotte Harbor, Pine Island Sound, offshore regions of Sanibel and Captive Islands, and the Caloosahatchee, Loxahatchee and St. Lucie Rivers. In addition, a need for an Estero Bay and Charlotte Harbor estuarine mixing model has been identified by the Southwest Florida Regional Restoration Coordination Team and the Southwest Florida Feasibility Study. In order to create an accurate numerical model, current bathymetric data must be obtained. Bathymetry data is also needed for the creation of a seagrass vision maps (an NEP effort) and to populate the species response models being created as assessment tools for several restoration programs.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hist_salinity_wq_veg_bb_04", + "title": "Historical Changes in Salinity, Water Quality, and Vegetation in Biscayne Bay", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-02-24", + "end_date": "2007-04-03", + "bbox": "-80.42, 25.17, -80.08, 26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553154-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553154-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hist_salinity_wq_veg_bb_04", + "description": "The objectives of this project are to examine in broad context the historical changes in the Biscayne Bay ecosystem at selected sites on a decadal-centennial scale, and to correlate these changes with natural events and anthropogenic alterations in the South Florida region. Specific emphasis will be placed on historical changes to 1) amount, timing, and sources of freshwater influx and the resulting effects on salinity and water quality; 2) shoreline and sub-aquatic vegetation; and 3) the relationship between sea-level change, onshore vegetation, and salinity. In addition, a detailed examination of historical seasonal salinity patterns will be derived from biochemical analyses of molluscs, ostracodes, foraminifera and corals. The corals will allow us to compare marine and estuarine trends, examine the linkage between the two systems, and will provide precise chronological control. Land management agencies (principally SFWMD, ACOE and Biscayne NP) can use the data derived from this project to establish performance criteria for restoring natural flow, and to understand the consequences of altered flow. These data can also be used to forecast potential problems as upstream changes in water delivery are made during restoration. During the last century, Biscayne Bay has been greatly affected by anthropogenic alteration of the environment through urbanization of the Miami/Dade County area, and alteration of natural flow. The sources, timing, delivery, and quality of freshwater flow into the Bay, and the shoreline and sub-aquatic vegetation have changed. Current restoration goals are attempting to restore natural flow of fresh water into Biscayne and Florida Bays and to restore the natural vegetation, but first we must address of what the was environment prior to significant human alteration in order to establish targets for restoration. This project is designed to examine the natural patterns of temporal change in salinity, water quality, vegetation, and benthic fauna in Biscayne Bay over the last 100-300 years and to examine the causes of change.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hlms_physical_data", + "title": "Florida Bay Physical Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-02-24", + "end_date": "1997-06-13", + "bbox": "-81.75, 24.75, -80.1, 26.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549037-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549037-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hlms_physical_data", + "description": "The dataset contains the core number, depth (cm), wet bulk density, dry bulk density, accumulated dry bulk density, dry bulk fines, total % H2O content, % insoluble residue, % loss on ignition, coarse (% dry wt.) > 0.062 mm, fines (% dry wt.) < 0.062 mm, and total Pb-210 activity (dpm/g and error). The use of radioactive isotopes is founded on the known physical property of radioactive material, the half-life. A half-life of an isotope is the amount of time it takes for half of a given number of atoms to \"decay\" to another element. The age of objects that contain radioactive isotopes with known half-lives can be calculated by determining the percent of the remaining radioactive material. To use this method successfully certain other prerequisites must be met. These are: 1. the chemistry of the nuclide (element) is known; 2. once the nuclide is incorporated into the substrate the only change is radioactive decay, and 3. in order to be useful, it is relatively easy to measure.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hlms_radchem_data", + "title": "Florida Bay Radiochemical data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-02-07", + "end_date": "1997-06-13", + "bbox": "-81.75, 24.75, -80.1, 26.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548761-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548761-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hlms_radchem_data", + "description": "The datasets contains the core number, depth (cm), and Ra-226 activity(dpm/g) and Ra-226 error (dpm/g). The use of radioactive isotopes is founded on the known physical property of radioactive material, the half-life. A half-life of an isotope is the amount of time it takes for half of a given number of atoms to \"decay\" to another element. The age of objects that contain radioactive isotopes with known half-lives can be calculated by determining the percent of the remaining radioactive material. To use this method successfully certain other prerequisites must be met. These are: 1. the chemistry of the nuclide (element) is known; 2. once the nuclide is incorporated into the substrate the only change is radioactive decay, and 3. in order to be useful, it is relatively easy to measure.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hlmsclog", + "title": "Florida Bay core logs disposition and analysis parameters", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-02-07", + "end_date": "1997-06-13", + "bbox": "-81.096, 24.9843, -80.4082, 25.2091", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554350-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554350-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hlmsclog", + "description": "The data set contains the core number, location, latitude/longitude, date collected, storage location, core surface description, and analyses for cores taken from Rabbitt Key, Cluett Key, Whipray Basin, Bob Allen Key, Rankin Bight, Lake Ingraham, Russell Bank, Johnson Key, Porjoe Key, Trout Creek, Little Madeira Bay, Crocodile Point, Pass Key, and Park Key. The use of radioactive isotopes is founded on the known physical property of radioactive material, the half-life. A half-life of an isotope is the amount of time it takes for half of a given number of atoms to \"decay\" to another element. The age of objects that contain radioactive isotopes with known half-lives can be calculated by determining the percent of the remaining radioactive material. To use this method successfully certain other prerequisites must be met. These are: 1. the chemistry of the nuclide (element) is known; 2. once the nuclide is incorporated into the substrate the only change is radioactive decay, and 3. in order to be useful, it is relatively easy to measure.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hydro_flow_TT", + "title": "Modeling hydrologic flow and vegetation response across the Tamiami Trail and coastal watershed of Ten Thousand Islands NWR", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2006-01-01", + "end_date": "2010-09-30", + "bbox": "-81.7, 25.6, -81.4, 26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553905-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553905-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hydro_flow_TT", + "description": "The proposed study capitalizes on field expertise and existing decision support tools to assess the benefits and/or consequences of CERP hydrologic goals and projects on mangrove/marsh habitat for park and refuge lands of the Greater Everglades system. The primary goal of this study is to monitor and model surface water, groundwater, and evapotranspiration fluxes across a major hydrological barrier in south Florida (U.S. Hwy. 41, Tamiami Trail), and across the oligohaline-estuarine gradient of Ten Thousand Islands National Wildlife Refuge (TTINWR). This research will record the rate and stage of water flow under varying climatic conditions (e.g., wet and dry season) across the coastal margin of TTINWR prior to and following implementation of hydrologic restoration outlined for the Picayune Strand Restoration Project (and Southern Golden Gate Estates Hydrologic Restoration). Overall project tasks and objectives include: gaging hydrologic conditions, surveying ground and water elevations, correlating hydroperiod and plant associations, monitoring intra-annual growth response to climate and hydrology, and modeling hydrologic coupling and vegetative succession. Major restoration projects have been proposed to restore freshwater flow across the Tamiami Trail (U.S. 41) into coastal marshes and estuaries of the northern Everglades including Big Cypress National Preserve and Ten Thousand Islands National Wildlife Refuge (TTINWR) with little or no understanding of the hydrologic coupling and potential impact to vegetation communities. Monitoring activities and models are needed to assess the hydrologic exchange across the Tamiami Trail and at the estuarine interface within the coastal watersheds of TTINWR. Under the proposed Picayune Strand Restoration Project, plugs and culverts will be installed to shunt more freshwater across the Tamiami Trail north-to-south akin to historic flows which will alter the stage, discharge, timing, and distribution of flow across the marsh/mangrove coastal margin. There is a critical need for current hydrologic and vegetation data to understand current processes and relations controlling hydroperiod, salinity, and plant succession under pre-project conditions and climate in order to build models and to predict how increasing freshwater flow and sea-level rise will impact future habitat quality and distribution. This study will establish a stratified network of gaging stations to monitor continuous water levels and salinity conditions associated with vegetation type and growth response and to produce a hydrodynamic model to predict changes in hydroperiod and salinity under different rates of freshwater inflow, pre- and post-project. Gaging stations will be surveyed to vertical datum to create a digital elevation model of both land and water surface that can be used to calibrate hydroperiod and salinity relations that control vegetation growth and succession. Model applications will be extended to predict vegetation migration and succession under changing freshwater delivery regimes and changing sea-level under projected climate change. For additional information about this project, please contact : Ken Krauss 700 Cajundome Blvd. Lafayette, LA, 70506 voice: 337 266-8882 fax: 337 266-8592 email: kkrauss@usgs.gov", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hydro_mon_joe_bay", + "title": "Hydrologic Monitoring in Joe Bay", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-05-01", + "end_date": "2006-09-30", + "bbox": "-80.59, 25.223, -80.524, 25.232", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550553-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550553-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hydro_mon_joe_bay", + "description": "The datasets contain values collected at 15 minute and hourly intervals for stage (water level), discharge (flow), salinity, and temperature between 1999 and 2006. The stage is measured in feet relative to NAVD 88, the discharge in cubic feet per second (cfs), temperature in degrees Celsius, and salinity in parts per thousand (ppt). The data are referenced to date and time in hours and minutes. Joe Bay is the primary hydrologic connection between the freshwater Everglades and northeastern Florida Bay. Flow and salinity monitoring by the U.S. Geological Survey (USGS) has determined that Trout Creek is the largest contributor of freshwater flow to northeastern Florida Bay and is connected to Joe Bay (Hittle and others 2001). Sources of freshwater to Joe Bay include Taylor Slough and the C-111 Canal. Hydrologic parameters such as water level, discharge, and salinity observations in conjunction with water quality sampling have been useful in determining contributions of freshwater flow from Taylor Slough and C-111 Canal to Joe Bay (Zucker 2003). Hourly salinity data has been collected at four locations in Joe Bay since May 1999. In 2001, three index velocity stations were installed at Joe Bay 2E, Joe Bay 5C, and Joe Bay 8W. The current monitoring network in Joe Bay can assist with determining the effect upstream restoration efforts have on the timing and distribution of freshwater flows into northeastern Florida Bay.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hydro_mon_net", + "title": "Hydrologic Monitoring Network: Data Mining and Modeling to Separate Human and Natural Hydrologic Dynamics", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2004-10-01", + "end_date": "2008-09-30", + "bbox": "-81, 25.75, -80.25, 26.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550030-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550030-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hydro_mon_net", + "description": "The objectives of the study include: (1) integration of hydrologic analysis and synthesis with biological studies; (2) separation of water level, stream flow, and salinity time series into the natural (tidal, climate) and anthropogenic components; and, (3) identification of additional areas where application of data mining techniques can address the DOI science needs in South Florida. New technologies in environmental monitoring have made it cost effective to acquire tremendous amounts of hydrologic and water-quality data. Although these data are a valuable resource for understanding environmental systems, often there is seldom a thorough analysis of the data. The monitoring network(s) supported by the Comprehensive Everglades Restoration Plan (CERP) records tremendous amounts of data each day and the data base incorporates millions of data points describing the environmental response of the system to changing conditions. To enhance the evaluation of the CERP data base, there is an immediate need to apply new methodologies to systematically analyze the data set to answer critical questions such as relative impacts of controlled freshwater releases, tidal dynamics, and meteorological forcing on streamflow, water level, and salinity. There also is a need to integrate longer-term hydrologic data with shorter-term hydrologic data collected for biological resource studies. This study will be undertaken as a series of pilot studies to demonstrate the efficacy of data mining techniques to evaluate CERP data and address hydrologic issues important to DOI's efforts in South Florida. In addition, preliminary assessment of the complete set of hydrologic data networks for further integration and analysis using data mining techniques will be conducted.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hydro_restoration_impacts_SW_FL", + "title": "Impacts of Hydrological Restoration on Three Estuarine Communities of the Southwest Florida Coast and on Associated Animal Inhabitants", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-10-01", + "end_date": "2004-09-30", + "bbox": "-81.375, 24.75, -80.25, 26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553398-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553398-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hydro_restoration_impacts_SW_FL", + "description": "This project sought to characterize habitat relationships between selected faunal groups and their mangrove environment on the Southwest Florida coast. We described how mangrove associated fish species are distributed in fringing forest habitat along a salinity gradient in the tidal portions of the Shark River; the ecology and population dynamics of diamondback terrapins in the Big Sable Creek complex; experimentally determined the preferred habitat of the specialist fish Rivulus marmoratus via field and laboratory experiments; and how the conversion of mangrove forests to intertidal mud flats in the Big Sable Creek complex has affected fish composition and use of those habitats. The overall strategy was to collect robust empirical field data on forage fish distribution and abundance that can serve multiple purposes: as performance measures in restoration assessment; as the beginning of a long-term dataset analogous to three very powerful datasets from other locales in the Greater Everglades Ecosystem: 15-20 yr from freshwater marshes, 10 yr from the mangrove ecotone of Taylor Slough and adjacent tidal creeks, and 10-12 yr from Florida Bay; and contribute to the basic ecological understanding of mangrove-associated fishes.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hydro_wq_ofr_00-168", + "title": "Hydrologic measurements and water quality in ENR, WCA2 and WCA3 (OFR 00-168 appendixes)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-06-01", + "end_date": "1998-12-21", + "bbox": "-80.45, 26.59, -80.37, 26.7", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411631-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411631-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hydro_wq_ofr_00-168", + "description": "At present there are few reliable estimates of hydrologic fluxes between groundwater and surface water in the Everglades. This gap in hydrological investigations not only leaves the water budget of the Everglades uncertain, it also hampers progress in understanding the processes that determine mobility and transformation of contaminants, such as mercury, sulfate and nutrients. The objective of this project is to quantify hydrologic exchange fluxes between groundwater and surface water and its effects on transport of contaminants in the Everglades. The research furthermore relates surface water and ground water interactions to past, present, and proposed management of surface-water levels and flows in the Everglades. The principal research sites are the Everglades Nutrient Removal Project (ENR), Water Conservation Area 2A (WCA-2A), and the freshwater wetlands of Everglades National Park. Results are being used to quantify ground-water exchange with surface flow, and to quantify the enhancement of chemical transformations of contaminants during transport across the interface between surface water and ground water. The datasets available in the appendixes of the OFR provide information on site locations and measurements in the Everglades Nutrient Removal (ENR) area and Water Conservation Area (WCA) 2A. For restoration of the Everglades to succeed there must be comprehensive knowledge about physical, chemical, and biological processes throughout the system. A key measure of success in the Everglades is the improvement or protection of water quality under changing hydrologic conditions. Although there is a basic understanding of how interactions between groundwater and surface water will affect water budgets under restoration, there is only a rudimentary understanding of how interactions between groundwater and surface water will affect water quality. Only field-oriented research and modeling can determine whether interactions between groundwater and surface water are currently storing pollutants in groundwater, how long those pollutants are likely to be stored in the aquifer, and under what changing management conditions associated with restoration will those pollutants be returned into the surface water system.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hydro_wq_ofr_00-483", + "title": "Hydrologic Measurements and Water-Quality Data for Taylor Slough and vicinity (OFR 00-483 appendixes)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-09-22", + "end_date": "1999-10-28", + "bbox": "-80.69, 25.25, -80.52, 25.38", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551903-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551903-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hydro_wq_ofr_00-483", + "description": "The data in the appendixes of the report are products of an investigation that quantified interactions between ground water and surface water in Taylor Slough in Everglades National Park. In order to define basic hydrologic characteristics of the wetland, depth of wetland peat was mapped and hydraulic conductivity and vertical hydraulic gradients in peat were determined. During specific time periods representing both wet and dry conditions in the area, the distribution of major ions, nutrients, and water stable isotopes throughout the slough were determined. The purpose of the chemical measurements was to identify an environmental tracer that could be used to quantify ground-water discharge. Data available in the appendixes include site locations and hydrologic characteristics of peat and individual tables for data collected on September 22-October 2, 1997; November 10, 1997; November 19-20, 1997; December 11-17, 1997; June 3-6, 1998; July 20-23, 1998; September 20-October 5, 1999; and October 25-28, 1999. For restoration of the Everglades to succeed there must be comprehensive knowledge about physical, chemical, and biological processes throughout the system. A key measure of success in the Everglades is the improvement or protection of water quality under changing hydrologic conditions. Although there is a basic understanding of how interactions between groundwater and surface water will affect water budgets under restoration, there is only a rudimentary understanding of how interactions between groundwater and surface water will affect water quality. Only field-oriented research and modeling can determine whether interactions between groundwater and surface water are currently storing pollutants in groundwater, how long those pollutants are likely to be stored in the aquifer, and under what changing management conditions associated with restoration will those pollutants be returned into the surface water system.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_hydrology_data_zwp", + "title": "Hydrology Data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "2007-12-31", + "bbox": "-81.39, 24.96, -80.35, 25.87", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554877-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554877-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_hydrology_data_zwp", + "description": "The data were produced by four separate projects: Coastal Gradients of Flow, Salintiy, and Nutrients; Freshwater Flows to Northeastern Florida Bay; Hydrologic Monitoring in Joe Bay; and Southwest Florida Coastal and Wetland Systems Monitoring. Data are available for 43 separate sites. The Water Resources Development Act (WRDA) of 2000 authorized the Comprehensive Everglades Restoration Plan (CERP) as a framework for modifications and operational changes to the Central and Southern Florida Project needed to restore the south Florida ecosystem. Provisions within WRDA 2000 provide for specific authorization for an adaptive assessment and monitoring program. A Monitoring and Assessment Plan (MAP) has been developed as the primary tool to assess the system-wide performance of the CERP by the REstoration, COordination and VERification (RECOVER) program. The MAP presents the monitoring and supporting enhancement of scientific information and technology needed to measure the responses of the South Florida ecosystem. Hydrologic information throughout the Everglades ecosystem is key to the development of restoration strategies and for future evaluation of restoration results. There are significant hydrologic information gaps throughout the Everglades wetlands and estuaries that need to be addressed, particularly along Florida\u0092s southwest coast. Among these gaps are flow, water level, and salinity data.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_impacts_20thcent", + "title": "Impact of 20th Century Water-Management and Land-Use Practices on the Coastal Hydrology of Southeast Florida", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1850-01-01", + "end_date": "2000-12-31", + "bbox": "-81, 25, -80, 27", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555185-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555185-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_impacts_20thcent", + "description": "The data are available as Arc/Info coverages from USGS Circular 1275. The Landuse coverages are in Florida State Plane Cordinate System, east zone, units feet, zone 3601, datum NAD27. All other coverages are in UTM Coordinate System, unit meters, zone 17, datum NAD27. Saltwater intrusion into the surficial aquifer is a direct consequence of water-management practices, concurrent agricultural and urban development, and natural drought conditions. An important part of this synthesis is to link water-management practices (canal-discharge), consumptive water use, water levels within the surficial aquifer system, chloride concentrations, ground-water discharge, and Holocene paleohistory of the Florida Bay and Biscayne Bay. For example, a series of water table maps for specific selected 5-year increments have been developed to spatially identify the areal extent where long-term water levels within the surficial aquifer have declined and to compare these changes with movement of the interface. Such changes are also being compared with changes in coastal outflows from major canals to distinquish between long-term declines caused by regional drainage and a large number of municipal pumping centers. Paleontologic data are being used to prepare maps illustrate temporal changes in salinity within the Biscayne Bay over the last 150 years. Salinity changes within the bay are largely attributed to a decrease in ground-water and surface water discharge. This is a completed project. The GIS data layers have been updated as of 4/26/2006. The previous layers available from SOFIA have been replaced with the updated layers.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_int_surf_water_flows_04", + "title": "Internal Surface-water Flows", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-01-01", + "end_date": "2004-12-31", + "bbox": "-80.96, 25.13, -80.81, 26.26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554603-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554603-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_int_surf_water_flows_04", + "description": "Proposed modified water deliveries to Indian Tribal Lands, Big Cypress National Preserve, and Water Conservation Area 3A require that flow and nutrient loads at critical points in the interior surface water network be measured. Defining the foundation for water levels, flows, and nutrient loads has become an important baseline for Storm Treatment Area 5 and 6 development, recent C-139 Basin flow re-diversions, and future L-28 Interceptor Canal de-compartmentaliztion including flow rerouting into the Big Cypress Preserve. Flow monitoring for the two primary flow routes for both L-28 Interceptor Canal and L-28 is key to developing this network. Data are available for L-28 Interceptor Canal below S-190, L-28 Canal above S-140, and L-28 Interceptor South . The accurate determination of flow through the interior canal networks south of Lake Okeechobee and the C-139 basin remains critical for water budgets and regional model calibrations as defined by the Everglades Forever Act of 1994 and due to the Comprehensive Everglades Restoration Plan (CERP) initiative to reroute Big Cypress Preserve flows. The implementation of strategically located stream flow gaging points and associated data collection for nutrients has helped define future surface-water flow requirements and has provided valuable baseline flow data prior to the establishment of the recently constructed northern Storm Treatment Areas (STA\u0092s 5 and 6) and the Rotenberger Wildlife Management Area. Generating continuous flow data at selected impact points for interior basins has complemented the existing eastern coastal canal discharge network, and has allowed for more accurately timed surface-water releases while providing flow and nutrient monitoring after recent STA implementation. A unique multi-agency experiment was conducted with much success with the focus on cooperation and development of new instrumentation and acoustic flow-weight auto-sampler protocols. The original data collection and processing was provided by three separate entities at each site with responsibilities originally allocated between the U.S. Geological Survey (USGS), the Seminole Tribe of Florida, and SFWMD. USGS provides calibration, analysis and processing of acoustic velocity meters (AVM\u0092s) and side-looking Doppler systems and stage shaft encoders, SFWMD provides data loggers with real-time flow-weighted algorithms, and radio frequency (RF) telemetry instrumentation. The Seminole Tribe provides auto-sampler service and funds nutrient load analysis through the USGS Ocala Lab.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_integrating_manatee", + "title": "Effects of hydrological restoration on manatees: integrating data and models for the Ten Thousand Islands and Everglades", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2005-12-01", + "end_date": "2008-09-30", + "bbox": "-81.85, 25, -80.5, 26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552686-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552686-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_integrating_manatee", + "description": "This project will extend previous studies into ENP, where manatees have not been intensively studied. To ascertain how restoration may affect the distribution and abundance of manatees in the region, an individual-based model has been under development, but completion of that model requires a hydrologic model for the rivers and estuaries affected by the accelerated Picayune Strand restoration. This study will provide integrated regional hydrologic models covering nearly the entire southwest coast below Naples, including portions of Picayune Strand and Big Cypress, providing much needed hydrologic modeling capabilities for evaluating restoration effects on coastal, estuarine, and freshwater ecosystems. This effort will enable us to model manatee response to restoration, and more adequately address science and management needs. Three tasks will be undertaken to develop the necessary components for this regional model: (1) Link the TIME hydrology model and the ATLSS manatee model to assess restoration effects in the Everglades and Picayune Strand, (2) Model changes to manatee thermal refugia due to hydrological restoration, and (3) Design and implement a regional manatee monitoring program using aerial surveys and use robust statistical analysis techniques to estimate manatee distribution and abundance before restoration. A significant population of the endangered West Indian manatee occurs in southwest Florida, throughout extensive estuarine and coastal areas within the Ten Thousand Islands (TTI; managed primarily by FWS) and Everglades National Park (ENP; managed by NPS). Planned restoration activities for the Everglades and Picayune Strand (an Acceler-8 project which discharges into TTI) may impact manatees by changing availability of freshwater for drinking, the quality and availability of seagrass forage, and the quality and availability of passive thermal basins used for refuge from lethal winter cold fronts. Changes in freshwater availability and forage are expected to result in a shift in manatee distribution, which could necessitate new management actions to reduce human-manatee interactions. Restoration also could negatively impact important passive thermal refugia by increasing cold sheet flow during winter or disrupting haloclines that maintain warm bottom layers of salty water. Recent telemetry and aerial survey studies of manatees in TTI have revealed much about their use of this area: this project will extend the study into ENP.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_karst_model", + "title": "Linking a conceptual karst hydrogeologic model of the Biscayne aquifer to ground-water flow simulations from Everglades National Park to Biscayne National Park - Phase 1", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2005-01-01", + "end_date": "2009-12-31", + "bbox": "-81.5, 25, -80, 26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550454-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550454-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_karst_model", + "description": "This project in being undertaken to develop a high-resolution 3-dimensional karst hydrogeologic framework of the Biscayne aquifer between Everglades National Park (ENP) and Biscayne National Park (BNP) using test coreholes, borehole geophysical logging, cyclostratigraphy, hydrostratigraphy, and hydrologic modeling. The development of an expanded conceptual karst hydrogeologic framework in this project will be used to assist development of procedures for numeric simulations to improve the monitoring and assessment of the response of the ground-water system to hydrologic changes caused by CERP-related changes in stage within the Everglades wetlands, including seepage-management pilot project implementation. Specifically, the development of procedures for ground-water modeling of the karst Biscayne aquifer in the area of Northern Shark Slough will help determine the appropriate hydrologic response to rainfall and translate that information into appropriate performance targets for input into the design and operating rules to manage water levels and flow volumes for the two Seepage Management Areas. Mapping of the karstic stratiform ground-water flow passageways in the Biscayne aquifer is recent and limited to a small area of Miami-Dade County adjacent to the Everglades wetlands. Extension of this karst framework between the Everglades wetlands and coastal Biscayne Bay will aid in the simulation of coupled ground-water and surface-water flows to Biscayne Bay. The development of procedures for modeling in the karst Biscayne aquifer will useful to the establishment of minimum flows and levels to the Biscayne Bay and seasonal flow patterns. Also, these improved procedures for simulations will assist in ecologic modeling efforts of Biscayne Bay coastal estuaries. Research is needed to determine how planned Comprehensive Everglades Restoration Plan (CERP) seepage control actions within the triple-porosity karstic Biscayne aquifer in the general area of Northeast Shark Slough will affect ground-water flows and recharge between the Everglades wetlands and Biscayne Bay. A fundamental problem in the simulation of karst ground-water flow and solute transport is how best to represent aquifer heterogeneity as defined by the spatial distribution of porosity, permeability, and storage. The triple porosity of the Biscayne aquifer is principally: (1) matrix of interparticle and separate-vug porosity, providing much of the storage and, under dynamic conditions, diffuse-carbonate flow; (2) touching-vug porosity creating stratiform ground-water flow passageways; and (3) less common conduit porosity composed mainly of bedding plane vugs, thin solution pipes, and cavernous vugs. The objectives of this project are to: (1) build on the Lake Belt area hydrogeologic framework (recently completed by the principal investigator), mainly using cyclostratigraphy and digital optical borehole images to map porosity types and develop the triple-porosity karst framework between the Everglades wetlands and Biscayne Bay; and (2) develop procedures for numerical simulation of ground-water flow within the Biscayne aquifer multi-porosity system.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_kendall_stable_isotopes", + "title": "Application of Stable Isotope Techniques to Identifying Foodweb Structure, Contaminant Sources, and Biogeochemical Reactions in the Everglades", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-03-01", + "end_date": "1999-10-31", + "bbox": "-81.0202, 25.2475, -80.3069, 26.6712", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553952-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553952-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_kendall_stable_isotopes", + "description": "This is the largest isotope foodweb study ever attempted in a marsh ecosystem, and combines detailed, long-term, trophic and biogeochemical studies at selected well-monitored USGS/SFWMD/FGFFC sites with limited synoptic foodweb data from over 300 sites sampled during 1996 and 1999 by a collaboration with the EPA-REMAP program. The preliminary synthesis of the biota isotopes at USGS and 1996 REMAP sites provides a mechanism for extrapolating the detailed foodwebs developed at the intensive USGS sites to the entire marsh system sampled by REMAP. Furthermore, this unique study strongly suggests that biota isotopes provide a simple means for monitoring how future ecosystem changes affect the role of periphyton (vs. macrophyte-dominated detritus) in local foodchains, and for predictive models for foodweb structure and MeHg bioaccumulation under different proposed land-management changes. Data are available for the following sites: Cell 4, ENR-OUT, L7, Cell 3, LOX, North Holeyland, E0, F1, U3/Glory Hole, L35B, 2BS, L67, 3A-15, 3A-TH, Lostmans Creek, North Prong Creek, TS-7, and TS-9 for the plants and animals found at each site. A first step of the Everglades restoration efforts is \"getting the water right\". However, the underlying goal is actually to re-establish, as much as possible, the \"pre-development\" spatial and temporal distribution of ecosystems throughout the Everglades. Stable isotope compositions of dissolved nutrients, biota, and sediments provide critical information about current and historic ecosystem conditions in the Everglades, including temporal and spatial variations in contaminant sources, biogeochemical reactions in the water column and shallow subsurface, and trophic relations. Hence, the scientific focus of this project is to use stable isotope techniques to examine ecosystem responses (especially variations in foodweb base and trophic structure) to temporal and spatial variations in hydroperiod and contaminant loading for the entire freshwater Everglades. The major \"long-term\" objectives of this project have been to: (1) determine the stable C, N, and S isotopic compositions of Everglades biota, (2) use bulk and compound-specific isotopic ratios to determine relative trophic positions for major organisms, (3) examine the spatial and temporal changes in foodweb structures across the ecosystem, especially with respect to the effect of anthropogenically derived nutrients and contaminants from agricultural land uses on foodwebs, (4) evaluate the effectiveness of isotopic techniques vs. gut content analysis for determining trophic relations in the Everglades, (5) evaluate the role of algae vs. detritus/microbial materials in foodwebs for the entire freshwater marsh part of the Everglades, and (6) work with modelers to correctly incorporate food web and MeHg bioaccumulation information into predictive models. More recent and specific objectives include: (1) link our data on seasonal and temporal differences in foodweb bases and trophic levels with SFWMD, FGFFC, and USGS Hg datasets (first for large fish and, more recently, for lower trophic levels), (2) investigate the effects of seasonal/spatial changes in nutrients, water levels, and reactions on the isotopic compositions at the base of the foodweb (that affect our interpretation of relative trophic positions of organisms), and (3) continue our efforts to link our foodweb isotope data from samples collected at USGS-ACME and EPA-REMAP sites with the spatial environmental patterns observed by the REMAP program. This work started as part of the Aquatic Cycling of Mercury in the Everglades (ACME) project in 1996 and was made a separate project in 2000.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_kitchens_snail_kite", + "title": "Estimation of Critical Parameters in Conjunction with Monitoring the Florida Snail Kite Population", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-10-01", + "end_date": "2003-09-30", + "bbox": "-83.32674, 24.229189, -79.897285, 29.138569", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550848-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550848-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_kitchens_snail_kite", + "description": "Life history traits and the population dynamics of the snail kite may vary considerably across space and over time. Understanding the influence of environmental (spatial and temporal) variation on demographic parameters is essential to understanding the population dynamics of a given species. Recognition of information needs for management decisions and conservation strategies has resulted in an increased emphasis on correlations to spatial and temporal environmental variation in relation to demographic studies. The purpose if this study is to provide valid estimates of the demographic parameters of the snail kite, including temporal and spatial variability due to environmental factors. These parameters will be used in a predictive model of the snail kite already developed under the ATLSS Program (Mooij et al. 2002). The snail kite (Rostrhamus sociabilis) is an endangered species that resides in the highly fluctuating ecosystem in the central and southern Florida wetlands. Many demographic traits, such as stage-dependent survival, reproduction, and movement of the snail kite vary both temporally and spatially. How these demographic parameters vary as a function of environmental conditions, hydrology in particular, is crucial for understanding how the snail kite will respond to proposed changes in water regulation in South and Central Florida. In particular, these data are needed for testing and improving the existing spatially-explicit, individual-based ATLSS snail kite model, developed by Mooij and Bennetts, which has recently been delivered to Department of Interior and other agencies (Mooij et al. 2002). From these data and the model, projections can be made on snail kite response to any hydrologic scenario. Also, continued estimates will be made of the rate of population growth. Assessing the demographic parameters is critical for identifying and evaluating the effectiveness of management actions and conservation strategies. In addition, new modeling techniques, such as structural modeling are being explored to better understand the effects of hydrology on the snail kite. The objectives of this project are the following: 1. To monitor the status of the snail kite population trends in central and southern Florida. 2. To provide estimates of demographic parameters for the spatially explicit individual-based model in ATLSS. 3. To collaborate with Dr. Wolf Mooij of the Netherlands Institute of Ecology to use snail kite data to validate the snail kite model.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_la_florida", + "title": "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from \"Down-Scaled\" AOGCM Climate Scenarios in Combination with Ecological Modeling", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "2000-12-31", + "bbox": "-92, 23, -75, 38.24", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554072-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554072-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_la_florida", + "description": "The objectives of this project are to develop the knowledge necessary to make accurate predictions of the response of species and their ecosystems to climate change. We propose to down-scale predictions from a suite of coupled Atmosphere-Ocean General Circulation Models (AOGCMs) to make regional scale predictions for the southeastern United States. For the time being the hydrologic and biologic models are confined to Florida. Climate outputs will then be used as inputs to a suite of species / habitat / ecosystem models that are currently being used in two key areas: the Greater Everglades and Suwannee River-Big Bend as a proof of concept that down-scaled climate results can work in ecological forecast models. We will run three scenarios of Land Use/Land Cover (LULC): past (circa 1900), present, and future (2041-2070). Additional climate model runs will address the contribution of green house gasses to climate variability and change over the Florida peninsula. Model perturbation experiments will be performed to address sources of variability and their contribution to the output regional climate change scenarios. We will develop scenarios that specifically address potential changes in temperature (land and near sea surface) and rainfall fields over the peninsula. We will then provide these scenarios and modeling results to resource management groups (NGOs, state and federal) via workshops in which the scenarios will be used to predict responses of additional selected species, habitats and ecosystems. Our approach is to develop regional climate predictions and subsequent ecological predictions for two 30-year long time periods as well as for the present. The first 30-year period is the recent past, spanning the period from 1971-2000. This will be used as a control, with copious observations of both climate variables (e.g. rainfall, ET) and species (e.g. densities, ranges) to verify both climate and ecology model outputs and to serve as a baseline to systematically judge the impacts of an altered climate. The second 30-year time period will begin 30 years in the future and extend for the thirty years from 2041-2070. This is a time horizon that is immediately relevant to habitat management.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_lake_okee_bathy_data", + "title": "Lake Okeechobee Bathymetry data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-09-01", + "end_date": "", + "bbox": "-81.125, 26.625, -80.5, 27.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550957-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550957-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_lake_okee_bathy_data", + "description": "The data from the bathymetric mapping of Lake Okeechobee are provided in two forms: as raw data files and as elevation contour maps. High resolution acoustic bathymetric surveying is a proven method to map sea and lake floor elevations. Of primary interest to the South Florida Water Management District (SFWMD) is the quantification of the present day lakebed in Lake Okeechobee. This information can be used by water-management decision-makers to better assess the water capacity of the lake at various levels.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_land_margin_ecosystems", + "title": "Dynamics of Land Margin Ecosystems: Historical Change, Hydrology, Vegetation, Sediment, and Climate", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-10-01", + "end_date": "2009-12-31", + "bbox": "-81.75, 25, -80.25, 26.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552313-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552313-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_land_margin_ecosystems", + "description": "This project has three objectives (tasks): 1) operate and maintain the Mangrove Hydrology sampling network; 2) study the dynamics of coastal vegetation (mangroves, marshes) in relation to sea-level, fire, disturbance and restoration; and, 3) measure rates of sediment surface elevation change and soil accretion or loss in coastal mangrove forests and brackish marshes of the Everglades and determine how sediment elevation varies in relation to hydrology (i.e. the restoration).", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_lbwfbay", + "title": "Ecosystem History: Florida Bay and Southwest Coast", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-02-01", + "end_date": "2003-02-06", + "bbox": "-80.75, 24.75, -80.33, 25.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553226-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553226-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_lbwfbay", + "description": "Recent negative trends in the Florida Bay ecosystem have been attributed to human activities, however, neither the natural patterns of change, nor the pre-human baseline for the environment have been determined. The major objectives of this project are 1) to determine patterns of faunal and floral change over the last 150-200 years, and 2) to explore associations between biotic changes and anthropogenically-induced changes and/or natural changes in the physical environment. Environmental managers and policy makers responsible for restoring the Everglades ecosystem to a \"natural state\" can use these data to make economical and realistic decisions about restoration goals and to determine interim steps to ameliorate further damage to the ecosystem. The history of the ecosystem during the last 150-200 years is studied by analysis of faunal and floral assemblages from a series of shallow cores taken in Florida Bay. Cores are located at strategic sites in Florida Bay, with initial emphasis on the northeast and northern portions of the Bay where the most significant changes are thought to have occurred. These cores are submitted for Pb 210 analysis to determine the age and degree of disruption of the sediments. Cores that present a good stratigraphic record are sampled at closely spaced intervals for all macro-and micro-fauna and flora present. Quantitative down-core assemblage diagrams are drawn up and the various faunal and floral data are compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment are made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora, and data from Florida Bay will supplement and be correlated to onshore data and to Biscayne Bay (Ecosystems History: Terrestrial and Fresh Water Ecosystems of Southern Florida Project and Ecosystems History: Biscayne Bay and the southeast coast Project). The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment. This project is one component of an interdisciplinary study of the ecosystem history in Florida Bay. A number of USGS and other agencies scientist's are examining a series of shallow cores (~1-2 m) collected from Florida Bay. By studying the patterns of change that have occurred in the ecosystem over the last two centuries, we gain insight into the natural processes, including the natural range of variability that exists within any ecosystem. We can then determine the degree to which anthropogenic-induced change has effected the system. This understanding is critical to the restoration effort; otherwise we will be attempting to restore the system to a targeted snapshot in time, without understanding how realistic or obtainable those goals are. The ecosystem history component of the initiative will save time and money by providing realistic, economical, obtainable goals. Our component of this study is to analyze the down-core faunal and floral assemblages, over the last 150-200 years. Cores are located at strategic sites in Florida Bay, with initial emphasis on the northeast and northern portions of the Bay where the most significant changes are thought to have occurred. These cores are submitted for Pb 210 analysis to determine the age and degree of disruption of the sediments. Cores that present a good stratigraphic record are sampled at closely spaced intervals for all macro- and micro-fauna and flora present. Quantitative down-core assemblage diagrams are drawn up and the various faunal and floral data are compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment are made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora, and data from Florida Bay will supplement and be correlated to onshore data and to Biscayne Bay. The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_levesque_field_params", + "title": "Field Parameters Data (Levesque)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-01-01", + "end_date": "1998-12-31", + "bbox": "-81.25, 25.33, -81, 25.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549719-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549719-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_levesque_field_params", + "description": "In 1996, the U.S. Geological Survey began a 4-year study of the flow and nutrient characteristics of three major streams that drain parts of the Everglades National Park. An upward looking acoustic Doppler current profiler, a water-level sensor, and two specific conductance sensors were installed at each site. Monthly discharge measurements are made with an acoustic Doppler current profiler to develop discharge ratings. Data collected at the Broad River, Harney River, and Shark River stations include water level, water velocity, specific conductance and temperature, total and dissolved phosphorus species, pH, and dissolved oxygen. These three stations were established in 1997. The southwest coast of Florida is part of a wilderness area with unique hydraulic characteristics that has historically been described as the \"River of Grass\". Flat terrain and lack of controlled topographic information has made it difficult to define drainage divides. Low gradients, coupled with tidal effects, create complex conditions under which to measure river flow. It has been almost thirty years since any effort has been made to monitor flow characteristics continuously in the area. Significant technological advancements have occurred during this time and this new technology can be applied to help obtain the information needed to make informed decisions about the future of this unique coastal area. Flow, nutrient concentrations, and nutrient load data will provide part of the basic information needed to understand the hydrologic and water-quality characteristics for a part of the southwest coast of Florida. The analysis of these measurements will help characterize the current conditions for the three sites and explain the relation between upgradient water levels and southwest coastal stream flows, and the possible interaction between southwest coastal waters and the waters of Florida Bay. The data can also be used as input to hydrodynamic and water-quality models. This project is now part of the Tides and Inflows in the Mangrove Ecotone (TIME) Model Development project.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_levesque_flow", + "title": "Flow Data (Levesque)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-10-01", + "end_date": "1998-12-31", + "bbox": "-81.25, 25.33, -81, 25.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553587-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553587-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_levesque_flow", + "description": "In 1996, the U.S. Geological Survey began a 4-year study of the flow and nutrient characteristics of three major streams that drain parts of the Everglades National Park. An upward looking acoustic Doppler current profiler, a water-level sensor, and two specific conductance sensors were installed at each site. Monthly discharge measurements are made with an acoustic Doppler current profiler to develop discharge ratings. Nutrient data are collected monthly at each site. Data collected at the Broad River, Harney River, and Shark River stations include water level, water velocity, specific conductance and temperature, total and dissolved phosphorus species, pH, and dissolved oxygen. These three stations were established in 1997. The southwest coast of Florida is part of a wilderness area with unique hydraulic characteristics that has historically been described as the \"River of Grass\". Flat terrain and lack of controlled topographic information has made it difficult to define drainage divides. Low gradients, coupled with tidal effects, create complex conditions under which to measure river flow. It has been almost thirty years since any effort has been made to monitor flow characteristics continuously in the area. Significant technological advancements have occurred during this time and this new technology can be applied to help obtain the information needed to make informed decisions about the future of this unique coastal area. Flow, nutrient concentrations, and nutrient load data will provide part of the basic information needed to understand the hydrologic and water-quality characteristics for a part of the southwest coast of Florida. The analysis of these measurements will help characterize the current conditions for the three sites and explain the relation between upgradient water levels and southwest coastal stream flows, and the possible interaction between southwest coastal waters and the waters of Florida Bay. The data can also be used as input to hydrodynamic and water-quality models. SUPPLEMENTAL INFORMATION: This project is now part of the Tides and Inflows in the Mangrove Ecotone (TIME) Model Development project.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_mangrove_modeling_04", + "title": "Mangrove Modeling of Landscape, Stand-Level and Soil-Nutrient Processes for the ATLSS Program and Everglades Restoration Project", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-12-15", + "end_date": "2005-12-30", + "bbox": "-81.30333, 25.125, -80.26212, 25.847113", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552486-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552486-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_mangrove_modeling_04", + "description": "This project provides an integrated suite of vegetation and nutrient resource models of the land-margin ecosystem compatible with and undergirding other restoration models of hydrology and higher trophic levels identified as critical. This modeling project fills the gaps and needs of existing restoration models, ELM and ATLSS, for a vegetation and nutrient dynamics component and complements continuing empirical studies within the land-margin ecosystem of the Everglades restoration program. The proposed work has eight major objectives: 1. Re-measurement and analysis of mangrove permanent plots 10 years after the passage of Hurricane Andrew to verify forest structure models (SELVA-MANGRO) and to re-calibrate output accordingly. 2. Map historic marsh-mangrove ecotone boundaries in selected southwest Florida regions. 3. Survey land/water datums across the intertidal and develop tidal ebb/flow synoptic functions for incorporation into SELVA-MANGRO. 4. Site quality characterization across the mangrove landscape using ground surveys and research studies, aerial photography, and aerial videography. 5. Develop external SELVA-MANGRO model linkages and WEB-based access to SELVA-MANGRO for Everglades restoration evaluations. 6. Verify HYMAN (hydrology), NUMAN (nutrient/organic matter decomposition), and FORMAN (forest structure/primary productivity) unit ecological simulation models with application to Everglades restoration evaluations. 7. Link SALSA (Hydrology BOX model) to HYMAN and FORMAN models to develop a better link between vegetation response and hydrological fluxes to the Everglades system. 8. Conduct field and greenhouse studies on nutrient biogeochemistry and determine the effects of nutrients and hydroperiod on forest biomass allocation and soil formation. Land-margin ecosystems (mangrove forests, brackish marshes, and coastal lakes) comprise some 40% of Everglades National Park. They support the important detrital foodwebs, fisheries, and wading bird colonies of the coastal zone. These systems are at the receiving end for the water management decisions made upstream which will impact the spatial distribution, timing, and quantity of freshwater flow. Additional factors which are important include disturbance history related to hurricanes and potential effects of projected sea-level rise. This project integrates the suite of spatial simulation models necessary to evaluate the response of land-margin ecosystems to upstream water management. Included are algorithms and databases of critical processes and spatio-temporal relations operating at the landscape, stand-level, and soil interface. These process and modeling studies are critical to the extended applications of the ATLSS and ELM modeling programs into the land-margin ecosystems of the Everglades.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_mcivor_hydroimpact", + "title": "Impacts of Hydrological Restoration on Three Estuarine Communities of the SW Florida Coast", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-10-01", + "end_date": "2004-09-30", + "bbox": "-81.375, 24.75, -80.25, 26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548456-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548456-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_mcivor_hydroimpact", + "description": "This project seeks to characterize habitat relationships between selected faunal groups and their mangrove environment on the Southwest Florida coast. We are describing how mangrove associated fish species are distributed in fringing forest habitat along a salinity gradient in the tidal portions of the Shark River; the ecology and population dynamics of diamondback terrapins in the Big Sable Creek complex; experimentally determining the preferred habitat of the specialist fish Rivulus marmoratus via field and laboratory experiments; and how the conversion of mangrove forests to intertidal mud flats in the Big Sable Creek complex has affected fish composition and use of those habitats. The overall strategy is to collect robust empirical field data on forage fish distribution and abundance that can serve multiple purposes: as performance measures in restoration assessment; as the beginning of a long-term dataset analogous to three very powerful datasets from other locales in the Greater Everglades Ecosystem: 15-20 yr from freshwater marshes, 10 yr from the mangrove ecotone of Taylor Slough and adjacent tidal creeks, and 10-12 yr from Florida Bay; and contribute to the basic ecological understanding of mangrove-associated fishes. A primary goal of Everglades restoration is the recreation of water flows and water quality more closely approximating pre-drainage conditions in both freshwater and estuarine ecosystems within Everglades National Park. These estuarine systems include submerged aquatic vegetation (SAV), mangroves (tidal forests), and brackish marshes. Four primary groups of animals are closely associated with, and often dependent upon, one or more of these ecosystems: fish and decapod crustaceans (shrimp, crabs), diamondback terrapins, manatees, and wading birds. This research focuses on fish and decapod crustaceans and diamondback terrapins in mangrove tidal forests and associated creeks. Concern about the fate of mangrove ecosystems derives from their known use as habitat for a wide range of aquatic animal species, especially fishes and decapod crustaceans of forage as well as of commercial and recreational importance. Additionally, in South Florida, mangroves on Cape Sable support a seemingly healthy population of diamondback terrapins, a species at risk in many salt marsh environments on the Gulf and Atlantic coasts. This project is being undertaken to: (1) determine what fish species make routine use of flooded fringing mangrove forests along the tidal portion of the major drainage of the historical Everglades, i.e., Shark River, and to develop empirical relationships that link species composition, density and biomass to environmental variables at those sites; (2) describe the population structure of a species of special concern, the diamondback terrapin, in mangrove tidal creek habitat within the complex of creeks that make up Big Sable Creek on Cape Sable, and secondarily to determine how this population is related to other populations on the Atlantic and Gulf coasts via DNA analysis; (3) experimentally determine via field and lab experiments the preferred habitat of a species of special concern but a common fish along the Shark River salinity gradient, mangrove rivulus; (4) determine the fisheries impact of the hurricane-induced conversion of mangrove forests to intertidal mudflats in the Big Sable Creek complex.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_mdcsoil", + "title": "Miami-Dade County FL soil map", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1947-01-01", + "end_date": "", + "bbox": "-81, 25, -80, 26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548633-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548633-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_mdcsoil", + "description": "The data sets consist of two files, an ARC/INFO shape file with associated files and an export file, of a composite of soil maps for Miami-Dade County, Florida issued by the Soil Conservation Service in April, 1958. The data is at 1:40,000 scale. Getting geographic information into a form that can be analyzed in a Geographic Information System (GIS) has always been a labor-intensive process. Graphic information was historically captured using variations of manual digitizing techniques. Users either digitized directly from printed materials on digitizing tablets or tables or by a variation of heads-up digitizing from scanned graphics displayed on computer monitors. Data collection involves considerable interaction between the user and a computer to capture and manipulate graphical data into a GIS layers. By using inexpensive image processing software to process and manipulate scanned images before processing these images in the GIS, features can be semi-automatically extracted from the scanned graphics, virtually eliminating the process of manual delineation. Common photo editing techniques combined with GIS expertise can dramatically decrease the time required to collect GIS data layers. The mentioning of specific software brands or registered trademarks does not constitute a commercial endorsement; their mention is done for clarity only. Mention of software products in the description of graphic processing techniques should be viewed as a use of available tools and not a recommendation for a software product.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_metholms", + "title": "Geochronology in the South Florida Ecosystem and Associated Ecosystem Programs", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-02-01", + "end_date": "1999-09-30", + "bbox": "-81.75, 24.75, -80.1, 26.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550723-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550723-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metholms", + "description": "In order to manage an ecosystem, it is imperative to define the rate at which ecologic, physical and chemical changes have occurred. The lack of historical records documenting ecological changes dictates that other methods are used to measure the rate of change. A common method of \"dating\" change is to measure the decay of naturally occurring radioactive nuclides. The use of radioactive isotopes is founded on the known physical property of radioactive material, the half-life. A half-life of an isotope is the amount of time it takes for half of a given number of atoms to \"decay\" to another element. The age of objects that contain radioactive isotopes with known half-lives can be calculated by determining the percent of the remaining radioactive material. To use this method successfully certain other prerequisites must be met. These are: 1. the chemistry of the nuclide (element) is known; 2. Once the nuclide is incorporated into the substrate the only change is radioactive decay, and 3. in order to be useful, it is relatively easy to measure.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_metish", + "title": "Ecosystem History of Biscayne Bay and the Southeast Coast", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-03-01", + "end_date": "2000-01-31", + "bbox": "-80.3, 25.1, -80, 26", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549217-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549217-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metish", + "description": "Historical changes in South Florida related to rapid population growth in the early to mid-1900's have led to significant alteration of the natural hydrocycles and water quality of Florida and Biscayne Bays. The Biscayne Bay ecosystem shows increasing signs of distress; declines in fisheries, increased pollution, and dramatic changes in nearshore vegetation. Northern and central Biscayne Bay are strongly affected by the urban development associated with the growth of Miami. Southern Biscayne Bay is influenced by drainage from the Everglades, which has been altered by canals and agricultural activities. Restoration and preservation of Biscayne Bay and Biscayne National Park are dependent on a comprehensive understanding of the linkages between the hydrologic system and the bay ecosystem, and of the natural versus human-induced variability of the ecosystem. In this project modern surface samples were collected from 26 sites in Biscayne Bay. The primary biota analyzed were 1) benthic foraminifera, 2) ostracodes, 3) mollusks, 4) dinoflagellate cysts, 5) pollen and macro-plant material. The distribution of the biota was quantified to determine relationships with environmental conditions. These results were used to interpret historical faunal and floral changes recorded in shallow sediment cores. Water samples, ostracode and foraminiferal shells collected from the modern sediment samples are being analyzed for trace element geochemistry to derive a calibration equation to calculate absolute salinity in down-core samples. Shallow cores (1-2 meters) were collected along a north-south transect within Biscayne Bay for analysis of the downcore faunal and floral assemblages over the last 150 years. Quantitative down-core assemblage diagrams will be drawn up and the various faunal and floral data will be compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment will be made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora; data from Biscayne Bay will supplement and be correlated to onshore data and to data from Florida Bay. The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment. Living assemblages will be collected twice a year to provide data on habitat distribution, preferred substrates and seasonality of the living biota for interpretation of the down-core assemblages. Recent negative trends have been observed in the ecosystem of Florida Bay, including algal blooms, seagrass die-offs, and declining numbers or shellfish, adversely affecting the fishing and tourist industries. Many theories of cause and effect exist to explain the adverse trends, but these theories have not been scientifically tested. Prior to finalizing plans for ecosystem restoration, the relative roles of human activities versus natural ecosystem variations need to be established. This project addresses this need by focusing on two primary goals. First, to determine the characteristics of the ecosystem prior to significant human alteration, including the natural range of variation in the system; this establishes the baseline for restoration. Second, to establish the extent, range, and timing of changes to the ecosystem over approximately the last 150 years and to determine if these changes correlate to human alteration, meteorological patterns, or a combination of factors. In addition, data on recovery times of certain components of the ecosystem will be obtained allowing biologists to estimate responses to proposed restoration efforts. This project is planned as a five year study, to be completed in 2000. This project is one segment in a group of coordinated USGS projects examining the biota, geochronology, geochemistry, sedimentology, and hydrology of southern Florida, Florida Bay and the surrounding areas. Data are being compiled from terrestrial, marine, and freshwater environments in onshore and offshore sites in order to reconstruct the ecosystem history for the entire region over the last 150 years.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_metjen", + "title": "Effect of Wind on Surface-Water Flows", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-04-01", + "end_date": "1999-09-30", + "bbox": "-81.25, 24.75, -80.3, 25.8", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552339-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552339-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metjen", + "description": "Flows in and through the Everglades wetlands and bordering subtidal embayments are often characterized by very low velocities that are driven or controlled at various scales by wind, gravity, pressure, and vegetative resistance. Little is known about the effect of wind on water movement in these environments, and no focused efforts are currently underway to assess its importance. This project will examine the effect of wind on surface-water flows. With insight into the functional relationships and into the scales at which wind forcing data must be collected for model input gained from the field efforts, the treatment of wind forcing in models can be improved. This, in turn, can lead to enhanced understanding of the significance of wind effects on flow, transport, and horizontal mixing in the SICS (Southern inland coastal systems of Dade County) study area. This project has been integrated into the TIME project (http://time.er.usgs.gov/)", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_metkotra", + "title": "Geochemical Processes in Organic-rich Sediments of South Florida - Mercury and Metals", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-10-01", + "end_date": "1998-10-01", + "bbox": "-81.25, 24.75, -80.3, 26.6", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550057-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550057-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metkotra", + "description": "Human activities have led to the deterioration of the productivity, biodiversity, and stability of the south Florida ecosystem. The fate of anthropogenic contaminants incorporated into the organic-rich sediments is not fully understood. Physical, chemical, and biological processes may remobilize some of the contaminants and reintroduce them into water, atmosphere, and the biological community. Other contaminants may be transformed during diagenesis and remain in surficial materials until the system is disturbed. This project examined the occurrence and cycling of mercury and metals in organic-rich sediments, pore fluids, and plants at selected sites in south Florida. An understanding of the relationship between diagenesis, concentration, speciation, and historical variation of elements of environmental significance is essential for planners in developing long-term remediation and management strategies for wetlands of south Florida. A better understanding of the controls on the cycling of these elements is critical for making informed decisions regarding the regulation of water levels and anticipating the effect of water regulation.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_metlang", + "title": "Ground-Water Discharge to Biscayne Bay", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-06-01", + "end_date": "1998-12-31", + "bbox": "-80.63, 25.12, -80.12, 25.9", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554984-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554984-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metlang", + "description": "The purpose of this project was to quantify the rates of ground water discharge to Biscayne Bay. This was achieved through the collection of field data and the development of two- and three-dimensional numerical models to simulate variable-density ground water flow. As part of this project, the SEAWAT code, which represents variable-density ground water flow, was developed to simulate ground water discharge. Monitoring wells were installed offshore and inland along three transects perpendicular to the shore of Biscayne Bay. Several surveys during the late 19th and early 20th centuries describe the occurrence of large quantities of ground-water flow to Biscayne Bay by way of underground channels or conduits. The construction of the drainage and flood-control network in southeastern Florida began during the early 20th century for the purpose of managing the water resources of the area. This drainage canal network affected the hydrologic pattern in southeastern Florida by replacing sheetflow with canal flow, thereby significantly reducing the altitude of the water table and diminishing ground-water flow to Biscayne Bay. This led to the inland movement of the saltwater interface. In 1960, there was still ground water discharging to the bottom of Biscayne Bay, but no quantification of the amount of ground-water discharges to the bay was made at the time. In 1967, discharges to the bay in the Cutler Ridge area were estimated by assuming Darcian flow and considering the tidal cycle. It was estimated that 210 cubic feet per square foot of flow section area was discharged during a 12.5-hour tidal cycle. The U.S. Army Corps of Engineers (COE) is planning to construct gated spillways and culverts to allow for the restoration of natural sheetflow conditions to Everglades National Park (ENP). These proposed changes may further affect the hydrologic conditions of ENP and other parts of the ecosystem, thus leading to the following questions: (1) Is ground water flowing to Biscayne Bay a significant component of the water budget in south Florida? (2) Would the quantity of ground water flowing to Biscayne Bay be greatly affected by changes in the operation of gates and control structures in canals? (3) How much change in ground-water discharges to Biscayne Bay has occurred due to modifications to the hydrologic system? Quantification of ground water flowing to Biscayne Bay is needed as input to two interagency projects: the South Florida Ecosystem Restoration Program and the Biscayne Bay Feasibility Study. The principal objective of the Biscayne Bay Feasibility Study is to investigate ongoing construction/dredging projects and propose solutions to alleviate adverse factors that affect the bay and to aid in the development of guidelines for future management of the natural resources of Biscayne Bay. The Biscayne Bay Feasibility Study includes the implementation of a surface-water circulation model which will be developed by the Waterway Experimental Station of the COE. Quantification of ground-water discharges to Biscayne Bay is needed as input to the bay water circulation model.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_metlietz", + "title": "Determination of Nutrient Loads to East Coast Canals", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-05-01", + "end_date": "1997-10-31", + "bbox": "-80.39, 25.35, -80.15, 25.94", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552186-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552186-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metlietz", + "description": "The objectives of this project were threefold: 1) to determine if historical water-quality data collected as grab samples at 0.5 and c1.0 m below the surface near the centroid of flow adequately represent stream cross-sectional chemistry, 2) to develop reliable estimates of nitrogen and phosphorus loads for east coast canals based on statistical models developed from utilizing the techniques of ordinary least squares regression, and 3) to summarize water-quality data and determine temporal trends for water-quality constituents at two sites that are strategic to Biscayne Bay and the south Florida ecosystem. During phase 1 of the project an intensive field sampling and data collection effort was undertaken. Depth-integrated samples were collected by the equal-width-increment method as well as grab samples at each canal. During Phase 2 data analysis was done. Nutrient data were collected upstream of 15 coastal control structures in Miami-Dade County. Samples were collected over a typical hydrologic period during various flow conditions. Sampling began at 5 sites in May 1996 and at 10 sites in October 1996. Constituents collected included ammonia, nitrite, nitrate, orthophosphate, and total phosphorus. Of major concern in many coastal areas around the Nation is the ecological health of bays and estuaries. A common problem in many of these areas is increased nutrient loads as a result of agricultural, commercial, industrial, and urban processes. Biscayne Bay is a shallow subtropical estuary along the southern coast of Florida. The Biscayne Bay ecosystem provides an aquatic environment that is a habitat to a diverse array of plant and animal communities. Nutrients are essential compounds for the growth and maintenance of all organisms and especially for the productivity of aquatic environments. Nitrogen and phosphorus compounds are especially important to seagrass, macroalgae, and phytoplankton. However, heavy nutrient loads to bays and estuaries can result in conditions conducive to eutrophication and the attendant problems of algal blooms and high phytoplankton productivity. Additionally, reduced light penetration in the water column because of phytoplankton blooms can adversely affect seagrasses, which many commercial and sport fish rely on for their habitat. Providing reliable estimates of nonpoint source nutrient loads to Biscayne Bay is important to the development of nutrient budgets as well as input to eutrophication models. Understanding the effects of these nutrient loads is a necessary initial step in planning restoration of the ecological health of Biscayne Bay. Nutrient data have been collected from the east coast canals for many years by various government agencies. Much of the data collected have been from grab samples at 0.5 or 1.0 meter below the stream surface near the centroid of flow. The degree to which these samples adequately represent nitrogen and phosphorus concentrations within the water column of the canals of south Florida is presently unknown and limits confidence in loading estimates. Furthermore, the relation between discharge and nutrient concentration that occurs in natural uncontrolled streams in other parts of the Nation may not apply to the artificially controlled canals of south Florida. Both of these issues need to be addressed to develop nutrient budgets and to plan effective restoration strategy now and in the future.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_metorem", + "title": "Geochemistry of Wetland Sediments from South Florida", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-01-01", + "end_date": "1999-12-31", + "bbox": "-81.3, 24.4, -80.1, 27", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549943-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549943-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metorem", + "description": "This project is examining (1) sources of nutrients (nitrogen and phosphorus), sulfur, and carbon to wetlands of south Florida, (2) the important role of chemical and biological processes in the wetland sediments (biogeochemical processes) in the cycling of these elements, and (3) the ultimate fate (i.e. sinks) of these elements in the ecosystem. The focus on nutrients and carbon reflects the problem of eutrophication in the northern Everglades, where excess phosphorus from agricultural runoff has dramatically altered the biology of the ecosystem. Major project objectives are as follows - (1) use isotope and other tracer methods to examine the major sources of nutrients, carbon, and sulfur to the south Florida ecosystem, (2) use geochemical methods to examine the major forms of nutrients, carbon, and sulfur in the sediments, the stabilities of the observed chemical species, and sinks of these elements in the sediments, (3) examine the biogeochemical processes controlling the cycling of nutrients, carbon, and sulfur in the ecosystem, and use geochemical modeling of porewater and sediment chemical data to determine the rates of these recycling processes, (4) develop geochemical sediment budgets for nutrients, carbon, and sulfur on a regional scale, including accumulation rates of these elements in the sediments, fluxes out of the sediments, and sequestration rates, (5) collaborate with mercury projects (USGS ACME team and others) to examine the role of sulfur and sulfate reduction in the production of methyl mercury in wetlands of south Florida, and the bioaccumulation of mercury in fish and other wildlife, (6) develop a geochemical history of the south Florida ecosystem from an examination of changes downcore in the concentration, speciation, and isotopic composition of nutrients, carbon and sulfur; use organic marker compounds and stable isotopes to develop a model of seagrass history in Florida Bay, (7) incorporate information from nutrient studies in overall ecosystem nutrient model, and results from sulfur studies in ecosystem mercury model. This project addresses three major areas of interest to land and water managers in south Florida: (1) nutrients and eutrophication of the Everglades, (2) the role of sulfur in the methylation of mercury and its bioaccumulation, and (3) the geochemical history of the south Florida ecosystem. Our nutrient studies are focused on using isotope methods to examine the sources of nutrients to the ecosystem, and on using sediment and porewater geochemical studies to determine the rates of nutrient recycling and nutrient sinks within the sediments. A nutrient sediment budget will be developed for incorporation in the nutrient model for the ecosystem. Results will assist managers in determining the fate of excess nutrients (especially phosphorus) stored in contaminated sediments (e.g. will the excess nutrients be buried, or recycled for movement further south into protected areas). The sediment studies will also provide managers with information relevant to the effectiveness of planned remediation methods. Studies of sulfur within the ecosystem relate directly to the issue of methyl mercury production and bioaccumulation, a serious threat to both wildlife and to the human population. Microbial sulfate reduction in wetlands (an anaerobic process) is the primary driver of mercury methylation. Understanding the source of sulfate to the wetlands of south Florida may be a key to understanding why mercury methylation rates are so high, and on how remediation efforts in the Everglades may impact mercury methylation rates. We are also examining the sulfur geochemistry of sediments on a regional scale, with emphasis on areas that are methyl mercury \"hotspots\". We are emphasizing co-sampling with USGS mercury researchers (ACME team). The geochemical history component of this project will provide information on historical changes in the chemical conditions existing in south Florida wetlands. This will provide wetland managers with baseline information on the water quality goals needed to achieve \"restoration\" of the ecosystem. It will also provide land managers with an estimate of the range of water quality and environmental conditions that have affected the south Florida ecosystem in the past. Geochemical history data in combination with information from paleontologic studies of the USGS paleoecology group and others will also provide insights on how organisms in the south Florida ecosystem have responded to environmental change in the past, and predict how these organisms will likely respond to changes in the ecosystem resulting from restoration efforts.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_metroys", + "title": "Evaluation of Methods to Determine Ground-water Seepage Below Levee 30, Miami-Dade County Florida", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-02-01", + "end_date": "1996-12-17", + "bbox": "-80.3, 25.85, -80.29, 25.85", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552379-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552379-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metroys", + "description": "Ground-water flow models were developed to calculate a water budget, including seepage losses, for a transect perpendicular to Levee 30. Data required for input to and calibration of the models were obtained from: (1) previous studies conducted in the area, (2) analysis of a geologic core and geophysical logs from a new monitor well drilled along the transect, (3) ground-water-level data from monitor wells along the transect, (4) surface-water-stage data in Water Conservation Area 3B and in the Levee 30 canal, (5) discharge measurement made at several locations under varying conditions in the Levee 30 canal, and (6) vertical seepage fluxes between surfacewater and groundwater in Water Conservation Area 3B obtained from seepage meters. Determining the volume of water seeping from the water-conservation areas to the underlying aquifers is important in managing water levels in the conservation areas and freshwater deliveries to Everglades National Park. An accurate water budget to meet the competing natural and anthropogenic needs cannot be determined without this information. From Water Conservation Area 3B, water seeps into the Biscayne aquifer, which is about 80 feet thick directly beneath Levee 30 and thickens to the east, and flows relatively fast (due to high permeability of the aquifer) toward the urban and agricultural areas to the east. Water is also discharged to the canal along the eastern part of Levee 30. The rate of discharge is controlled by structures at the northern and southern ends of the canal. This seepage to the aquifer and canal discharge of water are critical for water-supply wells to the east and for preventing the inland movement of saltwater from the coast. However, lowering of ground-water levels to the east has resulted in higher ground-water seepage and canal discharge, reducing flows to the south in the water-conservation area. As a result, Levees 67A and 67C were constructed to direct water southward toward the central region of Everglades National Park. This water-management scheme has been effective in delivering water to the southwest; however, it reduced the flow to the southeast (northeastern part of Everglades National Park). The altering of historical flow directions and water-level durations has caused significant adverse effects to parts of the Everglades ecosystem. Water managers want to restore predevelopment flow conditions for the Everglades to survive, while also taking into consideration the urban and agricultural needs. The objective of this project was to evaluate approaches for quantifying ground-water seepage beneath Levee 30. The accounting of all significant hydrologic inflows and outflows to the Everglades ecosystem of the south Florida mainland is a key element of the South Florida Ecosystem Program.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_metschaf", + "title": "Canal and Wetland Flow/Transport Interaction", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-09-23", + "end_date": "", + "bbox": "-80.6, 25.25, -80.4, 25.35", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554720-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554720-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metschaf", + "description": "Significant canal and wetland flow exchanges can potentially occur along the southwest overbank area of canal C-111 between hydraulic control structures S-18C and S-197. This coupled flow system is of particular concern to restoration efforts in that it provides a pathway for fresh water to nearshore embayments in Florida Bay. New construction modifications and operational strategies proposed for C-111 under the Central and Southern Florida \"Restudy\" Project are intended to enhance sheet flow to these subtidal embayments. The objectives of the canal and wetland flow/transport interaction project were to (1) develop numerical techniques and algorithms to facilitate the coupling of existing generic models for improved simulation of canal and wetland interactions, (2) translate recent findings of ongoing process studies within the South Florida Ecosystem Program (SFEP) into new mathematical formulations, empirical expressions, and numerical approximations to enhance generic simulation model capabilities for the south Florida ecosystem, (3) investigate new instrument capabilities and field deployment approaches to collect the refined data needed to identify and quantify the important flow-controlling forces and landscape features for model implementation, (4) integrate process-study findings and the results of physiographic mapping and remote sensing efforts specific to the C-111 basin into a numerical simulation model of the interconnected canal and wetland flow system, and (5) use the resultant model and data to study, evaluate, and demonstrate the significance of driving forces relative to controlling flow exchanges between canal C-111 and its bordering wetlands. Discharge data for Tamiami Canal are also available for water years 1986-1999, 2000, and 2001. A complex network of canals, levees, and control structures, designed to control flooding and provide a continuous supply of fresh water for household and agricultural use, has altered naturally occurring flow patterns through the Everglades and into Florida Bay. Quantification of dynamic flow conditions within the south Florida ecosystem is vital to assessing implications of the residence time of water, potentially nutrient-enriched (with nitrates or phosphates) or contaminant-laden (with metals or pesticides), that can alter plant life and affect biological communities. Improved numerical techniques are needed not only to more accurately evaluate discrete forces governing flow in the canals and wetlands but also to analyze their complex interaction in order to facilitate coupled representation of transport processes. Flow and transport processes are integrally linked meaning that precise quantification of the fluid dynamics is required to accurately evaluate the transport of waterborne constituents. Robust models that employ highly accurate numerical methods to invoke coupled solution of the most appropriately formulated and representative equations governing flow and transport processes are needed. Through strategic use of a model, cause-and-effect relations between discharge sources, flow magnitudes, transport processes, and changes in vegetation and biota can be systematically investigated. The effects of driving forces on nutrient cycling and contaminant transport can then be quantified, evaluated, and more effectively factored into the development of remedial management plans. A well-developed model can be used to evaluate newly devised plans to improve freshwater deliveries to Florida Bay prior to implementation. This project ended in 1999. Related work can be found at http://time.er.usgs.gov/. For additional information about this project contact either: Eric Swain, edswain@usgs.gov, 954 377-5925 or Chris Langevin, langevin@usgs.gov, 954 377-5917", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_metweed", + "title": "Hydrogeology of the Surficial Aquifer System in Southwest Florida", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-02-01", + "end_date": "1999-12-31", + "bbox": "-81.7, 25.73, -80.86, 26.18", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548516-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548516-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_metweed", + "description": "The objective of this project is to provide to hydrologic modelers a three-dimensional database of the geologic and hydrologic properties of the sediments and rocks of the surficial aquifer system in southwest Florida, in Collier and Monroe Counties. Emphasis will be placed on the geologic framework of the aquifer. Two independent methods are used in this study to estimate the age of the aquifer rocks and sediments. Samples from cores will be examined for fossil dinoflagellate cysts, pollen, mollusks, foraminifers, and ostracodes, and their age determined by correlation to other distant sites that have been dated isotopically. Age also will be estimated by the isotopic composition of strontium in unaltered shells. The ratio of the stable isotopes of strontium in the oceans has varied over geologic time such that, in the last 40 million years, there has been a unique relation between age and isotopic composition. Marine invertebrates incorporate the strontium isotopic ratio of the ocean into their shells as they grow, thereby preserving evidence of their age. Geophysical logs provide a continuous downhole record of the properties of the rocks that form the aquifer. They are especially valuable in providing physical and chemical properties of the corehole where particular intervals of core recovery are poor. Also, they allow extension of hydrologic test data from discrete samples to the rest of the core. Geophysical logs, combined with aquifer water properties and flow measurements, will be used to relate large-scale ground-water circulation to the distribution of hydrologic properties of the aquifer. For example, flowmeter logs can confirm that the most permeable intervals, as inferred from core measurements, coincide with the intervals that conduct the most flow in the vicinity of test wells. Geophysical logs also will indicate which confining units act to separate the aquifer system into discrete aquifers having different water quality and hydraulic head. Restoration and management of the south Florida ecosystem will be guided by hydrologic models that simulate water flowing through the wetlands and shallow subsurface aquifers beneath them. The restoration of the ecosystem is, essentially, the restoration of the natural hydrologic system. As surface water is re-diverted from manmade canals to its more natural sstate and overland flow, several changes are predicted to occurr. First, because water flowing over land moves more slowly than in canals, overland flow should remain in the wetlnad ecosystem for a longer period each year. Second, as flowing water spreads out over the wetlands, recharge to the shallow aquifers should increase as more of that water infiltrates into the ground. The U.S. Corps of Engineers (USACE) and the South Florida Water Management District (SFWMD) will use hydrologic models to anticipate the consequences of these proposed restoration plans. This reseaerch project is designed to provide essential subsurface data to improve hydrologic models for land and water managers in southwest Florida where subsurface information is lacking. Obtaining hydrogeological data involves core drilling, corehole testing, and rock and sediment analysis. Understanding the geologic history of the sediments and rocks of the aquifer system is necessary to place the hydrologic properties of that system into a geologic framework.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_mmarvin", + "title": "Bacterial demethylation of methylmercury in the South Florida Ecosystem", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-06-01", "end_date": "", "bbox": "-81.25, 24.8, -80.3, 25.8", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550592-CEOS_EXTRA.umm_json", @@ -184287,6 +192204,19 @@ "description": "Methylmercury (MeHg) degradation was investigated along an eutrophication gradient in the Florida Everglades by quantifying 14CH4 and 14CO2 production after incubation of anaerobic sediments with 14C-MeHg. Degradation rate constants (k) were consistently <=0.1 per day, and decreased with sediment depth. Higher k values were observed when shorter incubation times and lower MeHg amendment levels were used, and k increased two-fold as in-situ MeHg concentrations were approached. The average floc layer k was 0.046 +/- 0.023/ d (n=17) for 1-2 day incubations. In-situ degradation rates were estimated to be 0.02 to 0.5 ng MeHg/g dry sed/d, increasing from eutrophied to pristine areas. Nitrate-respiring bacteria did not demethylate MeHg, and NO3- addition partially inhibited degradation in some cases. MeHg degradation rates were not affected by PO4-3 addition. 14CO2 production in all samples indicated that oxidative demethylation (OD) was an important degradation mechanism. OD occurred over five orders of magnitude of applied MeHg concentration, with lowest limits (1-18 ng MeHg/g dry sediment) in the range of in-situ MeHg levels. Sulfate reducers and methanogens were the primary agents of anaerobic OD, although it is suggested that methanogens dominate degradation at in-situ MeHg concentrations. Specific pathways of OD by these two microbial groups are proposed. The objective of this research is to provide ecosystem managers with MeHg degradation rate data from a number of study sites that represent a diversity of hydrologic and nutrient regimes common to the Everglades, and to forge a better understanding of the microbial and geochemical controls regulating MeHg degradation in this system.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_monitor_sav_rs_fb_04", + "title": "Monitoring Sub-aquatic Vegetation Through Remote Sensing: a Pilot Study in Florida Bay", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-10-01", + "end_date": "2004-09-30", + "bbox": "-80.87, 24.9, -80.75, 25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553656-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553656-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_monitor_sav_rs_fb_04", + "description": "This pilot study will focus on Florida Bay, a region that suffered the loss of 40,000 ha of turtle grass in a die-off event that began in 1987, and a small, localized die-off in 1999. These events were well documented and provide a baseline for testing methods of monitoring grass beds remotely. Remote sensing data, including aerial photos and satellite imagery data, and data extracted from sediment cores will be used to examine the long-term sequences of events leading up to seagrass die-off events. The objectives of this pilot study are to develop a methodology for monitoring spatial and temporal changes in sub-aquatic vegetation using remote sensing, satellite imagery, and aerial photography, and to analyze potential causes of seagrass die-off using geographic, geologic and biologic tools. The ultimate goal is to develop a method for forecasting potential sea-grass die-offs and to determine if remediation efforts would be cost-effective. Florida Bay is selected for the pilot study because the thorough documentation of the 1987-1988 die-off event provides a baseline for examining data preceding and succeeding the event. In addition, a small well studied die-off occurred in 1999-2000 at Barnes Key in Florida Bay. A 10-15 km2 portion of Florida Bay that encompasses areas affected by the 1987 and 1999 die-offs will be analyzed for this pilot study. Current remotely sensed data, aerial photos and satellite images from this area will be used to test different platforms, determine detection limits, and to attempt to isolate distinct signals for different types of vegetation. When ground-truthing is completed, archived remotely sensed data and/or aerial photographs can then be used to examine the sequences of events leading up to the die-offs. The remotely sensed data can be compared and compiled with the data collected by seagrass biologists in 1987 and 1999, and to sediment core data collected at the sites of seagrass die-off. Sediment cores provide a long-term perspective on changes in nutrient geochemistry, substrate, water chemistry (salinity, temperature, oxygen), and changes in the biota. The geologic, biologic and remotely sensed data will be integrated and analyzed to determine the patterns of change and sequences of events that occur in healthy seagrass beds and in beds undergoing a die-off. Several remote sensor types will be compared in this study to determine the ideal sensor bands and spatial resolution necessary to detect and monitor the health of seagrass beds. The sensors to be tested include Landsat 7 (30m multi-spectral spatial resolution), ASTER (15 and 30m multi-spectral), Quickbird (2.5m multi-spectral and <1m panchromatic), and large-scale aerial photography (anticipated spatial resolution .25m with visible and near-infrared bands). Imagery with bands in the blue wavelength may help to penetrate water and infrared or near-infrared bands are predicted to perform better for resolving vegetation. It is theorized that through a combination of blue, and infrared bands and higher spatial resolution it will be possible to map the extent of seagrass beds. Although Landsat ETM+ 7 has several bands in desirable wavelengths, this sensor is predicted to be too course of a dataset to resolve individual seagrass beds. Landsat ETM+ may be used to develop an index of chlorophyll values that may be translated into a measure of seagrass health. ASTER\u0092s multiple infrared bands and increased spatial resolution may be successful in distinguishing between the types of vegetation, but these bands are not designed for water penetration. Higher spatial resolution platforms are predicted to have better mapping capabilities. The Quickbird sensor can provide 2.5m spatial resolution with multi-spectral capability. The multi-spectral bands include a blue band for water penetration and a near-infrared band for vegetation detection. Finally, aerial photography flown at low altitude represents the highest spatial resolution (.25m) and can be collected in visible and near-infrared to allow processing of blue and infrared bands. A combination of sensor types to maximize both spatial resolution and spectral signatures may provide the best solution for mapping and monitoring seagrass beds. Seagrass beds are essential components of any marine ecosystem because they provide feeding grounds, nurseries, and habitats for many forms of marine life, including commercially valuable species; they are important foraging grounds for migratory birds; and they anchor sediments and impede resuspension and coastal erosion during storms. This valuable natural resource has been suffering die-offs around the world in recent years, yet the causes of these die-offs are undetermined. The purpose of this project is to use a number of tools - geographic, geologic, and biologic - to investigate the causes of seagrass die-offs and to develop methods that can be used to monitor the health of seagrass meadows. If we understand the causes of the die-offs and can easily monitor the health of seagrass beds, then resource managers have a tool for forecasting areas of potential die-offs. By integrating remotely sensed data, biological data and core data the long-term (decadalscale) sequences of events leading up to die-off events can be examined. These data can be contrasted to normal seasonal changes that occur in healthy grass beds to establish criteria for identifying areas that may be on the threshold of experiencing a decline. This provides a very powerful predictive tool for resource managers. By examining the causes of die-off and the natural patterns of change in seagrass meadows over biologically significant periods of time we can determine the components of change that may be related to anthropogenic activities versus natural cycles of change. This information would allow resource managers to make informed decisions about the cost-effectiveness of and mechanisms for remediation, if an area of decline was identified via the predictive tool. Once the predictive tools and potential remediation tools have been developed in this pilot study, in well-studied seagrass meadows, the tools can be applied to threatened coastal ecosystems around the country and worldwide.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_nssmet", "title": "Cycling and Speciation of Mercury in the Food Chain of South Florida", @@ -184300,6 +192230,45 @@ "description": "Methylmercury, a neurotoxin, is found in the game fish of south Florida. Samples of periphyton, the assemblage of microalgae that live in shallow submerged substrates which is home to, and food for, creatures that are the foundation of the food chain, have concentrations of methylmercury that range from non-detectable to tenths of a part per million on a dry weight basis. The report produced from this project presents data for samples of periphyton and water collected in 1995 and 1996 from Water Conservation Areas, the Big Cypress National Preserve, and the Everglades National Park in south Florida. Periphyton samples were analyzed for concentrations of total mercury, methylmercury, nitrogen, phosphorus, organic carbon, and inorganic carbon. Water-column samples collected on the same dates as the periphyton samples were analyzed for concentrations of major ions. The goal of this project is to answer the question - How does mercury produced in the aquatic environment enter the food chain and become part of the body burden of animals such as game fish in south Florida?", "license": "proprietary" }, + { + "id": "USGS_SOFIA_nuts_S_orgmat_04", + "title": "Integrated Biogeochemical Studies of Contaminants in the Everglades: Task 1 -Nutrients, Sulfur, and Organic Matter", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-10-01", + "end_date": "2005-09-30", + "bbox": "-82, 24.4, -80.1, 28", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553342-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553342-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_nuts_S_orgmat_04", + "description": "The scientific focus of this project is to examine the complex interactions (synergistic and antagonistic) of contaminants (externally derived nutrients, mercury, sulfur, pesticides, herbicides, polycyclic aromatic and aliphatic hydrocarbons, and other metals), ecosystem responses to variations in contaminant loading (time and space dimensions), and how imminent ecosystem restoration steps may affect existing contaminant pools. The major objectives of this project are to use an integrated biogeochemical approach to examine: (1) anthropogenic-induced changes in the water chemistry of the Everglades ecosystem, (2) biogeochemical processes within the ecosystem affecting water chemistry, and (3) the predicted impacts of restoration efforts on water chemistry. The project uses a combination of field investigations, experimental approaches (mesocosm experiments in the ecosystem, and controlled laboratory experiments), and modeling to achieve these objectives. Contaminants of concern will include nutrients, sulfur, mercury, organic compounds, and other metals. Protocols for the collection of samples and chemical analyses developed during earlier studies will be employed in these efforts. Integration of the individual tasks within the project is achieved by colocation of field sampling sites, and cooperative planning and execution of laboratory and mesocosm experiments. Results from all tasks within the project are archived within a single database for use in Decision Management GIS systems and ecosystem models. This project is an integration of a number of individual but interrelated tasks that address environmental impacts in the south Florida ecosystem using geochemical approaches. The Everglades restoration program is prescribing ecosystem-wide changes to some of the physical, hydrological, and chemical components of this ecosystem. However, it reamins uncertain what overall effects will occur as these components react to the perturbations especially of the biological and chemical components and toward what type of \"new ecosystem\" the Everglades will evolve. Results of these geochemical investigations will provide the critical elements for building ecosystem models and screening-level risk assessment for contaminants in the ecosystem.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_orem_fb_sed_geochem", + "title": "Florida Bay sediment geochemical data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "1996-12-31", + "bbox": "-80.75, 25, -80.5, 25.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552868-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552868-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_orem_fb_sed_geochem", + "description": "The data set contains the sample ID, depth (cm), sediment size, fine sediment fraction (<60m), total C %, organic C %, total N %, total P %, C/N, C/P, and N/P. This project is examining (1) sources of nutrients, sulfur, and carbon to wetlands of south Florida, (2) the important role of chemical and biological processes to the wetland sediments (biogeochemical processes) in the cycling of these elements, and (3) the ultimate fate (i.e. sinks) of these elements in the ecosystem. The focus on nutrients and carbon reflects the problem of eutrophication in the northern Everglades, where excess phosphorus from agricultural runoff has dramatically altered the biology of the ecosystem. Results will be used by land and water managers to predict the fate of nutrients (especially phosphorus) in contaminated areas of the Everglades, and to evaluate the long-term effectiveness of buffer wetlands being constructed as nutrient removal areas. Studies of sulfur in the ecosystem are important for understanding the processes involved in mercury methylation in the Everglades. Methyl mercury (a potent neurotoxin) poses a severe health risk to organisms in the south Florida ecosystem and to humans. Sediment studies conducted by this project will also be used to construct a geochemical history of the ecosystem. An understanding of past changes in the geochemical environment of south Florida provides land and water managers with baseline information on what water quality goals for the ecosystem should be, and on how the ecosystem has responded to past environmental change and will likely respond to the changes that will accompany restoration.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_panther_refuge_hydro", + "title": "Hydrologic monitoring and synthesis of existing hydrologic data in the Florida Panther National Wildlife Refuge and surrounding areas", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2005-10-01", + "end_date": "2007-09-30", + "bbox": "-81.5, 26.15, -81.3, 26.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549236-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549236-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_panther_refuge_hydro", + "description": "The objectives of this project are to 1. Inventory existing hydrologic data available in the vicinity of the Florida Panther National Wildlife Refuge (FPNWR) including all data that can be used for determining past and current conditions. 2. Design and install a hydrologic monitoring network for the FPNWR. The network will include continuous and intermittently monitored ground-water level and surface water stations. The network will be used to monitor hydrologic conditions within the FPNWR and to evaluate the relationship between ground water and surface water. 3. Collect other hydrologic data as needed to assist in determining the hydrologic conditions in the area. Examples of other types of data include stable isotopes, which can be used to determine sources of water in a sample, evapotranspiration data, surface and borehole geophysical data, seepage measurements. 4. Evaluate historical and current data to determine trends and baseline conditions at and in the vicinity of the FPNWR. The biologic communities of the Florida Panther National Wildlife Refuge (FPNWR) and surrounding areas have been historically impacted by the changes in hydrology associated with past highway and canal construction and will be impacted by future plans for hydrologic restoration. Currently, little hydrologic data is collected in the vicinity of the FPNWR. Two continuous recording stations located up gradient in Big Cypress National Park (stations A1 and A2) are the nearest wetland stations to the FPNWR. Additional stations are located in the canals near the FPNWR. Information on current hydrologic conditions and a monitoring network are needed in order to determine the impact of the planned Picayune Strand Hydrologic Restoration on the hydrology of the area. These hydrologic changes will have effects on the threatened and endangered species as well as other biologic communities in the FPNWR. There are two components to the hydrology of the area that have an impact on the ecology, surface water, and shallow ground water. The surface water consists of wetlands within and canals bordering the FPNWR. Canals bordering the refuge have a major impact on the hydrology in the area. The FPNWR currently maintains a hydrologic monitoring program of 8 stations (Larry Richardson, verbal communication). These hydrologic monitoring stations have not been surveyed to a vertical datum, which is required to adequately evaluate the data being collected. The survey information is required to determine the relationship between ground water and surface water in the area. Additional information needed to evaluate the hydrology of the area include stage and flow rates in the canals bordering the FPNWR.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_rice_alligators_04", "title": "American Alligator Ecology and Monitoring for CERP", @@ -184313,6 +192282,58 @@ "description": "This project will accomplish several tasks with a combination of field data collection, GIS mapping, and computer simulation. Our main objectives are designed to answer questions critical to restoration success and to provide the tools necessary for evaluation: 1. Develop monitoring methods necessary for evaluation of restoration success in alligator populations. 2. Understand the effects of decompartmentalization and other CERP (Comprehensive Everglades Restoration Plan) projects on restoration of alligator populations. 3. Identify and quantify the extent of aquatic refugia maintained by alligators throughout the system and develop relationships necessary to predict restoration of refugia. 4. Validate and update ecological models for use in prediction of the effects of restoration. Many important questions concerning the effects of Everglades restoration on alligator populations remain unanswered such as the impacts of decompartmentalization, the role of alligator holes as aquatic refugia, and the effects of hydrology on population growth and condition. Further, the methods for monitoring and evaluating restoration success are not clear or have not been adapted for use during CERP. Also, we need to continue to update and validate restoration tools such as population models for use in alternative selection, performance measure development, and prediction. This project will directly address the questions outlined above, develop monitoring methods, and validate restoration tools for use in CERP. All project tasks have been requested by management agencies in South Florida (NPS, USFWS), listed as critical CERP priority research needs (see USGS Ecological Modeling Workshop at http://sofia.usgs.gov/publications/infosheets/ecoworkshop/ ), and/or highlighted as science objectives for CESI ", "license": "proprietary" }, + { + "id": "USGS_SOFIA_robblee_fb_shrimp_04", + "title": "Empirical studies in Support of Florida Bay and Adjacent Marine Ecosystems Restoration", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-10-01", + "end_date": "2003-09-30", + "bbox": "-81.25, 24.75, -80.375, 25.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551802-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551802-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_robblee_fb_shrimp_04", + "description": "The objectives of these activities are broadly: 1) to develop and implement (with other agency members) a program of research to support the restoration of Florida Bay; 2) with other PDT members to develop and evaluate restoration alternatives for Florida Bay and 3) with other committee members to develop performance measures and assess restoration alternatives affecting Florida Bay, Biscayne Bay, Barnes Sound and Manatee Bay and the lower southwest coast mangrove estuaries. Florida Bay lies downstream of the Everglades ecosystem. Perceived deterioration of the Everglades over the last century - and Florida Bay since the mid-1980\u0092s - is generally viewed as linked to changes in freshwater flow and water quality associated with water management in South Florida. The pink shrimp is a species of special interest in each of the above studies because it has been chosen as an indicator species for use in restoration of south Florida estuaries. Empirical and experimental data developed in these studies will be used to support the development of a pink shrimp landscape simulation model and restoration performance measures.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_robblee_shrimp", + "title": "Empirical Studies in Support of a Pink Shrimp, Farfantepenaeus duorarum, Simulation Model for Florida Bay", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-10-01", + "end_date": "2004-09-30", + "bbox": "-81.25, 24.75, -80.375, 25.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549090-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549090-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_robblee_shrimp", + "description": "A Tortugas/Florida Bay pink shrimp simulation model has been identified as a priority need in CERP by the South Florida Water Management District, NOAA, NPS and USGS. This model has been under development through the collaboration of a team of NMFS, USGS and University of Miami (UM) researchers since 1997. To date this project has been funded by NOAA's Coastal Oceans Program, DOI's Critical Ecosystem Studies Initiative and by USGS base funds. The purpose of the model is to assist in designing and refining restoration alternatives by predicting their impact on production of pink shrimp in Florida Bay and on shrimp recruitment from Florida Bay to the Tortugas fishery. A series of monitoring or empirical studies either have been completed or are ongoing. NMFS continues to monitor Tortugas pink shrimp harvest and develop the simulation model and has completed pink shrimp salinity/temperature tolerance experiments. USGS is continuing to monitor pink shrimp distribution and abundance in relation to environmental conditions and habitat in Florida Bay and to measure water flow in order to estimate postlarval transport within the Bay. With UM a critical collaborative study to identify and quantify the seasonality and magnitude of pathways of postlarval immigration to Florida Bay is continuing. Statistical studies of these and other data are ongoing relating pink shrimp to salinity, temperature and habitat in Florida Bay. Florida Bay lies downstream of the Everglades ecosystem. Perceived deterioration of the Everglades over the last century - and Florida Bay since the mid-1980's - is generally viewed as linked to changes in freshwater flow and water quality associated with water management in South Florida. A pink shrimp simulation model is being developed to assist in designing and refining restoration alternatives by predicting their impact on production of pink shrimp in Florida Bay and on shrimp recruitment from Florida Bay to the Tortugas fishery. The pink shrimp is a good indicator of the health and productivity of the Bay. The effect of salinity and temperature on pink shrimp growth and survivorship and of habitat on juvenile density provide a basis for predicting the abundance of pink shrimp juveniles in Florida Bay and thus the magnitude of recruitment to the Tortugas fishery. A landscape model is needed to express pink shrimp performance measures as functions of spatially complex factors acting across the Bay. Florida Bay is a complex shallow water ecosystem with distinct zones of different physical and biological characteristics (Fourqurean and Robblee 1999) that differ in their potential to support pink shrimp. The influence of upstream water management on pink shrimp recruitment from Florida Bay is expected to express itself principally through changes in salinity and seagrass habitat associated with changes in freshwater inflow. Predictions of the effect of these changes on the Bay's productive capacity require consideration not only of the resulting salinity and seagrass changes but also the resulting change in the area of overlap of these factors favorable to the pink shrimp (Browder and Moore 1981; Browder 1991). Critical long-term databases exist for pink shrimp that are suitable for developing empirical relationships and baselines.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_rsl30dv", + "title": "Levee 30 Water Level Daily Values", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-02-01", + "end_date": "1996-12-31", + "bbox": "-80.49, 25.86, -80.48, 25.86", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553711-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553711-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_rsl30dv", + "description": "This data set contains daily maximum water level elevation in feet below mean sea level(feet msl) for 21 groundwater wells and daily mean stage in feet msl for 2 surface water stations for 1996 along a transect, approximately 1,000 feet long that is perpendicular to and bisected by Levee 30. Determining the volume of water seeping from the water-conservation areas to the underlying aquifers is important in managing water levels in the conservation areas and freshwater deliveries to Everglades National Park. An accurate water budget to meet the competing natural and anthropogenic needs cannot be determined without this information. From Water Conservation Area 3B, water seeps into the Biscayne aquifer, which is about 80 feet thick directly beneath Levee 30 and thickens to the east, and flows relatively fast (due to high permeability of the aquifer) toward the urban and agricultural areas to the east. Water is also discharged to the canal along the eastern part of Levee 30. The rate of discharge is controlled by structures at the northern and southern ends of the canal. This seepage to the aquifer and canal discharge of water are critical for water-supply wells to the east and for preventing the inland movement of saltwater from the coast. However, lowering of ground-water levels to the east has resulted in higher ground-water seepage and canal discharge, reducing flows to the south in the water-conservation area. As a result, Levees 67A and 67C were constructed to direct water southward toward the central region of Everglades National Park. This water-management scheme has been effective in delivering water to the southwest; however, it reduced the flow to the southeast (northeastern part of Everglades National Park). The altering of historical flow directions and water-level durations has caused significant adverse effects to parts of the Everglades ecosystem. Water managers want to restore predevelopment flow conditions for the Everglades to survive, while also taking into consideration the urban and agricultural needs. The objective of this project was to evaluate approaches for quantifying ground-water seepage beneath Levee 30. The accounting of all significant hydrologic inflows and outflows to the Everglades ecosystem of the south Florida mainland is a key element of the South Florida Ecosystem Program.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_rsl30uv", + "title": "Levee 30 Water Level Unit Values", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-02-01", + "end_date": "1996-12-31", + "bbox": "-80.3, 25.8, -80.29, 25.85", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550828-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550828-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_rsl30uv", + "description": "This data set contains hourly readings for water level elevation in feet below mean sea level(feet msl) for 21 groundwater wells and stage in feet msl for 2 surface water stations for 1996 along a transect, approximately 1,000 feet long that is perpendicular to and bisected by Levee 30. Determining the volume of water seeping from the water-conservation to the underlying aquifers is important in managing water levels in the conservation areas and freshwater deliveries to Everglades National Park. An accurate water budget to meet the competing natural and anthropogenic needs cannot be determined without this information. From Water Conservation Area 3B, water seeps into the Biscayne aquifer, which is about 80 feet thick directly beneath Levee 30 and thickens to the east, and flows relatively fast (due to high permeability of the aquifer) toward the urban and agricultural areas to the east. Water is also discharged to the canal along the eastern part of Levee 30. The rate of discharge is controlled by structures at the northern and southern ends of the canal. This seepage to the aquifer and canal discharge of water are critical for water-supply wells to the east and for preventing the inland movement of saltwater from the coast. However, lowering of ground-water levels to the east has resulted in higher ground-water seepage and canal discharge, reducing flows to the south in the water-conservation area. As a result, Levees 67A and 67C were constructed to direct water southward toward the central region of Everglades National Park. This water-management scheme has been effective in delivering water to the southwest; however, it reduced the flow to the southeast (northeastern part of Everglades National Park). The altering of historical flow directions and water-level durations has caused significant adverse effects to parts of the Everglades ecosystem. Water managers want to restore predevelopment flow conditions for the Everglades to survive, while also taking into consideration the urban and agricultural needs. The objective of this project was to evaluate approaches for quantifying ground-water seepage beneath Levee 30. The accounting of all significant hydrologic inflows and outflows to the Everglades ecosystem of the south Florida mainland is a key element of the South Florida Ecosystem Program.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_rtt_sfwmd", "title": "CERP/RECOVER restoration technology transfer: USGS-CERP liaison with SFWMD", @@ -184352,6 +192373,45 @@ "description": "The major products are planned as a series of USGS Open-File Reports, one for each complete, or near complete, set of photos. A photoset is defined as a collection of aerial photos that were taken during a discrete time, generally 30-60 days, with the same scale, film type, and camera. All OFRs will be distributed on CD-ROM and several on DVD. Each report will encompass a photoset with descriptive text sections such as Introduction, Metadata & Procedures, Study Area, and Acknowledgements. All scanned images will be in a downloadable format. A foundation for Everglades restoration must include a clear understanding of the pre-drainage south Florida landscape. Knowledge of the spatial organization and structure of pre-drainage landscape communities such as mangrove forests, marshes, sloughs, wet prairies, and pinelands, is essential to provide potential endpoints, restoration goals and performance measures to gauge restoration success Information contained in historical aerial photographs of the Everglades can aid in this endeavor. The earliest known aerial photographs, from the mid to late 1920s, and resulted in the production of T-Sheets (Topographic Sheets) for the coasts and shorelines of south Florida. The T-Sheets are remarkably detailed, delineating features such as shorelines, ponds, and waterways, in addition to the position of the boundary between differing vegetation communities. If followed through time changes in the position of these ecotones could potentially be used to judge effects of changes in the landscape of the Everglades ecosystem, providing a standard by which restoration success can be ascertained. The overall objective is to create a digital archive of historical aerial photographs of Everglades national park and surrounding area of the greater Everglades and south Florida. The archive will be in readily available Geographic Information System formats for ease of accessibility. Each set of photos will be broadly disseminated to client agencies, academic institutions and the general public via Open-File Reports and through the Internet.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_solomet", + "title": "Evaluation of Methods to Determine Ground-water Seepage Below Levee 31N, Dade County Florida", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-07-01", + "end_date": "1999-09-30", + "bbox": "-80.75, 25.5, -80.33, 25.83", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549232-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549232-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_solomet", + "description": "The primary objective of this investigation is to quantify seepage below Levee L31N. The amount of water lost to the L-31N Canal versus the fraction that flows below the canal will be estimated. A conceptual model is currently being developed for the site based upon results from an on-going stable isotope (oxygen -18 and deuterium) study. Quantification of seepage rates will be based upon a computer model, MODBRANCH, which couples both groundwater and surface water flows. Particular attention will be devoted to model performance under transient conditions caused by fluctuations in the stage of the L-31N Canal and pumping operations of the West Wellfield. In addition, an alternative leakage relationship based on reach transmissivity will be incorporated into MODBRANCH; this relationship is believed to be more suitable for transient conditions. The reach transmissivity relationship will be evaluated in comparison to MODBRANCH's existing leakage relationship, which is based on Darcian flow through the bed of the surface water channel. Modeling results will be used to develop an algorithm for real time estimation of seepage beneath Levee L31N. It is expected that this algorithm will estimate seepage using head differences at monitoring stations in the vicinity of the levee. Plans to restore historical hydrologic conditions in the northeast section of Everglades National Park (ENP) include the raising of water levels in ENP and water conservation area 3B, which overlie the Biscayne aquifer, an extremely permeable aquifer. The increase in water levels is likely to cause an increase in seepage losses to the east. Quantifying this seepage loss is necessary for water management purposes as well as for models of the Everglades and coastal systems. Levee L-31N has been identified as a critical area for potential water losses. The L-31N study site includes a wetland area within ENP on the west; the L-31N Canal flows from north to south through the longitudinal center of the site, and the eastern portion of the region is a suburban area of Miami which includes a major municipal wellfield, the West Wellfield, and rock mining activities. This project was completed in 1999.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_sus_parts", + "title": "Effect of Water Flow on Transport of Solutes, Suspended Particles, and Particle-Associated Nutrients in the Everglades Ridge and Slough Landscape", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2004-01-01", + "end_date": "2006-12-31", + "bbox": "-81, 25, -80.25, 26.75", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554693-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554693-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_sus_parts", + "description": "The objectives of the study are: To quantify through detailed field experiments previously unstudied processes in the Everglades, such as rates of fine-particle movement and filtration by vegetation as well as advective solute exchange between surface water and zones of solute storage in relatively stagnant waters (in areas of thick vegetation and in peat pore water). Our study focuses on determining the effects of these processes on chemical reactions of the contaminants as well as overall effects on downstream transport. At least initially, the emphasis will be on improved understanding of factors influencing transport of dissolved and fine particle forms of phosphorus. To apply the new knowledge gained from field measurements first in our own transport models (which are necessarily limited in time and space) and then to encourage application in more widely used water-quality models (e.g. DMSTA, ELM), and water quality models currently in development (e.g. extension of USGS SICS model in Taylor Slough). The goal is more accurate simulation of the effects of restoration on Everglades water quality, thus allowing more reliable use of water-quality models for prediction of the effects of restoration. To guide the use of improved water-quality models to estimate potential rates of transport, storage, and remobilization of phosphorus (and other contaminants) in WCA-2A, Shark and Taylor Sloughs in Everglades National Park, and Loxahatchee Wildlife Refuge, with a goal to predict potential rates of downstream movement of phosphorus in these systems under \"restored\" flows. A key measure of success in the Everglades restoration is protecting water quality while increasing the quantity of water flowing through the Everglades. The restoration's goal of increasing surface-water flow through the wetlands could have the unintended consequence of transporting contaminants farther into the Everglades than ever before. Thus, the need to augment water delivery will at times inevitably result in using water with higher than desirable total dissolved solids, particulate organic matter, sulfate, nutrients, and mercury. In addition, greater water flows may increase transport of those contaminants farther into the wetlands than ever before. Our investigation seeks a better understanding of the fundamental processes that affect the rates at which contaminants are transported in wetlands, focusing especially on critical unknowns - 1) rates of contaminant transport in association with fine suspended particles, and 2) rates of solute exchange between surface water and storage areas reservoirs in relatively stagnant surface waters (in thick vegetation and subsurface pore water in peat). Our studies are planned to be the definitive experimental investigations of solute and particle transport in the Everglades.", + "license": "proprietary" + }, + { + "id": "USGS_SOFIA_sw-pore_water_DOC_SUVA", + "title": "Everglades Water Chemistry - Surface water DOC, pore water DOC and SUVA data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-03-01", + "end_date": "1998-06-30", + "bbox": "-80.9, 25.59, -80.1, 26.79", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554408-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554408-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_sw-pore_water_DOC_SUVA", + "description": "The data are for dissolved organic carbon (DOC) and specific ultraviolet absorbance (SUVA) for surface water and pore water in the South Florida Water Management District (SFWMD) water conservation areas. It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylamine and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiage research project.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_target_sal_vals", "title": "Determining Target Salinity Values for South Florida's Estuaries: The Combined Effects of Climate, Sea Level, and Water Management Practices", @@ -184365,6 +192425,19 @@ "description": "The primary objective of this project is to provide information to Comprehensive Everglades Restoration Plan (CERP) managers that can be used to establish target salinity values and performance measures for the estuaries and coastal ecosystems. The information provided will consider the contribution of climate, sea level rise, and anthropogenic alteration of salinity values in the estuaries and coastal ecosystems. The four areas of focus for the project are: 1. Refine existing modern analog dataset by completing analyses of modern samples collected between 1996 and 2004 and applying the data to core data compiled in the Synthesis Task 2. Collect new cores (if necessary) within the southern estuaries to fill in information gaps identified by the land management agencies (Everglades National Park (ENP), and Biscayne National Park (BNP) and the Southern Estuaries Subteam of the Regional Evaluation Team (RET) of Restoration Coordination and Verification (RECOVER) 3. Select a few sites in the transition zones to collect cores in a transect moving perpendicular to shore to analyze the rate of sea level rise in the region 4. Work with collaborators to input all of the combined paleoecology data into linear regression models that can hindcast salinity for different parts of the system. The importance and application of ecosystem history research to restoration goals has been previously identified. The Department of the Interior (DOI) Science Plan lists as one of three primary restoration activities the need to \"ensure that hydrologic performance targets accurately reflect the natural predrainage hydrology and ecology\". The primary goal of this project is to determine the predrainage and ecology of critical regions within the estuaries and coastal ecosystems of south Florida identified by the groups charged with setting performance measures and targets for these coastal zones.", "license": "proprietary" }, + { + "id": "USGS_SOFIA_terrapin_mark-recap_data", + "title": "Mangrove Terrapin Mark Recapture Study data", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-11-01", + "end_date": "2003-10-31", + "bbox": "-81.16, 25.26, -81.14, 25.28", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550522-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550522-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_terrapin_mark-recap_data", + "description": "In 2001 a mark-recapture study on mangrove terrapins (Malaclemys terrapin) in the Big Sable Creek (BSC) complex within Everglades National Park was initiated. The summary data for terrapins in BSC were collected over 5 sampling trips in a two-year period (November 2001 - October 2003) and from analysis of individual terrapin capture histories. Study objectives were to estimate adult survival probablility, capture probablilty, and abundance of terrapins at this study site. This allowed the establishment of the first baseline assessment for mangrove terrapins in the coastal Everglades.", + "license": "proprietary" + }, { "id": "USGS_SOFIA_willard_tree_islands_04", "title": "Development and Stability of Everglades Tree Islands, Ridge & Slough, & Marl Prairies", @@ -184378,6 +192451,19 @@ "description": "Analysis of 209 pollen assemblages from surface samples in ten vegetation types in the Florida Everglades form the basis to identify wetland sub-environments from the pollen record. This calibration dataset makes it possible to infer past trends in hydrology and disturbance regime based on pollen assemblages preserved in sediment cores. Pollen assemblages from sediment cores collected in different vegetation types throughout the Everglades provide evidence on wetland response to natural fluctuations in climate as well as impacts of human alteration of Everglades hydrology. Sediment cores were located primarily in sawgrass marshes, cattail marshes, tree islands, sawgrass ridges, sloughs, marl prairies, and mangroves. The datasets contain raw data on pollen abundance as well as pollen concentration (pollen grains per gram dry sediment). Everglades restoration planning requires an understanding of the impact of natural and human-induced environmental change on wetland stability, and this project focuses specifically on three wetland types: tree islands, the sawgrass ridge and slough system, and marl prairies. Tree islands are considered key indicators of the health of the Everglades ecosystem because of their sensitivity to both flooding and drought conditions. Tree islands also act as a sink for nutrients in the ecosystem and may play an important role in regulating nutrient dynamics. Although management strategies to restore and even create tree islands are being formulated, the published data on their age, developmental history, geochemistry, and response to hydrologic alterations is limited. To address these issues, this project integrates floral and geochemical data with geologic and vegetational mapping activities to establish the timing of tree-island formation and impacts of both flooding and droughts on tree islands throughout the Everglades.", "license": "proprietary" }, + { + "id": "USGS_SOFIF_Fbbtypes", + "title": "Florida Bay Bottom Types map", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "1997-01-31", + "bbox": "-81.25, 24.75, -80.25, 25.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553334-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553334-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIF_Fbbtypes", + "description": "The map shows the bottom types for Florida Bay that resulted from site surveys and boat transects (summer 1996-January 1997) compared with aerial photographs (December 1994-January 1995) and SPOT satellite imagery (1987). The purpose of this map is to describe the bottom types found within Florida Bay for use in 1) assessing bottom friction associated with sediment and benthic communities and 2) providing a very general description for other research needs. For these purposes, two descriptors were considered particularly important: density of seagrass cover and sediment texture. Seagrass estimates are visual estimates of the amount of seagrass cover including both number of plants and leaf length. Therefore, seagrass cover may be greater in areas with long leaves than in areas with short blades, even though the number of shoots may be the same. Seagrass cover is a different measure than density (Zieman et al., 1989 or Durako et al., 1996). It is used here to more accurately reflect hydrodynamic influence than the standing crop of seagrass. The use and definitions of dense, intermediate and sparse seagrass cover are similar to those used by Scoffin (1970). This map and associated descriptions are not meant to assess ecologic communities or detail sedimentological facies. The resolution of the map has been selected in an effort to define broad regions for use in modeling efforts. For these purposes, small-scale changes in bottom type (e.g. small seagrass patches) are not delineated.", + "license": "proprietary" + }, { "id": "USGS_SOIL_CHEMISTRY", "title": "Chemical Analyses of Soils and Other Surficial Materials of the Conterminous United States", @@ -184391,6 +192477,84 @@ "description": "The following abstract was taken from the the Chemical Analyses of Soils and Other Surficial Materials of the Conterminous United States Metadata, written by David B. Smith, Research Geologist, U.S. Geological Survey, Denver, Colorado. This metadata may be viewed in HTML at \"http://minerals.usgs.gov/\". ABSTRACT This data set contains geochemical data from soils and other regoliths collected and analyzed by Hans Shacklette and colleagues beginning in 1958 and continuing until about 1976. The samples were collected at a depth of about 20 cm from sites that, insofar as possible, had surficial materials that were very little altered from their natural condition and that supported native plants. The sample material at most sites could be termed \"soil\" because it was a mixture of disintegrated rock an organic matter. Some of the sampled deposits, however, were not soils as defined above, but were other regolith types. These included desert sands, sand dunes, some loess deposits, and beach and alluvial deposits that contained little or no visible organic material. The samples were chemically analyzed by a variety of techniques in the U.S. Geological Survey laboratories in Denver, CO. DATA The data set contains 1,323 samples for a sampling density of approximately one sample per 6,000 square kilometers. The data set is currently the only national geochemical data set collected and analyzed according to standardized protocols. The data are most appropriately used to provide information on background concentrations of elements in soil. ANALYSIS METHODS The data was acquired using various chemical analysis methods. In summary the methods used were: 1)Emission spectrography for Al, Ba, Be, B, Ca, Ce, Cr, Co, Cu, Ga, Fe, La, Pb, Mg, Mn, Mo, Nd, Ni, Nb, P, K, Sc, Na, Sr, Ti, V, Yb, Y, Zn, and Zr; 2)EDTA titration for Ca; 3)Colorimetric methods for P and Zn, 4)Flame photometry for K; 5)Flame atomic absorption for Hg, Li, Mg, Na, Rb, and Zn; 6)Flameless atomic absorption for Hg; 7)X-ray fluorescence spectrometry for Ca, Ge, Fe, K, Se, Ag, S, and Ti; 8)Combustion for total carbon; and 9)Neutron activation analysis for U and Th. THE DATA The data file is an ArcVie Shapefile and has been compressed using the WinZip program. The usere will need to uncompress the file with WinZip or compatible software before attempting to import the file into ArcView or ArcInfo. Shapefiles can only be de-compressed with programs that recognize multi-file archives. The shapefiles are designed for use with Arc/Info and ArcView, which are GIS/Mapping software marketed by ESRI. By visiting \"http://www.esri.com/software/arcexplorer/index.html\" you may download ESRI's free Arc Explorer software for viewing shape files on Windows 95, 98, or NT.", "license": "proprietary" }, + { + "id": "USGS_Sherman_QUAD_1.0", + "title": "Digital Geologic Map of Sherman Quadrangle, North-Central Texas", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1967-01-01", + "end_date": "1991-01-01", + "bbox": "-98.02595, 32.982605, -95.999886, 34.01737", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554058-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554058-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_Sherman_QUAD_1.0", + "description": "This data set was created for use in a regional ground-water model of the Lake Texoma watershed for a project by the U.S. Environmental Protection Agency, National Risk Management Research Laboratory located in Ada, Oklahoma, titled \"Development of protocols and decision support tools for assessing watershed system assimilative capacity, in support of risked-based ecosystem management and restoration practices.\" Although this data set was created for use in a specific project, it may be used to make geologic maps, and determine approximate areas and locations of various geologic units. This digital data set contains geologic formations for the 1:250,000-scale Sherman quadrangle, Texas and Oklahoma. The original data are from the Bureau of Economic Geology publication, \"Geologic Atlas of Texas, Sherman sheet\", by J.H. McGowen, T.F. Hentz, D.E. Owen, M.K. Pieper, C.A. Shelby, and V.E. Barnes, 1967, revised 1991. Additional geology data sets are available for Oklahoma at URL \"http://ok.water.usgs.gov/gis/geology/index.html\". The source maps for three counties in the Oklahoma panhandle are at a scale of 1:125,000. The source maps for the rest of Oklahoma are at a scale of 1:250,000. The original geology source map was published in the Transverse Mercator Projection, Zone 14. This data set was projected to an Albers Equal Area projection (Synder, 1987), cast on the North American Datum of 1983. This electronic report was subjected to the same review standard that applies to all U.S. Geological Survey reports. Reviewers were asked to check the topological consistency, tolerances, attribute frequencies and statistics, projection, and geographic extent. Reviewers were given digital data sets and paper plots for checking against the source maps to verify the linework and attributes. The reviewers were asked to check the metadata and accompanying files for completeness and accuracy.", + "license": "proprietary" + }, + { + "id": "USGS_TamiamiFlowMonitoring_2007-2010", + "title": "Flow monitoring along the western Tamiami Trail between County Road 92 and State Road 29 in support of the Comprehensive Everglades Restoration Plan, 2007-20101", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2006-03-01", + "end_date": "2010-09-30", + "bbox": "-81.90686, 25.831032, -81.449066, 26.413366", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550120-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550120-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_TamiamiFlowMonitoring_2007-2010", + "description": "he construction of U.S. Highway 41 (Tamiami Trail), the Southern Golden Gate Estates development, and the Barron River Canal has altered the flow of freshwater to the Ten Thousand Islands estuary of Southwest Florida. Two restoration projects, the Picayune Strand Restoration Project and the Tamiami Trail Culverts Project, both associated with the Comprehensive Everglades Restoration Plan, were initiated to address this issue. Quantifying the flow of freshwater to the estuary is essential to assessing the effectiveness of these projects. The U.S. Geological Survey conducted a study between March 2006 and September 2010 to quantify the freshwater flowing under theTamiami Trail between County Road 92 and State Road 29 in southwest Florida, excluding the Faka Union Canal (which is monitored by South Florida Water Management District). The study period was after the completion of the Tamiami Trail Culverts Project and prior to most of the construction related to the Picayune Restoration Project. The section of the Tamiami Trail that was studied contains too many structures (35 bridges and 16 culverts) to cost-effectively measure each structure on a continuous basis, so the area was divided into seven subbasins. One bridge within each of the subbasins was instrumented with an acoustic Doppler velocity meter. The index velocity method was used to compute discharge at the seven instrumented bridges. Periodic discharge measurements were made at all structures, using acoustic Doppler current profilers at bridges and acoustic Doppler velocity meters at culverts. Continuous daily mean values of discharge for the uninstrumented structures were calculated on the basis of relations between the measured discharge at the uninstrumented stations and the discharge and stage at the instrumented bridge. Estimates of daily mean discharge are available beginning in 2006 or 2007 through September 2010 for all structures. Subbasin comparison is limited to water years 2008?2010. The Faka Union Canal contributed more than half (on average 60 percent) of the flow under the Tamiami Trail between State Road 29 and County Road 92 during water years 2008?2010. During water years 2008?2010, an average 9 percent of the flow through the study area came from west of the Faka Union Canal and an average 31 percent came from east of the Faka Union Canal. Flow data provided by this study serve as baseline information about the seasonal and spatial distribution of freshwater flow under the Tamiami Trail between County Road 92 and State Road 29, and study results provide data to evaluate restoration efforts.", + "license": "proprietary" + }, + { + "id": "USGS_VOLCANO", + "title": "Global Volcanoes", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-179.2176, 17.67469, -64.56616, 72.40623", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552074-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552074-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_VOLCANO", + "description": "This data shows the location of all known volcanoes in the world. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_WHFC_SUPERDIF3", + "title": "Massachusetts Coastal Marine Time Series Data Held by the USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-73.68, 41.06, -69.75, 43.07", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551788-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551788-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WHFC_SUPERDIF3", + "description": "Time-series oceanographic data for the coast of Massachusetts collected by the USGS or used in conjunction with USGS projects. These data are stored as NetCDF files using conventions developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with their EPIC system. The hourly data is available online through the USGS Coastal Marine Time Series Browser \"http://stellwagen.er.usgs.gov/\". Variables include current, temperature, pressure, conductivity, light transmission (beam attenuation) and others. Available data sets for the coast of Massachusetts: * Buzzards Bay (Jul 1982 - Oct 1985) * Cape Cod Bay (Feb 1986 - Apr 1986) * Cape Cod Misc (Jul-Aug 1980) * Long Term Observations (MWRA) (Jan 1990 - Present) * Massachusetts Bay Circulation Experiment (Sep 1990 - Jun 1991) * Massachusetts Bay Internal Wave Experiment (Aug-Sep 1998) * Stellwagen Bank (Feb 1994 - Apr 1995) * Western Massachusetts Bay (Jan-May 1987)", + "license": "proprietary" + }, + { + "id": "USGS_WHFC_SUPERDIF4", + "title": "New Jersey Outer Continental Shelf (Middle Atlantic Bight) Marine Time Series Data Held by the USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-75.69, 38.8, -73.78, 41.47", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550932-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550932-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WHFC_SUPERDIF4", + "description": "Time-series oceanographic data for the New Jersey outer continental shelf (Middle Atlantic Bight) collected by the USGS or used in conjunction with USGS projects. These data are stored as NetCDF files using conventions developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with their EPIC system. The hourly data is available online through the USGS Coastal Marine Time Series Browser \"http://stellwagen.er.usgs.gov/\". Variables include current, temperature, pressure, conductivity, light transmission (beam attenuation) and others. Available data sets for the Middle Atlantic Bight: * Deep Water Dump Site 106 (Sep 1989 - Jul 1990) * Hudson Shelf Valley (Dec 1999 - Apr 2000) * Middle Atlantic Bight (Dec 1975 - Oct 1980) * New England Continental Slope (Nov 1982 - Nov 1984)", + "license": "proprietary" + }, + { + "id": "USGS_WHFC_SUPERDIF6", + "title": "Gulf of Mexico (Alabama coast) Coastal Marine Time Series Data Held by the USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-98, 25, -82, 34", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550393-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550393-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WHFC_SUPERDIF6", + "description": "Time-series oceanographic data for the Gulf of Mexico (Alabama coast) collected by the USGS or used in conjunction with USGS projects. These data are stored as NetCDF files using conventions developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with their EPIC system. The hourly data is available online through the USGS Coastal Marine Time Series Browser \"http://stellwagen.er.usgs.gov/\". Variables include current, temperature, pressure, conductivity, light transmission (beam attenuation) and others. Available data sets for the Gulf of Mexico: Chandeleur Islands (Jul - Nov 2010) * Deep Reef (May 2001) * Lake Ponchartrain (Mar-Jul 1995) * Mobile Bay (Apr-Jul 1990; May 1991 - May 1992)", + "license": "proprietary" + }, { "id": "USGS_WHFC_SUPERDIF7", "title": "California Coastal Marine Time Series Data Held by the USGS", @@ -184404,6 +192568,97 @@ "description": "Time-series oceanographic data for the coast of California collected by the USGS or used in conjunction with USGS projects. These data are stored as NetCDF files using conventions developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with their EPIC system. The hourly data is available online through the USGS Coastal Marine Time Series Browser \"http://stellwagen.er.usgs.gov/\". Variables include current, temperature, pressure, conductivity, light transmission (beam attenuation) and others. Available data sets for California: * California Area Monitoring Program (CAMP) (May-Jun 1987, Dec 1988 - Feb 1989) * Farallones (May 1989 - Aug 1990; Nov 1997 -Nov 1998) * Monterey Bay National Marine Sanctuary (May 1985 - Aug 1998) * Monterey Canyon (Aug 1993 - May 1995) * Orange County, CA (Jun 2001 - Jan 2003) * Palos Verdes Shelf (May 1992 - Mar 1993) * Southern California (Nov 1997 - Mar 2000) * Sediment Transport on Shelves and Slopes (STRESS) (Dec 1988 - May 1989; Nov 1990 - Mar 1991)", "license": "proprietary" }, + { + "id": "USGS_WHFC_SUPERDIF8", + "title": "Johnston Atoll of Hawaii Pacific Marine Time Series Data Held by the USGS", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-170, 16, -168, 18", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553024-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553024-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WHFC_SUPERDIF8", + "description": "Time-series oceanographic data for the Pacific Ocean in the vicinity of Johnston Atoll, collected by the USGS or used in conjunction with USGS projects. These data are stored as NetCDF files using conventions developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with their EPIC system. The hourly data is available online through the USGS Coastal Marine Time Series Browser \"http://stellwagen.er.usgs.gov/\". Variables include current, temperature, pressure, conductivity, light transmission (beam attenuation) and others. Available data sets for Hawaii: * Mamala Bay (Jun 1996 - Aug 1997) * Molokai (Jan-Apr 2001; Nov 2001 - Feb 2002)", + "license": "proprietary" + }, + { + "id": "USGS_WHSC_MassBay_89-06_3.0", + "title": "Long-Term Oceanographic Observations in Massachusetts Bay, 1989-2006 - USGS_WHSC_MassBay_89-06", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1989-12-01", + "end_date": "2006-02-28", + "bbox": "-71, 42, -70.5, 42.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550427-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550427-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WHSC_MassBay_89-06_3.0", + "description": "This data report presents long-term oceanographic observations made in western Massachusetts Bay at long-term site LT-A (42\ufffd 22.6' N., 70&\ufffd 47.0' W.; nominal water depth 32 meters) from December 1989 through February 2006 and long-term site B LT-B (42\ufffd 9.8' N., 70\ufffd 38.4' W.; nominal water depth 22 meters) from October 1997 through February 2004. The observations were collected as part of a U.S. Geological Survey (USGS) study designed to understand the transport and long-term fate of sediments and associated contaminants in Massachusetts Bay. The observations include time-series measurements of current, temperature, salinity, light transmission, pressure, oxygen, fluorescence, and sediment-trapping rate. About 160 separate mooring or tripod deployments were made on about 90 research cruises to collect these long-term observations. This report presents a description of the 16-year field program and the instrumentation used to make the measurements, an overview of the data set, more than 2,500 pages of statistics and plots that summarize the data, and the digital data in Network Common Data Form (NetCDF) format. This research was conducted by the USGS in cooperation with the Massachusetts Water Resources Authority and the U.S. Coast Guard.", + "license": "proprietary" + }, + { + "id": "USGS_WILMA_COASTAL_IMPACT", + "title": "Hurricane Wilma Impact Studies", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-86, 22, -74, 35", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550249-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550249-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WILMA_COASTAL_IMPACT", + "description": "Hurricane Wilma made landfall as a category 2 storm south of Fort Meyers, Florida on October 24, 2005. The U.S. Geological Survey (USGS), NASA, and the U.S. Army Corps of Engineers are cooperating in a research project investigating coastal change that might result from Hurricane Wilma. Pre-landfall vulnerability estimates for west Florida's barrier islands falling within the cone of uncertainty for Wilma's path are available. These maps highlight the extreme vulnerability of the West-Florida coastline to a direct hit from a storm of Wilma's predicted magnitude. Aerial video, still photography, and laser altimetry surveys of post-storm beach conditions will be collected for comparison with earlier data as soon as weather allows. The comparisons will show the nature, magnitude, and spatial variability of coastal changes such as beach erosion, overwash deposition, and island breaching. These data will also be used to further refine predictive models of coastal impacts from severe storms. The data will be made available to local, state, and federal agencies for purposes of disaster recovery and erosion mitigation. [Summary provided by the USGS.]", + "license": "proprietary" + }, + { + "id": "USGS_WRD_NWIS-W", + "title": "National Water Information System (NWISWeb)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-125, 25, -66, 50", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232314067-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232314067-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_WRD_NWIS-W", + "description": "The National Water Information System database (NWIS)provide access to water-resources data collected at approximately 1.5 million sites in all 50 States, the District of Columbia, and Puerto Rico. Online access to this data is organized around these categories: - Surface Water -Ground Water -Water Quality The USGS investigates the occurrence, quantity, quality, distribution, and movement of surface and underground waters and disseminates the data to the public, State and local governments, public and private utilities, and other Federal agencies involved with managing our water resources. [Summary adapted from: \"http://waterdata.usgs.gov/usa/nwis/\"]", + "license": "proprietary" + }, + { + "id": "USGS_YosemiteRockFalls", + "title": "Historical rock falls in Yosemite National Park, California (1857-2011)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-119.8863, 37.4948, -119.1995, 38.1863", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554256-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554256-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_YosemiteRockFalls", + "description": "Inventories of rock falls and other types of landslides are valuable tools for improving understanding of these events. For example, detailed information on rock falls is critical for identifying mechanisms that trigger rock falls, for quantifying the susceptibility of different cliffs to rock falls, and for developing magnitude-frequency relations. Further, inventories can assist in quantifying the relative hazard and risk posed by these events over both short and long time scales. This report describes and presents the accompanying rock fall inventory database for Yosemite National Park, California. The inventory database documents 925 events spanning the period 1857\u20132011. Rock falls, rock slides, and other forms of slope movement represent a serious natural hazard in Yosemite National Park. Rock-fall hazard and risk are particularly relevant in Yosemite Valley, where glacially steepened granitic cliffs approach 1 km in height and where the majority of the approximately 4 million yearly visitors to the park congregate. In addition to damaging roads, trails, and other facilities, rock falls and other slope movement events have killed 15 people and injured at least 85 people in the park since the first documented rock fall in 1857. The accompanying report describes each of the organizational categories in the database, including event location, type of slope movement, date, volume, relative size, probable trigger, impact to humans, narrative description, references, and environmental conditions. The inventory database itself is contained in a Microsoft Excel spreadsheet (Yosemite_rock_fall_database_1857-2011.xlsx). Narrative descriptions of events are contained in the database, but are also provided in a more readable Adobe portable document format (pdf) file (Yosemite_rock_fall_database_narratives_1857-2011.pdf) available for download separate from the database. ", + "license": "proprietary" + }, + { + "id": "USGS_ag_chem_1.0", + "title": "Estimates of agricultural-chemical use in counties in the conterminous United States as reported in the 1987 Census of Agriculture", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-128.07002, 22.67775, -65.25698, 48.26194", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548606-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548606-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ag_chem_1.0", + "description": "This coverage contains estimates of agricultural-chemical use in counties in the conterminous United States as reported in the 1987 Census of Agriculture (U.S. Department of Commerce, 1989a). Agricultural-chemical use data are reported as either acres on which used, tons, or as a percentage of county area. Agricultural-chemical use estimates were generated from surveys of all farms where $1,000 or more of agricultural products were sold, or normally would have been sold, during the census year. Most of the attributes summarized represent 1987 data, but some information from the 1982 Census of Agriculture also was included. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970).", + "license": "proprietary" + }, + { + "id": "USGS_ag_stock_1.0", + "title": "Estimates of livestock holdings in counties in the conterminous United States as reported in the 1987 Census of Agriculture", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-128.07002, 22.67775, -65.25698, 48.26194", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552987-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552987-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ag_stock_1.0", + "description": "The livestock holdings estimates in this coverage are intend for use in estimating regional livestock holdings, and in producing visual displays and mapping relative amounts of agricultural livestock holdings across broad regions of the United States. This coverage contains estimates of livestock holdings in counties in the conterminous United States as reported in the 1987 Census of Agriculture (U.S. Department of Commerce, 1989a). Livestock holdings data are reported as either a number (for example, number of milk cows), number of farms, or in thousands of dollars. Livestock holdings estimates were generated from surveys of all farms where $1,000 or more of agricultural products were sold, or normally would have been sold, during the census year. Most of the attributes summarized represent 1987 data, but some information for the 1982 Census of Agriculture also was included. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). Livestock Census of Agriculture Counties United States Procedures_Used: CENSUS DATA An automated procedure was developed for processing the raw census data into ARC/INFO coverage attributes. The procedure is summarized below: 1) copy county2m coverage to coverage representing type of census data (i.e. ag_expn or ag_land), 2) run agadd.aml for each item added to the coverage, giving coverage name and attribute field number as arguments. The agadd.aml program runs a fortran program to extract field data from the raw census data files, and then processes that raw data finally adding it as a column of attribute data to the county coverage. Other programs were developed to calculate summary statistics of the census attribute data, and to make graphics representing attribute values across the United States. COUNTY BOUNDARIES This series of maps was published as part of the National Atlas of the United States (U.S.Geological Survey, 1970). The maps for the conterminou United States were digitized in 15 sheets and published in the Digital Lin Graph (DLG) format as described by Domeratz and others (1983). Each sheet was prepared by reading the DLG files of the political and water bodies layers, converting them to ARC/INFO, extracting the county boundaries and the coastline, respectively, and joining the two layers. FIPS codes were assigned to all polygons by using available sources and were checked manually. Boundaries with adjacent sheets of the 15-sheet set were edgematched manually, arbitrarily choosing one of the sheets as the \"correct\" border. Edgematching operations adjusted the linework as far as was necessary so that the coverages would fit to a tolerance of 100 meters. The coverage (referred to herein as Version 1.0) was stored as 49 separate coverages (48 States and the District of Columbia) because the ARC/INFO software in use at the time could not process the entire coverage. Individual States could be joined by specifying a tolerance of 100 meters. From time to time, adjustments were made to the State coverages to reflect changes in U.S. counties. It is believed the accuracy of these adjustments is comparable to the original linework. For Version 2.0, all State coverages were rejoined and manually edited to produce a perfect edgematch between all States. For States on the original map sheet boundaries, this adjustment averaged less than 20 meters and in no case was more than 100 meters. The whole coverage was CLEANed to a tolerance of 20 meters, which resulted in few, if any, effect on small offshore islands. The coverage also was checked to ensure that it represented current U.S. counties or county equivalents. The coverage in Version 1.0 stopped at the coastline. There was no attempt to depict offshore areas. This created some problems when the coverage was used to assign county codes to sampling stations located near the coast. To help in this matter, Version 2.0 includes offshore extensions of the county polygons. The (water) boundaries of many of these polygons are arbitrary. The Canadian Great Lakes features are another new addition to Version 2. They were added to improve the utility of the coverage for visual displays Although the Canadian Great Lakes are logically represented by a single polygon, practical considerations -- the inability of some software to plo polygons with a large number of vertices -- made it necessary to separate them into four polygons. The dividing lines are located in narrow channel to minimize interference with plotting patterns. Canadian islands within the Great Lakes also were included. All ticks were relocated to places that are easily visible on maps of the United States, to help in registering maps that may not otherwise have adequate registration information. To expedite accessing parts of the coverage, certain items have been indexed with the procedure, INDEX_COUNTY.AML. See Section 3 above. A spatial index also was created. When using this coverage to clip or intersect other coverages, a tolerance as low as 2 meters can be used. The processing used to derive this coverage moved boundaries from their positions on the original maps. In cases of conflicting lines, preference was given to forming the correct topology. Strictly speaking, this coverage is not identical to the source materials. These changes were unavoidable in producing a continuous coverage of the conterminous United States. Revisions: COUNTY POLYGON DATA Revision 1.0, 12/17/90. This revision represents numerous corrections and minor modifications made to this set of coverages from its construction in 1985 through the revision date. Revision 2.0, 3/18/91. Major reworking of the coverage, combining all State coverages. Reviews_Applied_to_Data: The Census of Agriculture data processing procedure and attribute data have been peer reviewed in 1993 by Leonard Orzol and Barbara Ruddy, both hydrologist with the USGS. The county boundaries in this coverage have received no formal review. They have, however, been used in numerous applications where serious error would have been obvious. Some State coverages were corrected following such use. The offshore polygon extensions and the Canadian Great Lakes polygons have had no review. Related_Spatial_and_Tabular_Data_Sets: This coverage is part of series of 1:2,000,000-scale base maps covering the United States. Layers in this set include: COUNTY -- County boundaries. STATE -- State boundaries (formed from COUNTY). WATERBOD -- Water Bodies. STREAM -- Streams. HUC -- Hydrologic cataloging units (basins).", + "license": "proprietary" + }, { "id": "USGS_arapbase_Version 1.0, July 22, 1998", "title": "COVERAGE ARAPBASE -- Structure contours of base of upper Arapahoe aquifer", @@ -184417,6 +192672,19 @@ "description": "This data set was created to display the altitude of the base of the upper Arapahoe aquifer as depicted in Robson and others (1998). This digital geospatial data set consists of structure contours on the base of the upper member of the Arapahoe aquifer. The U.S. Geological Survey developed this data set as part of a project described in the report, \"Structure, Outcrop, and Subcrop of the Bedrock Aquifers Along the Western Margin of the Denver Basin, Colorado\" (Robson and others, 1998).", "license": "proprietary" }, + { + "id": "USGS_benchmark_1.0", + "title": "Locations of NASQAN benchmark stations", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-127.042595, 27.19216, -69.387886, 48.367382", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550268-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550268-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_benchmark_1.0", + "description": "This coverage was created for the 1990-91 National Water Summary. The coverage shows locations of NASQAN benchmark stations. Procedures_Used: The point coverage was created from data taken from U.S. Geological Survey computer files.", + "license": "proprietary" + }, { "id": "USGS_cir89_Version 1.0", "title": "Color-infrared composite of Landsat data for the Sarcobatus Flat area of the Death Valley regional flow system, Nevada and California, 1989", @@ -184482,6 +192750,45 @@ "description": "This data set consists of digital water-table contours for the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins. The U.S. Geological Survey constructed a water-table map of the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins for ground-water levels measured during the period January-September 1996. Water-level data were collected from 632 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:175,512. The contour interval ranges from 3,400 to 1,550 feet above sea level. [Summary provided by the USGS.]", "license": "proprietary" }, + { + "id": "USGS_erf1_Version 1.2, August 01, 1999", + "title": "ERF1 -- Enhanced River Reach File 1.2", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-01-07", + "end_date": "1999-01-07", + "bbox": "-127.8169, 23.247017, -65.55541, 48.19323", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552175-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552175-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_erf1_Version%201.2%2C%20August%2001%2C%201999", + "description": "ERF1 was designed to be a digital data base of river reaches capable of supporting regional and national water-quality and river-flow modeling and transport investigations in the water-resources community. ERF1 has been recently used at the U.S. Geological Survey to support interpretations of stream water-quality monitoring network data (see Alexander and others, 1996; Smith and others, 1995). In these analyses, the reach network has been used to determine flow pathways between the sources of point and nonpoint pollutants (e.g., fertilizer use, municipal wastewater discharges) and downstream water-quality monitoring locations in support of predictive water-quality models of stream nutrient transport. The digital data set ERF1 includes enhancements to the U.S. Environmental Protection Agency's River Reach File 1 (RF1)to ensure the hydrologic integrity of the digital reach traces and to quantify the time of travel of river reaches and reservoirs [see U.S.EPA (1996) for a description of the original RF1]. Any use of trade, product, or firm names is for descriptive", + "license": "proprietary" + }, + { + "id": "USGS_erfi-2_2.0, November 19, 2001", + "title": "ERF1-2 -- Enhanced River Reach File 2.0", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-01-07", + "end_date": "1999-01-07", + "bbox": "-127.85945, 23.243486, -65.37739, 48.194405", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551816-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551816-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_erfi-2_2.0%2C%20November%2019%2C%202001", + "description": "This report describes the process of enhancements to the stream reach network, ERF1, which is an enhanced version of EPA's RF1. The U.S. Environmental Protection Agency's reach file (RF1) is a database of interconnected stream segments or \"reaches\" that comprise the surface water drainage system for the United States. A variety of attributes have been assigned to each reach in support of spatial analysis and mapping applications. ERF1-2 was designed to be a digital database of river reaches capable of supporting regional and national water-quality and river-flow modeling by the water-resources community. ERF1, on which ERF1-2 is based, is used at the U.S. Geological Survey to support national-level water-quality monitoring modeling with the SPARROW model (see Alexander and others, 2000; Smith and others, 1997). In the current and earlier analyses, the reach network is used to determine flow pathways between the sources of point and nonpoint pollutants (e.g., fertilizer use, municipal wastewater discharges) and downstream water-quality monitoring locations in support of predictive water- quality models of stream nutrient transport. Acknowledgements The authors would like to thank Richard Smith, a co-developer of the SPARROW approach, Kristine Verdin, and Stephen Char, all of the U.S. Geological Survey, for providing technical assistance. The reviewers of this report, Dave Stewart, and Mike Wieczorek, are also acknowledged for their significant contributions. The digital segmented network based on watershed boundaries, ERF1-2, includes enhancements to the U.S. Environmental Protection Agency's River Reach File 1 (RF1) (USEPA, 1996; DeWald and others, 1985) to support national and regional-scale surface water-quality modeling. Alexander and others (1999) developed ERF1, which assessed the hydrologic integrity of the digital reach traces and calculated the mean water time-of-travel in river reaches and reservoirs. ERF1-2 serves as the foundation for SPARROW (Spatially Referenced Regressions (of nutrient transport) On Watershed) modeling. Within the context of a Geographic Information System, SPARROW estimates the proportion of watersheds in the conterminous U.S. with outflow concentrations of several nutrients, including total nitrogen and total phosphorus, (Smith, R.A., Schwarz, G.E., and Alexander, R.B., 1997). This version of the network expands on ERF1 (version 1.2; Alexander et al. 1999), and includes the incremental and total drainage area derived from 1-kilometer (km) elevation data for North America. Previous estimates of the water time-of-travel were recomputed for reaches with water- quality monitoring sites that included two reaches. The mean flow and velocity estimates for these split reaches are based on previous estimation methods (Alexander et al., 1999) and are unchanged in ERF1-2. Drainage area calculations provide data used to estimate the contribution of a given nutrient to the outflow. Data estimates depend on the accuracy of node connectivity. Reaches split at water- quality or pesticide-monitoring sites indicate the source point for estimating the contribution and transport of nutrients and their loads throughout the watersheds. The ERF1-2 coverage extends the earlier ERF1 coverage by providing digital-elevation-model (DEM-based estimates of reach drainage area founded on the 1-kilometer data for North America (Verdin, 1996; Verdin and Jenson, 1996). A 1-kilometer raster grid of ERF1-2 projected to Lambert Azimuthal Equal Area, NAD 27 Datum (Snyder, 1987), was merged with the HYDRO1K flow direction data set (Verdin and Jenson, 1996) to generate a DEM-based watershed grid, ERF1_2WS. The watershed boundaries are maintained in a raster (grid cell) format as well as a vector (polygon) format for subsequent model analysis. Both the coverage, ERF1-2, and the grid, ERF1-2WS are available at: \"http://water.usgs.gov/orh/nrwww/sparrow_section5_nolan.pdf\". Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology.", + "license": "proprietary" + }, + { + "id": "USGS_etsite_Version 1.0", + "title": "Evapotranspiration sites within the Ash Meadows and Oasis Valley discharge areas, Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1993-01-01", + "end_date": "1999-01-01", + "bbox": "-116.73254, 36.37027, -116.296814, 37.063698", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552240-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552240-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_etsite_Version%201.0", + "description": "The digital data set was created to display site locations at which micrometeorological data were collected in Ash Meadows and Oasis Valley, Nev. The digital data set provides locations and general descriptions of sites instrumented to collect micrometeorological data from which mean annual ET rates were computed. Sites are located in Ash Meadows and Oasis Valley, Nevada. Data were collected December 1993 through present. Introduction The digital data set was created in cooperation with the U.S. Department of Energy. The data set was created as part of a study to refine current estimates of ground-water discharge from the major discharge areas of the Death Valley regional flow system. This digital data set provides locations and general descriptions of sites instrumented during recent studies of evapotranspiration in Ash Meadows and Oasis Valley, Nevada. Data were collected December 1993 through 2001. Reviews The digital data set has gone through a multi-level, quality-control process to ensure that the data are a reasonable representation of source points. Reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers also were asked to check the topological consistency, tolerances, projections, and geographic extent. Notes Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although the data set has been used by the U.S. Geological Survey, U.S. Department of the Interior, no warranty expressed or implied is made by the U.S. Geological Survey as to the accuracy of the data and related materials. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in non-proprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology. Users should exercise caution and judgment in applying these data, and be aware that errors may be present in any or all of the digital image data. If errors are encountered in this data set, it will be appreciated if the user would pass this information to the Metadata_Contact.", + "license": "proprietary" + }, { "id": "USGS_etunit_Version 1.0", "title": "Classification of evapotranspiration units in major discharge areas of Death Valley regional flow system, Nevada and California", @@ -184495,6 +192802,19 @@ "description": "The data set was created to delineate the aerial extent and quantify acreage of the different ET units found within the many major discharge areas of the Death Valley regional flow system. The raster-based classification of evapotranspiration (ET) units is for nine major discharge areas in the Death Valley regional flow system. The ET units delineate general areas of similar vegetation and soil-moisture conditions. Classifications were derived from Landsat Thematic Mapper imagery data acquired June 13, 1992; Sept. 1, 1992; and June 21, 1989. Introduction The raster-based classification of ET units within the major discharge areas of the Death Valley regional flow system determined from Landsat Thematic Mapper (TM) imagery data acquired June 13, 1992, Sept. 1, 1992; and June 21, 1989. Background information on classification procedures can be found in American Society of Photogrammetry (1983). Except for Sarcobatus Flat, all discharge areas were classified using the 1992 TM imagery. An accurate classification of Sarcobatus Flat could not be attained from 1992 TM imagery because of extensive cloud cover over the area. Instead, Sarcobatus Flat was classified from TM data acquired June 21, 1989. Reviews The final classification of ET units within each major discharge area was checked for consistency and accuracy during data processing. Two external reviews were done. The reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers also were asked to check the topological consistency, tolerances, projections, and geographic extent.", "license": "proprietary" }, + { + "id": "USGS_gpwa_utm27f_met", + "title": "Mean annual precipitation for Ohio, 1931-80", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1931-01-01", + "end_date": "1980-12-31", + "bbox": "-84.90978, 38.42526, -80.48513, 41.977966", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553599-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553599-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_gpwa_utm27f_met", + "description": "This coverage is intended as a data layer representing the spatial distribution of mean annual precipitation in Ohio for the years 1931-80. Information contained in this coverage has been used to obtain values of mean annual precipitation at basin centroid locations. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology. This is a Triangulated Irregular Network (TIN) of mean annual precipitation for the period 1931-80 for Ohio. A 1:1,100,000 scale (approximate) paper isoline map of mean annual precipitation from Harstine (1991) was digitized as arcs directly into an Albers equal-area projection. The arc coverage was projected to the State Plane Coordinate system, zone 5001, and then converted to a TIN by means of the \"arctin\" command.", + "license": "proprietary" + }, { "id": "USGS_ha24_hum", "title": "COVERAGE HA24_HUM - 1:24,000-scale Hydrographic Areas for Humboldt River Basin, Nevada", @@ -184508,6 +192828,110 @@ "description": "This data set was created to display the topographic and administrative hydrographic area boundaries for the Humboldt River Basin at 1:24,000-scale. This data set contains the topographic and administrative hydrographic area boundaries for the Humboldt River Basin at 1:24,000-scale. Introduction The hydrographic area (HA) boundaries for the State of Nevada were delineated on 1:250,000-scale maps, in cooperation with the U.S. Geological Survey (USGS), and then redrawn and published at 1:500,000-scale (Cardinalli and others, 1968). This 1:500,000-scale map is the current reference for HAs in Nevada and is used as a guide in delineating the HAs at 1:24,000-scale. This data set contains the topographic and administrative HA boundaries for the Humboldt River Basin. The Humboldt River Basin HAs were delineated and digitized from 1993 to 1998 using 1:24,000-scale USGS topographic maps. Reviews The digital data in this data base has gone through a rigorous, multi-level, quality-control process that ensures the data set is a fair representation of the source map. If errors are encountered in this data set, it will be appreciated if the user would pass this information to the Metadata_Contact. Two reviews were done. The reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers were asked to check the topological consistency, tolerances, projections, and geographic extent. Notes It should be noted that, although the boundary lines between hydrographic areas generally coincide with true topographic basin divides, some of the lines are arbitrary divisions that have no basis in topography, but are administrative and specified by Nevada Division of Water Resources. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in non-proprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology.", "license": "proprietary" }, + { + "id": "USGS_herbicide2_1.0", + "title": "Estimates of herbicide use for the twenty-first through the fortieth most-used herbicides in the conterminous United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-128.07002, 22.67775, -65.25698, 48.26194", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549262-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549262-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_herbicide2_1.0", + "description": "The herbicide-use estimates in this coverage are intended for use as a means for estimating regional herbicide use, and for producing maps showing relative rates of herbicide use across broad regions of the United States. This coverage contains estimates of herbicide use for the twenty-first through the fortieth most-used herbicides in the conterminous United States as reported in Gianessi and Puffer (1991). Herbicide-use estimates in this coverage are reported for each county polygon as acres treated, pounds of active ingredient used, and pounds used per square mile. The herbicide-use estimates provided by Gianessi and Puffer (1991) list acres treated and pounds of active ingredient applied for a given crop in each county for which use has been estimated. Cropping data are from the 1987 Census of Agriculture, and are subject to occasional suppressions of acreage estimates at the county level due to problems of confidentiality and census disclosure rules. The herbicide-use estimates included in this coverage are totals of use on all crops treated in a given county. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). [Summary provided by USGS]", + "license": "proprietary" + }, + { + "id": "USGS_herbicide3_1.0", + "title": "Estimates of herbicide use for the forty-first through the sixtieth most-used herbicides in the conterminous United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-128.07002, 22.67775, -65.25698, 48.26194", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549121-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549121-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_herbicide3_1.0", + "description": "The herbicide-use estimates in this coverage are intended for use as a means for estimating regional herbicide use, and for producing maps showing relative rates of herbicide use across broad regions of the United States. This coverage contains estimates of herbicide use for the forty-first through the sixtieth most-used herbicides in the conterminous United States as reported in Gianessi and Puffer (1991). Herbicide-use estimates in this coverage are reported for each county polygon as acres treated, pounds of active ingredient used, and pounds used per square mile. The herbicide-use estimates provided by Gianessi and Puffer (1991) list acres treated and pounds of active ingredient applied for a given crop in each county for which use has been estimated. Cropping data are from the 1987 Census of Agriculture, and are subject to occasional suppressions of acreage estimates at the county level due to problems of confidentiality and census disclosure rules. The herbicide-use estimates included in this coverage are totals of use on all crops treated in a given county. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). [Summary provided by USGS]", + "license": "proprietary" + }, + { + "id": "USGS_herbicide4_1.0", + "title": "Estimates of herbicide use for the sixty-first through the eightieth most-use herbicides in the conterminous United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-128.07002, 22.67775, -65.25698, 48.26194", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550114-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550114-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_herbicide4_1.0", + "description": "The herbicide-use estimates in this coverage are intended for use as a means for estimating regional herbicide use, and for producing maps showing relative rates of herbicide use across broad regions of the United States. This coverage contains estimates of herbicide use for the sixty-first through the eightieth most-used herbicides in the conterminous United States as reported in Gianessi and Puffer (1991). Herbicide-use estimates in this coverage are reported for each county polygon as acres treated, pounds of active ingredient used, and pounds used per square mile. The herbicide-use estimates provided by Gianessi and Puffer (1991) list acres treated and pounds of active ingredient applied for a given crop in each county for which use has been estimated. Cropping data are from the 1987 Census of Agriculture, and are subject to occasional suppressions of acreage estimates at the county level due to problems of confidentiality and census disclosure rules. The herbicide-use estimates included in this coverage are totals of use on all crops treated in a given county. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). [Summary provided by USGS]", + "license": "proprietary" + }, + { + "id": "USGS_herbicidel_01_1.0", + "title": "Estimates of herbicide use for the 20 most-used herbicides in the conterminous United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-128.07002, 22.67775, -65.25698, 48.26194", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550842-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550842-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_herbicidel_01_1.0", + "description": "The herbicide-use estimates in this coverage are intended for use as a means for estimating regional herbicide use, and for producing maps showing relative rates of herbicide use across broad regions of the United States. This coverage contains estimates of herbicide use for the 20 most-used herbicides in the conterminous United States as reported in Gianessi and Puffer (1991). Herbicide-use estimates in this coverage are reported for each county polygon as acres treated, pounds of active ingredient used, and pounds used per square mile. The herbicide-use estimates provided by Gianessi and Puffer (1991) list acres treated and pounds of active ingredient applied for a given crop in each county for which use has been estimated. Cropping data are from the 1987 Census of Agriculture, and are subject to occasional suppressions of acreage estimates at the county level due to problems of confidentiality and census disclosure rules. The herbicide-use estimates included in this coverage are totals of use on all crops treated in a given county. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). Herbicides Herbicide use Counties United States Procedures_Used: HERBICIDE-USE DATA An automated procedure was developed to process the raw herbicide-use data into ARC/INFO coverage attributes. The procedure is summarized below: (1) copy county2m coverage to coverage called herbicide%#%, and (2) run the AML herbadd.aml for each herbicide to be added. The herbadd.aml program runs a fortran program to total estimates of herbicide use on all crops by county, then processes these data, finally adding them as three columns of attribute data to the county coverage. Other programs were developed to calculate summary statistics of the herbicide-attribute data and to produce maps that show attribute values across the United States. COUNTY BOUNDARIES This series of maps was published as part of the National Atlas of the United States (U.S.Geological Survey, 1970). The maps for the conterminous United States were digitized in 15 sheets and published in the Digital Line Graph (DLG) format as described by Domeratz and others (1983). Each sheet was prepared by reading the DLG files of the political and water-bodies layers, converting them to ARC/INFO; extracting the county boundaries and the coastline, respectively; and joining the two layers. FIPS codes were assigned to all polygons by using available sources and were checked manually. Boundaries with adjacent sheets of the 15-sheet set were edgematched manually; one of the sheets was chosen arbitrarily as the \"correct\" border. Edgematching operations were used to adjust the linework as far as was necessary so that the coverages would fit to a tolerance of 100 meters (328.1 feet). The coverage (referred to herein as Version 1.0) was stored as 49 separate coverages (48 States and the District of Columbia) because the ARC/INFO software in use at the time could not process the entire coverage. Individual States could be joined by specifying a tolerance of 100 meters. From time to time, adjustments were made to the State coverages to reflect changes in counties. The accuracy of these adjustments is believed to be comparable to that of the original linework. For Version 2.0, all State coverages were rejoined and manually edited to produce a perfect edgematch between all States. For States on the original map-sheet boundaries, this adjustment averaged less than 20 meters and in no case was more than 100 meters. The whole coverage was Cleaned to a tolerance of 20 meters (65.6 feet), which resulted in few, if any, effects on small offshore islands. The coverage also was checked to ensure that it represented current counties or county equivalents. The coverage in Version 1.0 ended at the coastline. No attempt was made to depict offshore areas. This created problems when the coverage was used to assign county codes to sampling stations located near the coast. To help in this matter, Version 2.0 includes offshore extensions of the county polygons. The (water) boundaries of many of these polygons are arbitrary. The Canadian Great Lakes features are another new addition to Version 2.0. They were added to improve the utility of the coverage for visual displays. Although the Canadian Great Lakes are represented logically by a single polygon, practical considerations--the inability of some software to plot polygons with a large number of vertices--made it necessary to separate them into four polygons. The dividing lines are located in narrow channels to minimize interference with plotting patterns. Canadian islands within the Great Lakes also were included. All tick marks were relocated to places that are easily visible on maps of the United States, to help in registering maps that otherwise may not have adequate registration information. To expedite accessing parts of the coverage, certain items have been indexed with the procedure INDEX_COUNTY.AML. See Section 3 above. A spatial index also was created. When this coverage is used to clip or intersect other coverages, a tolerance as low as 2 meters (6.6 feet) can be used. The processing used to derive this coverage moved boundaries from their positions on the original maps. In cases of conflicting lines, preference was given to forming the correct topology. Strictly speaking, this coverage is not identical to the source materials. These changes were unavoidable in producing a continuous coverage of the conterminous United States.", + "license": "proprietary" + }, + { + "id": "USGS_hgmr_Version 1", + "title": "Hydrogeomorphic Regions in the Chesapeake Bay Watershed.", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552541-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552541-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_hgmr_Version%201", + "description": "This data set was used to compare base-flow and ground-water nitrate loads to assess the significance of ground-water discharge as a source of nitrate load to non tidal streams in the Chesapeake Bay watershed. Generalized lithology (rock type) and physiography based on geologic formations were used to characterize hydrgeomorphic regions (HGMR) within the Chesapeake Bay watershed. These HGMRs were used in conjunction with existing data to assess the significance of ground-water discharge as a source of nitrate load to non tidal streams in the Chesapeake Bay watershed (Bachman and others, 1998). This work is part of the U.S. Geological Survey's (USGS) Chesapeake Bay initiative to develop an understanding and provide scientific information for the restoration of the Chesapeake Bay and its watershed (Phillips and Caughron, 1997). Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Geological Survey. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in non proprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology. The HGMR data set is the result of combining digital data sets of physiography and rock type from numerous sources.", + "license": "proprietary" + }, + { + "id": "USGS_hydmain_hum_Version 1.0, (September, 2001)", + "title": "Humboldt River main stem, Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-01-01", + "end_date": "1994-12-31", + "bbox": "-118.4891, 40.048546, -115.24806, 41.067688", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549056-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549056-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_hydmain_hum_Version%201.0%2C%20(September%2C%202001)", + "description": "This dataset was created as a layer of a geographic information system (GIS) to calculate river miles on the Humboldt River. The currentness and accuracy of the digital orthophoto quadrangle (DOQ) source exceeded that of other available data. This data set contains the main stem of the Humboldt River as defined by Humboldt Project personnel of the U.S. Geological Survey Nevada District, 2001. The data set was digitized on screen using digital orthophoto quadrangles from 1994. Reviews The digital data in this data set has gone through a rigorous, multi-level, quality-control process that ensures the data set is a fair representation of the source map. If errors are found in this data set, it will be appreciated if the user would pass this information to the Metadata_Contact. Two formal reviews were done. The reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers were asked to check the topological consistency, tolerances, projections, and geographic extent. Notes Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology.", + "license": "proprietary" + }, + { + "id": "USGS_landfills_1.1", + "title": "Map of landfill locations in United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1986-01-01", + "end_date": "1986-12-31", + "bbox": "-125.460815, 23.556513, -66.02105, 47.045097", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552322-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552322-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_landfills_1.1", + "description": "This is a point coverage of landfills shown in the 1986 National Water Summary Report (U.S. Geological Survey, 1987). ", + "license": "proprietary" + }, + { + "id": "USGS_landuse_1", + "title": "Digital map file of major land uses in the United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1991-01-01", + "end_date": "1991-01-01", + "bbox": "-127.87006, 23.24801, -65.40621, 48.20435", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552526-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552526-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_landuse_1", + "description": "The intended use of this coverage was for the state sections of the 1990-91 National Water Summary on surface-water quality. Each state report contains a map of the state's major land uses and, where possible, discusses the influence of land use on water quality in the state. This is a polygon coverage of major land uses in the United States. The source of the coverage is the map of major land uses in the 1970 National Atlas of the United States, pages 158-159, which was adapted from U.S. Department of Agriculture, \"Major Land Uses in the United States,\" by Francis J. Marschner, revised by James R. Anderson, 1967.", + "license": "proprietary" + }, { "id": "USGS_lfhbase_Version 1.0, July 09, 1998", "title": "COVERAGE LFHBASE -- Structure contours of base of Laramie-Fox Hills and Arapahoe aquifers", @@ -184547,6 +192971,292 @@ "description": "These estimates are intended for large-scale ground- and surface-water analyses of nutrient sources or changes in these sources. These data on nutrients in manure can be compared to fertilizer inputs of nutrients. This data set contains county estimates of nitrogen and phosphorus content of animal wastes produced annually for the years 1982, 1987, and 1992. The estimates are based on animal populations for those years from the 1992 Census of Agriculture (U.S. Bureau of the Census, 1995) and methods for estimating the nutrient content of manure from the Soil Conservation Service (1992). The data set includes several components.. 1. Spatial component - generalized county boundaries in ARC/INFO format/1/, including nine INFO lookup tables containing animal counts and nutrient estimates keyed to the county polygons using county code. (The county lines were not used in the nutrient computations and are provided for displaying the data as a courtesy to the user.) The data is organized by 5-digit state/county FIPS (Federal Information Processing Standards) code. Another INFO table lists the county names that correspond to the FIPS codes. 2. Tabular component - Nine tab-delimited ASCII lookup tables of animal counts and nutrient estimates organized by 5-digit state/county FIPS (Federal Information Processing Standards) code. Another table lists the county names that correspond to the FIPS codes. The amount of nitrogen and phosphorus present in manure (in kilograms) has been calculated for each county of the United States. The procedure is identical to that of Smith and others (1997), which covered the year 1987. Nutrient estimates for the years 1982 and 1987 were computed again for the data set here, and the results were checked against the results computed previously by Alexander (written commun., 1992) for those years to ensure that they were identical. Limitations: The estimates are county level and are based on estimates of the nutrient content of animal manure produced per 1,000 pounds of animal weight on a daily basis. One important limitation of the animal population numbers from the Census of Agriculture is that for some counties and animal classes, no data are reported. This limitation reportedly is the result of restrictions on including animal population data for counties where animal production is dominated or limited to one business or farm. These data therefore are considered trade secrets and may not be included in the county-based data. This limitation on population data at the county level results in discrepancies when county-based data are summed and compared to national animal population totals. At the present we have no way of estimating animal populations for those counties with missing data and further have no way of determining which counties are missing data. Therefore, the animal manure, nitrogen and phosphorus estimates for some counties are an underestimate of the total nutrient form animal manure in those counties.", "license": "proprietary" }, + { + "id": "USGS_map-2653_1.0", + "title": "Geologic Map of the Eminence Quadrangle, Shannon County, Missouri", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "1999-12-31", + "bbox": "-91.3778, 37.1233, -91.2472, 37.2518", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553858-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553858-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_map-2653_1.0", + "description": "The purpose of this geologic map and database is to support and be part of a three-dimensional geologic framework study of south-central Missouri. The framework will be used to assess environmental impacts of lead and zinc mining in the Mark Twain National Forest on the hydrologic system of the Ozark National Scenic Riverways. The geology of the Eminence 7 1/2-minute quadrangle , Shannon County, Missouri was mapped from 1996 through 1997 as part of the Midcontinent Karst Systems and Geologic Mapping Project, Eastern Earth Surface Processes Team. The map supports the production of a geologic framework that will be used in hydrogeologic investigations related to potential lead and zinc mining in the Mark Twain National Forest adjacent to the Ozark National Scenic Riverways (National Park Service). Digital geologic coverages will be used by other federal and state agencies in hydrogeologic analyses of the Ozark karst system and in ecological models. Bedrock, Quaternary , residual units, faults, and structural data are each stored in separate coverages. See readme.txt file for explanation of organization.", + "license": "proprietary" + }, + { + "id": "USGS_mapi-1300_Version 1.0", + "title": "Geologic and structure map of the Choteau 1 x 2 degree quadrangle, western Montana: a digital database.", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "", + "bbox": "-114, 47.5, -112, 48", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553685-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553685-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-1300_Version%201.0", + "description": "This dataset was developed to provide geologic map GIS of the Choteau 1:250,000 quadrangle for use in future spatial analysis by a variety of users. These data can be printed in a variety of ways to display various geologic features or used for digital analysis and modeling. This database is not meant to be used or displayed at any scale larger than 1:250,000 (e.g. 1:100,000 or 1:24,000). The geologic and structure map of Choteau 1 x 2 degree quadrangle (Mudge and others, 1982) was originally converted to a digital format by Jeff Silkwood (U.S. Forest Service and completed by the U.S. Geological Survey staff and contractor at the Spokane Field Office (WA) in 2000 for input into a geographic information system (GIS). The resulting digital geologic map (GIS) database can be queried in many ways to produce a variey of geologic maps. Digital base map data files (topography, roads, towns, rivers and lakes, etc.) are not included: they may be obtained from a variety of commercial and government sources. This database is not meant to be used or displayed at any scale larger than 1:250,000 (e.g. 1:100,000 or 1:24,000. The digital geologic map graphics and plot files (chot250k.gra/.hp/.eps and chot-map.pdf) that are provided in the digital package are representations of the digital database. They are not designed to be cartographic products. This GIS consists of two major Arc/Info datasets, a line and polygon file (chot250k) containing geologic contact and structures (lines) and geologic map rock units (polygons), and a point file (chot250kp) containing structural point data for plunging folds.", + "license": "proprietary" + }, + { + "id": "USGS_mapi-1509A_version 1.0", + "title": "Geologic and structure maps of the Wallace 1 deg. x 2 deg. quadrangle, Montana and Idaho: A digital database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-116, 47, -114, 48", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551139-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551139-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-1509A_version%201.0", + "description": "This dataset was developed to provide a geologic map GIS of the Wallace 1x2 degree quadrangle for use in future spatial analysis by a variety of users. This database is not meant to be used or displayed at any scale larger than 1:250,000 (e.g., 1:100,000 or 1:24,000) This dataset was digitized by the U.S. Geological Survey EROS Data Center and U.S. Geological Survey Spokane Field Office for input into an Arc/Info geographic information system (GIS) The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps. This GIS consists of two major and Arc/Info datasets: one line and polygon file (wal250k) containing geologic contacts and structures (lines) and geologic map rock units (polygons), and one point file (wal250bc) containing breccia outcrops.", + "license": "proprietary" + }, + { + "id": "USGS_mapi-1803_1.0", + "title": "Geologic map of the Dillon 1 x 2 degree quadrangle, Idaho and Montana", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-01-01", + "end_date": "", + "bbox": "-114, 45, -112, 46", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551047-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551047-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-1803_1.0", + "description": "This GIS database was prepared to provide digital geologic coverage for the Dillon 1 degree by 2 degree quadrangle of southwest Montana and east-central Idaho. The digital ARC/INFO databases included in this website provide a GIS database for the geologic map of the Dillon 1 degree by 2 degree quadrangle of southwest Montana and east-central Idaho. The geologic map was orginally published as U.S. Geological Survey Miscellaneous Investigations Series Map I-1803-H. This website directory contains ARC/INFO format files that can be used to query or display the geology of USGS Map I-1803-H with GIS software. (\"http://pubs.usgs.gov/imap/1993/i-1803-h/\")", + "license": "proprietary" + }, + { + "id": "USGS_mapi-1819_1.0", + "title": "Geologic Map of the Challis 1 x 2 Degree Quadrangle, Idaho", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "", + "bbox": "-116, 44, -114, 45", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553193-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553193-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-1819_1.0", + "description": "This dataset was developed to provide a geologic map GIS database of Challis 1x2 Quadrangle, Idaho for use in spatial analysis. The paper version of The geology of the Challis 1 x 2 quadrangle, was compiled by Fred Fisher, Dave McIntyre and Kate Johnson in 1992. The geology was compiled on a 1:250,000 scale topographic base map. TechniGraphic System, Inc. of Fort Collins Colorado digitized this map under contract for N.Shock. G.Green edited and prepared the Challis digital version for publication as a geographic information system database. The digital geologic map database can be queried in many ways to produce a variety of geologic maps.", + "license": "proprietary" + }, + { + "id": "USGS_mapi-2267", + "title": "Geologic and structure maps of the Kalispell 1:250,000 quadrangle, Montana, and Alberta and British Columbia: a digital database.", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-116, 48, -114, 49", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550407-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550407-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2267", + "description": "This dataset was developed to provide geologic map GIS of the Kalispell 1:250,000 quadrangle for use in the future spatial analysis by a variety of users. This database is not meant to be used or displayed at any scale larger than 1:250,000 (e.g., 1:100,000 or 1:24,000). This dataset was digitized by the U.S. Geological Survey EROS Data Center and U.S. Geological Survey Spokane Field Office for input into an Arc/Info geographic information system (GIS). The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps. This GIS dataset consists of one major Arc/Info dataset: a line and polygon file (kal250k) that contains geologic contacts and structures (lines) and geologic map rock units (polygons).", + "license": "proprietary" + }, + { + "id": "USGS_mapi-2395_1.0", + "title": "Geologic map of the eastern part of the Challis National Forest and vicinity, Idaho", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-114.5, 43.5, -112.75, 45", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554798-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554798-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2395_1.0", + "description": "This dataset was developed to provide a geologic map GIS database of Challis National Forest, Idaho for use in spatial analysis by a variety of users. The paper version of the Geologic Map of the eastern part of the Challis National Forest and vicinity, Idaho was compiled by Anna Wilson and Betty Skipp in 1994. The geology was compiled on a 1:250,000 scale topographic base map. TechniGraphic System, Inc. of Fort Collins Colorado digitized this map under contract for N.Shock. G.Green edited and prepared the digital version for publication as a GIS database. The digital geologic map database can be queried in many ways to produce a variety of geologic maps.", + "license": "proprietary" + }, + { + "id": "USGS_mapi-2494_1.0", + "title": "Generalized Thermal Maturity Map of Alaska", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "1996-12-31", + "bbox": "-175, 52, -130, 71.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552378-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552378-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2494_1.0", + "description": "The files in this directory are those that were used to create the Generalized Thermal Maturity Map of Alaska (USGS Miscellaneous Investigations Map I-2494), published in 1996. These files are necessary for importing the map in digital form into a Geographical Information System. Output files in several formats also are included in this directory; these can be used any time a digital version of the complete map is needed. The map is based, in large part, on the vitrinite-reflectance (VR) and conodont color-alteration-index (CAI) data in USGS Open-File Report 92-409, an updated version of which also is included on this CD-ROM. Alaska is a complex amalgamation of tectonic blocks with diverse histories. Sedimentary basins that are formed on these blocks both before amalgamation and as a result of collisions between them record the tectonic history of this complex region. Thermal-maturity data-indicators of maximum burial temperatures-provide important constraints both on basin evolution and on terrane amalgamation. To help elucidate these relations, and to provide constraints for hydrocarbon assessments, the U.S. Geological survey (USGS) has compiled thermal-maturity data from Alaska for many decades. This report is a digital release of our current understanding of thermal-maturity patters in Alaska. The 10 ARC/INFO coverages used to construct the map, together with the directory \"INFO\" (needed by ARC/INFO to support the coverages), are found in the \"coverages\" subdirectory. These coverages can be used by any GIS capable of importing files in ARC/INFO format. Export version of these coverages are found in the subdirectory \"export files.\" These files can be imported into ARC/INFO with the \"import\" command. Shapefile versions of 9 of the ARC/INFO coverages are found in the subdirectory \"shapefiles.\" Each shapefile actually consists of three files, with extensions .shp, .shx., and .dbf; all are needed for importation into a GIS supporting the shapefile format. Inset figures and text, as well as the map title, headers, and latitude-longitude ticks, were created as a separate file. This file is available in Adobe Illustrator 6.0 format (insets.ill) and in Encapsulated PostScript format (insets.eps) in the subdirectory \"insetfigures.\" The subdirectory \"miscfiles\" contains many important files needed to use these coverages in ARC/INFO or another GIS. ", + "license": "proprietary" + }, + { + "id": "USGS_mapi-2634_2.0", + "title": "Geologic map of the Sedan quadrangle, Gallatin and Park Counties, Montana", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1965-01-01", + "end_date": "1971-12-31", + "bbox": "-111, 45.75, -110.75, 46", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553897-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553897-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2634_2.0", + "description": "The geology of the Sedan quadrangle was mapped as part of a regional study of the western Crazy Mountains Basin. It was digitized for ease of production of the printed version and for greater distribution for analytical use. This quadrangle lies 6.4 km (4 mi) northeast of Bozeman, Mont., in southwestern Montana. Metamorphic, sedimentary, and volcanic rocks of Precambrian to Tertiary age are exposed in the Bridger Range and southwestern margin of the Crazy Mountains Basin in a crustal cross section and a structural triangle zone. Surface geology records Precambrian extension, Late Paleocene east-vergent contraction, including backthrusts, and Holocene basin-range extension. A preliminary map was published as a U.S. Geological Survey Open-File Report in 1971. The geologic data was interpreted 1965-93, the interpretation being informed by data from two wells in addition to the original field work. The digital files for the map were released in November 1998. The map-on-demand edition, released in January 2000, includes supplemental figures,three cross sections, and interpretive text. Users should be aware that of the many faults mapped, the only active one is the range front fault on the west side of the Bridger Range. The dataset for the Sedan quadrangle consists of 10 coverages: geo_net, geo_pnt, stru_net, stru_pnt, data_net, data_pnt, pnt_sym, pnt_graphic, stpnt_graphic, and dvalues. The three coverages pnt_graphic, stpnt_graphic, and dvalues are not \"true\" ARC/INFO coverages. They contain the graphic representations of symbols used on the geologic map: >pnt_sym = pnt_graphic, >stru_pnt and geo_pnt = stpnt_graphic, and >dvalues = annotation for stru_pnt and geo_pnt.", + "license": "proprietary" + }, + { + "id": "USGS_mapi-2645_version 1.0", + "title": "Geologic Map of the Central Marysvale Volcanic Field, Southwestern Utah", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-112.625, 38.25, -112, 38.708332", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553120-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553120-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2645_version%201.0", + "description": "This database was developed to improve upon previous mapping in the central Marysvale volcanic field and compile older mapping at a consistent scale. This area is an important mining district, and a regional understanding of the geology and mineral deposits will assist in understanding genesis of deposits and in exploration for new deposits. The area is also an important part of the transition zone between the Colorado Plateau to the east and the Great Basin to the west. This tectonically significant province may hold keys to the style and mechanisms of continent-scale deformation in the Western United States. The geologic map of the central Marysvale volcanic field, southwestern Utah, shows the geology at 1:100,000 scale of the heart of one of the largest Cenozoic volcanic fields in the Western United States. The map shows the area of 38 degrees 15' to 38 degrees 42'30\" N., and 112 degrees to 112 degrees 37'30\" W. The Marysvale field occurs mostly in the High Plateaus, a subprovince of the Colorado Plateau and structurally a transition zone between the complexly deformed Great Basin to the west and the stable, little-deformed main part of the Colorado Plateau to the east. The western part of the field is in the Great Basin proper. The volcanic rocks and their source intrusions in the volcanic field range in age from about 31 Ma (Oligocene) to about 0.5 Ma (Pleistocene). These rocks overlie sedimentary rocks exposed in the mapped area that range in age from Ordovician to early Cenozoic. The area has been deformed by thrust faults and folds formed during the late Mesozoic to early Cenozoic Sevier deformational event, and later by mostly normal faults and folds of the Miocene to Quaternary basin-range episode. The map revises and updates knowledge gained during a long-term U.S. Geological Survey investigation of the volcanic field, done in part because of its extensive history of mining. The investigation also was done to provide framework geologic knowledge suitable for defining geologic and hydrologic hazards, for locating hydrologic and mineral resources, and for an understanding of geologic processes in the area. A previous geologic map (Cunningham and others, 1983, U.S. Geological Survey Miscellaneous Investigations Series I-1430-A) covered the same area as this map but was published at 1:50,000 scale and is obsolete due to new data. This new geologic map of the central Marysvale field, here published as U.S. Geological Survey Geologic Investigations Series I-2645-A, is accompanied by gravity and aeromagnetic maps of the same area and the same scale (Campbell and others, 1999, U.S. Geological Survey Geologic Investigations Series I-2645-B).", + "license": "proprietary" + }, + { + "id": "USGS_mapi-2690", + "title": "Geologic map of the Ennis 30' X 60' quadrangle, Madison and Gallatin Counties, Montana, and Park County, Wyoming", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-112, 45, -111, 45.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553215-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553215-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2690", + "description": "This map forms part of the Montana State Geological Map. The Ennis 1:100,000 quadrangle lies within both the Laramide (Late Cretaceous to early Tertiary) foreland province of southwestern Montana and the northeastern margin of the middle to late Tertiary Basin and Range province. The oldest rocks in the quadrangle are Archean high-grade gneiss, and granitic to ultramafic intrusive rocks that are as old as about 3.0 Ga. The gneiss includes a supracrustal assemblage of quartz-feldspar gneiss, amphibolite, quartzite, and biotite schist and gneiss. The basement rocks are overlain by a platform sequence of sedimentary rocks as old as Cambrian Flathead Quartzite and as young as Upper Cretaceous Livingston Group sandstones, shales, and volcanic rocks. The Archean crystalline rocks crop out in the cores of large basement uplifts, most notably the \"Madison-Gravelly arch\" that includes parts of the present Tobacco Root Mountains and the Gravelly, Madison, and Gallatin Ranges. These basement uplifts or blocks were thrust westward during the Laramide orogeny over rocks as young as Upper Cretaceous. The thrusts are now exposed in the quadrangle along the western flanks of the Gravelly and Madison Ranges (the Greenhorn thrust and the Hilgard fault system, respectively). Simultaneous with the west-directed thrusting, northwest-striking, northeast-side-up reverse faults formed a parallel set across southwestern Montana; the largest of these is the Spanish Peaks fault, which cuts prominently across the Ennis quadrangle. Beginning in late Eocene time, extensive volcanism of the Absorka Volcanic Supergroup covered large parts of the area; large remnants of the volcanic field remain in the eastern part of the quadrangle. The volcanism was concurrent with, and followed by, middle Tertiary extension. During this time, the axial zone of the \"Madison-Gravelly arch,\" a large Laramide uplift, collapsed, forming the Madison Valley, structurally a complex down-to-the-east half graben. Basin deposits as thick as 4,500 m filled the graben. Pleistocene glaciers sculpted the high peaks of the mountain ranges and formed the present rugged topography. Compilation scale is 1:100,000. Geology mapped between 1988 and 1995. Compilation completed 1997. Review and revision completed 1997. Archive files prepared 1998-02.", + "license": "proprietary" + }, + { + "id": "USGS_mapi-2691_1.0", + "title": "Geologic map of the Alligator Ridge area, White Pine County, Nevada", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "", + "bbox": "-115.625, 39.626, -115.41, 39.875", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553430-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553430-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2691_1.0", + "description": "Digital representation of geologic mapping facilitates the presentation and analysis of earth-science data. Digital maps may be displayed at any scale or projection, however the geologic data in this coverage is not intended for use at a scale larger that 1:24,000. Data set describes the geology of Paleozoic through Quaternary units in the Alligator Ridge area, which hosts disseminated gold deposits. These digital files were used to create the 1:24,000-scale geologic map of the Buck Mountain East and Mooney Basin Summit Quadrangles and parts of the Sunshine Well NE and Long Valley Slough Quadrangles, White Pine County, east-central Nevada.", + "license": "proprietary" + }, + { + "id": "USGS_mapi-2737", + "title": "Digital spatial data for the map \"Earthquakes in and near the northeastern United States, 1638-1998\"", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1638-06-11", + "end_date": "1998-12-29", + "bbox": "-81, 38, -66, 48", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552226-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552226-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2737", + "description": "The map is an educational tool with which to inform the public about the existence and the broad, regional nature of earthquake hazard in the Northeast. The data were created digitally in order to ease and speed production and publication of the map. Text on the map cautions against using the map for scientific or engineering purposes, or to estimate hazard in small areas or at single sites. Entries in Lineage under Data_Quality_Information explain the reasons for this caution (see also Wheeler, 2000; reference in Lineage). The earthquake catalog was constructed in such a way that it should not be utilized in scientific, engineering, or hazards use (Wheeler, 2000; reference in Lineage). Accordingly, the catalog is not being published separately, in order to minimize the potential for misuse. It is available only as part of the digital files from which the entire map was made. The data are those used to make a large-format, colored map of earthquakes in the northeastern United States and adjacent parts of Canada and the Atlantic Ocean (Wheeler, 2000; Wheeler and others, 2001; references in Data_Quality_Information, Lineage). The map shows the locations of 1,069 known earthquakes of magnitude 3.0 or larger, and is designed for a non-technical audience. Colored circles represent earthquake locations, colored and sized by magnitude. Short descriptions, colonial-era woodcuts, newspaper headlines, and photographs summarize the dates, times of day, damage, and other effects of notable earthquakes. The base map shows color-coded elevation, shaded to emphasize relief. This metadata record describes the data on earthquakes and topography. Other data, such as for roads and urban areas, were obtained elsewhere and we lack metadata for them. Instead, this field cites the sources of these data that were obtained elsewhere.", + "license": "proprietary" + }, + { + "id": "USGS_mapi-2740_1.0", + "title": "Geologic Map of Colorado National Monument and Adjacent Areas, Mesa County, Colorado", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-108.792, 38.958, -108.583, 39.125", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553872-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553872-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-2740_1.0", + "description": "To update the interpretation and increase the scale of geologic mapping, provide a geologic map for the public to use at Colorado National Monument, and to provide sufficient geologic information for land-use and land-management decisions. New 1:24,000-scale geologic mapping in the Colorado National Monument Quadrangle and adjacent areas, in support of the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping Project, provides new interpretations of and data for the stratigraphy, structure, geologic hazards in the area from the Colorado River in Grand Valley onto the Uncompahgre Plateau. The plateau drops abruptly along northwest-trending structures toward the northeast 800 m to the Redlands area and the Colorado River in Grand Valley. In addition to common alluvial and colluvial deposits, surficial deposits include Holocene and late Pleistocene charcoal-bearing valley-fill deposits, late to middle Pleistocene river-gravel terrace deposits, Holocene to middle Pleistocene younger, intermediate, and old fan-alluvium deposits, late to middle Pleistocene local gravel deposits, Holocene to late Pleistocene rock-fall deposits, Holocene to middle Pleistocene young and old landslide deposits, Holocene to late Pleistocene sheetwash deposits and eolian deposits, and Holocene Cienga-type deposits. Only the lowest part of the Upper Cretaceous Mancos Shale is exposed in the map area near the Colorado River. The Upper and Lower Cretaceous Dakota Formation and the Lower Cretaceous Burro Canyon Formation form resistant dipslopes in the Grand Valley and a prominent ridge on the plateau. Less resistant strata of the Upper Jurassic Morrison Formation consisting of the Brushy Basin, Salt Wash, and Tidwell Members form slopes on the plateau and low areas below the mountain front of the plateau. The Middle Jurassic Wanakah Formation nomenclature replaces the previously used Summerville Formation. Because an upper part of the Middle Jurassic Entrada Formation is not obviously correlated with strata found elsewhere, it is therefore not formally named; however, the lower rounded cliff former Slickrock Member is clearly present. The Lower Jurassic silica-cemented Kayenta Formation forms the cap rock for the Lower Jurassic carbonate-cemented Wingate Sandstone, which forms the impressive cliffs of the monument. The Upper Triassic Chinle Formation was deposited on the eroded and weathered Middle Proterozoic meta-igneous gneiss, pegmatite dikes, and migmatitic gneiss. Structurally the area is deceptively challenging. Nearly flat-lying strata on the plateau are folded by northwest-trending fault-propagation folds into at least two S-shaped folds along the mountain front of the plateau. Strata under Grand Valley dip at about 6 degrees to the northeast. In the absence of local evidence, the uplifted plateau is attributed to Laramide deformation by dated analogous structures elsewhere in the Colorado Plateau. The major exposed fault records high-angle reverse relationships in the basement rocks but dissipates strain as a triangular zone of distributed microfractures and cataclastic flow into overlying Mesozoic strata that absorb the fault strain, leaving only folds. Evidence for younger, probably late Pliocene or early Pleistocene, uplift does exist at the antecedent Unaweep Canyon south and east of the map area. To what degree this younger deformation affected the map area is unknown. Several geologic hazards affect the area. Middle and late Pleistocene landslides involving the smectite-bearing Brushy Basin Member of the Morrison Formation are extensive on the plateau and common in the Redlands below the plateau. Expansive clay in the Brushy Basin and other strata create foundation stability problems for roads and homes. Flash floods create a serious hazard to people on foot in narrow canyons in the monument and to homes close to water courses downstream from narrow restrictions close to the monument boundary. Map political location: Mesa County, Colorado Compilation scale: 1:24,000 Geology mapped in 1998. Geospatial data files included in this data set: cnmpoly: geologic units cnmline: faults, fold axes, dikes, and other line features cnmpoint: strike and dip measurements and other point features cnmsym: cartographic decorations-strike/dip symbols, leaders, line decorations, etc. cnmtext: text labels for map units cnmborder: neatline of map cnmboundary: boundary of Colorado National Monument cnmhydro: hydrologic features cnmhypso: elevation contours cnmrailroads: railroads cnmroads: roads", + "license": "proprietary" + }, + { + "id": "USGS_mapi-797Scan_Version 1.0", + "title": "Geologic map of the Stillwater Complex, Montana: scanned source map images", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1974-01-01", + "end_date": "1974-12-31", + "bbox": "-110.247635, 45.327175, -109.73657, 45.520306", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550921-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550921-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-797Scan_Version%201.0", + "description": "This set of images was developed to provide georeferenced digital images of the 1:12000-scale geologic map (Page and Nokleberg, 1974) These images can be used in conjunction with the vector data files now available as part of the I-797 dataset. This database is not meant to be used or displayed at any scale larger than 1:12000 (for example, 1:2000). This collection of four georeferenced MrSID files and one TIFF file provides raster images of the five map sheets comprising the Geologic map of the Stillwater Complex, Montana by Page and Nokleberg (1974). Paper copies of the four geologic map sheets and the explanation were scanned, and the geologic map sheets were georeferenced to the Montana State Plane South coordinate system. Each georeferenced MrSID image consists of a package of three files with the extensions: sid, .sdw (MrSID world file), and .aux (ArcInfo 8.1 georeferencing information). The MrSID and TIFF files are listed below: i797origs1.sid/.sdw/.aux - Sheet 1 - east end of the Stillwater Complex i797origs2.sid/.sdw/.aux - Sheet 2 - east central part of the Stillwater Complex i797origs3.sid/.sdw/.aux - Sheet 3 - west central part of the Stillwater Complex i797origs4.sid/.sdw/.aux - Sheet 4 - west end of the Stillwater Complex i797origs5.tif - Sheet 5 - explanation of map symbols, correlation of map units and map unit descriptions used on Sheets 1 through 4.", + "license": "proprietary" + }, + { + "id": "USGS_mapi-797Topo_Version 1.0", + "title": "Geologic map of the Stillwater Complex, Montana: topographic base map image", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1943-01-01", + "end_date": "1943-12-31", + "bbox": "-110.247635, 45.327175, -109.73657, 45.520306", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555237-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555237-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-797Topo_Version%201.0", + "description": "This image was prepared for archival purposes and is not meant to be used or displayed at any scale larger than 1:12000 (for example. 1:2000). Four film positives of topography [which was prepared in 1943 and subsequently used by Page and Nokleberg (1974) for a base map for the geology of the Stillwater Complex, Montana] were scanned, and the resulting TIFF images were then geoferenced, rectified, and spliced together to create i797base.tif.", + "license": "proprietary" + }, + { + "id": "USGS_mapi-797_Version 1.0", + "title": "Geologic map of the Stillwater Complex, Montana: a digital database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-01-01", + "end_date": "2002-12-31", + "bbox": "-110.247635, 45.327175, -109.73657, 45.520306", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550321-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550321-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mapi-797_Version%201.0", + "description": "This dataset was developed to provide a spatial database of the 1:12,000 scale geologic map of the Stillwater Complex for use in future spatial analysis. These data can be printed in a variety of ways to display various geologic features or used for digital analysis and modeling. This database is not meant to be used or displayed at any scale larger than 1:12000. The digital geologic map graphics and plot files (i797.gra/ps and i797-map.pdf) that are provided in the digital package are representations of the digital database. They are not designed to be cartographic products. This report provides a digital version of the Geologic map of the Stillwater Complex, Montana originally published by N. Page and W. Nokleberg (1974). Paper copies of the four geologic map sheets from the original report were scanned and initially attributed by Optronics Specialty Company (Northridge, CA) and remitted to the U.S. Geological Survey for further attribution and publication of the geospatial digital files. The resulting digital geologic dataset can be queried in a geographic information system (GIS) in many ways to produce a variety of geological maps. This GIS dataset consists of two Arc/Info datasets. The first is a line and polygon file (i797) containing geologic contacts and structures (lines) and geologic map rock units (polygons). A second file contains structural point data (i797p). Since the topographic base map for the original publication is no longer readily available, a georeferenced image (tiff) of the original basemap is also included.", + "license": "proprietary" + }, + { + "id": "USGS_mdnet_Version 1.3 (July 06, 2001)", + "title": "Maryland Ground-Water Observation Well Network, 2001", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2001-01-01", + "bbox": "-79.40369, 37.991196, -75.122604, 39.74173", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549594-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549594-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mdnet_Version%201.3%20(July%2006%2C%202001)", + "description": "The dataset MDNET was created to provide locations of Maryland ground-water observation wells for use within a Geographic Information System. MDNET is a point coverage that represents the locations and names of a network of observation wells for the State of Maryland. Additional information on water conditions at these sites can be found in the Ground-Water Site Inventory System (GWSI) database, which is maintained by the U.S. Geological Survey. Site information can be accessed on the internet at URL: \"http://waterdata.usgs.gov/nwis/\". Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology.", + "license": "proprietary" + }, + { + "id": "USGS_mdwu_98_Version 1.3, July 06, 2001", + "title": "Maryland Water-Use Data, 1998", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1998-01-01", + "end_date": "1998-01-01", + "bbox": "-79.40369, 37.991196, -75.122604, 39.74173", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552746-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552746-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_mdwu_98_Version%201.3%2C%20July%2006%2C%202001", + "description": "The dataset MDWU98 was created to provide the locations of MDE permitted ground-water withdrawal sites in Maryland for use within a Geographic Information System. MDWU98 is a point coverage that represents the locations of wells for the State of Maryland that are permitted to withdraw 10,000 gallons or more per day by the Maryland Department of the Environment (MDE). Each site has the permit number, permit amount, reported withdrawal, aquifer code, and type of use. Information contained in the dataset comes from the U.S.Geological Survey site-specific water-use database (SWUDS). Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology.", + "license": "proprietary" + }, + { + "id": "USGS_msavi_92_Version 1.0", + "title": "Modified soil adjusted vegetation index for the Death Valley regional flow system, Nevada and California, 1992", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-06-13", + "end_date": "1992-06-13", + "bbox": "-117.550385, 35.378323, -115.251015, 37.653557", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551531-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551531-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_msavi_92_Version%201.0", + "description": "The data set was created to determine areas of regional plant-cover information for use in the report \"Ground-water discharge determined from estimates of evapotranspiration, Death Valley regional flow system, Nevada and California.\" The raster-based Modified Soil Adjusted Vegetation Index was derived from Landsat Thematic Mapper imagery data acquired during June 1992 for the Death Valley regional flow system. The index has been shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a \"vegetation signal\" to \"soil noise\" ratio. The data set was used in determining phreatophyte boundaries for a ground-water evapotranspiration study and relative differences in vegetation density between discharge areas. Introduction The raster-based Modified Soil Adjusted Vegetation Index (MSAVI) was derived from Landsat Thematic Mapper (TM) imagery data acquired during June 1992 for the Death Valley regional flow system. Background and formulas of the MSAVI are detailed in Qi and others (1994). The MSAVI has been shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a \"vegetation signal\" to \"soil noise\" ratio. The MSAVI is a type of Soil Adjusted Vegetation Index (SAVI). The SAVI includes a constant soil adjustment factor L. The MSAVI uses the Normalized Difference Vegetation Index (NDVI) and Weighted Difference Vegetation Index (WDVI) to compute the L value in the SAVI for each picture element or pixel. This is referred to as a self-adjusting L function in Qi and others (1994, p. 123). The slope of the soil line used in the equations was 1.06. This was used by Qi and others (1994, p. 123) and was determined to be an acceptable value for this study. Reviews The MSAVI image for 1992 was checked for consistency and accuracy during the data processing. Two external reviews were done. The reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers also were asked to check the topological consistency, tolerances, projections, and geographic extent.", + "license": "proprietary" + }, + { + "id": "USGS_msavi_Version 1.0", + "title": "Modified soil adjusted vegetation index of the Sarcobatus Flat area of the Death Valley regional flow system, Nevada and California, 1989", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1989-06-21", + "end_date": "1989-06-21", + "bbox": "-117.216324, 36.997658, -116.66944, 37.40421", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553264-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553264-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_msavi_Version%201.0", + "description": "The data set was created to determine areas of regional plant-cover information for use in the report, \"Ground-water discharge determined from estimates of evapotranspiration, Death Valley regional flow system, Nevada and California.\" The raster-based Modified Soil Adjusted Vegetation Index was derived from Landsat Thematic Mapper imagery data acquired during June 1989 for Sarcobatus Flat. The index has been shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a \"vegetation signal\" to \"soil noise\" ratio. The data set was used in determining phreatophyte boundaries for a ground-water evapotranspiration study and relative differences in vegetation density between discharge areas. Introduction The raster-based Modified Soil Adjusted Vegetation Index (MSAVI) was derived from Landsat Thematic Mapper (TM) imagery data acquired during June 1989 for the Sarcobatus Flat area of the Death Valley regional flow system. Background and formulas of the MSAVI are detailed in Qi and others (1994). The MSAVI has been shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a \"vegetation signal\" to \"soil noise\" ratio. The MSAVI is a type of Soil Adjusted Vegetation Index (SAVI). The SAVI includes a constant soil adjustment factor L. The MSAVI uses the Normalized Difference Vegetation Index (NDVI) and Weighted Difference Vegetation Index (WDVI) to compute the L value in the SAVI for each picture element or pixel. This is referred to as a self-adjusting L function in Qi and others (1994, p. 123). The slope of the soil line used in the equations was 1.06. This was used by Qi and others (1994, p. 123) and was determined to be an acceptable value for this study. Reviews The MSAVI image for 1989 was checked for consistency and accuracy during the data processing. Two external reviews were done. The reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers also were asked to check the topological consistency, tolerances, projections, and geographic extent.", + "license": "proprietary" + }, + { + "id": "USGS_nit85_1.0", + "title": "Estimates of nitrogen-fertilizer sales for the conterminous United States in 1985", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-128.07002, 22.67775, -65.25698, 48.26194", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554641-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554641-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_nit85_1.0", + "description": "NITROGEN-FERTILIZER SALES DATA Estimates of nitrogen-fertilizer sales by county were generated by the U.S. Environmental Protection Agency (1990) and by Jerald Fletcher (West Virginia University, written commun., 1992) by using the following procedure: (1) compiling annual State fertilizer-sales data reported as tonnages to the National Fertilizer and Environmental Research Center of the TVA; (2) calculating the ratio of expenditures for commercial fertilizers by county to expenditures for commercial fertilizers by States from the 1987 Census of Agriculture (U.S. Department of Commerce, 1989a); and (3) computing annual county-level nitrogen-fertilizer sales, in tons, by multiplying estimates of annual States sales by the ratio of county expenditures to States expenditures. In some counties no fertilizer sales were reported, but some fertiliz use was reported in the Census Data. Although fertilizer expenditures estimates (in $1,000) represent the 1987 growing year, the nitrogen-fertilizer sales estimates (tons) generally reflect 1985 amounts. Estimates of nitrogen-fertilizer sales by county were constructed fro a combination of data reported to State regulatory agencies and from data in the 1987 Census of Agriculture. Fertilizer-sales data submitted annually to State regulatory agencies by fertilizer dealers reflect total sales without regard to the land use for which it was bought, or the State (or county) in which the fertilizer was actually used. In the Census of Agriculture sampling and statistics were used to account for non responding farm operations (U.S. Department of Commerce, 1989b) Thus, the information that describes county-level fertilizer sales is subject to sampling variability as well as reporting and coverage errors. Census disclosure rules also prevent the publication of information that would reveal the operation of individual farms. COUNTY BOUNDARIES The original files for this map were provided in 15 sections. Boundaries near the edges of sections have been adjusted in edgematching. Polygons that extend into the water (an ocean or the Great Lakes) should be considered arbitrary. Originating_Center: (required) Group: Reference End_Group Group: Summary The nitrogen-fertilizer sales estimates in this coverage are intended for use in estimating regional fertilizer sales, and in producing visual displays and mapping relative rates of fertilizer sales across broad regions of the United States. This coverage contains estimates of nitrogen-fertilizer sales for the conterminous United States in 1985 as reported by the U.S. Environmental Protection Agency (1990) and by Jerald Fletcher (West Virginia University, written commun., 1992). Nitrogen-fertilizer sales estimates in this coverage are reported for each county polygon in tons of actual nutrient sold (inorganic nitrogen, phosphate, and potash) as distinct from total tons of fertilizer product. The data are summarized for fertilizer years (i.e. the 1987 fertilizer year runs from July 1, 1986 to June 30, 1987). The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) file representing the 1:2,000,000-scale map in the National Atlas of the United States (1970).", + "license": "proprietary" + }, { "id": "USGS_ofr00-265-geol_Version 1.0, May 9, 2000", "title": "Bedrock Geology of the Turkey Creek Drainage Basin", @@ -184560,6 +193270,110 @@ "description": "This data set was created for analysis of the ground-water system of the study area. This geospatial data set describes bedrock geology of the Turkey Creek drainage basin in Jefferson County, Colorado. It was digitized from maps of fault locations and geologic map units based on age and lithology. Created for use in the Jefferson County Mountain Ground-Water Resources Study, it is to be used at a scale no more detailed than 1:50,000. The source materials for the generation of this data set consist of bedrock geology mapped on U.S. Geological Survey (USGS) topographic quadrangles at a scale of 1:24,000 by the USGS. The source materials were converted to digital format, topologically developed, and attributed on a quadrangle-by- quadrangle basis before being combined into one data set. The procedures for converting the materials to digital format differed for each quadrangle and are summarized as follows: Conifer The original camara-ready transparency of the map publication, Reconnaissance Geologic Map of the Conifer Quadrangle, Jefferson County, Colorado, was obtained from the USGS. A film-positive was made from this transparency. To simplify the linework, this film-positive was then traced by hand onto mylar. The mylar was then digitally scanned at 300 dots per inch (dpi) and stored as a TIFF image. Using Arc/INFO software from Environmental Systems Research Institute, the image was georeferenced to real-world coordinates and converted into an Arc/INFO raster data set format known as a grid, which was then vectorized into an Arc/INFO vector data set format known as a coverage. A quadrangle boundary outline that was generated from quadrangle boundary coordinates and then projected into real-world coordinates was added to the coverage, which was then converted to a coverage with polygon topology. Line features in the coverage were attributed according to fault type classification, and the polygon features were attributed according to bedrock geologic map unit and fault zone classification. Evergreen An incomplete collection of the original pre-press mylar separates for the map publication, Geologic Map of the Evergreen Quadrangle, Jefferson County, Colorado, was obtained from the USGS. Mylar separates of Quaternary geologic contacts and faults were identified and digitally scanned at 300 dpi into TIFF images. All other geologic contacts in the area of interest were traced onto mylar from a paper print of the map publication. Furthermore, an enclosing polygon outline outside of the area of interest was drawn on the mylar so that the traced contacts would form polygon features. The mylar was then digitally scanned at 300 dpi into a TIFF image. All the images were then georeferenced to real-world coordinates, converted into grids, and vectorized into three separate coverages, one for each of the two mylar sources, and one for the traced source. These coverages were then combined into one coverage. One of the authors of the map publication provided updated nomenclature for Precambrian map units (Bruce Bryant, U.S. Geological Survey, oral communication, 1998) so that the nomenclature would match that of adjacent quadrangles. The line features in the coverage were attributed according to fault type, and polygon features were attributed according to geologic map unit and fault zone classification. Indian Hills A paper print of the map publication, Geologic Map of the Indian Hills Quadrangle, Jefferson County, Colorado, was obtained from the USGS. For the area of interest on the quadrangle, two mylars were hand-traced from this paper print. One mylar consisted of geologic contacts and an enclosing polygon outline outside of the area of interest that was drawn so that the contacts would form polygon features. The other mylar consisted of fault traces. The two mylars were then digitally scanned at 300 dpi into TIFF images. These images were georeferenced to real-world coordinates and converted into grids which were then vectorized into coverages. The coverages were then combined into one coverage. One of the authors of the map publication provided updated nomencla- ture for Precambrian map units (Bruce Bryant, U.S. Geological Survey, oral communication, 1998) so that the nomenclature would match that of adjacent quadrangles. Line features in the coverage were attributed according to fault type, and polygon features were attributed according to geologic map unit and fault zone classification. Meridian Hill A paper photocopy of preliminary geologic mapping consisting of faults and geologic contacts for the Meridian Hill Quadrangle, Clear Creek, Jefferson, and Park Counties, was obtained from the USGS. For the area of interest on this quadrangle, all the linework was traced onto mylar. Furthermore, an enclosing polygon outline outside of the area of interest was drawn on the mylar so that the traced contacts would form polygon features. The mylar was then digitally scanned at 300 dpi into a TIFF image. The image was georeferenced to real-world coordinates and converted into a grid which was then vectorized into a coverage. Line features in the coverage were then attributed according to fault type, and the polygon features were attributed according to geologic map unit and fault zone classification. Morrison The original pre-press mylar separates for the map publication Geologic Map of the Morrison Quadrangle, Jefferson County, Colorado, were obtained from the USGS. The mylar separate of geologic contacts was digitally scanned at 300 dpi into a TIFF image. This image was georeferenced to real-world coordinates and converted into a grid which was then vectorized into a coverage. The fault linework was digitized into a coverage from another mylar separate of the same publication that had too many other themes on it and was therefore too difficult to scan and vectorize. The fault coverage was then transformed to real-world coordinates. The coverages were then combined into one coverage. An enclosing polygon outline outside of the area of interest was digitized into the coverage so that the geologic contacts would form polygon features. Line features in the coverage were attributed according to fault type, and polygons were attributed according to geologic map unit and fault zone classification. Once the polygon and vector topology was developed for each quadrangle, the individual coverages were combined into one coverage. No edgematching was performed. A study-area outline of the Turkey Creek Watershed was delineated in Arc/INFO with USGS Digital Elevation Model data sets. A 500-meter buffer polygon of this outline was used to clip the geology coverage. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.", "license": "proprietary" }, + { + "id": "USGS_ofr00-96_wlc80_97_1.0", + "title": "Digital map of water-level changes in the High Plains Aquifer, 1980 to 1997", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-01-01", + "end_date": "1999-12-31", + "bbox": "-106.015, 31.652, -96.26, 43.806", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552580-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552580-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00-96_wlc80_97_1.0", + "description": "This data set was created to document the original map (McGuire, V.L. and Fischer, B.C., 1999) produced by the High Plains Water-Level Monitoring Project and to make available the data on this map for use with geographic information systems. This data set consists of digital water-level-change contours for the High Plains aquifer in the central United States, 1980 to 1997. The High Plains aquifer extends from south of 32 degrees to almost 44 degrees north latitude and from 96 degrees 30 minutes to 104 degrees west longitude. The aquifer underlies about 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This digital data set was created from 5,233 wells measured in both 1980 and 1997. The water-level-change contours were drawn manually on mylar at a scale of 1:1,000,000. The contours then were converted to a digital map. Introduction -- The information provided in this introduction is found in U.S. Geological Survey Professional Paper 1400-B (Gutentag and others, 1984). This data set consists of digital water-level-change contours for the High Plains aquifer in the United States, 1980 to 1997. The High Plains aquifer, which underlies about 174,000 square miles in parts of eight states, is the principal water source in one of the nation's major agricultural areas. In 1980, about 170,000 wells pumped water from the aquifer to irrigate about 13 million acres. The High Plains aquifer is a regional water-table aquifer consisting mostly of near-surface sand-and-gravel deposits. In 1980, the maximum saturated thickness of the aquifer was about 1,000 feet and averaged about 200 feet. Hydraulic conductivity and specific yield of the aquifer depend on sediment types, which vary significantly both horizontally and vertically. Hydraulic conductivity ranged from less than 25 to greater than 300 feet per day and averaged 60 feet per day. Specific yields ranged from less than 10 to 30 percent and averaged about 15 percent. The High Plains aquifer boundaries were determined by erosional extent of associated geologic units and by hydraulic and physiographic boundaries where the High Plains aquifer extends eastward from the Great Plains physiographic province (Fenneman, 1931). In most of the area, the erosional extent of the hydraulically connected Tertiary and Quaternary deposits were used as the aquifer boundary. In eastern Nebraska, streams and physiographic boundaries were used as the aquifer boundary. Reviews Applied to Data -- This electronic report was subjected to the same review standards that apply to all U.S. Geological Survey reports. Reviewers were asked to check the topological consistency, tolerances, attribute frequencies and statistics, projection, and geographic extent. Reviewers were given digital data sets for checking against the source maps to verify the linework and attributes. The reviewers checked the metadata files for completeness and accuracy.", + "license": "proprietary" + }, + { + "id": "USGS_ofr00471_ddwdscon_Version 1.0, March 27, 2000", + "title": "Geologic structure contours for the top of the Deadwood Formation, Black Hills, South Dakota", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-07-14", + "end_date": "1999-07-14", + "bbox": "-104.07985, 43.117573, -102.968216, 44.78429", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549083-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549083-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00471_ddwdscon_Version%201.0%2C%20March%2027%2C%202000", + "description": "This data set was created as part of the Black Hills Hydrology Study (BHHS). The BHHS is a long-term investigation that was initiated in 1990 as a cooperative effort between the U.S. Geological Survey (USGS), the South Dakota Department of Environment and Natural Resources (DENR), and the West Dakota Water Development District. West Dakota represents various local and county cooperators. The purpose of the study is to assess the quantity, quality, and distribution of surface and ground water in the Black Hills area of western South Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer, Fall River, Lawrence, Meade, and Pennington counties in South Dakota. This data set represents geologic structure contours for the top of the Deadwood Formation, Black Hills, South Dakota.", + "license": "proprietary" + }, + { + "id": "USGS_ofr00471_hydrogeo_Version 1.0, April 27, 2000", + "title": "Hydrogeologic Units in the Black Hills area, South Dakota", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1998-06-09", + "end_date": "1998-06-09", + "bbox": "-104.07994, 43.117134, -102.95288, 44.786495", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550572-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550572-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00471_hydrogeo_Version%201.0%2C%20April%2027%2C%202000", + "description": "This data set was created as part of the Black Hills Hydrology Study (BHHS). The BHHS is a long-term investigation that was initiated in 1990 as a cooperative effort between the U.S. Geological Survey (USGS), the South Dakota Department of Environment and Natural Resources (DENR), and the West Dakota Water Development District. West Dakota represents various local and county cooperators. The purpose of the study is to assess the quantity, quality, and distribution of surface and ground water in the Black Hills area of western South Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer, Fall River, Lawrence, Meade, and Pennington counties in South Dakota. This data set represents surficial hydrogeology for the Black Hills of South Dakota. This electronic report was subjected to the same review standard that applies to all U.S. Geological Survey reports. Reviewers were asked to check the topological consistency, tolerances, attribute frequencies and statistics, projection, and geographic extent. The reviewers checked the metadata and a_readme.1st files for completeness and accuracy.", + "license": "proprietary" + }, + { + "id": "USGS_ofr00471_inkrscon_Version 1.0, March 17, 2000", + "title": "Geologic structure contours for the top of the Inyan Kara Group, Black Hills, South Dakota", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-07-14", + "end_date": "1999-07-14", + "bbox": "-104.079315, 43.11753, -102.95605, 44.786076", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552963-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552963-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00471_inkrscon_Version%201.0%2C%20March%2017%2C%202000", + "description": "This data set was created as part of the Black Hills Hydrology Study (BHHS). The BHHS is a long-term investigation that was initiated in 1990 as a cooperative effort between the U.S. Geological Survey (USGS), the South Dakota Department of Environment and Natural Resources (DENR), and the West Dakota Water Development District. West Dakota represents various local and county cooperators. The purpose of the study is to assess the quantity, quality, and distribution of surface and ground water in the Black Hills area of western South Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer, Fall River, Lawrence, Meade, and Pennington counties in South Dakota. This data set represents geologic structure contours for the top of the Inyan Kara Group, Black Hills, South Dakota.", + "license": "proprietary" + }, + { + "id": "USGS_ofr00471_mnktscon_Version 1.0, March 21, 2000", + "title": "Geologic structure contours for the top of the Minnekahta Limestone, Black Hills, South Dakota", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-07-14", + "end_date": "1999-07-14", + "bbox": "-104.07972, 43.11754, -102.95606, 44.784607", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552592-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552592-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00471_mnktscon_Version%201.0%2C%20March%2021%2C%202000", + "description": "This data set was created as part of the Black Hills Hydrology Study (BHHS). The BHHS is a long-term investigation that was initiated in 1990 as a cooperative effort between the U.S. Geological Survey (USGS), the South Dakota Department of Environment and Natural Resources (DENR), and the West Dakota Water Development District. West Dakota represents various local and county cooperators. The purpose of the study is to assess the quantity, quality, and distribution of surface and ground water in the Black Hills area of western South Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer, Fall River, Lawrence, Meade, and Pennington counties in South Dakota. This data set represents geologic structure contours for the top of the Minnekahta Limestone, Black Hills, South Dakota.", + "license": "proprietary" + }, + { + "id": "USGS_ofr00471_mnlsscon_Version 1.0, March 21, 2000", + "title": "Geologic structure contours for the top of the Minnelusa Formation, Black Hills, South Dakota", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-07-14", + "end_date": "1999-07-14", + "bbox": "-104.07984, 43.1174, -102.957466, 44.784397", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551478-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551478-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00471_mnlsscon_Version%201.0%2C%20March%2021%2C%202000", + "description": "This data set was created as part of the Black Hills Hydrology Study (BHHS). The BHHS is a long-term investigation that was initiated in 1990 as a cooperative effort between the U.S. Geological Survey (USGS), the South Dakota Department of Environment and Natural Resources (DENR), and the West Dakota Water Development District. West Dakota represents various local and county cooperators. The purpose of the study is to assess the quantity, quality, and distribution of surface and ground water in the Black Hills area of western South Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer, Fall River, Lawrence, Meade, and Pennington counties in South Dakota. This data set represents geologic structure contours for the Minnelusa Formation, Black Hills, South Dakota.", + "license": "proprietary" + }, + { + "id": "USGS_ofr00471_mnlssurf_Version 1.0, March 21, 2000", + "title": "Geologic structure contours for the top of the Minnelusa Formation, Black Hills, South Dakota - USGS_ofr00471_mnlssurf", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-07-14", + "end_date": "1999-07-14", + "bbox": "-104.07984, 43.1174, -102.957466, 44.784397", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552095-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552095-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr00471_mnlssurf_Version%201.0%2C%20March%2021%2C%202000", + "description": "This data set was created as part of the Black Hills Hydrology Study (BHHS). The BHHS is a long-term investigation that was initiated in 1990 as a cooperative effort between the U.S. Geological Survey (USGS), the South Dakota Department of Environment and Natural Resources (DENR), and the West Dakota Water Development District. West Dakota represents various local and county cooperators. The purpose of the study is to assess the quantity, quality, and distribution of surface and ground water in the Black Hills area of western South Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer, Fall River, Lawrence, Meade, and Pennington counties in South Dakota. This data set represents geologic structure contours for the Minnelusa Formation, Black Hills, South Dakota.", + "license": "proprietary" + }, + { + "id": "USGS_ofr02-007_lithogeo_1.0, February, 2002", + "title": "Lithogeochemical Character of Near-Surface Bedrock in the New England Coastal Basins", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-11-16", + "end_date": "2001-11-16", + "bbox": "-72.19068, 41.134434, -69.031586, 45.902214", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411626-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232411626-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr02-007_lithogeo_1.0%2C%20February%2C%202002", + "description": "The lithogeochemical data layer was compiled to provide the NECB NAWQA study area with digital geologic information that could be used in the analysis of surface- and ground-water quality. Goals of the NAWQA program are to describe the status and trends of a large representative part of the Nation's surface- and ground-water resources and to identify the natural and human factors that affect the quality of these resources (Leahy and others, 1990). The data layer presented here was intended to characterize the bedrock units in the study area in terms of mineralogic and chemical parameters relevant to water quality, such that the geologic data could be used in GIS to plan NAWQA study-unit activities, and to analyze and interpret water-quality and ecosystem conditions. This geographic information system (GIS) data layer shows the generalized lithologic and geochemical, termed lithogeochemical, character of near-surface bedrock in the New England Coastal Basins (NECB) study area of the U.S. Geological Survey's National Water Quality Assessment (NAWQA) Program. The area encompasses 23,000 square miles in western and central Maine, eastern Massachusetts, most of Rhode Island, eastern New Hampshire and a small part of eastern Connecticut. The NECB study area includes the Kennebec, Androscogginn, Saco, Merrimack, Charles, and Blackstone River Basins, as well as all of Cape Cod. Bedrock units in the NECB study area are classified into 38 lithogeochemical units based on the relative reactivity of their constituent minerals to dissolution and the presence of carbonate or sulfide minerals. The 38 lithogeochemical units are generalized into 7 major groups: (1) carbonate-bearing metasedimentary rocks; (2)primarily noncalcareous, clastic sedimentary rocks with restricted deposition in discrete fault-bounded sedimentary basins of Mississipian or younger age; (3) primarily noncalcareous, clastic sedimentary rocks at or above biotite-grade of regional metamorphism; (4) mafic igneous rocks and their metamorphic equivalents; (5) ultramafic rocks; (6) felsic igneous rocks and their metamorphic equivalents; and (7) unconsolidated and poorly consolidated sediments. The classification scheme used was first developed as part of the USGS's study of the Connecticut, Housatonic, and Thames River Basins (CONN), an adjacent NAWQA study area (Robinson and others, 1999). The classification scheme is based on geochemical principles, previous studies of the relations among water-quality and ecosystem characteristics and rock type, and the regional geology of New England. The classification scheme and data set are intended to provide a general, flexible framework for classifying and mapping bedrock units in the study area for all types of water-quality analysis. The data set is a lithologic map that has been coded to reflect the potential influence of geology on water quality. The classification scheme provides flexibility because the user can reclassify the 38 lithogeochemical units into other groups for other types of data analysis. The bedrock units in this study area have been mapped defined by time- stratigraphic and other geologic criteria which may not be directly relevant to water quality. Bedrock units depicted on the State geologic maps are inconsistent across state boundaries in some areas (See Data_Quality_Information section of this document for explanation on how these discrepancies were addressed with the classification scheme). Thus, a study-area-wide coding scheme was developed to classify the geologic map units according to mineralogical and chemical characteristics that are relevant for water-quality investigations. Bedrock units were classified for water-quality purposes according to the chemical composition and relative susceptibility to weathering of their constituent minerals. Although weathering rates may vary, the relative stability of different minerals during weathering in moist climates is generally consistent (Robinson, 1997). However, the degree to which a rock weathers reflects the proportions of its constituent mineral as well as many other factors such as degree of induration and relative amount of mineral surfaces exposed to water through primary and secondary porosity. Thus, although largely based on the relative stability of rock constituent minerals, the classification scheme to group bedrock units according to effects on water quality is more complex than mineral- stability sequences. Most common rock-forming minerals are only sparingly soluble, so that small amounts of highly reactive minerals can have large effects of water quality (Robinson, 1997). For example, carbonate minerals are more rapidly weathered and tend to produce higher solute concentrations in natural waters than other rock types. In contrast, granites, schists and quartzites, which are rich in alkali-feldspar, muscovites, and quartz, produce low solute concentrations because they react to a lesser degree and at slower rates than other rock types in humid temperate climates (Robinson, 1997). The lithogeochemical classification scheme used in this data set incorporates the relative stability of minerals classifications criteria such as used in previous studies, and the characteristics of bedrock geology specific to the study area (such as the presence of a discrete fault bounded sedimentary basins of Mississipian or younger age). Further description of the lithogeochemical classification scheme and the expected water- quality and ecosystem characteristics associated with each lithogeochemical unit is explained in Robinson (1997). Thirty-eight lithogeochemical units have been defined for the NECB study area based on the mineral and textural properties of the bedrock unit's constituent minerals, presence of carbonate and sulfide minerals and for some of the granitic units, relative age. The classification scheme used descriptions from State geologic maps (Osberg and others, 1985; Lyons and others, 1997; Zen and others, 1985;Hermes and others, 1994; and Rogers, 1985) of the lithology, mineralogy, and weathering characteristics of the bedrock units. For example, \"rusty-weathering\" serves as an indicator of sulfidic-bearing bedrock units (Robinson, 1997). Carbonate and sulfide minerals predominate in the classification scheme because these highly reactive minerals have a disproportionately large effect on water chemistry compared to other minerals commonly found in the rocks of this region. In the Maine data set, information about metamorphic grade was also used to classify bedrock units. A digital data layer of generalized regional metamorphic zones (Guidotti, 1985, shown in Osberg and others,1985), was obtained from the Maine Geological Survey. This layer was intersected with the digital bedrock geology to determine the regional metamorphic grade of each polygon in the bedrock geology data layer. Polygons lying within two metamorphic zones were split at the metamorphic-zone boundary. Metamorphic grade and geochemical composition of the protolith (pre-metamorphism source rock) were used to classify polygons into lithogeochemical units. For example, bedrock units with protoliths of \"limestone and(or) dolostone\" were classified as \"limestone, dolomite, and carbonate-rich clastic sediments\" (lithogeochemical unit \"11u\") in areas of none or weak regional metamorphism and as \"marble, may include some calc-silicate rock\" (lithogeochemical unit \"12u\") in areas of greenschist facies or high grade metamorphism. The 38 lithogeochemical units defined for the NECB study area result from the combination of a lithology code (numeric) with a modifier code (alphabetic). There are 17 lithology codes that represent the influences on water chemistry of lithology, metamorphic grade, and geologic setting. Each bedrock unit is assigned one of 17 lithology codes based on the description of the bedrock unit from the State bedrock geologic maps. There are 13 modifier codes used to identify minor amounts of carbonate and(or) sulfide minerals, and subdivide granitic units into subgroups based on their chemical and mineral characteristics and relative age. A description of the 38 lithogoechemical units in the NECB study area and their potental effects on water quality can be found in the Supplemental_Information section of this document. The 38 lithogeochemical units are generalized into 7 major groups that share similarities in overall geochemistry and lithology: (1) carbonate-bearing metasedimentary rocks; (2) primarily noncalcareous, clastic sedimentary rocks deposited in fault-bounded sedimentary basins of Mississipian or younger age; (3) primarily noncalcareous, clastic sedimentary rocks at or above biotite-grade of regional metamorphism; (4) mafic igneous rocks and their metamorphic equivalents; (5) ultramafic rocks; (6) felsic igneous rocks and their metamorphic equivalents; and (7) unconsolidated and poorly consolidated sediments. Major group 7 encompasses areas in the south-coastal part of the NECB study area where the bedrock is overlain by thick glacial sediments at the surface. These surficial glacial deposits are the primary aquifer for these areas. An example of how this data set has been used in study design strategies and in analyzing water-quality characteristic by lithogeochemical units and major groups is provided in Ayotte and others (1999). The bedrock units shown on the individual State maps for the NECB were classified according to a lithogeochemical scheme modified from Robinson and others (1999). Specifically, the modification included the subdivision of granitic bedrock units into additional lithogeochemical units with modifying attributes to indicate relative age. However, this modification to the classification system is evident in the lithogeochemical units. Thus, the CONN and the NECB data set can be readily merged together to create a larger regional product with these difference being more frequent when the data set is viewed with the lithogeochemical units showing and less frequent when the data set is viewed with the major groups showing. Overall, the bedrock units in the two study units are classified in a consistent manner to a create regional product that can be used to evaluate the influences of bedrock geology on water-quality characteristics. Quality Assurance procedures: The scientific content of this digital data set underwent technical review by two USGS scientists who have knowledge of the regional geology,and GIS and spatial-data production. The data set was evaluated on positional accuracy, contextual accuracy, attribute accuracy, and topological consistency.", + "license": "proprietary" + }, { "id": "USGS_ofr02-338_depth2wt", "title": "Depth/Colorado Front Range Infrastructure Resources Project (FRIRP)", @@ -184586,6 +193400,45 @@ "description": "This data set was created to display the outline of the study area as depicted in (Robson and others, 1998). This digital geospatial data set consists of outlines of the study area in the report \"Structure, Outcrop, and Subcrop of the Bedrock Aquifers Along the Western Margin of the Denver Basin, Colorado\" (Robson and others, 1998).", "license": "proprietary" }, + { + "id": "USGS_ofr96-443_cond_1.0", + "title": "Digital hydraulic conductivity values of the Antlers aquifer in southeastern Oklahoma", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "1992-12-31", + "bbox": "-97.4976, 33.7288, -94.4684, 34.3644", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549511-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549511-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-443_cond_1.0", + "description": "This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data set that could be used in ground-water vulnerability analysis. This data set consists of digitized polygons of a constant hydraulic conductivity value for the Antlers aquifer in southeastern Oklahoma. The Early Cretaceous-age Antlers Sandstone is an important source of water in an area that underlies about 4,400-square miles of all or part of Atoka, Bryan, Carter, Choctaw, Johnston, Love, Marshall, McCurtain, and Pushmataha Counties. The Antlers aquifer consists of sand, clay, conglomerate, and limestone in the outcrop area. The upper part of the Antlers aquifer consists of beds of sand, poorly cemented sandstone, sandy shale, silt, and clay. The Antlers aquifer is unconfined where it outcrops in about an 1,800-square-mile area. The hydraulic conductivity polygons were developed from the hydraulic conductivity value used as input into a ground-water flow model and from published digital data sets of the surficial geology of the Antlers Sandstone except in areas overlain by alluvial and terrace deposits near streams. Some of the lines were interpolated where the Antlers aquifer is overlain by alluvial and terrace deposits. The interpolated lines are very similar to the aquifer boundaries shown on maps published in a ground-water modeling report for the Antlers aquifer. The constant hydraulic conductivity value used as input to the ground-water flow model was estimated as 5.74 feet per day. Ground-water flow models are numerical representations that simplify and aggregate natural systems. Models are not unique; different combinations of aquifer characteristics may produce similar results. Therefore, values of hydraulic conductivity used in the model and presented in this data set are not precise, but are within a reasonable range when compared to independently collected data.", + "license": "proprietary" + }, + { + "id": "USGS_ofr96-444_cond_1.0", + "title": "Digital hydraulic conductivity values of the Vamoosa-Ada aquifer in east-central Oklahoma", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1986-01-01", + "end_date": "1986-12-31", + "bbox": "-96.7807, 34.8562, -96.0003, 37.001", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550240-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550240-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-444_cond_1.0", + "description": "This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digitized polygons of constant hydraulic conductivity values for the Vamoosa-Ada aquifer in east-central Oklahoma. The Vamoosa-Ada aquifer is an important source of water that underlies about 2,320-square miles of parts of Osage, Pawnee, Payne, Creek, Lincoln, Okfuskee, and Seminole Counties. Approximately 75 percent of the water withdrawn from the Vamoosa-Ada aquifer is for municipal use. Rural domestic use and water for stock animals account for most of the remaining water withdrawn. The Vamoosa-Ada aquifer is defined in a ground-water report as consisting principally of the rocks of the Late Pennsylvanian-age Vamoosa Formation and overlying Ada Group. The Vamoosa-Ada aquifer consists of a complex sequence of fine- to very fine-grained sandstone, siltstone, shale, and conglomerate interbedded with very thin limestones. The water-yielding capabilities of the aquifer are generally controlled by lateral and vertical distribution of the sandstone beds and their physical characteristics. The Vamoosa-Ada aquifer is unconfined where it outcrops in about an 1,700-square-mile area. The hydraulic conductivity of the Vamoosa-Ada aquifer was computed as 3 feet per day in a ground-water report. Most of the hydraulic conductivity polygons were extracted from published digital geology data sets. The lines in the digital geology data sets were scanned or digitized from maps published at a scale of 1:250,000 and represent geologic contacts. Some of the lines in the data set were interpolated in areas where the Vamoosa-Ada aquifer is overlain by alluvial and terrace deposits near streams and rivers.", + "license": "proprietary" + }, + { + "id": "USGS_ofr96-444_wlelev_1.0", + "title": "Digital water-level elevation contours for the Vamoosa-Ada aquifer in east-central Oklahoma", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1986-01-01", + "end_date": "1986-12-31", + "bbox": "-96.8152, 34.8716, -96.0525, 37.0008", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552418-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552418-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-444_wlelev_1.0", + "description": "This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digitized water-level elevation contours for the Vamoosa-Ada aquifer in east-central Oklahoma. The Vamoosa-Ada aquifer is an important source of water that underlies about 2,320-square miles of parts of Osage, Pawnee, Payne, Creek, Lincoln, Okfuskee, and Seminole Counties. Approximately 75 percent of the water withdrawn from the Vamoosa-Ada aquifer is for municipal use. Rural domestic use and water for stock animals account for most of the remaining water withdrawn. The Vamoosa-Ada aquifer is defined in a ground-water report as consisting principally of the rocks of the Late Pennsylvanian-age Vamoosa Formation and overlying Ada Group. The Vamoosa-Ada aquifer consists of a complex sequence of fine- to very fine-grained sandstone, siltstone, shale, and conglomerate interbedded with very thin limestones. The water-yielding capabilities of the aquifer are generally controlled by lateral and vertical distribution of the sandstone beds and their physical characteristics. The Vamoosa-Ada aquifer is unconfined where it outcrops in about an 1,700-square-mile area. The water-level elevation contours were digitized from a mylar map, at a scale of 1:250,000, used to publish a plate in a ground-water report about the Vamoosa-Ada aquifer. The water-level elevation contours in this data set extend west of the aquifer outcrop to areas where Vanoss Group rocks overlie the Ada Group. The data set also includes a water-level elevation contour for a terrace deposit east of the aquifer outcrop near the North Canadian River. Water-level elevations range from 800 to 1,000 feet above sea level for the Vamoosa-Ada aquifer.", + "license": "proprietary" + }, { "id": "USGS_ofr96-445_aqbound_1.0", "title": "Digital boundaries of the alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie in northwestern Oklahoma", @@ -184599,6 +193452,32 @@ "description": "This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data set that could be used in ground-water vulnerability analysis. This data set consists of digital aquifer boundaries for the alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie in northwestern Oklahoma. Ground water in 1,305 square miles of Quaternary-age alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. Alluvial and terrace deposits are composed of interfingering lenses of clay, sandy clay, and cross-bedded poorly sorted sand and gravel. The aquifer is composed of hydraulically connected alluvial and terrace deposits that uncomfortably overlie the Permian-age Formations. The aquifer boundaries along geological contacts were extracted from published digital geology data sets. Additional boundaries defining the geographic limits of the aquifer and areas of less than 5 feet saturated thickness were digitized from a mylar map, at a scale of 1:250,000. The maps were published at a scale of 1:900,000.", "license": "proprietary" }, + { + "id": "USGS_ofr96-445_cond_1.0", + "title": "Digital hydraulic conductivity values of the alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie in northwestern Oklahoma", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "1996-12-31", + "bbox": "-99.0874, 35.7774, -97.5243, 36.8974", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550236-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550236-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-445_cond_1.0", + "description": "This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data set that could be used in ground-water vulnerability analysis. This data set consists of digital polygons of constant hydraulic conductivity values for the alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie in northwestern Oklahoma. Ground water in 1,305 square miles of Quaternary-age alluvial and terrace deposits along the the Cimarron River from Freedom to Guthrie is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. Alluvial and terrace deposits are composed of interfingering lenses of clay, sandy clay, and cross-bedded poorly sorted sand and gravel. The aquifer is composed of hydraulically connected alluvial and terrace deposits that uncomfortably overlie the Permian-age Formations. The hydraulic-conductivity values for alluvial and terrace deposits used in this data set were published in a steady-state ground-water flow modeling report. The aquifer boundaries along geological contacts were extracted from published digital geology data sets. Boundaries defining the geographic limits of the aquifer were digitized from a mylar map, at a scale of 1:250,000. The maps were published at a scale of 1:900,000. The hydraulic conductivity values are 104.5 feet per day for the alluvial deposits and 47.5 feet per day for the terrace deposits. Ground-water flow models are numerical representations that simplify and aggregate natural systems. Models are not unique; different combinations of aquifer characteristics may produce similar results. Therefore, values of hydraulic conductivity used in the model and presented in this data set are not precise, but are within a reasonable range when compared to independently collected data.", + "license": "proprietary" + }, + { + "id": "USGS_ofr96-445_wlelev_1.0", + "title": "Digital water-level elevation contours for the alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie in northwestern Oklahoma", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1985-01-01", + "end_date": "1986-12-31", + "bbox": "-99.0721, 35.8204, -97.5609, 36.8137", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549989-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549989-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-445_wlelev_1.0", + "description": "This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data set that could be used in ground-water vulnerability analysis. This data set consists of digital water-level elevation contours for the alluvial and terrace deposits along the Cimarron River in northwestern Oklahoma during 1985-86. Ground water in 1,305 square miles of Quaternary-age alluvial and terrace deposits along the the Cimarron River from Freedom to Guthrie is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. Alluvial and terrace deposits are composed of interfingering lenses of clay, sandy clay, and cross-bedded poorly sorted sand and gravel. The aquifer is composed of hydraulically connected alluvial and terrace deposits that unconformably overlie the Permian-age Formations. Water-level elevations measured in 1985 and 1986 ranged from 1,650 feet to 950 feet above sea level. Regional ground-water flow is generally southeast to southwest towards the Cimarron River, except where the flow direction is affected by perennial tributaries. The water-level elevation contours were digitized from a mylar map at a scale of 1:250,000. The maps were published at a scale of 1:900,000.", + "license": "proprietary" + }, { "id": "USGS_ofr96-446_aqbound_1.0", "title": "Digital boundaries of the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma", @@ -184612,6 +193491,45 @@ "description": "This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digital aquifer boundaries for the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma. Ground water in 830 square miles of the Quaternary-age alluvial and terrace aquifer is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. The aquifer consists of poorly sorted, fine to coarse, unconsolidated quartz sand with minor amounts of clay, silt, and basal gravel. The hydraulically connected alluvial and terrace deposits unconformably overlie the Tertiary-age Ogallala Formation and Permian-age formations. The aquifer boundaries established in a ground-water flow model for the aquifer include areas: 1) where the terrace deposits pinch out against relatively impermeable Permian-age formations; 2) where the alluvium has been deposited against relatively impermeable Permian-age formations; 3) where the alluvial and terrace deposits have been eroded and underlying Permian-age formations are exposed at the surface; 4) where the aquifer extends beyond the geographic limit of the study area; and 5) where the aquifer has little or no saturated thickness. The lines in the data set representing aquifer boundaries along geological contacts were extracted from a published digital surficial geology data set based on a scale of 1:250,000. The geographic limits of the aquifer and areas of little or no saturated thickness were digitized from a folded paper map, at a scale of 1:250,000 from a ground-water modeling report.", "license": "proprietary" }, + { + "id": "USGS_ofr96-446_cond_1.0", + "title": "Digital hydraulic conductivity values", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1981-01-01", + "end_date": "1981-01-01", + "bbox": "-99.965, 36.0439, -98.5487, 36.9727", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554806-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554806-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-446_cond_1.0", + "description": "This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digital hydraulic conductivity values for the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma. Ground water in 830 square miles of the Quaternary-age alluvial and terrace aquifer is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. The aquifer consists of poorly sorted, fine to coarse, unconsolidated quartz sand with minor amounts of clay, silt, and basal gravel. The hydraulically connected alluvial and terrace deposits unconformably overlie the Tertiary-age Ogallala Formation and Permian-age formations. Six zones of ranges of hydraulic conductivity values for the alluvial and terrace deposits reported in a ground-water modeling report are used in this data set. The hydraulic conductivity values range from 0 to 160 feet per day, and average 59 feet per day. The features in the data set representing aquifer boundaries along geological contacts were extracted from a published digital surficial geology data set based on a scale of 1:250,000. The geographic limits of the aquifer and zones representing ranges of hydraulic conductivity values were digitized from folded paper maps, at a scale of 1:250,000 from a ground-water modeling report. Ground-water flow models are numerical representations that simplify and aggregate natural systems. Models are not unique; different combinations of aquifer characteristics may produce similar results. Therefore, values of hydraulic conductivity used in the model and presented in this data set are not precise, but are within a reasonable range when compared to independently collected data.", + "license": "proprietary" + }, + { + "id": "USGS_ofr96-446_recharg_1.0", + "title": "Digital recharge rate for the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1981-01-01", + "end_date": "1981-12-31", + "bbox": "-99.965, 36.0439, -98.5487, 36.9727", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550385-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550385-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofr96-446_recharg_1.0", + "description": "This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digital polygons of a constant recharge value for the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma. Ground water in 830 square miles of the Quaternary-age alluvial and terrace aquifer is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. The aquifer consists of poorly sorted, fine to coarse, unconsolidated quartz sand with minor amounts of clay, silt, and basal gravel. The hydraulically connected alluvial and terrace deposits unconformably overlie the Tertiary-age Ogallala Formation and Permian-age formations. A recharge rate of 1 inch per year was estimated in the ground-water modeling report for the alluvial and terrace deposits and used in this data set. The recharge rate was estimated using a base-flow method and a monthly-water-balance method. The features in the data set representing boundaries along geological contacts were extracted from a published digital surficial geology data set based on a scale of 1:250,000. The geographic limits of the aquifer were digitized from a folded paper map, at a scale of 1:250,000 in the ground-water modeling report. Ground-water flow models are numerical representations that simplify and aggregate natural systems. Models are not unique; different combinations of aquifer characteristics may produce similar results. Therefore, values of recharge used in the model and presented in this data set are not precise, but are within a reasonable range when compared to independently collected data.", + "license": "proprietary" + }, + { + "id": "USGS_ofroo-300_SATTK9697_1.0", + "title": "Digital map of the saturated thickness of the High Plains Aquifer, 1996-97", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-01-01", + "end_date": "1999-12-31", + "bbox": "-106.015, 31.652, -96.26, 43.806", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549394-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549394-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_ofroo-300_SATTK9697_1.0", + "description": "This data set was created to document the original map (McGuire and Fischer, 1999) produced by the High Plains Water-level Monitoring project and make available the data on this map for use with geographic information systems. This digital data set consists of saturated thickness contours for the High Plains aquifer in Central United States, 1996-97. The High Plains aquifer extends from south of 32 degrees to almost 44 degrees north latitude and from 96 degrees 30 minutes to 104 degrees west longitude. The aquifer underlies about 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This data set was based on 10,085 water-level measurements, 49 stream elevations, (March 1997) and 10,036 water-level elevations from wells (1,370 from 1996 and 8,666 from 1997) and the base of aquifer value for each measurement location. The saturated thickness at each measurement location was determined by subtracting the water-level elevation from the base of aquifer at that location. Introduction -- The information provided in this introduction is found in U.S. Geological Survey Professional Paper 1400-B (Gutentag and others,1984). This data set consists of saturated thickness contours for the High Plains aquifer in Central United States, 1996-97 (modified from Weeks and Gutentag, 1981; Cederstrand and Becker, 1999). The High Plains aquifer, which underlies about 174,000 square miles in parts of eight states, is the principal water source in one of the nation's major agricultural areas. In 1980, about 170,000 wells pumped water from the aquifer to irrigate about 13 million acres. The High Plains aquifer is a regional water-table aquifer consisting mostly of near-surface sand and gravel deposits. In 1980, the maximum saturated thickness of the aquifer was about 1,000 feet and averaged about 200 feet. Hydraulic conductivity and specific yield of the aquifer depend on sediment types, which vary significantly both horizontally and vertically. Hydraulic conductivity ranged from less than 25 to greater than 300 feet per day and averaged 60 feet per day. Specific yields ranged from less than 10 to 30 percent and averaged about 15 percent. The High Plains aquifer boundaries were determined by erosional extent of associated geologic units and by hydraulic and physiographic boundaries where the High Plains aquifer extends eastward from the Great Plains physiographic province (Fenneman, 1931). In most of the area, the erosional extent of the hydraulically connected Tertiary and Quaternary deposits were used as the aquifer boundary. In eastern Nebraska, streams and physiographic boundaries were used as the aquifer boundary. Reviews Applied to Data -- This electronic report was subjected to the same review standard that applies to all U.S. Geological Survey reports. Reviewers were asked to check the topological consistency, tolerances, attribute frequencies and statistics, projection, and geographic extent. Reviewers were given digital data sets for checking against the source maps to verify the linework and attributes. The reviewers checked the metadata files for completeness and accuracy.", + "license": "proprietary" + }, { "id": "USM_pCO2_0", "title": "University of Southern Mississippi (USM) - partial pressure of carbon dioxide (pCO2) project", @@ -184625,6 +193543,19 @@ "description": "Measurements of pCO2 taken by the University of Southern Mississippi in the Gulf of Mexico near the Louisiana coast in 2005 and 2006", "license": "proprietary" }, + { + "id": "US_FOREST_FRAGMENTATION", + "title": "Forest Fragmentation in the United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-128, 24, -65, 50", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549003-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549003-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/US_FOREST_FRAGMENTATION", + "description": "National Land Cover Data (NLCD) was reclassified into three categories: forest, other natural (e.g., grassland and wetland), and anthropogenic use (e.g., agricultural and urban). Three new grids were created, one for each edge type (forest, forest, forest natural, and forest anthropogenic). The values in these grids were calculated as the number of edges with the appropriate type in the window divided by the total number of forest edges, regardless of neighbor. These grids represented forest connectivity (forest forest edges), naturally caused forest fragmentation (forest natural edges), and human-caused forest fragmentation (forest anthropogenic edges). In the map, forest connectivity is displayed in green, natural fragmentation in blue, and human fragmentation in red. Pure green identifies areas where most or all forest edges are shared by another forest pixel. Pure red areas are where forest edges are largely shared with human land use. Pure blue areas show where most or all forest edges are shared with another natural land cover type. Different mixes of the three edge types can produce other colors. Two common examples in the map are yellow and cyan. Yellow identifies areas with roughly equal amounts of forest connectivity and anthropogenic fragmentation. Cyan is where forest connectivity and natural fragmentation are approximately equal. Black represents areas with no forest in the window, and white represents ignored areas, mostly water, as well as state boundaries. With few exceptions, forest fragmentation by other natural land cover types is confined to the western United States, while most human-caused forest fragmentation is in the East and Midwest. The yellow and red areas around Yellowstone in northwest Wyoming are a result of the wildfires in 1988. The burned areas are classified as \"transitional\" in the NLCD, which are treated as anthropogenic use. The Mississippi River valley was largely forested at one time but has been almost entirely converted to agricultural use, resulting in a display of black and red. Las Vegas, Nevada, is visible as a patch of red in the Mojave Desert due to an \"urban forest\" effect from trees planted by residents. Riparian corridors are highly visible in arid and developed areas, especially the West and Midwest. In arid areas, climate often confines trees to riparian zones that are displayed in shades of blue. In the intensely farmed Midwest, intact and restored riparian vegetation is depicted in yellow or red. Southern Atlantic coastal plain riparian zones are wider; forest is better connected and is shown in green.", + "license": "proprietary" + }, { "id": "US_MODIS_NDVI_1299_3", "title": "MODIS NDVI Data, Smoothed and Gap-filled, for the Conterminous US: 2000-2015", @@ -184664,6 +193595,71 @@ "description": "This data set portrays the 1990 State and county boundaries of the United States, Puerto Rico, and the U.S. Virgin Islands. The data set was created by extracting county polygon features from the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey. These files were then merged into a single file and the boundaries were modified to what they were in 1990. This is a revised version of the March 2000 data set.", "license": "proprietary" }, + { + "id": "UTC_TNgeologicmaps", + "title": "Geologic Maps of Tennessee", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1966-01-01", + "end_date": "1966-12-31", + "bbox": "-90.31191, 34.983253, -81.64822, 36.679295", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549514-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549514-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UTC_TNgeologicmaps", + "description": "This data set is a digital representation of the printed 1:250,000 geologic maps from the Tennessee Department of Environment and Conservation, Division of Geology. The coverage was designed primarily to provide a more detailed geologic base than the 1:2,500,000 King and Beikman (1974). 1:24,000 scale coverage of the state is available for about 40 percent of the state. Formation names and geologic unit codes used in the coverage are from the Tennessee Division of Geology published maps and may not conform to USGS nomenclature. The Tennessee Division of Geology can be contacted at (615) 532-1500.", + "license": "proprietary" + }, + { + "id": "UTC_TRIfacilities", + "title": "Facilities in the Toxic Release Inventory", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-12-31", + "end_date": "", + "bbox": "-127.61431, 23.24277, -65.505165, 51.523094", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553589-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553589-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UTC_TRIfacilities", + "description": "This data set is a subset of the U.S. Environmental Protection Agency (USEPA) Envirofacts point data set which includes facilities included in the the Toxic Release Inventory. Information on total pounds of volatile organic compounds released in 1995 (from USEPA's Toxic Release Inventory CD-ROM) has been included. This data set is designed to locate or plot manufacturing facilities included in the Toxic Release Inventory and display or analysis of volatile organic compounds releases in pounds per year. The following are the volatile organic compounds (VOC's) selected to calculate the total releases at each facility. Not all of these chemicals actually appear in the TRI data set, but this list was used to select releases to sum for each facility. CAS-ID Chemical name > ---------- ---------------------------- > 1 630-20-6 1,1,1,2-Tetrachloroethane > 2 71-55-6 1,1,1-Trichloroethane > 3 79-34-5 1,1,2,2-Tetrachloroethane > 4 76-13-1 1,1,2-Trichloro-1,2,2-trifluoroethane > 5 79-00-5 1,1,2-Trichloroethane > 6 75-34-3 1,1-Dichloroethane > 7 75-35-4 1,1-Dichloroethene > 8 563-58-6 1,1-Dichloropropene > 9 87-61-6 1,2,3-Trichlorobenzene > 10 96-18-4 1,2,3-Trichloropropane > 11 120-82-1 1,2,4-Trichlorobenzene > 12 95-63-6 1,2,4-Trimethylbenzene > 13 96-12-8 1,2-Dibromo-3-chloropropane > 14 106-93-4 1,2-Dibromoethane > 15 95-50-1 1,2-Dichlorobenzene > 16 107-06-2 1,2-Dichloroethane > 17 78-87-5 1,2-Dichloropropane > 18 108-67-8 1,3,5-Trimethylbenzene > 19 541-73-1 1,3-Dichlorobenzene > 20 142-28-9 1,3-Dichloropropane > 21 106-46-7 1,4-Dichlorobenzene > 22 95-49-8 1-Chloro-2-methylbenzene > 23 106-43-4 1-Chloro-4-methylbenzene > 24 594-20-7 2,2-Dichloropropane > 25 71-43-2 Benzene > 26 108-86-1 Bromobenzene > 27 74-97-5 Bromochloromethane > 28 75-27-4 Bromodichloromethane > 29 74-83-9 Bromomethane > 30 108-90-7 Chlorobenzene > 31 75-00-3 Chloroethane > 32 75-01-4 Chloroethene > 33 74-87-3 Chloromethane > 34 124-48-1 Dibromochloromethane > 35 74-95-3 Dibromomethane > 36 75-71-8 Dichlorodifluoromethane > 37 75-09-2 Dichloromethane > 38 1330-20-7 Dimethylbenzenes > 39 100-42-5 Ethenylbenzene > 40 100-41-4 Ethylbenzene > 41 87-68-3 Hexachlorobutadiene > 42 98-82-8 Isopropylbenzene > 43 1634-04-4 Methyl tert-butyl ether > 44 108-88-3 Methylbenzene > 45 91-20-3 Naphthalene > 46 127-18-4 Tetrachloroethene > 47 56-23-5 Tetrachloromethane > 48 75-25-2 Tribromomethane > 49 79-01-6 Trichloroethene > 50 75-69-4 Trichlorofluoromethane > 51 67-66-3 Trichloromethane > 52 156-59-2 cis-1,2-Dichloroethene > 53 10061-01-5 cis-1,3-Dichloropropene > 54 104-51-8 n-Butylbenzene > 55 103-65-1 n-Propylbenzene > 56 99-87-6 p-Isopropyltoluene > 57 135-98-8 sec-Butylbenzene > 58 98-06-6 tert-Butylbenzene > 59 156-60-5 trans-1,2-Dichloroethene > 60 10061-02-6 trans-1,3-Dichloropropene Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology.", + "license": "proprietary" + }, + { + "id": "UTC_USdams", + "title": "Major Dams in the United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "-162.93422, 18.016077, -66.01461, 68.06759", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555196-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555196-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UTC_USdams", + "description": "This data set portrays major dams of the United States, including Puerto Rico and the U.S. Virgin Islands. The data set was created by extracting dams 50 feet or more in height, or with a normal storage capacity of 5,000 acre- feet or more, or with a maximum storage capacity of 25,000 acre-feet or more, from the 75,187 dams in the U.S. Army Corps of Engineers National Inventory of Dams. These data are intended for geographic display and analysis at the national level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:2,000,000-scale data. No responsibility is assumed by the U.S. Geological Survey in the use of these data. In the online, interactive National Atlas of the United States, at scales smaller than 1:4,850,000 the data is thinned for display purposes. For scales between 1: 4,850,000 and 1:22,000,000, dams are only shown if they have a height of 500 feet or more, or a normal storage capacity of 50,000 acre-feet or more, or a maximum storage capacity of 250,000 acre-feet or more (1173 dams). At scales smaller than 1:22,000,000, dams are only shown if they have a height of 5000 feet or more, or a normal storage capacity of 500,000 acre-feet or more, or a maximum storage capacity of 2,500,000 acre-feet or more (240 dams). The dams in this file were selected from the National Inventory of Dams (NID). First, a subset of the attributes contained in the NID was selected based on input from the Army Corps of Engineers. Using an ArcView query, the dams with a height of 50 feet or more were selected, along with the dams with a normal storage capacity of 5,000 acre-feet or more, and those with a maximum storage capacity of 25,000 acre-feet or more. (The International Committee on Large Dams considers dams over 50 feet to be large dams. The USGS Water Resources Division considers large reservoirs to be those with a normal storage capacity of 5,000 acre-feet or more, or with a maximum storage capacity of 25,000 acre-feet or more.) The resulting data set was converted to an ArcView shape file using the \"Convert to Shapefile\" command. 33 dams that fell outside the 50 States were deleted (1 in Guam, 1 in the Trust Territories, and 31 in Puerto Rico), and 78 dams without coordinates were also deleted. Several misspelled county names were corrected, and the entries in the FIPS_cnty (County FIPS) field were cleaned up. For all dams with a valid county name but no County FIPS, the FIPS code was added based on the listed county name. If two county names were given, the FIPS code used was for the first one listed, or for the county in the listed State. Where the county name was invalid or missing, the county was determined by comparing the dam location to the National Atlas counties file. If the dam fell on a State line, the county name and FIPS code used were those appropriate for the listed State. The shape file was converted to an Arc/Info coverage and then converted to NAD 83 for display purposes. The result was then converted back to shapefile format.", + "license": "proprietary" + }, + { + "id": "UTC_hydrography", + "title": "Hydrographic Features of the United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1999-12-31", + "bbox": "-177.1, 17, -64, 72", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551647-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551647-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UTC_hydrography", + "description": "The data set portrays the polygon and line water features of the United States, Puerto Rico, and the U.S. Virgin Islands. The file was produced by joining the individual State hydrographic layers from the 1:2,000,000- scale Digital Line Graph (DLG) data produced by the USGS. This is a revised version of the March 1999 data set. These data are intended for geographic display and analysis at the national level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:2,000,000-scale data. No responsibility is assumed by the U.S. Geological Survey in the use of these data.", + "license": "proprietary" + }, + { + "id": "UTC_landpolygonfeatures", + "title": "Federally Owned Land Polygon Features of the United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1972-01-01", + "end_date": "2000-12-31", + "bbox": "-179.13339, 17.674692, 179.78821, 71.3407", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554023-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554023-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UTC_landpolygonfeatures", + "description": "This data set consists of federally owned land polygon features of the United States. The data set was created by extracting federal land polygon features from the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey. These files were then appended into a single coverage. This is a revised version of the June 1998 data set. There may be private in holdings within the boundaries of Federal Lands in this data set. These data are intended for geographic display and analysis at the national level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:2,000,000-scale data. No responsibility is assumed by the U.S. Geological Survey in the use of these data.", + "license": "proprietary" + }, { "id": "UTC_majorgeologicunits", "title": "Boundaries and Tags for Major Geologic Units in the United States", @@ -184755,6 +193751,19 @@ "description": "This dataset provides measurements of vegetation biomass from 11 locations across Alaska during 2016 to 2018. Vegetation was harvested from plots that were located at the end of previously established 30-m transects at each site, except at one site where plots were randomly selected. Vascular vegetation was clipped from 50 cm x 50 cm plots, and non-vascular vegetation was clipped from 25 cm x 25 cm plots. All harvested vegetation was sorted by functional group or by species where identification was possible. The sorted vegetation was dried and then weighed to determine biomass. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma separated values (CSV) format.", "license": "proprietary" }, + { + "id": "VATECH_VAdust", + "title": "Dust Deposition in Southern Nevada and California", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1984-01-01", + "end_date": "1989-12-31", + "bbox": "-118, 32.5, -114, 38.25", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548569-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548569-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/VATECH_VAdust", + "description": "Dust samples taken annually for five years from 55 sites in southern Nevada and California provide an unparalleled source of information on modern rates of dust deposition, grain size, and mineralogical and chemical composition. The relations of modern dust to climatic factors, type and lithology of dust source, and regional wind patterns shed new light on the processes of dust entrainment and deposition. The average silt-plus-clay flux in southern Nevada and southeastern California ranges from 4.3 to 15.7 g/m2/yr, but in southwestern California the average flux is as high is 30 g/m2/yr. These rates are generally less than those of previous studies in the arid southwestern United States, probably due to differences in measurement techniques (other studies mostly used traps at lower heights and did not exclude bird- derived sediment). The climatic factors that affect dust flux interact with each other and with the factors of source type, source lithology, geographic area, and human disturbance. For example, average dust flux increases with mean annual temperature but is only weakly related to decreases in mean annual precipitation, because the prevailing winds bring dust to relatively wet areas. In contrast, annual dust flux mostly reflects changes in annual precipitation rather than temperature. Although playa and alluvial sources emit about the same amount of dust per unit area, the volume of dust from the more extensive alluvial sources is much larger. In addition, playa and alluvial sources respond differently to annual changes in precipitation. Most playas emit dust that is richer in soluble salts and carbonate than that from alluvial sources (except carbonate-rich alluvial fans), but the dust-deposition rates do not reflect this trend: salt flux tends to be larger in mountain ranges, and gypsum flux parallels carbonate flux. Gypsum dust may be produced by the interaction of carbonate dust and anthropogenic sulfates. Cultivated areas generally yield about 20 percent more dust than uncultivated areas. The dust flux in an arid urbanizing area may be as much as twice that before disturbance, but decreases when construction stops. The mineralogic and major-oxide composition of the dust samples indicate that sand and some silt is locally derived and deposited, whereas clay and some silt from different sources can be far-travelled. Dust deposited in the Transverse Ranges of California by the Santa Ana winds appears to be mainly derived from sources to the north and east. The sampling design for this study was not statistically based; rather, sites were chosen to provide data on dust influx at soil-study sites and to answer specific questions about the relations of dust to local source lithology and type, distance from source, and climate. Some sites were chosen for their proximity to potential dust sources of different lithologic composition (for example, playas versus granitic, calcic, or mafic alluvial fans). Other sites were placed along transects crossing topographic barriers downwind from a dust source. These transects include sites east of Tonopah (43-46) crossing the rhyolitic Kawich Range, sites downwind of northern (40, 35, 36) and central Death Valley (38, 39, 11-14) crossing the mixed-lithology Grapevine and Funeral Mountains, respectively, and sites downwind of Desert Dry Lake crossing the calcareous Sheep Range (47-50) north of Las Vegas. In addition, some sites were chosen for their proximity to weather stations. Specific locations for dust traps were chosen on the basis of the above criteria plus accessibility, absence of dirt roads or other artificially disturbed areas upwind, and inconspicuousness. The last factor is important because the sites are not protected or monitored; hence, most sites are at least 0.5 mile from a road or trail. Despite these precautions, dust traps are sometimes tampered with, often violently. This is a particular problem in areas close to population centers, and most of these sites (52-55 near Los Angeles and 17-19 and 22 near Las Vegas) have been abandoned. A few other sites, mostly those that appeared to be greatly influenced by nearby farming (20, 21, and 41), were eliminated in 1989. Dust traps were also generally placed in flat, relatively open areas to mitigate wind-eddy effects created by tall vegetation or topographic irregularities. The 55 sites established in 1984 and 1985 were sampled annually through 1989 in order to establish an adequate statistical basis to calculate annual dust flux. Sampling continues at 37 of these sites (many sites now have two or more dust traps) every two or three years as opportunity and funding permit.", + "license": "proprietary" + }, { "id": "VBEMI2AE_002", "title": "MISR Level 2 TOA/Cloud Aerosol Product subset for the VBBE region V002", @@ -191554,6 +200563,45 @@ "description": "Administration division of Poland created on a basis of digitization with manual generalisation proper for specific scales. Projection Albers; points and polygons; ARC/INFO and SINUS systems", "license": "proprietary" }, + { + "id": "WARd0004_108", + "title": "Land Use Division Maps of Poland", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "24, 14, 49, 54", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848834-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848834-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WARd0004_108", + "description": "Land use map of Poland acquisited form interpreted Landsat TM, MSS images by digitization. 24 classes of land use grouped in subjects (agriculture, grass lands, settlements and communication areas, forests, surface waters, industry, not used areas). Vector and raster format; projection Albers; ARC/INFO and SINUS systems", + "license": "proprietary" + }, + { + "id": "WARd0005_108", + "title": "Geomorphology Forms of Poland", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "24, 14, 49, 54", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848304-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848304-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WARd0005_108", + "description": "Geomorphological forms of Poland created within Central Scientific Programme 10.4/1989. Digitized from the map of relief types in Poland; Scale 1:1 000 000.", + "license": "proprietary" + }, + { + "id": "WARd0006_108", + "title": "Hunting Unit Border Maps of Poland", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "24, 14, 49, 54", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849207-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849207-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WARd0006_108", + "description": "Borders of hunting units digitized from the maps prepared by Polish Hunting Association within Central Scientific Programme 10.4/1989.", + "license": "proprietary" + }, { "id": "WARd0010_108", "title": "Catchments Division Maps of Poland", @@ -191567,6 +200615,32 @@ "description": "Four-level hydrographic division of Poland prepared in accordance to a new scheme of catchment division elaborated by the Institute of Meteorology and Water Management (IMGW). Scanned from the \"Hydrological Atlas of Poland\".", "license": "proprietary" }, + { + "id": "WARd0011_108", + "title": "Ecological Hazards in Poland", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "24, 14, 49, 54", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847578-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847578-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WARd0011_108", + "description": "Ecological hazards digitized from the map of protected landscape.", + "license": "proprietary" + }, + { + "id": "WARd0012_108", + "title": "Digitized Maps of Main Cities in Poland", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "24, 14, 49, 54", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846804-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846804-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WARd0012_108", + "description": "Main cities in Poland digitized from the Review Map of Poland", + "license": "proprietary" + }, { "id": "WATVP_D3_VIIRS_SNPP_1", "title": "VIIRS/SNPP Water Vapor Level-3 daily 0.5 x 0.5 degree grid", @@ -191710,6 +200784,32 @@ "description": "WCMC has provided services to the Convention on International Trade in Endangered Species CITES since 1980, computerising the trade records of species listed in the CITES Appendices, as reported by the Parties. This computer database is the largest of its kind, currently holding over 2 million records on trade in wildlife species and their derivative products. The information spans from 1975, when a mere 148 trade records were reported, to the present and is constantly being updated as further annual reports are received from CITES Parties. Since 1986 more than 200,000 trade records have been reported annually. In addition to the trade records, the database holds some 29,000 scientific names and synonyms. \"http://www.cites.org/\". The annual report data arrive in many different formats, ranging from copies of permits, hand-written or printed reports, to computer tapes, diskettes and electronic mail. The information is entered into the database, either manually or by direct electronic transfer, and customised translation programmes written in Perl at WCMC now enable automatic loading of most reports received on magnetic media. Now that WCMC is connected to the Internet, it is expected that many countries will be able to submit their information directly via the network, this process having already been successfully carried out by the Management Authority of Brazil. In order to investigate the further potential of this type of data collection WCMC has devised a questionnaire that the CITES Secretariat is circulating to all Party States. At the beginning of 1993 the trade data were transferred from a WANG computer to an Ingres relational database held on a Sunsparc 10/30. A large suite of custom-built programs allow sophisticated control, maintenance and manipulation of the data; for example, information on a species Appendix-listing is linked to the taxonomic file thus possible errors at the data input stage are reduced to a minimum. Current work at WCMC is linking the trade data with species distribution information and with the Centre's Geographical Information Systems (GIS), known as the Biodiversity Map Library, to ensure that the information, so laboriously collected, can be used in the best way to promote species conservation. Further links with information on national and international legislation may be possible in the future. In addition to input and maintenance of the trade data, WCMC collects information on protected areas, habitats and species of conservation concern, and can therefore provide comprehensive analyses and reports. The trade outputs usually comprise one of three standard formats: Gross/Net Trade Tabulation - will provide gross or net import/export data for a specified year(s), country, species and/or product, thus allowing yearly trends to be monitored. Comparative Tabulation - produces data from corresponding importing and exporting countries for a specific year, species, product, etc., thus allowing a comparison of the reporting between the two Parties and a chance to identify any potentially illegal trade. Annual Report - format will provide a complete printout of all CITES trade for a particular year reported by a specific CITES Party. Where Parties are unable to produce their own annual report, WCMC can produce one based on that country's returned permits. Regular requests for information from the database are made by the CITES Secretariat, Management and Scientific Authorities, the TRAFFIC Network, WWF, IUCN, Universities, NGO's, researchers, journalists and teachers, etc. With permission from the CITES Secretariat, WCMC can provide data in any of the above formats although a fee is charged to cover the production costs of the work. WCMC have carried out detailed analyses of the status and trade data have included the following: * selected species listed in Appendix II * Green and Hawksbill turtles * world trade in raw and worked ivory * Asian monitor lizards * South East Asian pythons * Crocodile farming and ranching LANGUAGE: English STATISTICAL INFORMATION: ACCESS AND DISTRIBUTION: WCMC makes information available through published media, through provision of datasets, and through the provision of either standard or customised reports. WCMC is committed to the principle of the free exchange of data with other institutions and users. In so far as is practical, it places its data in the public domain and encourages their wide distribution. However, costs may be incurred in accessing and distributing datasets, and where analysis and assessment provide an added-value service. Such costs are passed on to the user.", "license": "proprietary" }, + { + "id": "WCMC_157", + "title": "East Africa Biodiversity Metadatabase from the World Conservation Monitoring Centre (WCMC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "", + "bbox": "28, -12, 42, 5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847994-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847994-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WCMC_157", + "description": "Availability of Biodiversity Information for East Africa Launched in 1992 at the Conference Conservation of Biodiversity in Africa, Nairobi, the project represents a survey of the sources and types of information held on biodiversity for Kenya, Tanzania and Uganda by organisations both within and outside East Africa. The project came about in response to the need for a systematic review of data holdings for the region in support of conservation and sustainable development. This need has been subsequently underscored by provisions in the Convention on Biological Diversity calling for contracting parties to develop national strategies, plans or programmes, assisted by such baseline information. The study was a collaborative venture between the IUCN Regional Office for Eastern Africa (EARO), the World Conservation Monitoring Centre (WCMC), and key national institutions in each of the three countries. Funding was provided by The European Commission (B7-5040 Contract 92/11) and through a contract with the Food and Agriculture Organization of the United Nations, executing agency for a GEF/UNDP project entitled Institutional Support for the Protection of East African Biodiversity (UNO/RAF/006/GEF). Information for the study was collected using a standard questionnaire design, administered by means of face to face interviews for the majority of the 100 institutions surveyed within East Africa, by mailing the questionnaire to more than 1000 institutions outside the region, and by posting the questionnaire on \"News\", a global electronic bulletin board, with the potential of reaching another 1 million+ subscribers. A total of 350 questionnaires were completed and returned, the results of which were used in the production of the following outputs: The creation of a \"data sources\" database (metadatabase) of the sources and types of biodiversity information held for the region The production of a printed report including (1) summary information and analysis of results in terms of taxonomic and geographic coverage of biodiversity information; and (2) a catalogue of questionnaire entries Presentation of catalogue entries in Folio Views text-retrieval software, with accompanying User's Guide Important follow-up to this study includes forthcoming publication and distribution of hard-copy and electronic outputs, maintenance and updating of the database, ongoing provision of training in information collection and database use, and general support for biodiversity initiatives by promoting networking between institutions and accessibility to information. Consideration is being given to extending the study to other regions of Africa and elsewhere, and the experience gained from this initiative is being used in support of larger institutional capacity building projects currently being undertaken at WCMC. LANGUAGE: English STATISTICAL INFORMATION: ACCESS AND DISTRIBUTION: WCMC makes information available through published media, through provision of datasets, and through the provision of either standard or customised reports. WCMC is committed to the principle of the free exchange of data with other institutions and users. In so far as is practical, it places its data in the public domain and encourages their wide distribution. However, costs may be incurred in accessing and distributing datasets, and where analysis and assessment provide an added-value service. Such costs are passed on to the user.", + "license": "proprietary" + }, + { + "id": "WCMC_158", + "title": "List of Threatened Animals from the World Conservation Monitoring Centre (WCMC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848347-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848347-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WCMC_158", + "description": "As part of the species activities, WCMC maintains nomenclature, distribution and conservation status information on some 18,000 animal species and subspecies worldwide. WCMC stores animal information in a series of Foxpro database files which include data on single country endemics, globally threatened species, and species included on various International Conventions. These animal data are currently being converted from Foxpro to A-Rev. For further details of structure see Plant Species Database description. The basic data elements on species conservation include scientific and common names, distribution by country and conservation status. Additional information on population size, trends and habitat are sought wherever possible. For species subject to wildlife trade, information on levels of trade, impact on wild populations, protection and management measures are important. WCMC publishes the Red List of Threatened Animals in collaboration with IUCN and the Species Survival Commission. All the data are held on computer, including nomenclature, common names, distribution, conservation status, threats, etc. The Centre is actively seeking collaboration to prepare digital distribution maps for threatened animals and plants, but this work is at an early stage. LANGUAGE: English STATISTICAL INFORMATION: ACCESS AND DISTRIBUTION: WCMC makes information available through published media, through provision of datasets, and through the provision of either standard or customised reports. WCMC is committed to the principle of the free exchange of data with other institutions and users. In so far as is practical, it places its data in the public domain and encourages their wide distribution. However, costs may be incurred in accessing and distributing datasets, and where analysis and assessment provide an added-value service. Such costs are passed on to the user.", + "license": "proprietary" + }, { "id": "WC_LSMEM_SOILM_025_001", "title": "AMSR-E/Aqua surface soil moisture (LSMEM) L3 1 day 0.25 degree x 0.25 degree V001 (WC_LSMEM_SOILM_025) at GES DISC", @@ -191814,6 +200914,19 @@ "description": "The main purpose of the Wind spacecraft is to measure the incoming solar wind, magnetic fields and particles, although early on it will also observe the Earth's foreshock region. Wind, together with Geotail, Polar, SOHO, and Cluster projects, constitute a cooperative scientific satellite project designated the International Solar Terrestrial Physics (ISTP) program which aims at gaining improved understanding of the physics of solar terrestrial relations. This experiment is designed to measure the full three-dimensional distribution of suprathermal electrons and ions at energies from a few eV to over several hundred keV on the WIND spacecraft. Its high sensitivity, wide dynamic range, and good energy and angular resolution make it especially capable of detecting and characterizing the numerous populations of particles that are present in interplanetary space at energies above the bulk of the solar wind particles and below the energies typical of most cosmic rays. Data consists of ion moments, energy spectra, electron spectra, electron and ion omni directional energy spectra. Data are available from SSL at University of California, Berkeley (http://sprg.ssl.berkeley.edu/wind3dp/esahome.html) and at the NSSDC CDAWeb (http://cdaweb.gsfc.nasa.gov/cdaweb/)", "license": "proprietary" }, + { + "id": "WIR_98_4105", + "title": "Major-Ion, Nutrient, and Trace-Element Concentrations in the Steamboat Creek Basin", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-09-09", + "end_date": "1996-09-13", + "bbox": "-122.7, 42.3, -122.5, 43.6", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554333-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554333-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WIR_98_4105", + "description": "In September 1996, a water-quality study was done by the U.S. Geological Survey, in coordination with the U.S. Forest Service, in headwater streams of Steamboat Creek, a tributary to the North Umpqua River Basin in southwestern Oregon. Field measurements were made in and surface-water and bottom-sediment samples were collected from three tributaries of Steamboat Creek-Singe Creek, City Creek, and Horse Heaven Creek-and at one site in Steamboat Creek upstream from where the three tributaries flow into Steamboat Creek. Water samples collected in Singe Creek had larger concentrations of most major-ion constituents and smaller concentrations of most nutrient constitu ents than was observed in the other three creeks. City Creek, Horse Heaven Creek, and Steamboat Creek had primarily calcium bicarbonate water, whereas Singe Creek had primarily a calcium sulfate water; the calcium sulfate water detected in Singe Creek, along with the smallest observed alkalinity and pH values, suggests that Singe Creek may be receiving naturally occurring acidic water. Of the 18 trace elements analyzed in filtered water samples, only 6 were detected-aluminum, barium, cobalt, iron, manganese, and zinc. All six of the trace elements were detected in Singe Creek, at concentrations generally larger than those observed in the other three creeks. Of the detected trace elements, only iron and zinc have chronic toxicity criteria established by the U.S. Environmental Protection Agency (USEPA) for the protection of aquatic life; none exceeded the USEPA criterion. Bottom-sediment concentrations of antimony, arsenic, cadmium, copper, lead, mercury, zinc, and organic carbon were largest in City Creek. In City Creek and Horse Heaven Creek, concentrations for 11 constituents--antimony, arsenic, cadmium, copper, lead, manganese (Horse Heaven Creek only), mercury, selenium, silver, zinc, and organic carbon (City Creek only)--exceeded concentrations considered to be enriched in streams of the nearby Willamette River Basin, whereas in Steamboat Creek only two trace elements--antimony and nickel--exceeded Willamette River enriched concentrations. Bottom-sediment concentrations for six of these constituents in City Creek and Horse Heaven Creek--arsenic, cadmium, copper, lead, mercury, and zinc--also exceeded interim Canadian threshold effect level (TEL) concentrations established for the protection of aquatic life, whereas only four constituents between Singe Creek and Steamboat Creek--arsenic, chromium, copper (Singe Creek only), and nickel--exceeded the TEL concentrations. The data set checked for the concentrations of major ions, nutrients, and trace elements in water and bottom sediments collected in the four tributaries during the low-flow conditions of September 9-13, 1996. Stream-water chemistry results were contrasted, and trace-element concentrations were compared with U.S. Environmental Protection Agency chronic aquatic life toxicity criteria. Bottom-sediment trace-element results were also contrasted and compared with concentrations considered to be enriched in streams of the nearby Willamette River Basin and to interim Canadian threshold level (TEL) concentrations established for the protection of aquatic life. The area of study was Headwater streams of Steamboat Creek, a tributary to the north Umpqua River Basin in southwestern Oregon Field measurements and surface-water and bottom-sediment samples at each of the four sites included streamflow, stream temperature, specific conductance, dissolved oxygen, pH, alkalinity, major ions in filtered water (8 constituents), low-level concentrations of trace elements in filtered water (18 elements), and trace elements and carbon in bottom sediment (47 elements). Stream temperature, specific conductance, dissolved oxygen, and pH were measured using a calibrated Hydrolab multiparameter unit. Because stream widths were less than 8 feet, field measurements were made only near the center of flow at 1 foot or less below water surface. The Hydrolab unit was calibrated at each site before and after sampling. Stream temperatures were recorded to the nearest 0.1 degree Centigrade; specific conductance to the nearest 1 microsiemen per centimeter at 25 degrees Centigrade ; dissolved oxygen to the nearest 0.1 milligrams per liter; and pH to the nearest 0.1 pH units. Measurements of streamflow were made in accordance with standard USGS procedures (Rantz and others, 1982). Alkalinity measurements were made on filtered water samples using an incremental titration method (Shelton, 1994), and results were reported to the nearest 1 milligram per liter as calcium carbonate (CaCO3). Water samples were collected using 1-liter narrow-mouth acid-rinsed polyethylene bottles from a minimum of eight verticals in the cross section, suing an equal-width-increment method described by Edwards and Glysson (1988), and composited into a 8-liter polyethylene acid-rinsed churn splitter. Sample and compositing containers were prerinsed with native water prior to sample collection. Water samples were collected using clean procedures as outlined by Horowitz and others (1994). Samples were chilled on ice from time of sample collection until analysis, except when samples were processed. Processing of the field samples was accomplished either in the mobile field laboratory or in an area suitably clean for carrying out the filtering and preservation procedures. Samples for major ions, nutrients, and trace elements in filtered water (operationally defined as dissolved) were passed through 0.45 micrometer pore-size capsule filters into polyethylene bottles using procedures outlined by Horowitz and others (1994). Filtered-water trace-element samples were preserved with 0.5 milliliter of ultra-pure nitric acid per 250 mL of sample; nutrient samples were placed in dark brown polyethylene bottles and were chilled for preservation. All chemical samples were shipped to the USGS National Water Quality Laboratory (NWQL) in Arvada, Colorado, for analysis according to methods outlined by Fishman (1993). The information for this metadata was taken from the Online Publications of the Oregon District at http://oregon.usgs.gov/pubs_dir/online_list.html .", + "license": "proprietary" + }, { "id": "WISPMAWSON04-05_1", "title": "A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season", @@ -191918,6 +201031,32 @@ "description": "Excessive total dissolved gas pressure can cause gas-bubble trauma in fish downstream from dams on the Columbia River. In cooperation with the U.S. Army Corps of Engineers, the U.S. Geological Survey collected data on total dissolved gas pressure, barometric pressure, water temperature, and probe depth at eight stations on the lower Columbia River from the John Day forebay (river mile 215.6) to Camas (river mile 121.7) in water year 2000 (October 1, 1999, to September 30, 2000). These data are in the databases of the U.S. Geological Survey and the U.S. Army Corps of Engineers. Methods of data collection, review, and processing, and quality-assurance data are presented in this report. The purpose of TDG monitoring is to provide USACE with (1) real-time data for managing streamflows and TDG levels upstream and downstream from its project dams in the lower Columbia River and (2) reviewed and corrected TDG data to evaluate conditions in relation to water-quality criteria and to develop a TDG data base model for modeling the effect of various management scenarios of stream flow and spill on TDG levels. Instrumentation at each fixed station consisted of a TDG probe, an electronic barometer, a data-collection platform (DCP), and a power supply. The TDG probe was manufactured by Hydrolab Corporation. The probe had individual sensors for TDG, temperature, and probe depth (unvented sensor). The TDG sensor consisted of a cylindrical framework wound with a length of Silastic (dimethyl silicon) tubing. The tubing was tied off at one end and the other end was connected to a pressure transducer. After the TDG pressure in the river equilibrated with the gas pressure inside the tubing (about 15 to 20 minutes), the pressure transducer produced a measure of the TDG presure in the River. The water-temperature sensor was a thermocouple. The barometer was contained in the display unit of the Model TBO-L, a total dissolved gas meter manufactured by Common Sensing, Inc. More information abou the TDG probe is provided by Tanner, D. Q. And Johnston and M.W. 2001. The fixed station monitors were calibrated every 2 weeks from March 10 to September 15, 2000, and every three weeks for the remainder of the year, at which time Warrendale and Bonneville forebay were the only sites in operation. The general procedure was to check the operation of the TDG probe in the field without disturbing it, replace the field probe with one that had just been calibrated in the laboratory, and then check the operation of the newly deployed field probe. The details of the laboratory calibration procedure are outlined in Tanner and Johnston, 2001. Information for this metadata was obtained from the Technical Reports of the Oregon District available at http://oregon.usgs.gov/pubs_dir/online_list.html .", "license": "proprietary" }, + { + "id": "WRIR_97_4268", + "title": "Distribution of Dissolved Pesticides and Other Water Quality Constituents in Small Streams, and their Relation to Land Use, Willamette River Basin, Oregon", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-03-01", + "end_date": "1996-11-30", + "bbox": "-124, 43.5, -121.5, 46", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555249-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231555249-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WRIR_97_4268", + "description": "Water quality samples were collected at sites in 16 randomly selected agricultural and 4 urban subbasins as part of Phase III of the Willamette River Basin Water Quality Study in Oregon during 1996. Ninety-five samples were collected and analyzed for suspended sediment, conventional constituents (temperature, dissolved oxygen, pH, specific conductance, nutrients, biochemical oxygen demand, and bacteria) and a suite of 86 dissolved pesticides. The data were collected to characterize the distribution of dissolved pesticide concentrations in small streams (drainage areas 2.6? 13 square miles) throughout the basin, to document exceedances of water quality standards and guidelines, and to identify the relative importance of several upstream land use categories (urban, agricultural, percent agricultural land, percent of land in grass seed crops, crop diversity) and seasonality in affecting these distributions. A total of 36 pesticides (29 herbicides and 7 insecticides) were detected basinwide. The five most frequently detected compounds were the herbicides atrazine (99% of samples), desethylatrazine (93%), simazine (85%), metolachlor (85%), and diuron (73%). Fifteen compounds were detected in 12?35% of samples, and 16 compounds were detected in 1?9% of samples. Water quality standards or criteria were exceeded more frequently for conventional constituents than for pesticides. State of Oregon water quality standards were exceeded at all but one site for the indicator bacteria E. coli, 3 sites for nitrate, 10 sites for water temperature, 4 sites for dissolved oxygen, and 1 site for pH. Pesticide concentrations, which were usually less than 1 part per billion, exceeded State of Oregon or U.S. Environmental Protection Agency aquatic life toxicity criteria only for chlorpyrifos, in three samples from one site; such criteria have been established for only two other detected pesticides. However, a large number of unusually high concentrations (1?90 parts per billion) were detected, indicating that pesticides in the runoff sampled in these small streams were more highly concentrated than in the larger streams sampled in previous studies. These pulses could have had short term toxicological implications for the affected streams; however, additional toxicological assessment of the detected pesticides was limited because of a lack of available information on the response of aquatic life to the observed pesticide concentrations. Six pesticides, including atrazine, diuron, and metolachlor, had significantly higher (p<0.08 for metolachlor, p<0.05 for the other five) median concentrations at agricultural sites than at urban sites. Five other compounds ?carbaryl, diazinon, dichlobenil, prometon, and tebuthiuron?had significantly higher (p<0.05) concentrations at the urban sites than at the agricultural sites. Atrazine, metolachlor, and diuron also had significantly higher median concentrations at southern agricultural sites (dominated by grass seed crops) than northern agricultural sites. Other compounds that had higher median concentrations in the south included 2,4-D and metribuzin, which are both used on grass seed crops, and triclopyr, bromacil, and pronamide. A cluster analysis of the data grouped sites according to their pesticide detections in a manner that was almost identical to a grouping made solely on the basis of their upstream land use patterns (urban, agricultural, crop diversity, percentage of basin in agricultural production). In this way inferences about pesticide associations with different land uses could be drawn, illustrating the strength of these broad land use categories in determining the types of pesticides that can be expected to occur. Among the associations observed were pesticides that occurred at a group of agricultural sites, but which have primarily noncropland uses such as vegetation control along rights-of-way. Also, the amount of forested land in a basin was negatively associated with pesticide occurrence", + "license": "proprietary" + }, + { + "id": "WRIR_99_4196", + "title": "Inorganic Chemistry of Water and Bed Sediment in Selected Tributaries of the South Umpqua River, Oregon, 1998", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1998-08-01", + "end_date": "1998-09-01", + "bbox": "-122.83, 42.66, -122.5, 43.33", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551961-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551961-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/WRIR_99_4196", + "description": "Ten sites on small South Umpqua River tributaries were sampled for inorganic constituents in water and streambed sediment. In aqueous samples, high concentrations (concentrations exceeding U.S. Environmental Protection Agency criterion continuous concentration for the protection of aquatic life) of zinc, copper, and cadmium were detected in Middle Creek at Silver Butte, and the concentration of zinc was high at Middle Creek near Riddle. Similar patterns of trace-element occurrence were observed in streambed-sediment samples.The dissolved aqueous load of zinc carried by Middle Creek along the stretch between the upper site (Middle Creek at Silver Butte) and the lower site (Middle Creek near Riddle) decreased by about 0.3 pounds per day. Removal of zinc from solution between the upper and lower sites on Middle Creek evidently was occurring at the time of sampling. However, zinc that leaves the aqueous phase is not necessarily permanently lost from solution. For example, zinc solubility is pH-dependent, and a shift between solid and aqueous phases towards release of zinc to solution in Middle Creek could occur with a perturbation in stream-water pH. Thus, at least two potentially significant sources of zinc may exist in Middle Creek: (1) the upstream source(s) producing the observed high aqueous zinc concentrations and (2) the streambed sediment itself (zinc-bearing solid phases and/or adsorbed zinc). Similar behavior may be exhibited by copper and cadmium because these trace elements also were present at high concentrations in streambed sediment in the Middle Creek Basin.", + "license": "proprietary" + }, { "id": "WUS_UCLA_SR_1", "title": "Western United States UCLA Daily Snow Reanalysis V001", @@ -192776,6 +201915,19 @@ "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 2 aerosol products from MERIS for 2008, using the ALAMO algorithm, version 2.2. The data have been provided by Hygeos.For further details about these data products please see the linked documentation.", "license": "proprietary" }, + { + "id": "a78f0eb5-a146-4129-9066-519378e22fd8_1", + "title": "IUCN PROTECTED AREAS OF AFRICA", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1986-01-01", + "end_date": "1986-01-01", + "bbox": "-17.3, -34.6, 51.1, 38.2", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848904-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848904-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/a78f0eb5-a146-4129-9066-519378e22fd8_1", + "description": "The Protected Areas of Africa were provided by the International Union for the Conservation of Nature and Natural Resources (IUCN) - World Conservation Monitoring Center (WCMC) of Gland, Switzerland and Cambridge, UK, to UNEP/GRID-Geneva for digitization into computer form in 1986. The map was digitized in ARC/INFO and subsequently rasterized to a two-minute cell size in the ELAS software format. Today, the same data set resides at GRID on an IBM mainframe computer, but it has not been updated since the initial work was carried out.* The Protected Areas of Africa data set shows a series of 11 different types of parks, reserves and other unique areas which had some degree of protected status. The various types of Protected Areas are all shown as squares of varying size on a background map of Africa, with four square sizes which are proportional to the actual size of each area, and with center points approximately equal to the actual central location of each Protected Area. Thus, this data set is perhaps most useful for showing the general distribution of African Protected Areas by type, circa 1986. There are two versions of the Protected Areas of Africa data set (two data files) at UNEP/GRID-Geneva. Because there is significant overlap between the Protected Area squares, the first shows the various squares superimposed with the size of Protected Areas used as the criterion for which take precedence over others. The second version shows Protected Areas ranked by importance; that is, squares take precedence according to the order in which they appear in the legend, with the more highly ranked Protected Areas overlaying others of lower rank. Following is the legend which applies to the Protected Areas of Africa (categories were formulated by IUCN-WCMC): Values Category of Protected Area ------ -------------------------- 1 Scientific Reserve 2 National Park 3 National Monument 4 Wildlife Sanctuary 5 Protected Landscape 6 Resource Reserve 7 Anthropological Reserve 8 Multiple Use Management Area 9 Biosphere Reserve 10 World Heritage Site 11 Unclassified The Protected Areas data set from IUCN-WCMC covers the entire African continent at a spatial resolution of two minutes (120 seconds) of latitude/longitude, or approximately 3.7 kilometers. The data file consists of 2191 rows (lines/records) by 2161 columns (elements/pixels/ samples). Its upper-left or northwest corner origin is 38 degrees, 0 minutes and 45 seconds North latitude (38d 00' 45\" N), and -20 degrees, 1 minute and 15 seconds West longitude (-20d 01' 15\" W); and it extends to -35 degrees, 1 minute and 15 seconds South latitude (-35d 01' 15\" S), and 52 degrees, 0 minutes and 45 seconds East longitude (52d 00' 45\" E) at its terminal point in the lower-right or southeast corner. The data file comprises 4.74 Megabytes. The source of the Protected Area data is, as mentioned above, the International Union for the Conservation of Nature and Natural Resources (IUCN's) World Conservation Monitoring Center (WCMC) in Cambridge, UK. There is no published reference for this data set. * - Another more recent version of Protected Areas for Africa, with actual protected area boundaries, exists at UNEP/GRID-Nairobi. ", + "license": "proprietary" + }, { "id": "a7b87a912c494c03b4d2fa5ab8479d1c_NA", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change 1992-2014, v1.2", @@ -195415,6 +204567,19 @@ "description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the GOMOS instrument. The data are zonal mean time series (10\u00c2\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00c2\u00b0 latitude zones from 90\u00c2\u00b0S to 90\u00c2\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u00e2\u0080\u009cESACCI-OZONE-L3-LP-GOMOS_ENVISAT-MZM-2008.nc\u00e2\u0080\u009d contains monthly zonal mean data for GOMOS in 2008.", "license": "proprietary" }, + { + "id": "b480d7c8-3694-4772-8294-941f3d3ede9f_1", + "title": "European remote sensing forest/non-forest digital map", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "1993-09-28", + "bbox": "-12, 38, 44, 74", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847861-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847861-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/b480d7c8-3694-4772-8294-941f3d3ede9f_1", + "description": "The European Remote Sensing Forest/Non-forest Digital Map was originally prepared for the European Space Agency (ESA) as a contribution to the World Forest Watch project of the International Space Year (ISY), 1992. The actual production of the map was carried out by a consortium of four companies, GAF mbH (Munich FRG), the Swedish Space Corporation (Kiruna), SCOT Conseil (France) and the National Land Survey of Finland (Helsinki). It is based entirely on the digital classification of NOAA/AVHRR-HRPT* one-kilometer resolution multispectral data, approximately 70 scenes from the summer periods only of 1990 to 1992. As such, the European Forest/Non-forest Digital Map is reasonably up-to- date and based on a homogeneous data source. Because the methodology used to produce the digital map is documented and was \"economically\" accomplished, the product is presumably replicable and could therefore be updated and/or used for monitoring purposes at scales of up to 1:2 million (ESA/ESTEC, 1992). The following steps are a summary of those actually used by the consortium in the production of the digital map: - Satellite data selection (minimal cloud cover)/acquisition; - Data pre-processing for a) geometric correction and b) cloud masking; - Data subset stratification into homogeneous spectral zones; - Data subset classification (Bayesian maximum likelihood); - Accuracy assessment (using classified Landsat MSS); - Mosaicking of classified data subsets; - Merging of final results and overlays; - Cartographic preparation. The producers of the digital map used only data from AVHRR channels 1, 2 and 3 with \"maximal geometric and radiometric resolution\"; that is, the central 1200 to 1600 pixels of any given scan line, to map European forest areas greater than one square kilometer. Because the AVHRR sensor is not capable of distinguishing among different European forest types, many broad classes (Boreal, Central European and Mediterranean) are grouped together as \"forest\" in the digital map. * - the National Oceanic and Atmospheric Administration (NOAA) / satellite's Advanced Very High Resolution Radiometer (AVHRR) sensor - and High Resolution Picture Transmission (HRPT) data. For the 32 Landsat scenes compared with the NOAA/AVHRR forest/non-forest classification, the overall accuracy (percentage of pixels \"correctly\" classified) was calculated as 82.5%, and the surface area accuracy (degree of agreement in areal extent between the NOAA/AVHRR results and the Landsat MSS used as \"ground truth\") was found to be 93.8%. Format of the Original ESA/ESTEC-Provided Data Set The European Forest/Non-Forest Digital Map was provided to GRID on a single 150-Mb data cartridge, as a total of seven ARC/INFO-format data files for separate parts of the continent as follows: Northwest; North; Central; Southwest and Southeast Europe; the Commonwealth of Independent States (CIS, up to the Ural Mountains only); and North Africa. A total of 53 countries are included altogether. Within this original digital map, data are coded by country and category (i.e. forest, non-forest or water), but \"overall\" selections of one category or another are rendered difficult because the codes are in combination (i.e. country + category). Also, the large size of the seven individual ARC/INFO coverages all but prohibits working with the digital data for the entire pan-European area. Explanation of the Data Processing done by GRID GRID's objective in data processing of the European Forest/Non-forest Digital Map was to create a single seamless product covering most of the continent, for forestry and GIS studies at a pan-European level. The assemblage of the seven original individual coverages prepared for ESA/ESTEC into a single entity proved impractical due to both hardware and software limitations; thus, the seventh and largest portion for the Commonwealth of Independent States (CIS) was left out of the overall assemblage. Even so, it was still necessary to generalize the data somewhat, given the total number of polygons (>100000) and arcs (>170000) in the remaining six original coverages. Thus, the following methodology was followed to reduce the amount of data and assemble the six coverages into a single product (all data processing was done using commands in the ARC/INFO software): - Polygon elimination based on area - After several experiments, polygons with an area smaller than four square kilometers (sq. km.) were eliminated. This minimum area proved to be a good compromise between original forest patterns and number of polygons eliminated (total of 70%). The equivalent of four sq. km. at a central latitude within each of the six original coverages was calculated, and this value was used in the 'ELIMINATE' command. It would have been more accurate to perform the 'ELIMINATEs' with the data in an equal-area projection, but for practical reasons (space and time) they were not. - Assembling six coverages into one - The six coverages were put together using the 'MAPJOIN' command. The software limitation of a maximum 10000 arcs per polygon was circumvented by splitting the outer polygon of Europe into three separate parts. - Editing errors produced by step (2) - The 'MAPJOIN' command puts adjacent coverages together and recreates topology using an assigned distance known as the \"fuzzy tolerance\" factor. Any reasonable factor forces some lines to converge, creating dangling arcs and new polygons without IDs. As a result, interactive editing of the new coverage was necessary to delete dangling arcs, and to assign proper polygon IDs. - Update of the topology - After the modifications made in step (3), it was necessary to re-create the polygon topology using 'CLEAN'. - Addition of INFO item 'classes' - A new numeric item (format 3 3 I) was added in the polygon attribute table (.PAT) to contain the following values: 1) Forest; 2) Non-forest; and 3) Water. This item allows a user to select e.g. all of the European forested area polygons, as opposed to just those within a single country, in one simple INFO command. The European Forest/Non-forest data set is available from GRID as one ARC/INFO 'EXPORT'-format data file in the Geographic Projection, which covers an area from 20 to 80 degrees North latitude, and -30 degrees West to 60 degrees East longitude. The single data file \"EURO_FOR.E00\" comprises 77.25 Mb., but after being 'IMPORTed' to the equivalent ARC/INFO coverage, is reduced to 19.7 Mb in size. There is also the separate, original (non-generalized) data file which covers the CIS area alone; this additional 'EXPORT'-format data file \"CIS.E00\" comprises 68.262 Mb. Users who would prefer to have other original portions of the European Forest/Non-forest Digital Map listed above, as opposed to the GRID version documented herein, are requested to contact ESA/ESTEC at the address listed below. Reference and Source The source of the data set is the ESA/ESTEC ISY Office*, as modified by UNEP/GRID-Geneva. The proper reference to the data set is \"ESA, 1992, Remote sensing forest map of Europe (brochure), ESA/ESTEC, 18 pages.\" ESA/ESTEC also provides a paper entitled \"Digital data set of the remote sensing forest map of Europe; guidelines for data handling (as prepared by GAF-Munich in April 1993)\", which contains much useful information about their original digital data product and the seven individual data files they distribute as one entity. In addition, ESA/ESTEC distributes a paper map of the original product having the same name as above, at a scale of 1:6 000 000 (the paper map uses the Lambert Azimuthal Equal-Area projection). * - the European Space Agency/European Space Research and Technology Centre - the International Space Year; P. O. Box 299; 2200 AG Noordwijk; The Netherlands (Mr. K. Pseiner; fax = 01719-17400). ", + "license": "proprietary" + }, { "id": "b64b1a0ad7874fb39791e99c57b944bc_NA", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 3.1", @@ -196104,6 +205269,32 @@ "description": "Aerial photography (35mm film) of penguin colonies was acquired over some islands north east of Brattstrand Bluff islands (Eric Woehler). The penguin colonies were traced, then digitised (John Cox), and saved as DXF-files. Using the ArcView extension 'Register and Transform' (Tom Velthuis), The DXF-files were brought into a GIS and transformed to the appropriate islands. Update May 2015 - This dataset has been rename from \"Brattstrand Bluff penguin GIS dataset\" to \"Islands NE of Brattstrand Bluff penguin GIS dataset\" to better describe the location of the colonies. The penguin colonies are on a small group of islands approximately 12km north east of Brattstrand Bluff. Latitude 69.148 south and longitude 77.268 east. The Data Centre does not have a copy of the original photographs or described GIS data. In May 2015, the Data Centre has attached the following to this record: The DXF file produced by John Cox by digitising the aerial photography. Note this document is not georeferenced. Four photographs taken in 2009 by Barbara Wienecke, Seabird Ecologist, showing penguin colonies on these islands. A shapefile exists of the digitised colonies. The digitising by Ursula Harris, Australian Antarctic Data Centre, was done by georeferencing the DXF drawing over unprocessed Quickbird Image 05NOV15042413-M1BS-052187281010_01_P002. It was done in two parts, the largest island and then the two smaller islands. This allowed for better matching. The accuracy of this data is unknown.", "license": "proprietary" }, + { + "id": "brdglsc0001", + "title": "Great Lakes Commercial Fishing Database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1929-01-01", + "end_date": "2011-12-31", + "bbox": "-93, 41, -76, 49", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554766-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554766-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/brdglsc0001", + "description": "The Great Lakes Commercial Fishing Database contains commercial fishing data from the United States. The states of Illinois, Indiana, Michigan, Minnesota, New York, Ohio, Pennsylvania, and Wisconsin gather monthly fishing reports and forward them to the Great Lakes Center. The database provides the fisherman's name, information about the vessel, the estimated weight and estimated dollar value. Methodology: The database is not a scientific one. The data is reported by individual licensed fishermen to each state juridsdiction. The states gather monthly fishing reports and forward them to the Great Lakes Science Center. GLSC then compiles all of the information into a database each year and produces an annual summary that is called the NOAA report. It is sent to the National Marine Fishery Service (NMFS) and is included with commercial fishing data from the entire United States into a publication.", + "license": "proprietary" + }, + { + "id": "brdlsc0007", + "title": "Efficiency Of Adaptive Cluster Sampling for Estimating Density of Wintering Waterfowl", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-12-13", + "end_date": "1992-12-15", + "bbox": "-85, 25, -80, 30", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554639-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554639-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/brdlsc0007", + "description": "An evaluation of adaptive cluster sampling was based on a simulation experiment where samples were drawn from an enumeration of three species of waterfowl wintering in central Florida. The initial samples were taken either by simple random sampling or with probability proportional to available habitat. Efficiency of adaptive cluster sampling relative to simple random sampling was highest when 1) the within-network variance was close to the population variance, and 2) the final sampling fraction was close to the initial sampling fraction. The within-network variance is determined by the spatial distribution of the population, quadrat size, and the condition that determined when to adapt sampling. The final sampling fraction depends on the previous factors as well as the size and selection of the initial sample. Some combinations of these factors led to increased precision compared to simple random sampling and some did not. Geographic Description: Central Florida (5,000 km2). The study region extended 100 km east and 50 km north from the southwest corner at 0438000, 3056000 (Universal Transverse Mercator coordinates; zone 17). 1.5.2 Bounding Rectangle Coordinates Methodology: An effort was made to count every individual duck of the three waterfowl species in a 5,000 km2 area of central Florida by making systematic flights over the entire study region. Two biologists counted waterfowl from separate helicopters (Bell Jet Rangers) during 13-15 December, 1992 and used the LORAN-C and GPS systems to determine flock locations Field.", + "license": "proprietary" + }, { "id": "brdpier0004", "title": "Aspects of the Life History and Foraging Ecology of the Endangered Akiapolaau", @@ -196377,6 +205568,19 @@ "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily, monthly and yearly gridded aerosol products from the AATSR instrument on the ENVISAT satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 2002 to 2012. In the early period, it also contains data from the ATSR-2 instrument on the ERS-2 satellite. A separate ATSR-2 product covering the period 1995-2001 is also available, and together these form a continuous timeseries from 1995-2012.For further details about these data products please see the documentation.", "license": "proprietary" }, + { + "id": "c241e665-5175-4c26-b0cd-f0dfee32afdb", + "title": "Earthquakes events from ANSS 1970-March 2011", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-02", + "end_date": "2011-04-01", + "bbox": "-180, -58, 180, 85.03594", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847370-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847370-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/c241e665-5175-4c26-b0cd-f0dfee32afdb", + "description": "This dataset includes earthqakes events with magnitudes higher than 5.0 as reported by the Advanced national Seismic System (ANSS) Catalogue over the period 1970 - March 2011. UNEP/GRID-Europe processed the intensity buffer of each event following a methodology developped in GRAVITY I and II (http://www.grid.unep.ch/product/publication/download/ew_gravity1.pdf and http://www.grid.unep.ch/product/publication/download/ew_gravity2.pdf). Credit: Earthquakes events (USGS/ANSS), Intensity buffers UNEP/GRID-Europe. Attributes descriptions: EV_ID: Event ID ISO3YEAR: Country and year ISO3: Country ISO3 ID_NAT: Event ID and ISO3 ID_CAT: ANSS ID YEAR: Year START_DATE: Year, Month and Day (YYYYMMDD) MAG: Earthquake magnitude DEPTH: Earthquake depth (kilometer) RADIUS_M: Buffer radius following Gravity I and II methodology (meter) LATITUDE: Latitude (decimal degrees) ", + "license": "proprietary" + }, { "id": "c2af8764c84744de87a69db7fecf7af9_NA", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.1", @@ -196975,6 +206179,19 @@ "description": "The CAMEX-4 GOES-8 Products dataset was collected during the CAMEX-4 field campaign, which ocused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using NASA-funded aircraft and surface remote sensing instrumentation. In support of the fourth Convection and Moisture Experiment (CAMEX-4), imagery from the Geostationary Operational Environmental Satellite 8 (GOES-8) was collected and archived. Three channels were archived: channel 1-- visible (0.65 microns), channel 2-- infrared (11 microns) and channel 3-- known as the water vapor channel (6.75 microns). Data files are available in McIDAS format, and browse imagery is also available.", "license": "proprietary" }, + { + "id": "c5064da0-ce61-47fc-b17f-c837bd2847be", + "title": "Flood events", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-101.242195, -34.5123, 127.74636, 52.267735", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848956-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848956-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/c5064da0-ce61-47fc-b17f-c837bd2847be", + "description": "This dataset includes an estimate of flood events. It is based on two sources: 1) A GIS modeling using a statistical estimation of peak-flow magnitude and a hydrological model using HydroSHEDS dataset and the Manning equation to estimate river stage for the calculated discharge value. 2) Observed flood from 1999 to 2007, obtained from the Dartmouth Flood Observatory (DFO). This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing UNEP/GRID-Europe, with key support from USGS EROS Data Center, Dartmouth Flood Observatory 2008.", + "license": "proprietary" + }, { "id": "c65ce27928f34ebd92224c451c2a8bed_NA", "title": "ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI): Analysis long term product version 1.1", @@ -197274,6 +206491,19 @@ "description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the SCIAMACHY instrument on ENVISAT. The data are zonal mean time series (10\u00c2\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00c2\u00b0 latitude zones from 90\u00c2\u00b0S to 90\u00c2\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u00e2\u0080\u009cESACCI-OZONE-L3-LP-SCIAMACHY_ENVISAT-MZM-2008-fv0001.nc\u00e2\u0080\u009d contains monthly zonal mean data for SCIAMACHY in 2008.", "license": "proprietary" }, + { + "id": "cc4d85ee-6c72-4249-8775-a96e359457ad_1", + "title": "Global template for the GLASOD digital database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1991-07-01", + "end_date": "1991-07-01", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848766-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848766-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/cc4d85ee-6c72-4249-8775-a96e359457ad_1", + "description": "The Global Assessment of Human Induced Soil Degradation (GLASOD) was conducted by the International Soil Reference and Information Centre (ISRIC) at Wageningen, The Netherlands, as commissioned by the United Nations Environment Programme (UNEP). ISRIC produced a 1:10 million scale wall chart in 1990 and subsequently produced a digital data set. In essence, the GLASOD database contains information on soil degradation within map units as reported by numerous soil experts around the world through a questionnaire. It includes the type, degree, extent, cause and rate of soil degradation. From these data, the GRID-Nairobi center produced digital and hardcopy maps and made area calculations. The GLASOD database includes a topographic basemap or global template of continental coastlines, islands and lakes, which GRID-Nairobi extracted from the digital version of GLASOD's 1:10 million wall map. All of the boundaries that defined oceans and lakes were selected to create a new ARC/INFO coverage, which was subsequently used as a basemap for all the maps in UNEP's World Atlas of Desertification (see reference below). The global boundaries template contains 306 polygons of four types, which are coded in the data set as follows: 1) Oceans; 2) Lakes; 3) Continents; and 4) Islands. It is available from GRID as a single ARC/INFO 'EXPORT'-format file comprising 1.7 Mb when uncompressed. While the original projection ISRIC used for the GLASOD wall map was the Mercator to display the various continents with as little distortion as possible, it is distributed by GRID in either the Van der Grinten (a variation of Mercator) or the Geographic projection. The sources of the global boundaries template are ISRIC and UNEP/GRID, and the proper references are as follows: Oldeman, L. R., Hakkeling, R. T. A. and W. G. Sombroek. October 1990. \"World Map of the Status of Human-Induced Soil Degradation; Explanatory Note\". (The) Global Assessment of Soil Degradation, ISRIC and UNEP in cooperation with the Winand Staring Centre, ISSS, FAO and ITC; 27 pages. Deichmann, Uwe and Lars Eklundh. July 1991. \"Global digital data sets for land degradation studies: a GIS approach\". GRID Case Study Series No. 4; UNEP/GEMS & GRID; Nairobi, Kenya; 103 pages (mostly pp. 29-32). An additional reference is UNEP's 1992 World Atlas of Desertification (Edward Arnold, London, UK, 69 pages - see pages vii to ix). ", + "license": "proprietary" + }, { "id": "ccamlr_subareas_gis_1", "title": "CCAMLR Statistical Reporting Subareas GIS Dataset.", @@ -203891,6 +213121,149 @@ "description": "Cereals also includes other cereals such as mixed grains and buckwheat. Crop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption). When the production data available refers to a production period falling into two successive calendar years and it is not possible to allocate the relative production to each of them, it is usual to refer production data to that year into which the bulk of the production falls. Crop production data are stored in tonnes (T).", "license": "proprietary" }, + { + "id": "geodata_0028", + "title": "Improved Sanitation Coverage - Rural Population", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846604-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846604-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0028", + "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection.", + "license": "proprietary" + }, + { + "id": "geodata_0032", + "title": "Improved Drinking Water Coverage - Urban Population", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849241-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849241-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0032", + "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. Improved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine.", + "license": "proprietary" + }, + { + "id": "geodata_0048", + "title": "Improved Drinking Water Coverage - Rural Population", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847812-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847812-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0048", + "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. Improved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine.", + "license": "proprietary" + }, + { + "id": "geodata_0049", + "title": "Fertility", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1950-01-01", + "end_date": "2050-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847840-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847840-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0049", + "description": "The average number of children a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates of a given period and if they were not subject to mortality. It is expressed as children per woman.", + "license": "proprietary" + }, + { + "id": "geodata_0052", + "title": "Improved Sanitation Coverage - Urban Population", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848815-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848815-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0052", + "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. Improved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine.", + "license": "proprietary" + }, + { + "id": "geodata_0058", + "title": "Earthquake Intensity Zones", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1988-01-01", + "end_date": "1988-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847971-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847971-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0058", + "description": "The dataset shows earthquake intensity zones in accordance with the 1956 version of the Modified Mercalli Scale (MM). The intensity describes exclusively the effects of an earthquake on the surface of the earth and integrates numerous parameters (such as ground acceleration, duration of an earthquake, subsoil effects). It also includes historical earthquake reports. The risk grading is based on expectations for a period of 50 years, corresponding to the mean service life of modern buildings. The probability that degrees of intensity shown on the map will be exceeded in 50 years is 20 per cent. This probability figure varies with time; i.e., it is lower for shorter periods and higher for longer periods. In ARC/INFO, the item ZONE in the polygon attribute table (PAT) contains the following earthquake intensity values: Zone Probable maximum intensity once in 50 years (MM Scale) 0 V and below 1 VI 2 VII 3 VIII 4 IX and above 10 indicates main waterbodies", + "license": "proprietary" + }, + { + "id": "geodata_0059", + "title": "Ecological Zones (Holdridge Lifezones)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847949-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847949-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0059", + "description": "The Holdridge Life Zones data set is from the International Institute for Applied Systems Analyses (IIASA) in Laxenburg, Austria. The data set shows the Holdridge Life Zones of the World, a combination of climate and vegetation (ecological) types, under current, so-called \"normal\" climate conditions, as well as under a presumed doubling of atmospheric CO2. The Life Zones were devised using three indicators: biotemperature (based on the growing season length and temperature); mean annual precipitation; and a potential evapotranspiration ratio, linking biotemperature with annual precipitation to define humidity provinces. The data set has a spatial resolution of one-half degree latitude/longitude, and a total of 38 life-zone classes which are listed on the accompanying legend sheet. The Holdridge Life Zones data set includes a total of four data files. The first (HOLDNORM) is as described in the paragraph above; the second (HOLDDOUB) shows how the Life Zones would change given an assumed doubling of atmospheric CO2 (according to a General Circulation Model from the U.K. Office of Meteorology). The third and fourth data files show only those portions of the Life Zones which would undergo changes, that is for both the old classification (HOLDCHFR) before and the new classification (HOLDCHTO) after the theoretical doubling of CO2 (in effect, these areas have the appearance of 'sliver' polygons).", + "license": "proprietary" + }, + { + "id": "geodata_0060", + "title": "Human Induced Soil Degradation (GLASOD)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847802-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847802-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0060", + "description": "Soil degradation Severity : Overall severity by which the polygon is affected by soil degradation. This item takes the degree and extent of both types into account. For the classification from 1 (low) to 4 (very high), a look-up table created by ISRIC was used. This item should be used for mapping only, not for area calculations! ", + "license": "proprietary" + }, + { + "id": "geodata_0063", + "title": "Global Humidity Index", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1991-01-01", + "end_date": "1991-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847604-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847604-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0063", + "description": "The Global Humidity Index is based on a ratio of annual precipitation and potential evapotrans- piration (these data layers are described elsewhere) as P/PET, and largely follows the classification used in a 1984 UNESCO study. The Global Humidity Index surface shows mean annual potential moisture availability for the period 1951-1980, classified into four aridity zones and one humid zone, defined in this data set as follows: Hyper-Arid Zone P/PET less than 0.05 Arid Zone 0.05 less equal P/PET less than 0.20 Semi-Arid Zone 0.20 less equal P/PET less than 0.50 Dry-Subhumid Zone 0.50 less equal P/PET less than 0.65 Humid Zone 0.65 less equal P/PET", + "license": "proprietary" + }, + { + "id": "geodata_0065", + "title": "Matthews Cultivation Intensity", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1991-01-01", + "end_date": "1991-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847950-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847950-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0065", + "description": "Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro-magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow-free conditions except for permanently snow-covered continental ice. ", + "license": "proprietary" + }, + { + "id": "geodata_0066", + "title": "Matthews Vegetation", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1991-01-01", + "end_date": "1991-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848148-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848148-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0066", + "description": "Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro-magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow-free conditions except for permanently snow-covered continental ice", + "license": "proprietary" + }, { "id": "geodata_0067", "title": "Annual Precipitation", @@ -203943,6 +213316,175 @@ "description": "Biogeographical realms were established by Udvardy on the basis of geographic and historic elements, utilizing ground-breaking work as appears on this topic in the published literature. Udvardy's paper makes reference to at least three preceding reports on this topic, and also includes an extensive bibliography of five pages. There are 8 biogeographical realms recognized by Udvardy in Paper #18: the Palearctic, the Nearctic, the Afrotropical, the Indomalayan, the Oceanian, the Australian, the Antarctic and the Neotropical. The proper reference for this data set is \"Udvardy, Miklos D. F. 1975. A Classification of the Biogeographical Provinces of the World. IUCN Occasional Paper No. 18, prepared as a contribution to UNESCO's Man and the Biosphere (MAB) Program, Project No. 8. International Union for the Conservation of Nature and Natural Resources, Morges (now Gland), Switzerland, 49 pages.\" A source citation should include IUCN, as digitized by UNEP/GRID in 1986.", "license": "proprietary" }, + { + "id": "geodata_0165", + "title": "Mean Annual Rainfall", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-33.19, -41.41, 62.62, 40.04", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847515-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847515-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0165", + "description": "Precipitation is \"average annual\", and is expressed in terms of millimeters (mm.) per year; Average Days of Precipitation (\"Wet Days\") is number of days per year; and Average Windspeed is expressed in terms of meters per second (note that this is not maximum windspeed, nor is there any directional content included in this data set). It is GRID's assumption that the definition of a \"wet day\" is one in which enough precipitation occurred on a given day so as to be recordable by a gauging station at a particular location.", + "license": "proprietary" + }, + { + "id": "geodata_0179", + "title": "Forests - Original", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "9999-01-01", + "end_date": "9999-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849373-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849373-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0179", + "description": "UNEP-WCMC has been gathering and compiling spatial data on the extent and conservation status of forests since 1987. Until 1995, WCMC's work focused on tropical moist forests because of their high species diversity. GIS data were first assembled for closed moist tropical forests and used to publish the three volumes of the Conservation Atlas of Tropical Forests, covering Asia (1991), Africa (1992) and the Americas (1996). Because digital data were rare at this time, the process of assembling the forest cover data sets involved digitizing manually many paper maps. Continuing on from the tropical moist forest mapping, the next major initiative was to create the first 'World Forest Map'. This was produced in 1996 and was the first digital global forest map showing actual forest extent and protected areas with forested land. Since this achievement, significant work has been carried out to improve data sources and fill in gaps which occurred in this first attempt. This led to the production of the 'Global Overview of Forest Conservation CDROM' in 1997. ", + "license": "proprietary" + }, + { + "id": "geodata_0180", + "title": "Forests - Current", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849191-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849191-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0180", + "description": "UNEP-WCMC has been gathering and compiling spatial data on the extent and conservation status of forests since 1987. Until 1995, WCMC's work focused on tropical moist forests because of their high species diversity. GIS data were first assembled for closed moist tropical forests and used to publish the three volumes of the Conservation Atlas of Tropical Forests, covering Asia (1991), Africa (1992) and the Americas (1996). Because digital data were rare at this time, the process of assembling the forest cover data sets involved digitising manually many paper maps.", + "license": "proprietary" + }, + { + "id": "geodata_0181", + "title": "Mangroves", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849096-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849096-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0181", + "description": "The World Mangrove Atlas is the first significant attempt to provide an overview of the distribution of mangroves worldwide. Mapped data showing the extent of mangroves in over 100 countries have been gathered from a wide range of sources. Mangrove plants include trees, shrubs, ferns and palms. These plants are found in the tropics and sub- tropics on river banks and along coastlines, being unusually adapted to anaerobic conditions of both salt and fresh water environments. These plants have adapted to muddy, shifting, saline conditions. They produce stilt roots which project above the mud and water in order to absorb oxygen. Mangrove plants form communities which help to stabilise banks and coastlines and become home to many types of animals.", + "license": "proprietary" + }, + { + "id": "geodata_0199", + "title": "Forest Plantation Extent", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2010-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847295-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847295-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0199", + "description": "Forest plantation is a forest established by planting and/or seeding in the process of afforestation or reforestation. It consists of introduced species or, in some cases, indigenous species. Forest plantation and natural forests are included in the term forest, a term that refers to land with a tree cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 m. Young stands that have not yet reached, but are expected to reach, a crown density of 10m percent and tree height of 5 m are included under forest, as are temporarily unstocked areas. The term includes forests used for purposes of production, protection, multiple use or conservation (i.e. forest in national parks, nature reserves and other protected areas), as well as forest stands on agricultural lands (e.g. windbreaks and shelterbelts of trees with a width of more than 20 m) and rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems. ", + "license": "proprietary" + }, + { + "id": "geodata_0200", + "title": "Forest Plantation Annual Change", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2010-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847185-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847185-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0200", + "description": "Plantation Average Annual Change - is the annual change of a forest established by planting and/or seeding in the process of afforestation or reforestation. It consists of introduced species or, in some cases, indigenous species. Forest plantation and natural forests are included in the term forest, a term that refers to land with a tree cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 m. Young stands that have not yet reached, but are expected to reach, a crown density of 10m percent and tree height of 5 m are included under forest, as are temporarily unstocked areas. The term includes forests used for purposes of production, protection, multiple use or conservation (i.e. forest in national parks, nature reserves and other protected areas), as well as forest stands on agricultural lands (e.g. windbreaks and shelterbelts of trees with a width of more than 20 m) and rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems. ", + "license": "proprietary" + }, + { + "id": "geodata_0201", + "title": "Forests Certified by FSC- Accredited Certification Bodies", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-01-01", + "end_date": "2010-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847173-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847173-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0201", + "description": "FSC-endorsed certification of a forest site signifies that an independent evaluation by one of several FSC accredited certification bodies has shown that its management meets the internationally recognised FSC Principles and Criteria of Forest Stewardship.", + "license": "proprietary" + }, + { + "id": "geodata_0227", + "title": "General Government Final Consumption Expenditure", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2009-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849338-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849338-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0227", + "description": "General government final consumption expenditure (formerly general government consumption) includes all government current expenditures for purchases of goods and services (including compensation of employees). It also includes most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. Data are in current U.S. dollars. Source: World Bank national accounts data, and OECD National Accounts data files. ", + "license": "proprietary" + }, + { + "id": "geodata_0231", + "title": "Military Expenditures - Percent of GDP", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1988-01-01", + "end_date": "2009-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849225-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849225-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0231", + "description": "Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.) Source: Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security. Note: Data for some countries are based on partial or uncertain data or rough estimates. ", + "license": "proprietary" + }, + { + "id": "geodata_0237", + "title": "Life Expectancy", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1950-01-01", + "end_date": "2050-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848978-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848978-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0237", + "description": "Life expectancy: The average number of years of life expected by a hypothetical cohort of individuals who would be subject during all their lives to the mortality rates of a given period. It is expressed as years. ", + "license": "proprietary" + }, + { + "id": "geodata_0261", + "title": "Groundwater Produced Internally", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1958-01-01", + "end_date": "2012-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847626-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847626-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0261", + "description": "Groundwater produced internally: Long-term annual average groundwater recharge, generated from precipitation within the boundaries of the country. Renewable groundwater resources of the country are computed either by estimating annual infiltration rate (in arid countries) or by computing river base flow (in humid countries).", + "license": "proprietary" + }, + { + "id": "geodata_0271", + "title": "Fishery Production - Marine", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848156-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848156-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0271", + "description": "TOTAL PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. * includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals", + "license": "proprietary" + }, + { + "id": "geodata_0278", + "title": "Exclusive Fishing Zone (EFZ)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848807-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848807-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0278", + "description": "The exclusive fishing zone or fishery zone refers to an area beyond the outer limit of the territorial sea (12 nautical miles from the coast) in which the coastal State has the right to fish, subject to any concessions which may be granted to foreign fishermen. Some countries have made no claim beyond the territorial sea. Some States have claimed an exclusive fishing zone instead of the more encompassing 200 nautical mile Exclusive Economic Zone (EEZ). The United Nations Convention on the Law of the Sea (UNCLOS) is an international agreement that sets conditions and limits on the use and exploitation of the oceans. This Convention also sets the rules for the maritime jurisdictional boundaries of the different member states. The UNCLOS was opened for signature on 10 December 1982 in Montego Bay, Jamaica, and it entered into force on 16 November 1994. As of January 2000, there are 132 countries that have ratified UNCLOS. Under UNCLOS, coastal States can claim sovereign rights in a 200-nautical mile exclusive economic zone (EEZ). This allows for sovereign rights over the EEZ in terms of exploration, exploitation, conservation and management of all natural resources in the seabed, its subsoil, and overlaying waters. UNCLOS allows other states to navigate and fly over the EEZ, as well as to lay submarine cables and pipelines. The inner limit of the EEZ starts at the outer boundary of the Territorial Sea (i.e., 12 nautical miles from the low-water line along the coast). Some States have not ratified UNCLOS and many have not yet claimed their EEZ. Given the uncertainties surrounding much of the delimitation of the fishing zones, these figures should be used with caution. Further information on the Web site: http://www.maritimeboundaries.com/ ", + "license": "proprietary" + }, { "id": "geodata_0279", "title": "Claimed Exclusive Economic Zone (EEZ)", @@ -203982,6 +213524,19 @@ "description": "Construction of reservoirs became a worldwide activity in the second half of the twentieth century. The total storage capacity of the large reservoirs is more than 100 million cubic meters, which makes up more than 95% of water accumulated in all the reservoirs of the world. The total area of the more than 60,000 reservoirs that have been built in the last 50 years exceeds more than 100,000 square kilometers. This is an area equivalent to 11 water bodies the size of the Sea of Azov or five the size of Lake Superior. These man-made lakes affect natural and economic conditions over an area of 1.5 million square kilometers. Many of the world's large rivers, such as the Volga, Angara, Missouri, Colorado, and Parana Rivers, have been transformed into cascades of reservoirs. Construction and use of reservoirs cause inevitable changes in the environment, both positive and negative. Environmental changes can include overflowing and swamping; transformation of coasts; changes of soil, vegetation, and fauna; and changes of reproduction and habitat conditions of various aquatic organisms, especially fish and blue-green algae. The impact of reservoirs on the environment is diverse and contradictory. ", "license": "proprietary" }, + { + "id": "geodata_0295", + "title": "Global Vegetation Index 1983-1990", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1991-01-01", + "end_date": "1991-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848459-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848459-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0295", + "description": "The NOAA/GVI (Global Vegetation Index; see reference pg. 3) Eight-Year Mean Maximum data set was developed in the following manner. First, eight years of NOAA/GVI Monthly Maximum data were obtained from GRID's Geneva archive of these data*. At GRID-Nairobi, an analyst then used these data files (12 per year) to calculate yearly mean maximum images, and the eight yearly mean images were averaged in their turn, in order to create a single eight-year mean maximum image. The original idea had been to produce an eight-year :hp2.maximum:ehp2. value image, but this was abandoned due to the accretion of \"noise\" from spurious maximum-value pixels in the individual data files (UNEP/GRID, 1990). * - GRID-Geneva has compiled an archive of NOAA/GVI Weekly data from the U.S. National Oceanic and Atmospheric Administration / National Environmental Satellite Data and Information Service / National Climate Data Center / Satellite Data Services Division (or the NOAA / NESDIS / NCDC / SDSD). This collection covers the period from April 1982 to present. At GRID-Geneva, the Weekly data are used to create Monthly, Seasonal and Annual Maximum images, in addition to the archived NOAA/GVI Weekly data. ", + "license": "proprietary" + }, { "id": "geodata_0331", "title": "Agriculture Value Added - Percent of GDP", @@ -203995,6 +213550,110 @@ "description": "Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Source: World Bank national accounts data, and OECD National Accounts data files.", "license": "proprietary" }, + { + "id": "geodata_0335", + "title": "Industry Value Added - Percent of GDP", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2009-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848389-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848389-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0335", + "description": "Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Source: World Bank national accounts data, and OECD National Accounts data files. ", + "license": "proprietary" + }, + { + "id": "geodata_0337", + "title": "Fish Catch - Total", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848427-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848427-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0337", + "description": "CAPTURE PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is excluded. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals ", + "license": "proprietary" + }, + { + "id": "geodata_0344", + "title": "Energy Production - Total (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1971-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849265-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849265-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0344", + "description": "Total energy production is the production of primary energy, from, the total of all energy sources : hard coal, lignite/brown coal, peat, crude oil, NGLs, natural gas, combustible renewables and wastes, nuclear, hydro, geothermal, solar and the heat from heat pumps that is extracted from the ambient environment. Production is calculated after removal of impurities (e.g. sulphur from natural). ", + "license": "proprietary" + }, + { + "id": "geodata_0365", + "title": "European Remote Sensing Forest/Non-forest Digital Map", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "1992-12-31", + "bbox": "-27.72, 34.56, 68.1, 85.21", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847449-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847449-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0365", + "description": "As such, the European Forest/Non-forest Digital Map is reasonably up-to- date and based on a homogeneous data source. Because the methodology used to produce the digital map is documented and was \"economically\" accomplished, the product is presumably replicable and could therefore be updated and/or used for monitoring purposes at scales of up to 1:2 million (ESA/ESTEC, 1992). The following steps are a summary of those actually used by the consortium in the production of the digital map: - Satellite data selection (minimal cloud cover)/acquisition; - Data pre-processing for a) geometric correction and b) cloud masking; - Data subset stratification into homogeneous spectral zones; - Data subset classification (Bayesian maximum likelihood); - Accuracy assessment (using classified Landsat MSS); - Mosaicking of classified data subsets; - Merging of final results and overlays; - Cartographic preparation ", + "license": "proprietary" + }, + { + "id": "geodata_0368", + "title": "Map of the Natural Vegetation", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1987-01-01", + "end_date": "1987-12-31", + "bbox": "-27.72, 34.56, 68.1, 85.21", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849091-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849091-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0368", + "description": "The digital version of the Vegetation Map of the European Communities and the Council of Europe held by GRID covers all of Belgium, Denmark, France, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, the United Kingdom and the former West Germany, although the original map also covers Iceland, Norway, Sweden, Finland, Turkey and Cyprus. ", + "license": "proprietary" + }, + { + "id": "geodata_0418", + "title": "Diseases of the Respiratory System - Number of Deaths", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1979-01-01", + "end_date": "2003-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849038-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849038-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0418", + "description": "Diseases of the Respiratory System: Effects are generally irritation and reduced lung function with increased incidence of respiratory disease, especially in more susceptible members of the population such as young children, the elderly and asthmatics. Diseases of the Respiratory System includes: ICD-9 BTL codes B31-B32, ICD-9 code CH08 for some ex-USSR countries, ICD-9 code C052 for China, ICD-10 codes J00-J99, European mortality indicator database (HFA-MDB), available at www.euro.who.int for missing figures for some european countries: indicator \"3250 Deaths, Diseases of the Respiratory System\"", + "license": "proprietary" + }, + { + "id": "geodata_0436", + "title": "Disasters of Natural Origin - Affected People", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1975-01-01", + "end_date": "2004-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849260-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849260-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0436", + "description": "Affected: People requiring immediate assistance during a period of emergency, i.e. requiring basic survival needs such as food, water, shelter, sanitation and immediate medical assistance (included in the field \"total affected\"); Appearance of a significant number of cases of an infectious disease introduced in a region or a population that is usually free from that disease. (100 or more people affected). Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms. ", + "license": "proprietary" + }, + { + "id": "geodata_0438", + "title": "Disasters of Natural Origin - Killed People", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1975-01-01", + "end_date": "2004-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846577-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846577-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0438", + "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms. ", + "license": "proprietary" + }, { "id": "geodata_0449", "title": "Amphibians - Number of Threatened Species", @@ -204021,6 +213680,123 @@ "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild.", "license": "proprietary" }, + { + "id": "geodata_0458", + "title": "Mammals - Number of Threatened Species", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847714-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847714-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0458", + "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild.", + "license": "proprietary" + }, + { + "id": "geodata_0465", + "title": "Emissions of CO2 - from Cement Production (CDIAC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847865-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847865-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0465", + "description": "Emissions of CO2 - from Cement Production (CDIAC) is the amount of C02 created by the conversion of calcium carbonate to calcium oxide inside the kilns, and by burning large quantities of fossil fuels to heat the kilns to the 1450 C necessary for roasting limestone. The sum of emissions estimates for all countries is not equal to the estimate of global total emissions because: 1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals. 2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not. 3. National totals include annual changes in fuel stocks whereas the global total does not. 4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers. ", + "license": "proprietary" + }, + { + "id": "geodata_0469", + "title": "Emissions of CO2 - from Gas Flaring (CDIAC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848710-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848710-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0469", + "description": "Emissions of CO2 - from Gas Flaring (CDIAC): Annual estimations of CO2 emissions from Gas Flaring, primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration, Rotty (1974), and with a few national estimates provided by G. Marland. For information about the data collection methodology used by Rotty (1974) and G. Marland see http://cdiac.esd.ornl.gov. The sum of emissions estimates for all countries is not equal to the estimate of global total emissions because : 1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals. 2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not. 3. National totals include annual changes in fuel stocks whereas the global total does not. 4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers. ", + "license": "proprietary" + }, + { + "id": "geodata_0470", + "title": "Emissions of CO2 - from Gas Fuels Consumption (CDIAC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847712-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847712-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0470", + "description": "Emissions of CO2 - from Gas Fuel Consumption (CDIAC): Annual estimations of CO2 emissions from gas Gas Fuel Consumption, primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration, Rotty (1974), and with a few national estimates provided by G. Marland. For information about the data collection methodology used by Rotty (1974) and G. Marland see http://cdiac.esd.ornl.gov. The sum of emissions estimates for all countries is not equal to the estimate of global total emissions because : 1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals. 2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not. 3. National totals include annual changes in fuel stocks whereas the global total does not. 4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers. ", + "license": "proprietary" + }, + { + "id": "geodata_0473", + "title": "Emissions of CO2 - from Liquid Fuels Consumption (CDIAC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847566-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847566-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0473", + "description": "Emissions of CO2 - from Liquid Fuel Consumption (CDIAC): Annual estimations of CO2 emissions from Liquid Fuel Consumption, primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration, Rotty (1974), and with a few national estimates provided by G. Marland. For information about the data collection methodology used by Rotty (1974) and G. Marland see http://cdiac.esd.ornl.gov. The sum of emissions estimates for all countries is not equal to the estimate of global total emissions because : 1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals. 2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not. 3. National totals include annual changes in fuel stocks whereas the global total does not. 4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers. ", + "license": "proprietary" + }, + { + "id": "geodata_0476", + "title": "Emissions of CO2 - from Solid Fuels Consumption (CDIAC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848108-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848108-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0476", + "description": "Emissions of CO2 - from Solid Fuel Consumption (CDIAC): Annual estimations of CO2 emissions from Solid Fuel Consumption, primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration, Rotty (1974), and with a few national estimates provided by G. Marland. For information about the data collection methodology used by Rotty (1974) and G. Marland see http://cdiac.esd.ornl.gov. The sum of emissions estimates for all countries is not equal to the estimate of global total emissions because : 1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals. 2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not. 3. National totals include annual changes in fuel stocks whereas the global total does not. 4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers. ", + "license": "proprietary" + }, + { + "id": "geodata_0480", + "title": "Emissions of CO2 - from Fossil Fuels - Total (CDIAC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848729-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848729-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0480", + "description": "Total emissions of CO2 from fossil fuels are the sum of CO2 produced during the consumption of solid, liquid, and gaseous fuels, and from gas flaring, and cement manufacturing. The data is primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration (Rotty) and with a few national estimates provided by G. Marland. The sum of emissions estimates for all countries is not equal to the estimate of global total emissions because : 1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals. 2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not. 3. National totals include annual changes in fuel stocks whereas the global total does not. 4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers. ", + "license": "proprietary" + }, + { + "id": "geodata_0543", + "title": "Floods - Killed People", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1980-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849227-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849227-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0543", + "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Flood: Significant rise of water level in a stream, lake, reservoir or coastal region. ", + "license": "proprietary" + }, + { + "id": "geodata_0588", + "title": "Extreme Temperatures - Killed People", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1980-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847385-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847385-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0588", + "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Extreme temperature: Disaster type term comprising the two disaster subsets \"heat wave\" and \"cold wave\" (Long lasting period with extremely high or low surface temperature). ", + "license": "proprietary" + }, { "id": "geodata_0613", "title": "Crude Birth Rate", @@ -204034,6 +213810,19 @@ "description": "Crude birth rate: number of births over a given period divided by the person-years lived by the population over that period. It is expressed as number of births per 1,000 population.", "license": "proprietary" }, + { + "id": "geodata_0633", + "title": "Household Final Consumption Expenditure - Total", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2009-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848942-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848942-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0633", + "description": "Household final consumption expenditure (formerly private consumption) is the market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. Here, household consumption expenditure includes the expenditures of nonprofit institutions serving households, even when reported separately by the country. Data are in constant 2000 U.S. dollars. Source: World Bank national accounts data, and OECD National Accounts data files. ", + "license": "proprietary" + }, { "id": "geodata_0686", "title": "Arable Land", @@ -204047,6 +213836,19 @@ "description": "Arable Land: land under temporary crops (double-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable. ", "license": "proprietary" }, + { + "id": "geodata_0758", + "title": "Gross Domestic Product - Purchasing Power Parity", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1980-01-01", + "end_date": "2010-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847187-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847187-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0758", + "description": "PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2005 international dollars. Source: World Bank, International Comparison Program database.", + "license": "proprietary" + }, { "id": "geodata_0771", "title": "Arable and Permanent Crops", @@ -204060,6 +213862,19 @@ "description": "Arable Land: land under temporary crops (double-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable. Permanent Crops: land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee and rubber; this category includes land under flowering shrubs, fruit trees, nut trees and vines, but excludes land under trees grown for wood or timber. ", "license": "proprietary" }, + { + "id": "geodata_0776", + "title": "Infant Mortality Rate", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1950-01-01", + "end_date": "2050-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847531-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847531-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0776", + "description": "Infant mortality: probability of dying between birth and exact age 1. It is expressed as deaths per 1,000 births.", + "license": "proprietary" + }, { "id": "geodata_0839", "title": "Cereals - Yield", @@ -204073,6 +213888,58 @@ "description": "Cereals also includes other cereals such as mixed grains and buckwheat. The data reported under this element represent the harvested production per unit of harvested area for crop products. In most of the cases yield data are not recorded but obtained by dividing the data stored under production element by those recorded under element: area harvested. Data are recorded in hectogramme (100 grammes) per hectare (HG/HA).", "license": "proprietary" }, + { + "id": "geodata_0879", + "title": "Droughts - Killed People", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1980-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848029-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848029-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0879", + "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Drought: Period of deficiency of moisture in the soil such that there is inadequate water required for plants, animals and human beings. ", + "license": "proprietary" + }, + { + "id": "geodata_0885", + "title": "Disasters of Natural Origin - Total Affected People", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1975-01-01", + "end_date": "2004-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848224-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848224-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0885", + "description": "Total affected: In EM-DAT, people that have been injured, affected and left homeless after a disaster are included in this category. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms. ", + "license": "proprietary" + }, + { + "id": "geodata_0927", + "title": "Energy Production - Combustible Renewables and Waste (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1971-01-01", + "end_date": "2008-12-31", + "bbox": "62, -85, 64, -84", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848131-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848131-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0927", + "description": "Production refers to the quantities of fuels extracted or produced, calculated after any operation for removal of inert matter or impurities (e.g. sulphur from natural gas). Waste refers to the quantities of fuels extracted or produced from Industrial waste and Municipal waste. Industrial waste consists of solid and liquid products (e.g. tyres) combusted directly, usually in specialised plants, to produce heat and/or power and that are not reported in the category solid biomass. Municipal waste consists of products that are combusted directly to produce heat and/or power and comprises wastes produced by the residential, commercial and public services sectors that are collected by local authorities for disposal in a central location. Hospital waste is included in this category. A KTOE is defined as 41.868 TJ (Terajoules) or 11.630 GWh (Gigawatt-Hour). ", + "license": "proprietary" + }, + { + "id": "geodata_0930", + "title": "Forest Average Annual Change", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2010-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847940-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847940-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0930", + "description": "Forest Average Annual Change \u2013 Total is the net change in forests and includes expansion of forest plantations and losses and gains in the area of natural forests. Total Forest includes natural forests and forest plantations. The term is used to refer to land with a tree cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 m. Young stands that have not yet reached, but are expected to reach, a crown density of 10m percent and tree height of 5 m are included under forest, as are temporarily unstocked areas. The term includes forests used for purposes of production, protection, multiple use or conservation (i.e. forest in national parks, nature reserves and other protected areas), as well as forest stands on agricultural lands (e.g. windbreaks and shelterbelts of trees with a width of more than 20 m) and rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems. ", + "license": "proprietary" + }, { "id": "geodata_0932", "title": "Aquaculture Production - Total", @@ -204086,6 +213953,58 @@ "description": "AQUACULTURE PRODUCTION The annual series of aquaculture production begin in 1950 for the quantities and in 1984 for the values. Aquaculture is the farming of aquatic organisms including fish, mollusks, and crustaceans*. Farming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual or corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by the public as a common property resource, with or without appropriate licenses, are the harvest of fisheries. Production of fish, crustaceans and molluscs is expressed in live weight that is the nominal weight of the aquatic organisms at the time of capture Quantities are given in tonnes. *includes all FAOSTAT group excluded aquatic animals nei, aquatic plants, aquatic mammals ", "license": "proprietary" }, + { + "id": "geodata_0938", + "title": "Fish Catch - Inland Waters", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848676-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848676-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0938", + "description": "CAPTURE PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is excluded. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. * includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals ", + "license": "proprietary" + }, + { + "id": "geodata_0940", + "title": "Fish Catch - Marine", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846722-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846722-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0940", + "description": "CAPTURE PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is excluded. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. * includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals ", + "license": "proprietary" + }, + { + "id": "geodata_0960", + "title": "Forest Fire Extent", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2005-01-01", + "end_date": "2005-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848961-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848961-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0960", + "description": "Forest Fire Extent - Annual Average comprises the reported forest areas exposed to fire. Total Forest includes natural forests and forest plantations. The term is used to refer to land with a tree cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 m. Young stands that have not yet reached, but are expected to reach, a crown density of 10m percent and tree height of 5 m are included under forest, as are temporarily unstocked areas. The term includes forests used for purposes of production, protection, multiple use or conservation (i.e. forest in national parks, nature reserves and other protected areas), as well as forest stands on agricultural lands (e.g. windbreaks and shelterbelts of trees with a width of more than 20 m) and rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems. ", + "license": "proprietary" + }, + { + "id": "geodata_0992", + "title": "Energy Capacity - Nuclear", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "2010-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847536-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847536-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_0992", + "description": "Energy Capacity - Nuclear is the actual capacity of the nuclear electric power industry to describe the size of generating plants. \u201cMWe\u201d is the symbol for the actual output of a generating station in megawatts of electricity.", + "license": "proprietary" + }, { "id": "geodata_1011", "title": "Carbon to the Atm. From Land-Use Change - Ann. Net Flux", @@ -204112,6 +214031,32 @@ "description": "Desalinated water corresponds to the annual amount of fresh water generated by desalination of sea or brackish waters (annually estimated on the basis of the total capacity of water desalination installations).", "license": "proprietary" }, + { + "id": "geodata_1029", + "title": "Improved Sanitation Coverage - Total Population", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848426-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848426-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1029", + "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. Improved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine. ", + "license": "proprietary" + }, + { + "id": "geodata_1034", + "title": "Improved Drinking Water Coverage - Total Population", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847727-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847727-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1034", + "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. Improved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine. ", + "license": "proprietary" + }, { "id": "geodata_1085", "title": "Continental Shelf Area", @@ -204125,6 +214070,552 @@ "description": "According to the UN Convention of the Law of the Sea, the Continental Shelf is the area of the seabed and subsoil which extends beyond the territorial sea to a distance of 200 nautical miles from the territorial sea baseline and beyond that distance to the outer edge of the continental margin. Areas of continental shelf that are disputed by overlaping claims by one or more nations have been excluded from this table. Areas that are of cooperative joint development between two or more nations have also been excluded. Coastal States have sovereign rights over the continental shelf (the national area of the seabed) for exploring and exploiting it; the shelf can extend at least 200 nautical miles from the shore, and more under specified circumstances. The United Nations Convention on the Law of the Sea (UNCLOS) is an international agreement that sets conditions and limits on the use and exploitation of the oceans. This Convention also sets the rules on how the maritime jurisdictional boundaries of the different member states are set. The UNCLOS was opened for signature on 10 December 1982 in Montego Bay, Jamaica, and it entered into force on 16 November 1994. As of January 2000, there are 132 countries that have ratified UNCLOS. Further information on the Web site: http://www.maritimeboundaries.com/ ", "license": "proprietary" }, + { + "id": "geodata_1088", + "title": "Length of Coastline", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849208-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849208-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1088", + "description": "The measurement of an irregular and curving feature such as a nation's coastal length is scale-dependent and very difficult to measure. Maps of individual islands for example, frequently show great detail, whereas regional maps summarize complex coastlines into a few simple lines. In addition, coastal features are constantly changing due to erosion, etc. The only way to derive comparable statistics on coastline length is to use a single source which uses a constant scale. This is what has been attempted with the data presented in this table, however, highly complex coastlines will appear longer at higher resolutions. Estimates may differ from other published sources. Because of the difficulty in trying to measure coastline length, these figures should be interpreted as approximations and should be used with caution. Coastline length was derived from the World Vector Shoreline database at 1:250,000 kilometers. The estimates presented here were calculated using a Geographic Information System (GIS) and an underlying database consistent for the entire world. The methodology used to estimate length is based on the following: 1) A country's coastline is made up of individual lines, and an individual line has two or more vertices and/or nodes. 2) The length between two vertices is calculated on the surface of a sphere. 3) The sum of the lengths of the pairs of vertices is aggregated for each individual line, and 4) the sum of the lengths of individual lines was aggregated for a country. In general, the coastline length of islands that are part of a country, but are not overseas territories, are included in the coastline estimate for that country (i.e., Canary Islands are included in Spain). Coastline length for overseas territories and dependencies are listed separately. Disputed areas are not included in country or regional totals. ", + "license": "proprietary" + }, + { + "id": "geodata_1147", + "title": "Emissions of CO2 - from Public Electricity and Heat Production (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2004-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848176-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848176-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1147", + "description": "Emissions of CO2 from public electricity and heat production contain the sum of emissions from public electricity generation, public combined heat and power generation, and public heat plants. Public utilities are defined as those undertakings whose primary activity is to supply the public. They may be publicly or privately owned. Emissions from own on-site use of fuel should be included. This corresponds to IPCC Source/Sink Category 1 A 1 a. ", + "license": "proprietary" + }, + { + "id": "geodata_1150", + "title": "Emissions of CO2 - from Manufacturing Industries and Construction (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2004-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847956-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847956-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1150", + "description": "Emissions of CO2 from manufacturing industries and construction contain the emissions from combustion of fuels (coal, oil and gas) in industry. The IPCC Source/Sink Category 1 A 2 includes these emissions. However, in the Guidelines, the IPCC category also includes emissions from industry autoproducers that generate electricity and/or heat. The IEA data are not collected in a way that allows the energy consumption to be split by specific end-use and therefore, autoproducers are shown as a separate item (Unallocated Autoproducers). Manufacturing Industries and Construction also includes emissions from coke inputs into blast furnaces, which may be reported either in the transformation sector, the industry sector or the separate IPCC Source/Sink Category 2, Industrial Processes.", + "license": "proprietary" + }, + { + "id": "geodata_1153", + "title": "Emissions of CO2 - from Transport (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2004-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848166-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848166-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1153", + "description": "Emissions of CO2 from transport contain emissions from the combustion of fuel (coal, oil and gas) for all transport activity, regardless of the sector, except for international marine and aviation bunkers. This corresponds to IPCC Source/Sink Category 1 A 3. In addition, the IEA data are not collected in a way that allows the autoproducer consumption to be split by specific end-use.", + "license": "proprietary" + }, + { + "id": "geodata_1156", + "title": "Emissions of CH4 - from Agriculture (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847635-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847635-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1156", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). Emissions of CH4 - from Agriculture (RIVM). Emissions of CH4 (Methane) from the agricultural sector include emissions from: Rice cultivation (IPCC 4C); Animal breeding: enteric fermentation and animal waste management (IPCC 4A and 4B,); Savannah burning (IPCC 4E); Agricultural waste burning (IPCC 4F). The emissions from deforestation (IPCC 5A1) and vegetation fires (IPCC 5A2,3) are not included. ", + "license": "proprietary" + }, + { + "id": "geodata_1162", + "title": "Emissions of CH4 - Total (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848292-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848292-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1162", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of CH4 - Total (RIVM) include \"Energy\", \"Agriculture\", \"Waste\" and \"Others\" EDGAR subdivisions. \"Energy\" comprises production, handling, transmission and combustion of fossil fuels and biofuels (IPCC category 1A and 1B); \"Agriculture\" comprises animals, animal waste, rice production, agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); \"Waste\" comprises landfills, wastewater treatment, human wastewater disposal and waste incineration (non-energy) (IPCC category 6); \"Others\" include industrial process emissions and tropical and temperate forest fires (IPCC categories 2 and 5). ", + "license": "proprietary" + }, + { + "id": "geodata_1165", + "title": "Emissions of CH4 - Waste (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848626-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848626-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1165", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of CH4 - from Waste (RIVM) include emissions from: Landfills (including CH4 recovery) (IPCC 6A1,2); Wastewater treatment (including CH4 recovery) (IPCC 6B1,2); Human wastewater disposal (IPCC 6B2); Waste incineration (non-energy) (IPCC 6C). ", + "license": "proprietary" + }, + { + "id": "geodata_1198", + "title": "Emissions of Total GHG (CO2, CH4, N2O, HFCs, PFCs and SF6) (National Reports, UNFCCC), Including Land Use, Land-Use Change and Forestry", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848797-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848797-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1198", + "description": "Emissions of Total GHG (CO2, CH4, N2O, HFCs, PFCs and SF6) (UNFCCC), Excluding Land-Use Change and Forestry The Global Warming Potential (GWP) is an index used to translate the level of emissions of various gases into a common measure in order to compare the relative radiative forcing of different gases without directly calculating the changes in atmospheric concentrations. GWPs are calculated as the ratio of the radiative forcing that would result from the emissions of one kilogram of a greenhouse gas to that from the emission of one kilogram of carbon dioxide over a period of time (usually 100 years). Gases involved in complex atmospheric chemical processes have not been assigned GWPs. ", + "license": "proprietary" + }, + { + "id": "geodata_1204", + "title": "Emissions of N2O - from Agriculture (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848853-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848853-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1204", + "description": "Emissions of N2O - from Agriculture (RIVM). Emissions of CH4 (Methane) from the agricultural sector include emissions from: Arable Land (fertilizer use) (IPCC 4D); Animal waste management (IPCC 4B); Savannah burning (IPCC 4E); Agricultural waste burning (IPCC 4F); Crop production (IPCC 4D); Animal waste (deposited on soil - N2O) (IPCC 4B); Atmospheric deposition (IPCC 4D); Leaching and Run-off (IPCC 4D). The emissions from deforestation (IPCC 5A1), vegetation fires (IPCC 5A2,3) and deforestation post burn effects (IPCC 5B1) are not included.", + "license": "proprietary" + }, + { + "id": "geodata_1210", + "title": "Emissions of N2O - Total (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848860-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848860-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1210", + "description": "Emissions of N2O - Total (RIVM) include \"Energy\", \"Agriculture\", \"Waste\" and \"Others\" EDGAR subdivisions. \"Energy\" comprises combustion of fossil fuels and biofuels (IPCC category 1A and 1B); \"Agriculture\" comprises fertilizer use (synthetic and animal manure), animal waste management, agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); \"Waste\" comprises human sewage discharge and waste incineration (non-energy) (IPCC category 6); \"Others\" include industrial process emissions, N2O usage and tropical and temperate forest fires (IPCC categories 2, 3 and 5). ", + "license": "proprietary" + }, + { + "id": "geodata_1213", + "title": "Emissions of CO - Total (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848686-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848686-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1213", + "description": "Emissions of CO (carbon monoxide) - Total (RIVM) include the following EDGAR subdivisions: \"Fuel combustion\", \u201cBiofuel combustion\u201d, \u201cFugitive\u201d, \u201cIndustry\u201d, \u201cSolvent use\u201d, \"Agriculture\", \"Waste\" and \"Others\". \"Fuel combustion\" refers to fossil fuel combustion and evaporation of NMVOC in road transport (part of IPCC category 1A); \"Biofuel combustion\" refers to traditional biofuels as well as to wood waste, paper, ethanol, etc. (part of IPCC category 1A); \"Fugitive\" comprises flaring and venting of associated gas in oil and gas production, handling / transmission losses of oil and charcoal production (IPCC category 1B); \"Industry\" refers to non-combustion industrial processes, excluding solvent use (IPCC category 2); \"Solvent use\" refers to solvent use in industry and non-industry sectors (IPCC category 3); \"Agriculture\" comprises agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); \"Waste\" comprises waste incineration (non-energy) (uncontrolled residential burning and controlled non-residential burning) and hazardous waste handling (IPCC category 6); \"Others\" comprises tropical forest fires and temperate forest fires (IPCC category 5A). ", + "license": "proprietary" + }, + { + "id": "geodata_1216", + "title": "Emissions of NMVOC - Total (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848501-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848501-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1216", + "description": "Emissions of NMVOC (Non-Methane Volatile Organic Compounds) - Total (RIVM) include the following EDGAR subdivisions: \"Fuel combustion\", \u201cBiofuel combustion\u201d, \u201cFugitive\u201d, \u201cIndustry\u201d, \u201cSolvent use\u201d, \"Agriculture\", \"Waste\" and \"Others\". \"Fuel combustion\" refers to fossil fuel combustion and evaporation of NMVOC in road transport (part of IPCC category 1A); \"Biofuel combustion\" refers to traditional biofuels as well as to wood waste, paper, ethanol, etc. (part of IPCC category 1A); \"Fugitive\" comprises flaring and venting of associated gas in oil and gas production, handling / transmission losses of oil and charcoal production (IPCC category 1B); \"Industry\" refers to non-combustion industrial processes, excluding solvent use (IPCC category 2); \"Solvent use\" refers to solvent use in industry and non-industry sectors (IPCC category 3); \"Agriculture\" comprises agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); \"Waste\" comprises waste incineration (non-energy) (uncontrolled residential burning and controlled non-residential burning) and hazardous waste handling (IPCC category 6); \"Others\" comprises tropical forest fires and temperate forest fires (IPCC category 5A).", + "license": "proprietary" + }, + { + "id": "geodata_1219", + "title": "Emissions of NOx - Total (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848211-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848211-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1219", + "description": "Emissions of NOx (Nitrogen Oxides) - Total (RIVM) include the following EDGAR subdivisions: \"Fuel combustion\", \u201cBiofuel combustion\u201d, \u201cFugitive\u201d, \u201cIndustry\u201d, \u201cSolvent use\u201d, \"Agriculture\", \"Waste\" and \"Others\". \"Fuel combustion\" refers to fossil fuel combustion and evaporation of NMVOC in road transport (part of IPCC category 1A); \"Biofuel combustion\" refers to traditional biofuels as well as to wood waste, paper, ethanol, etc. (part of IPCC category 1A); \"Fugitive\" comprises flaring and venting of associated gas in oil and gas production, handling / transmission losses of oil and charcoal production (IPCC category 1B); \"Industry\" refers to non-combustion industrial processes, excluding solvent use (IPCC category 2); \"Solvent use\" refers to solvent use in industry and non-industry sectors (IPCC category 3); \"Agriculture\" comprises agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); \"Waste\" comprises waste incineration (non-energy) (uncontrolled residential burning and controlled non-residential burning) and hazardous waste handling (IPCC category 6); \"Others\" comprises tropical forest fires and temperate forest fires (IPCC category 5A). ", + "license": "proprietary" + }, + { + "id": "geodata_1222", + "title": "Emissions of SO2 - Total (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847778-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847778-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1222", + "description": "Emissions of SO2 (Sulfur dioxide) - Total (RIVM) include the following EDGAR subdivisions: \"Fuel combustion\", \u201cBiofuel combustion\u201d, \u201cFugitive\u201d, \u201cIndustry\u201d, \u201cSolvent use\u201d, \"Agriculture\", \"Waste\" and \"Others\". \"Fuel combustion\" refers to fossil fuel combustion and evaporation of NMVOC in road transport (part of IPCC category 1A); \"Biofuel combustion\" refers to traditional biofuels as well as to wood waste, paper, ethanol, etc. (part of IPCC category 1A); \"Fugitive\" comprises flaring and venting of associated gas in oil and gas production, handling / transmission losses of oil and charcoal production (IPCC category 1B); \"Industry\" refers to non-combustion industrial processes, excluding solvent use (IPCC category 2); \"Solvent use\" refers to solvent use in industry and non-industry sectors (IPCC category 3); \"Agriculture\" comprises agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); \"Waste\" comprises waste incineration (non-energy) (uncontrolled residential burning and controlled non-residential burning) and hazardous waste handling (IPCC category 6); \"Others\" comprises tropical forest fires and temperate forest fires (IPCC category 5A). ", + "license": "proprietary" + }, + { + "id": "geodata_1253", + "title": "Mangroves Forest Extent - Total Area", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1980-01-01", + "end_date": "2005-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847510-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847510-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1253", + "description": "Mangroves are commonly found along sheltered coastlines in the tropics and subtropics where they fulfil important socio-economic and environmental functions. These include the provision of a large variety of wood and non-wood forest products; coastal protection against the effects of wind, waves and water currents; conservation of biological diversity, including a number of endangered mammals, reptiles, amphibians and birds; protection of coral reefs, sea-grass beds and shipping lanes against siltation; and provision of habitat, spawning grounds and nutrients for a variety of fish and shellfish, including many commercial species. Mangrove trees and shrubs, including ferns and palms, are found along river banks and coastlines in tropical and subtropical countries. Their main characteristic is that they can tolerate salt and brackish water environments. Globally, there are seventy known species of mangroves. ", + "license": "proprietary" + }, + { + "id": "geodata_1256", + "title": "Mangroves Forest Extent - Protected Area", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846595-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846595-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1256", + "description": "The World Mangrove Atlas is the first significant attempt to provide an overview of the distribution of mangroves worldwide. Mapped data showing the extent of mangroves in over 100 countries have been gathered from a wide range of sources. Mangrove trees and shrubs, including ferns and palms, are found along river banks and coastlines in tropical and subtropical countries. Their main characteristic is that they can tolerate salt and brackish water environments. Globally, there are seventy known species of mangroves.", + "license": "proprietary" + }, + { + "id": "geodata_1261", + "title": "Emissions of CO2 - from All Anthropogenic Sources (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849166-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849166-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1261", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", + "license": "proprietary" + }, + { + "id": "geodata_1262", + "title": "Emissions of CO2 - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1262", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847196-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847196-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1262", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", + "license": "proprietary" + }, + { + "id": "geodata_1264", + "title": "Emissions of CO2 - from Cement Production (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849248-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849248-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1264", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). ", + "license": "proprietary" + }, + { + "id": "geodata_1265", + "title": "Emissions of CO2 - from Power Generation (Model Estimations, RIVM-MNP) - geodata_1265", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846672-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846672-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1265", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from Power Generation (public and auto, including cogeneration) corresponds to IPCC category 1A1a.", + "license": "proprietary" + }, + { + "id": "geodata_1266", + "title": "Emissions of CO2 - from Power Generation (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849296-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849296-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1266", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from Power Generation (public and auto, including cogeneration) corresponds to IPCC category 1A1a. ", + "license": "proprietary" + }, + { + "id": "geodata_1267", + "title": "Emissions of CO2 - from Residentials, Commercials and Other Sector (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849370-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849370-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1267", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from Residential, Commercials and Other sector corresponds to IPCC category 1A4. ", + "license": "proprietary" + }, + { + "id": "geodata_1268", + "title": "Emissions of CO2 - from Residentials, Commercials and Other Sector (Model Estimations, RIVM-MNP) - geodata_1268", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-12-31", + "end_date": "1995-01-01", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847928-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847928-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1268", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from Residential, Commercials and Other sector corresponds to IPCC category 1A4.", + "license": "proprietary" + }, + { + "id": "geodata_1269", + "title": "Emissions of CO2 - from Transport Road (Model Estimations, RIVM-MNP) - geodata_1269", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847835-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847835-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1269", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from transport road corresponds to IPCC category 1A3b.", + "license": "proprietary" + }, + { + "id": "geodata_1270", + "title": "Emissions of CO2 - from Transport Road (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849247-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849247-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1270", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from transport road; corresponds to IPCC category 1A3b.", + "license": "proprietary" + }, + { + "id": "geodata_1271", + "title": "Emissions of CH4 - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1271", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847506-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847506-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1271", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", + "license": "proprietary" + }, + { + "id": "geodata_1272", + "title": "Emissions of CH4 - from All Anthropogenic Sources (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849293-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849293-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1272", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). ", + "license": "proprietary" + }, + { + "id": "geodata_1273", + "title": "Emissions of CH4 - from Animal Breeding: Enteric Fermentation (Model Estimations, RIVM-MNP) - geodata_1273", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847615-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847615-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1273", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Methane production from herbivores is a by-product of enteric Emissions of CH4 from animal breeding corresponds to IPCC category 4A. All Anthropogenic Sources also includes international air traffic and international shipping.", + "license": "proprietary" + }, + { + "id": "geodata_1274", + "title": "Emissions of CH4 - from Animal Breeding: Enteric Fermentation (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849107-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849107-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1274", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Methane production from herbivores is a by-product of enteric,fermentation, a digestive process by which carbohydrates are broken down by micro-organisms into simple molecules for absorption into the bloodstream. Both ruminant (e.g. cattle, sheep) and non-ruminant animals (e.g. pigs, horses) produce CH 4 , although ruminants are the largest source (per unit of feed intake).", + "license": "proprietary" + }, + { + "id": "geodata_1275", + "title": "Emissions of N2O - from All Anthropogenic Sources (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849164-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849164-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1275", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", + "license": "proprietary" + }, + { + "id": "geodata_1276", + "title": "Emissions of N2O - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1276", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847278-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847278-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1276", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", + "license": "proprietary" + }, + { + "id": "geodata_1277", + "title": "Emissions of N2O - from Fertilizer Use in Arable Land (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848949-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848949-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1277", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of N2O from fertilizer use in arable land: synthetic and animal waste handling. ", + "license": "proprietary" + }, + { + "id": "geodata_1278", + "title": "Emissions of N2O - from Fertilizer Use in Arable Land (Model Estimations, RIVM-MNP) - geodata_1278", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846606-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846606-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1278", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of N2O from fertilizer use in arable land: synthetic and animal waste handling.", + "license": "proprietary" + }, + { + "id": "geodata_1279", + "title": "Emissions of CO - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1279", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847548-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847548-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1279", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", + "license": "proprietary" + }, + { + "id": "geodata_1280", + "title": "Emissions of CO - from All Anthropogenic Sources (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846739-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846739-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1280", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", + "license": "proprietary" + }, + { + "id": "geodata_1281", + "title": "Emissions of SO2 - from All Anthropogenic Sources (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846592-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846592-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1281", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", + "license": "proprietary" + }, + { + "id": "geodata_1282", + "title": "Emissions of SO2 - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1282", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847945-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847945-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1282", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC)", + "license": "proprietary" + }, + { + "id": "geodata_1283", + "title": "Emissions of NOx - from All Anthropogenic Sources (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847241-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847241-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1283", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). ", + "license": "proprietary" + }, + { + "id": "geodata_1284", + "title": "Emissions of NOx - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1284", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847681-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847681-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1284", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", + "license": "proprietary" + }, + { + "id": "geodata_1285", + "title": "Emissions of NMVOC (Non-Methane Volatile Organic Compounds) - from All Anthropogenic Sources (Model Estimations, RIVM-MNP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847549-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847549-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1285", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). ", + "license": "proprietary" + }, + { + "id": "geodata_1286", + "title": "Emissions of NMVOC (Non-Methane Volatile Organic Compounds) - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1286", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847440-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847440-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1286", + "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC)", + "license": "proprietary" + }, + { + "id": "geodata_1315", + "title": "Mean Annual Precipitation", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1961-01-01", + "end_date": "1990-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849152-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849152-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1315", + "description": "For the purpose of Desertification Atlas map production, the GRID-Nairobi data analysts required data from a fairly dense network of global climate stations. They therefore obtained both precipitation and temperature station data from UEA/CRU for two 30-year periods, 1930-59 and 1960-89. While the CRU database contained 950 precipitation station values, this number was not sufficient for interpolating two separate global surfaces to be used in a climate change study, for reasons of both temporal instability and inaccuracies of eventual area estimates. Thus, GRID decided in conjunction with UEA/CRU to produce a single, high-resolution preci- pitation surface for one time period only, using the maximum number of station means available. For this surface, data from the time period 1951-1980 were selected, both in order to avoid creation of a \"timeless\" data set, and to better match the period of the GLASOD study whose data were compiled in the late 1980s. ", + "license": "proprietary" + }, { "id": "geodata_1316", "title": "Annual Temperature", @@ -204138,6 +214629,487 @@ "description": "The original data took the form of a value for each month and each box on a 0.5 degree latitude / longitude grid. The annual values are the average of their constituent months, they have been calculated by GRID-Geneva. Original Data Station observations were first collected by national meteorological, hydrological and related services, and were acquired through the free and unrestricted exchange of meteorological and related data. These observations were gridded by collaborators at the Climatic Research Unit (www.cru.uea.ac.uk). The gridded data-set is publicly available, and has been published in a peer-reviewed scientific journal. Data Source: CRU TS 2.10 Jan 2004 T. D. Mitchell, Tyndall Centre Reference: Mitchell T.D. and Jones P.D. 2005 An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol. 25: 693-712. ", "license": "proprietary" }, + { + "id": "geodata_1351", + "title": "Modis Blue Marble Land Surface (Africa)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2001-12-31", + "bbox": "-33.19, -41.41, 62.62, 40.04", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848194-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848194-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1351", + "description": "The \"blue marble\" image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. Much of the information contained in this image came from a single remote-sensing device-NASA\u2019s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor\u2019s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey\u2019s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration\u2019s AVHRR sensor\u2014the Advanced Very High Resolution Radiometer. ", + "license": "proprietary" + }, + { + "id": "geodata_1352", + "title": "Modis Blue Marble Land Surface (Middle East)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2001-12-31", + "bbox": "29.09, 9.24, 63.99, 38.67", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847977-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847977-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1352", + "description": "The \"blue marble\" image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. Much of the information contained in this image came from a single remote-sensing device-NASA\u2019s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor\u2019s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey\u2019s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration\u2019s AVHRR sensor\u2014the Advanced Very High Resolution Radiometer. ", + "license": "proprietary" + }, + { + "id": "geodata_1353", + "title": "Modis Blue Marble Land Surface (Asia)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2001-12-31", + "bbox": "26.35, -10.61, 179.66, 89.32", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847943-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847943-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1353", + "description": "Ther \u201cblue marble\u201d image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. Much of the information contained in this image came from a single remote-sensing device-NASA\u2019s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor\u2019s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey\u2019s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration\u2019s AVHRR sensor\u2014the Advanced Very High Resolution Radiometer.", + "license": "proprietary" + }, + { + "id": "geodata_1354", + "title": "Modis Blue Marble Land Surface (South America)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2001-12-31", + "bbox": "-122.85, -55.78, -18.14, 30.46", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847662-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847662-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1354", + "description": "The \"blue marble\" image is the most detailed true-color image of the entire Earth to date. 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", + "license": "proprietary" + }, + { + "id": "geodata_1355", + "title": "Modis Blue Marble Land Surface (North America)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2001-12-31", + "bbox": "-178.97, 8.56, -11.98, 87.95", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847610-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847610-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1355", + "description": "The \u201cblue marble\u201d image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. 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", + "license": "proprietary" + }, + { + "id": "geodata_1358", + "title": "Global Forest Canopy Density (Africa)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "-33.19, -41.41, 62.62, 40.04", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848820-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848820-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1358", + "description": "Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover). ", + "license": "proprietary" + }, + { + "id": "geodata_1359", + "title": "Global Forest Canopy Density (Asia)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "26.35, -10.61, 179.66, 89.32", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848781-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848781-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1359", + "description": "Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover).", + "license": "proprietary" + }, + { + "id": "geodata_1360", + "title": "Global Forest Canopy Density (South America)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "-122.85, -55.78, -18.14, 30.46", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848276-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848276-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1360", + "description": "Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover).", + "license": "proprietary" + }, + { + "id": "geodata_1361", + "title": "Global Forest Canopy Density (North America)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "-178.97, 8.56, -11.98, 87.95", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848328-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848328-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1361", + "description": "Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover).", + "license": "proprietary" + }, + { + "id": "geodata_1362", + "title": "Global Forest Canopy Density (Europe)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "-27.72, 34.56, 68.1, 85.21", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848473-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848473-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1362", + "description": "Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover).", + "license": "proprietary" + }, + { + "id": "geodata_1363", + "title": "Global Forest Canopy Density (Middle East)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "29.09, 9.24, 63.99, 38.67", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848449-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848449-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1363", + "description": "Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover).", + "license": "proprietary" + }, + { + "id": "geodata_1364", + "title": "Global Forest Cover (Middle East)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "29.09, 9.24, 63.99, 38.67", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848843-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848843-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1364", + "description": "The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. ", + "license": "proprietary" + }, + { + "id": "geodata_1365", + "title": "Global Forest Cover (Africa)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "-33.19, -41.41, 62.62, 40.04", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848777-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848777-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1365", + "description": "The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. ", + "license": "proprietary" + }, + { + "id": "geodata_1366", + "title": "Global Forest Cover (Asia)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "26.35, -10.61, 179.66, 89.32", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848561-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848561-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1366", + "description": "The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. ", + "license": "proprietary" + }, + { + "id": "geodata_1367", + "title": "Global Forest Cover (South America)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "-122.85, -55.78, -18.14, 30.46", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848671-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848671-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1367", + "description": "The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. ", + "license": "proprietary" + }, + { + "id": "geodata_1368", + "title": "Global Forest Cover (North America)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "-178.97, 8.56, -11.98, 87.95", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847667-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847667-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1368", + "description": "The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water.", + "license": "proprietary" + }, + { + "id": "geodata_1369", + "title": "Global Forest Cover (Europe)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1996-12-31", + "bbox": "-27.72, 34.56, 68.1, 85.21", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847605-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847605-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1369", + "description": "The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. ", + "license": "proprietary" + }, + { + "id": "geodata_1370", + "title": "Global Burnt Area", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848826-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848826-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1370", + "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal).", + "license": "proprietary" + }, + { + "id": "geodata_1371", + "title": "Global Burnt Area (Middle East)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "29.09, 9.24, 63.99, 38.67", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848779-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848779-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1371", + "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC) http://www.gvm.sai.jrc.it/fire/default.htm In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal) Biomass burning contributes up to 50%, 40% and 16% of the total emissions of anthropogenic origin for carbon monoxide, carbon dioxide and methane respectively. Both the scientific community and the policy makers are looking for reliable and quantitative information on the magnitude and spatial distribution of biomass burning. Please visit the original source site at: http://www.grid.unep.ch/activities/earlywarning/preview/ims/gba/ where you can find more detailed information, downloads and the GBA2000-IMS application. The GBA2000-IMS application informs users of the status of the project. In addition, the user can overlay burnt area maps with other sources of information such as country borders, national park boundaries and a land cover map. Moreover users can zoom in and out, change the background, the month of observation in 2000, as well as download the data and access statistics. NOTE: The GBA2000 products are currently under development. Burnt area maps are still prototype versions and might be modified/improved to take into account the comments received from the scientific community.", + "license": "proprietary" + }, + { + "id": "geodata_1372", + "title": "Global Burnt Area (Africa)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-33.19, -41.41, 62.62, 40.04", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848559-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848559-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1372", + "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal).", + "license": "proprietary" + }, + { + "id": "geodata_1373", + "title": "Global Burnt Area (Asia)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "26.35, -10.61, 179.66, 89.32", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848673-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848673-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1373", + "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC) http://www.gvm.sai.jrc.it/fire/default.htm In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal).", + "license": "proprietary" + }, + { + "id": "geodata_1374", + "title": "Global Burnt Area (South America)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-122.85, -55.78, -18.14, 30.46", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848280-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848280-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1374", + "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal). ", + "license": "proprietary" + }, + { + "id": "geodata_1375", + "title": "Global Burnt Area (North America)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-178.97, 8.56, -11.98, 87.95", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848322-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848322-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1375", + "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal) . ", + "license": "proprietary" + }, + { + "id": "geodata_1376", + "title": "Global Burnt Area (Europe)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-27.72, 34.56, 68.1, 85.21", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848483-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848483-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1376", + "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal).", + "license": "proprietary" + }, + { + "id": "geodata_1395", + "title": "Length of Available Growing Period", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847793-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847793-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1395", + "description": "The concept of the length of the available growing period (LGP) combines temperature and moisture considerations to determine the length of time crops are able to grow, hence excluding periods which are too cold or too dry or both. LGP refers to the number of days within the period of temperatures above 5\u00b0C when moisture conditions are considered adequate. Under rain-fed conditions, the begin of the LGP is linked to the start of the rainy season. The growing period for most crops continues beyond the rainy season and, to a greater or lesser extent, crops mature on moisture stored in the soil profile. ", + "license": "proprietary" + }, + { + "id": "geodata_1398", + "title": "Dominant Type of Problem Lands", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848678-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848678-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1398", + "description": "Problem soils have been defined as soils with inherent physical or chemical constraints to agricultural production. In these soils degradation hazards are more severe and adequate soil management measures are more difficult or costly to apply. Such soils, if improperly used or inadequately managed will degrade rapidly, sometimes irreversibly. As a result the land itself might go out of production. The analysis is carried out in a sequential way. ", + "license": "proprietary" + }, + { + "id": "geodata_1399", + "title": "Easy Available Water", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848554-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848554-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1399", + "description": "This is an indicator for the amount of stored soil moisture readily available to crops.The water retention at 2 bar suction is used to separate easily available water (EAV) from water which is more tightly held at higher suctions and difficult to abstract, especially from deeper subsoils; and in the use of a conceptual model of effective rooting depth.", + "license": "proprietary" + }, + { + "id": "geodata_1425", + "title": "Net Reproduction Rate", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1950-01-01", + "end_date": "2050-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847838-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847838-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1425", + "description": "Net reproduction rate: The average number of daughters a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates and the mortality rates of a given period. It is expressed as number of daughters per woman.", + "license": "proprietary" + }, + { + "id": "geodata_1458", + "title": "Emissions of Organic Water Pollutants (BOD) - Total", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1989-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849007-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849007-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1458", + "description": "Emissions of organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants. Source: 1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, Industrial Pollution in Economic Development: Kuznets Revisited (available at www.worldbank.org/nipr). The data were updated through 2005 by the World Bank's Development Research Group using the same methodology as the initial study. ", + "license": "proprietary" + }, + { + "id": "geodata_1459", + "title": "Emissions of Organic Water Pollutants (BOD) - per Worker", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1989-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848968-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848968-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1459", + "description": "Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants. Source: World Bank and UNIDO's industry database.", + "license": "proprietary" + }, + { + "id": "geodata_1474", + "title": "Healthy Life Expectancy (HALE)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2002-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849320-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849320-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1474", + "description": "Healthy life expectancy (HALE) is based on life expectancy (LEX), but includes an adjustment for time spent in poor health. This indicator measures the equivalent number of years in full health that a newborn child can expect to live based on the current mortality rates and prevalence distribution of health states in the population. ", + "license": "proprietary" + }, + { + "id": "geodata_1480", + "title": "Economically Active Population", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1980-01-01", + "end_date": "2020-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847424-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847424-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1480", + "description": "The economically active population comprises all persons of either sex who furnish the supply of labour for the production of economic goods and services as defined by the United Nations systems of national accounts and balances during a specified time-reference period. According to these systems the production of economic goods and services includes all production and processing of primary products whether for the market for barter or for own consumption, the production of all other goods and services for the market and, in the case of households which produce such goods and services for the market, the corresponding production for own consumption.", + "license": "proprietary" + }, + { + "id": "geodata_1498", + "title": "Drylands - Total Area", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849159-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849159-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1498", + "description": "The concept of drylands continues to be debated. In this data set, drylands are taken as areas with a potential hazard of desertification. The hyperarid zone is not subject to desertification and is therefore excluded. Hence drylands are defined as the arid, semi-arid and dry subhumid zones, or areas with lengths of growing periods of 1-179 days.", + "license": "proprietary" + }, + { + "id": "geodata_1501", + "title": "Drylands - Percent of Total Area", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849375-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849375-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1501", + "description": "The concept of drylands continues to be debated. In this data set, drylands are taken as areas with a potential hazard of desertification. The hyperarid zone is not subject to desertification and is therefore excluded. Hence drylands are defined as the arid, semi-arid and dry subhumid zones, or areas with lengths of growing periods of 1-179 days.", + "license": "proprietary" + }, + { + "id": "geodata_1525", + "title": "Energy use (kg oil equivalent) per $1,000 GDP (Constant 2005 PPP $)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1980-01-01", + "end_date": "2009-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846723-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846723-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1525", + "description": "Energy use per GDP (Constant 2005 PPP $) is the kilogram of oil equivalent of energy use per gross domestic product converted to 2005 constant international dollars using purchasing power parity rates. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport. Gross Domestic Product (GDP) is the sum of gross value added by all resident producers in the economy plus any product taxes (less subsidies) not included in the valuation of output. Value added is the net output of an industry after adding up all outputs and subtracting intermediate inputs. The purchasing power parity (PPP) conversion factor is the number of units of a country's currency required to buy the same amounts of goods and services in the domestic market as the United States (U.S.) dollar would buy in the United States. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States.", + "license": "proprietary" + }, + { + "id": "geodata_1540", + "title": "Hazardous Waste - Production", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2002-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848009-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848009-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1540", + "description": "Definitions used in these data refer to the waste streams to be controlled according to the Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and their Disposal (seeAnnex IV of the convention for complete definition and methods of treatment, movement and disposal).", + "license": "proprietary" + }, { "id": "geodata_1571", "title": "Consumption of Ozone-Depleting Substances - Chlorofluorocarbons (CFCs)", @@ -204151,6 +215123,32 @@ "description": "Consumption of Ozone-Depleting CFCs is the sum of the consumption of the weighted tons of the individual substances in the group\u2014metric tons of the individual substance (defined in the Montreal Protocol on Substances That Deplete the Ozone Layer) multiplied by its ozone-depleting potential. Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. The consumption of CFCs is the national production plus imports, minus exports, minus destroyed quantities, minus feedstock uses of individual CFCs. National annual consumption of CFCs is the sum of the weighted tons (consumption in metric tons multiplied by the estimated ozone-depleting potential) of the individual CFCs. ", "license": "proprietary" }, + { + "id": "geodata_1624", + "title": "Disasters of Natural Origin - Total Affected per Million People", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1975-01-01", + "end_date": "2004-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847656-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847656-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1624", + "description": "Total affected: In EM-DAT, people that have been injured, affected and left homeless after a disaster are included in this category. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms. ", + "license": "proprietary" + }, + { + "id": "geodata_1627", + "title": "Disasters of Natural Origin - Killed per Million People", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1975-01-01", + "end_date": "2005-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847829-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847829-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1627", + "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms.", + "license": "proprietary" + }, { "id": "geodata_1628", "title": "Animal Species - Threatened", @@ -204177,6 +215175,97 @@ "description": "AQUACULTURE PRODUCTION The annual series of aquaculture production begin in 1950 for the quantities and in 1984 for the values. Aquaculture is the farming of aquatic organisms including fish, mollusks, and crustaceans*. Farming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual or corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by the public as a common property resource, with or without appropriate licenses, are the harvest of fisheries. Production of fish, crustaceans and molluscs is expressed in live weight that is the nominal weight of the aquatic organisms at the time of capture Quantities are given in tonnes. *includes all FAOSTAT group excluded aquatic animals nei, aquatic plants, aquatic mammals ", "license": "proprietary" }, + { + "id": "geodata_1646", + "title": "Irrigated Areas", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846631-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846631-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1646", + "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). ", + "license": "proprietary" + }, + { + "id": "geodata_1647", + "title": "Irrigated Areas (Africa)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-33.19, -41.41, 62.62, 40.04", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846641-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846641-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1647", + "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). ", + "license": "proprietary" + }, + { + "id": "geodata_1648", + "title": "Irrigated Areas (Asia)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "26.35, -10.61, 179.66, 89.32", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849231-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849231-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1648", + "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format)", + "license": "proprietary" + }, + { + "id": "geodata_1649", + "title": "Irrigated Areas (Europe)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-27.72, 34.56, 68.1, 85.21", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849226-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849226-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1649", + "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). ", + "license": "proprietary" + }, + { + "id": "geodata_1650", + "title": "Irrigated Areas (South America)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-122.85, -55.78, -18.14, 30.46", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847182-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847182-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1650", + "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). ", + "license": "proprietary" + }, + { + "id": "geodata_1651", + "title": "Irrigated Areas - geodata_1651", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "-178.97, 8.56, -11.98, 87.95", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848914-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848914-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1651", + "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format).", + "license": "proprietary" + }, + { + "id": "geodata_1652", + "title": "Irrigated Areas (Middle East)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-01-01", + "end_date": "1995-12-31", + "bbox": "29.09, 9.24, 63.99, 38.67", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846636-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846636-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1652", + "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). ", + "license": "proprietary" + }, { "id": "geodata_1672", "title": "Agricultural Area", @@ -204190,6 +215279,19 @@ "description": "Agricultural area, this category is the sum of areas under a) arable land - land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for Arable land are not meant to indicate the amount of land that is potentially cultivable; (b) permanent crops - land cultivated with long-term crops which do not have to be replanted for several years (such as cocoa and coffee); land under trees and shrubs producing flowers, such as roses and jasmine; and nurseries (except those for forest trees, which should be classified under \"forest\"); and (c) permanent meadows and pastures - land used permanently (five years or more) to grow herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land).", "license": "proprietary" }, + { + "id": "geodata_1685", + "title": "Land Area", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846605-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846605-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1685", + "description": "Land area is the total area of the country excluding area under inland water bodies. Possible variations in the data may be due to updating and revisions of the country data and not necessarily to any change of area.", + "license": "proprietary" + }, { "id": "geodata_1706", "title": "Consumption of Ozone-Depleting Substances - Methyl Bromide", @@ -204216,6 +215318,162 @@ "description": "Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. Hydrochlorofluorocarbons (HCFCs) were developed as the first major replacement for CFCs. While much less destructive than CFCs, HCFCs also contribute to ozone depletion. They have an atmospheric lifetime of about 1.4 to 19.5 years.", "license": "proprietary" }, + { + "id": "geodata_1717", + "title": "Disasters of Natural Origin (Excluding: Earthquakes, Insect Infestation, Volcanic Eruptions) - Total Affected per Million People", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1975-01-01", + "end_date": "2004-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849404-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849404-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1717", + "description": "Total affected: In EM-DAT, people that have been injured, affected and left homeless after a disaster are included in this category. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Extreme Temperatures, Floods, Slides, Wave/surges, Wild fires and Wind storms. ", + "license": "proprietary" + }, + { + "id": "geodata_1720", + "title": "Disasters of Natural Origin (Excluding: Earthquakes, Insect Infestation, Volcanic Eruptions) - Total Affected People", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1975-01-01", + "end_date": "2004-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847273-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847273-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1720", + "description": "Total affected: In EM-DAT, people that have been injured, affected and left homeless after a disaster are included in this category. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Extreme Temperatures, Floods, Slides, Wave/surges, Wild fires and Wind storms. ", + "license": "proprietary" + }, + { + "id": "geodata_1723", + "title": "Disasters of Natural Origin (Excluding: Earthquakes, Insect Infestation, Volcanic Eruptions) - Killed per Million People", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1975-01-01", + "end_date": "2004-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847492-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847492-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1723", + "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Extreme Temperatures, Floods, Slides, Wave/surges, Wild fires and Wind storms. ", + "license": "proprietary" + }, + { + "id": "geodata_1726", + "title": "Disasters of Natural Origin (Excluding: Earthquakes, Insect Infestation, Volcanic Eruptions) - Killed People", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1975-01-01", + "end_date": "2004-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846578-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846578-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1726", + "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Extreme Temperatures, Floods, Slides, Wave/surges, Wild fires and Wind storms. ", + "license": "proprietary" + }, + { + "id": "geodata_1730", + "title": "Energy Consumption for Total Transport Sector - Total (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1971-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847246-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847246-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1730", + "description": "The transport sector includes all fuels for transport except international marine bunkers [ISIC Divisions 60, 61 and 62]. It includes transport in the industry sector and covers road, railway, air, internal navigation (including small craft and coastal shipping not included under marine bunkers), fuels used for transport of materials by pipeline and non-specified transport. Fuel used for ocean, coastal and inland fishing should be included in agriculture. For many countries, the split between international civil aviation and domestic air appears to allocate fuel use for both domestic and international departures of domestically owned carriers to domestic air.", + "license": "proprietary" + }, + { + "id": "geodata_1741", + "title": "Energy Production - Crude Oil (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1971-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848235-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848235-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1741", + "description": "Energy production comprises crude oil, natural gas liquids, refinery feedstocks, and additives as well as other hydrocarbons such as synthetic oil, mineral oils extracted from bituminous minerals (in the row production) and oils from coal and natural gas liquefaction (in the row liquefaction). Production is calculated after removal of impurities (e.g. sulphur from natural gas). A TOE is defined as 41.868 gigajoules or 10 Exp 7 kilocalories. One terawatt-hour = 0.086 MTOE.", + "license": "proprietary" + }, + { + "id": "geodata_1744", + "title": "Energy Production - Natural gas (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1971-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847687-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847687-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1744", + "description": "Gas includes natural gas (excluding natural gas liquids) and gas works gas. The latter appears as a positive figure in the \"gas works\" row but is not part of production. A TOE is defined as 41.868 gigajoules or 10 Exp 7 kilocalories. One terawatt-hour = 0.086 MTOE.", + "license": "proprietary" + }, + { + "id": "geodata_1745", + "title": "Energy Production - Nuclear (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1971-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847568-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847568-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1745", + "description": "Energy production Nuclear shows the primary heat equivalent of the electricity produced by a nuclear power plant with an average thermal efficiency of 33 per cent.", + "license": "proprietary" + }, + { + "id": "geodata_1761", + "title": "Diseases of the Respiratory System - Number of Deaths per 100,000 Population", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1979-01-01", + "end_date": "2003-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848737-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848737-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1761", + "description": "Diseases of the Respiratory System: Effects are generally irritation and reduced lung function with increased incidence of respiratory disease, especially in more susceptible members of the population such as young children, the elderly and asthmatics. Diseases of the Respiratory System includes: ICD-9 BTL codes B31-B32, ICD-9 code CH08 for some ex-USSR countries, ICD-9 code C052 for China, ICD-10 codes J00-J99, European mortality indicator database (HFA-MDB), available at http://www.euro.who.int, for missing figures for some european countries: indicator \"3250 Deaths, Diseases of the Respiratory System\" ", + "license": "proprietary" + }, + { + "id": "geodata_1786", + "title": "Global Lakes and Wetlands Database (GLWD-3) - Level 3", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847563-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847563-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1786", + "description": "Drawing upon a variety of existing maps, data and information, a new Global Lakes and Wetlands Database (GLWD) has been created. Level 3 of the Global Lakes and Wetlands Database (GLWD) comprises lakes, reservoirs, rivers, and different wetland types in the form of a global raster map at 30-sec resolution.", + "license": "proprietary" + }, + { + "id": "geodata_1787", + "title": "Global Lakes and Wetlands Database (GLWD-2) - Level 2", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "9999-01-01", + "end_date": "9999-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847695-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847695-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1787", + "description": "Drawing upon a variety of existing maps, data and information, a new Global Lakes and Wetlands Database (GLWD) has been created. Level 2 of the Global Lakes and Wetlands Database (GLWD) comprises the shoreline polygons of permanent open water bodies with a surface area greater equal 0.1 square km, excluding the water bodies contained in GLWD-1. ", + "license": "proprietary" + }, + { + "id": "geodata_1788", + "title": "Global Lakes and Wetlands Database (GLWD-1) - Level 1", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848716-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848716-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1788", + "description": "Drawing upon a variety of existing maps, data and information, a new Global Lakes and Wetlands Database (GLWD) has been created.Level 1 of the Global Lakes and Wetlands Database (GLWD) comprises the shoreline polygons of the largest lakes (area greater equal 50 square km) and reservoirs (storage capacity greater equal 0.5 cubic km) worldwide, including extensive attribute data.", + "license": "proprietary" + }, { "id": "geodata_1824", "title": "Concentration of Biochemical Oxygen Demand (BOD) in Rivers, Lakes and Groundwater", @@ -204242,6 +215500,110 @@ "description": "In water, nitrogen (N) occurs as nitrates (NO3-) and nitrites (NO2-). These are naturally occurring ions that are part of the nitrogen cycle. Nitrate is used mainly in inorganic fertilizers, and sodium nitrite is used as a food preservative, especially in cured meats. In most countries, nitrate levels in drinking-water derived from surface water do not exceed 10 mg/liter, although nitrate levels in well water often exceed 50 mg/liter; nitrite levels are normally lower, less than a few milligrams per liter (WHO 2004).", "license": "proprietary" }, + { + "id": "geodata_1835", + "title": "Emissions of CO - Total (National Reports, UNFCCC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848463-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848463-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1835", + "description": "National figures only include data from UNFCCC Greenhouse Gas Inventory (GHG) Annex I Parties. These inventory data are provided in the national communications under the Convention by Annex I and non-Annex I Parties, and in addition Annex I Parties submit annual national greenhouse gas inventories. We use the category \"National Total\" from the GHG Database; this category does not include emissions resulting from fuel sold to ships or aircraft engaged in international transport (international bunker fuel emissions). Furthermore, the \"National Total\" does not include emissions from biomass burning or emissions or removals from the land-use change and forestry sector. ", + "license": "proprietary" + }, + { + "id": "geodata_1840", + "title": "Emissions of SO2 - Total (National Reports, UNFCCC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848973-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848973-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1840", + "description": "Sulfur dioxide is an air pollutant produced when fossil fuels containing sulfur are burned. It contributes to acid rain and can damage human health, particularly that of the young and the elderly. National figures only include data from UNFCCC Greenhouse Gas Inventory (GHG) Annex I Parties. These inventory data are provided in the national communications under the Convention by Annex I and non-Annex I Parties, and in addition Annex I Parties submit annual national greenhouse gas inventories. We use the category \"National Total\" from the GHG Database; this category does not include emissions resulting from fuel sold to ships or aircraft engaged in international transport (international bunker fuel emissions). Furthermore, the \"National Total\" does not include emissions from biomass burning or emissions or removals from the land-use change and forestry sector. ", + "license": "proprietary" + }, + { + "id": "geodata_1843", + "title": "Emissions of NMVOC - Total (National Reports, UNFCCC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849123-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849123-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1843", + "description": "National figures only include data from UNFCCC Greenhouse Gas Inventory (GHG) Annex I Parties. These inventory data are provided in the national communications under the Convention by Annex I and non-Annex I Parties, and in addition Annex I Parties submit annual national greenhouse gas inventories. We use the category \"National Total\" from the GHG Database; this category does not include emissions resulting from fuel sold to ships or aircraft engaged in international transport (international bunker fuel emissions). Furthermore, the \"National Total\" does not include emissions from biomass burning or emissions or removals from the land-use change and forestry sector.", + "license": "proprietary" + }, + { + "id": "geodata_1846", + "title": "Emissions of NOX - Total (National Reports, UNFCCC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2008-12-31", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849222-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849222-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1846", + "description": "Nitrogen dioxide is a poisonous, pungent gas formed when nitric oxide combines with hydrocarbons and sunlight, producing a photochemical reaction. These conditions occur in both natural and anthropogenic activities. Nitrogen dioxide is emitted by bacteria, motor vehicles, industrial activities, nitrogenous fertilizers, combustion of fuels and biomass, and aeorbic decomposition of organic matter in soils and oceans. National figures only include data from UNFCCC Greenhouse Gas Inventory (GHG) Annex I Parties. These inventory data are provided in the national communications under the Convention by Annex I and non-Annex I Parties, and in addition Annex I Parties submit annual national greenhouse gas inventories. We use the category \"National Total\" from the GHG Database; this category does not include emissions resulting from fuel sold to ships or aircraft engaged in international transport (international bunker fuel emissions). Furthermore, the \"National Total\" does not include emissions from biomass burning or emissions or removals from the land-use change and forestry sector. ", + "license": "proprietary" + }, + { + "id": "geodata_1848", + "title": "Global Land Cover 2000 (GLC 2000)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846682-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846682-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1848", + "description": "Each regional partner used the VEGA2000 dataset, providing a daily global image from the Vegetation sensor onboard the SPOT4 satellite. Each partner also used the Land Cover Classification System (LCCS) produced by FAO and UNEP (Di Gregorio and Jansen, 2000), which ensured that a standard legend was used over the globe. This hierarchical classification system allowed each partner to choose the most appropriate land cover classes which best describe their region, whilst also providing the possibility to translate regional classes to a more generalised global legend.", + "license": "proprietary" + }, + { + "id": "geodata_1890", + "title": "Emissions of Particulates Smaller than 2.5 Microns", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2002-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849230-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849230-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1890", + "description": "Particulate matter contributes significantly to visibility reduction and, as a carrier of toxic metals and other toxic substances, exerts pressures on human health, especially fine particulates. An effort has been made to present data on particulates smaller than 2.5 microns.", + "license": "proprietary" + }, + { + "id": "geodata_1896", + "title": "Human Poverty Index for Developing Countries (HPI-1)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-01-01", + "end_date": "2003-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848988-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848988-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1896", + "description": "While the HDI measures average achievement, the HPI-1 measures deprivations in the three basic dimensions of human development captured in the HDI: - A long and healthy life vulnerability to death at a relatively early age, as measured by the probability at birth of not surviving to age 40. - Knowledge exclusion from the world of reading and communications, as measured by the adult illiteracy rate. - A decent standard of living lack of access to overall economic provisioning, as measured by the unweighted average of two indicators, the percentage of the population without sustainable access to an improved water source and the percentage of children under weight for age. ", + "license": "proprietary" + }, + { + "id": "geodata_1897", + "title": "Ecological Footprint", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1961-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848979-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848979-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1897", + "description": "The Ecological Footprint (EF) is a measure of the consumption of renewable natural resources by a human population, be it that of a country, a region or the whole world. A population's EF is the total area of productive land or sea required to produce all the crops, meat, seafood, wood and fibre it consumes, to sustain its energy consumption and to give space for its infrastructure. The EF can be compared with the biologically productive capacity of the land and sea available to that population.", + "license": "proprietary" + }, { "id": "geodata_1930", "title": "Concentrations of Particulate Matter less than 10 microns (PM10)", @@ -204255,6 +215617,214 @@ "description": "Particulate matter concentrations refer to fine suspended particulates less than 10 microns in diameter (PM10) that are capable of penetrating deep into the respiratory tract and causing significant health damage. Data for countries and aggregates for regions and income groups are urban-population weighted PM10 levels in residential areas of cities with more than 100,000 residents. The estimates represent the average annual exposure level of the average urban resident to outdoor particulate matter. The state of a country\u2019s technology and pollution controls is an important determinant of particulate matter concentrations. Source: Kiren Dev Pandey, David Wheeler, Bart Ostro, Uwe Deichmann, Kirk Hamilton, and Katherine Bolt. \"Ambient Particulate Matter Concentrations in Residential and Pollution Hotspot Areas of World Cities: New Estimates Based on the Global Model of Ambient Particulates (GMAPS),\" World Bank, Development Research Group and Environment Department (2006). ", "license": "proprietary" }, + { + "id": "geodata_1933", + "title": "Global Mean Sea Level", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1870-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2226653620-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2226653620-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1933", + "description": "The reconstruction has used monthly-mean tide gauge data from the Permanent Service for Mean Sea Level (PSMSL) database [Woodworth and Player, 2003], together with Empirical Orthogonal Functions (EOFs) from a 12-year TOPEX/Poseidon + Jason-1 satellite altimeter data set to 'reconstruct' a GMSL curve from January 1870 to December 2001. ", + "license": "proprietary" + }, + { + "id": "geodata_1965", + "title": "Growing Stock in Forest", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2010-01-01", + "end_date": "2010-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848060-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848060-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1965", + "description": "Growing stock Volume over bark of all living trees more than X cm in diameter at breast height. Includes the stem from ground level or stump height up to a top diameter of Y cm, and may also include branches to a minimum diameter of W cm. Explanatory notes: 1. The countries must indicate the three thresholds (X, Y, W in cm) and the parts of the tree that are not included in the volume. The countries must also indicate whether the reported figures refer to volume above ground or above stump. 2. The diameter is measured at 30 cm above the end of the buttresses if these are higher than 1 meter. 3. Includes windfallen living trees. 4. Excludes: Smaller branches, twigs, foliage, flowers, seeds, and roots. Forest: Land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use. Explanatory notes 1. Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate. 2. Includes areas with bamboo and palms provided that height and canopy cover criteria are met. 3. Includes forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest. 4. Includes windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 ha and width of more than 20 m. 5. Includes plantations primarily used for forestry or protection purposes, such as rubberwood plantations and cork oak stands. 6. Excludes tree stands in agricultural production systems, for example in fruit plantations and agroforestry systems. The term also excludes trees in urban parks and gardens. The term is mainly related to FRA 2005 National Reporting Table T1. ", + "license": "proprietary" + }, + { + "id": "geodata_1966", + "title": "Forest Harvest Rate", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2010-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848238-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848238-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1966", + "description": "Forest harvest rates expressed as the ratio of roundwood production and growing stock in forests. After decades of increases, harvesting of roundwood from forests appears to have levelled off in recent years. However, roundwood production is still very high and largely exceeds growth of forest stock in Asia and the Pacific. Data source: GEO Data Portal, compiled from FAO, FAOStat forestry 2010, Forest Resources Assessment 2005 for 1990, 2000 and 2005, Forest Resources Assessment 2010 for 2010 ", + "license": "proprietary" + }, + { + "id": "geodata_1967", + "title": "Energy Production - Hydro (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1971-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848081-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848081-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1967", + "description": "Production is the production of primary energy, i.e. hard coal, lignite/brown coal, peat, crude oil, NGLs, natural gas, combustible renewables and waste, nuclear, hydro, geothermal, solar and the heat from heat pumps that is extracted from the ambient environment. Production is calculated after removal of impurities (e.g. sulphur from natural gas). Hydro shows the energy content of the electricity produced in hydro power plants. Hydro output excludes output from pumped storage plants. ", + "license": "proprietary" + }, + { + "id": "geodata_1977", + "title": "Emissions of NO2 (National Reports, UNFCCC), Including Land Use, Land-Use Change and Forestry", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2008-12-31", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847701-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847701-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1977", + "description": "Emissions of NO2, With LULUCF correspond to total emissions of NO2 and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land) ", + "license": "proprietary" + }, + { + "id": "geodata_1980", + "title": "Emissions of NO2 (National Reports, UNFCCC), Excluding Land Use, Land-Use Change and Forestry", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2008-12-31", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848249-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848249-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1980", + "description": "Emissions of NO2, Without LULUCF correspond to total emissions of NO2 without emissions and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land) ", + "license": "proprietary" + }, + { + "id": "geodata_1982", + "title": "Emissions of GHGs - from Waste (National Reports, UNFCCC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848524-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848524-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1982", + "description": "Emissions of ghgs from waste correspond to the total emissions from solid waste disposal on land, wastewater, waste incineration and any other waste management activity. Any CO2 emissions from fossil-based products (incineration or decomposition) are not included here. CO2 from organic waste handling and decay are not included here. ", + "license": "proprietary" + }, + { + "id": "geodata_1986", + "title": "Emissions of GHGs - from Industrial Processes (National Reports, UNFCCC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848538-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848538-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1986", + "description": "Emissions of ghgs from industrial processes corresponds to emissions by-product or fugitive emissions of greenhouse gases from industrial processes. Emissions from fuel combustion in industry are included under Fuel Combustion.", + "license": "proprietary" + }, + { + "id": "geodata_1988", + "title": "Emissions of GHGs - from Agriculture (National Reports, UNFCCC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847690-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847690-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1988", + "description": "Emission ghgs from agriculture correspond to all anthropogenic emissions from agriculture except for fuel combustion and sewage emissions.", + "license": "proprietary" + }, + { + "id": "geodata_1993", + "title": "Emissions of GHGs - from Transport (National Reports, UNFCCC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848703-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848703-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1993", + "description": "Emissions of ghgs from transport correspond to the emissions from the combustion and evaporation of fuel for all transport activity, regardless of the sector. Emissions from fuel sold to any air or marine vessel engaged in international transport (international bunker fuels) are not included. ", + "license": "proprietary" + }, + { + "id": "geodata_1995", + "title": "Emissions of CO2 - (National Reports, UNFCCC), Including Land Use, Land-Use Change and Forestry", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848391-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848391-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1995", + "description": "Emissions of CO2 with LULUCF corresponds to total emissions and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land). ", + "license": "proprietary" + }, + { + "id": "geodata_1998", + "title": "Emissions of CO2 - (National Reports, UNFCCC), Excluding Land Use, Land-Use Change and Forestry", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848100-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848100-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_1998", + "description": "Emissions of CO2 without LULUCF corresponds to total emissions and removals without activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land).", + "license": "proprietary" + }, + { + "id": "geodata_2001", + "title": "Emissions of CH4 - (National Reports, UNFCCC), Excluding Land Use, Land-Use Change and Forestry", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847544-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847544-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2001", + "description": "Emissions of CH4 without LULUCF: Total emissions and removals without emissions from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land).", + "license": "proprietary" + }, + { + "id": "geodata_2004", + "title": "Emissions of CH4 - (National Reports, UNFCCC), Including Land Use, Land-Use Change and Forestry", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846732-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846732-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2004", + "description": "Emissions of CH4 with LULUCF: Total emissions and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land).", + "license": "proprietary" + }, + { + "id": "geodata_2018", + "title": "Nitrogen (N Total Nutrients) - Production", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848954-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848954-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2018", + "description": "Fertilizer consumption refers to the application of nutrients in terms of nitrogen (N), phosphate (P2O5), and potash (K2O) consumed in agriculture by a country. All figures are given in Metric Tonnes (T) of plant nutrients (N total nutriens). Production P = (\u2013 M) + X + NF + C; Production = less Imports + Exports + Non fertilizer use + Consumption When the data of a country are all available then P = the country actual production; M = actual imports, X = actual exports, C =actual consumption and NF = actual non fertilizer use. ", + "license": "proprietary" + }, + { + "id": "geodata_2021", + "title": "Nitrogen (N Total Nutrients) - Consumption", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849307-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849307-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2021", + "description": "Fertilizer consumption refers to the application of nutrients in terms of nitrogen (N), phosphate (P2O5), and potash (K2O) consumed in agriculture by a country. All figures are given in Tonnes (T) of plant nutrients (N total nutrients). Consumption (C) = Production (P) + Imports (M) - Exports (X) - Non-Fertilizer use (NF); P + M \u2013 X \u2013 NF = C When data is not known for either fertilizer production or consumption, then the other items are used to derive the residual data. When this occurs, the data is labelled as apparent (e.g. apparent production). Apparent Consumption AC = P + M \u2013 (NF + X); Apparent consumption = production + imports - (non-fertilizer use + exports). Apparent consumption figures are developed based on the underlying assumption that supply equals consumption. However, actual apparent consumption may be underestimated due to the following: ( Non-fertilizer use assumed to be zero in the absence of data; Stocks of fertilizer assumed to be zero or stable; Country imports or exports of fertilizer data not available and assumed to be zero).", + "license": "proprietary" + }, { "id": "geodata_2024", "title": "Crustaceans - Number of Threatened Species", @@ -204268,6 +215838,32 @@ "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. ", "license": "proprietary" }, + { + "id": "geodata_2026", + "title": "Molluscs - Number of Threatened Species", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849175-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849175-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2026", + "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. ", + "license": "proprietary" + }, + { + "id": "geodata_2027", + "title": "Fishes - Threatened Species as Percent of Species Evaluated", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2007-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849102-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849102-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2027", + "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. A part from the mammals, birds, amphibians and gymnosperms (i.e., those groups completely or almost completely evaluated), the figures in the last column are gross over-estimates of the percentage threatened due to biases in the assessment process towards assessing species that are thought to be threatened, species for which data are readily available, and under-reporting of Least Concern species. The true value for the percentage threatened lies somewhere in the range indicated by the two right-hand columns. In most cases this represents a very broad range. For example, the true percentage of threatened insects lies somewhere between 0.07% and 50%. Hence, although 39% of all species on the IUCN Red List are listed as threatened, this figure needs to be treated with extreme caution given the biases described above. ", + "license": "proprietary" + }, { "id": "geodata_2028", "title": "Amphibians - Threatened Species as Percent of Species Evaluated", @@ -204294,6 +215890,32 @@ "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. A part from the mammals, birds, amphibians and gymnosperms (i.e., those groups completely or almost completely evaluated), the figures in the last column are gross over-estimates of the percentage threatened due to biases in the assessment process towards assessing species that are thought to be threatened, species for which data are readily available, and under-reporting of Least Concern species. The true value for the percentage threatened lies somewhere in the range indicated by the two right-hand columns. In most cases this represents a very broad range. For example, the true percentage of threatened insects lies somewhere between 0.07% and 50%. Hence, although 39% of all species on the IUCN Red List are listed as threatened, this figure needs to be treated with extreme caution given the biases described above. ", "license": "proprietary" }, + { + "id": "geodata_2031", + "title": "Mammals - Threatened Species as Percent of Species Evaluated", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2007-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849019-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849019-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2031", + "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. A part from the mammals, birds, amphibians and gymnosperms (i.e., those groups completely or almost completely evaluated), the figures in the last column are gross over-estimates of the percentage threatened due to biases in the assessment process towards assessing species that are thought to be threatened, species for which data are readily available, and under-reporting of Least Concern species. The true value for the percentage threatened lies somewhere in the range indicated by the two right-hand columns. In most cases this represents a very broad range. For example, the true percentage of threatened insects lies somewhere between 0.07% and 50%. Hence, although 39% of all species on the IUCN Red List are listed as threatened, this figure needs to be treated with extreme caution given the biases described above. ", + "license": "proprietary" + }, + { + "id": "geodata_2032", + "title": "Human Impact on Marine Ecosystems", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "9999-01-01", + "end_date": "9999-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849178-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849178-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2032", + "description": "The global map of Human Impact on Marine Ecosystems is an ecosystem-specific, multiscale spatial model to synthesize 17 global data sets of anthropogenic drivers of ecological change for 20 marine ecosystems.", + "license": "proprietary" + }, { "id": "geodata_2034", "title": "Anthropogenic Drivers of Change on Marine Ecosystems: Nutrient Pollution (Fertilizer)", @@ -204307,6 +215929,45 @@ "description": "The Nutrient Pollution (Fertilizer) dataset represents an anthropogenic driver of ecological change for marine ecosystem. ", "license": "proprietary" }, + { + "id": "geodata_2039", + "title": "Energy Production - Biodiesel (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846597-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846597-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2039", + "description": "Biodiesels includes biodiesel (a methyl-ester produced from vegetable or animal oil, of diesel quality), biodimethylether (dimethylether produced from biomass), Fischer Tropsh (Fischer Tropsh produced from biomass), cold pressed bio-oil (oil produced from oil seed through mechanical processing only) and all other liquid biofuels which are added to, blended with or used straight as transport diesel. Biodiesels includes the amounts that are blended into the diesel - it does not include the total volume of diesel into which the biodiesel is blended. A KTOE is defined as 41.868 TJ (Terajoules) or 11.630 GWh (Gigawatt-Hour). ", + "license": "proprietary" + }, + { + "id": "geodata_2045", + "title": "Energy Production - Biogasoline (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1992-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848504-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848504-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2045", + "description": "Biogasoline includes bioethanol (ethanol produced from biomass and/or the biodegradable fraction of waste), biomethanol (methanol produced from biomass and/or the biodegradable fraction of waste), bioETBE (ethyl-tertio-butyl-ether produced on the basis of bioethanol; the percentage by volume of bioETBE that is calculated as biofuel is 47%) and bioMTBE (methyl-tertio-butyl-ether produced on the basis of biomethanol: the percentage by volume of bioMTBE that is calculated as biofuel is 36%). Biogasoline includes the amounts that are blended into the gasoline - it does not include the total volume of gasoline into which the biogasoline is blended. A KTOE is defined as 41.868 TJ (Terajoules) or 11.630 GWh (Gigawatt-Hour). ", + "license": "proprietary" + }, + { + "id": "geodata_2048", + "title": "Energy Production - Other Liquid Biofuels (IEA)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847933-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847933-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2048", + "description": "Other liquid biofuels includes liquid biofuels used directly as fuel other than biogasoline or biodiesels. ", + "license": "proprietary" + }, { "id": "geodata_2063", "title": "Average Monthly Maximum Temperature - January", @@ -204632,6 +216293,19 @@ "description": "AQUACULTURE PRODUCTION The annual series of aquaculture production begin in 1950 for the quantities and in 1984 for the values. Aquaculture is the farming of aquatic organisms including fish, mollusks, and crustaceans*. Farming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual or corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by the public as a common property resource, with or without appropriate licenses, are the harvest of fisheries. Production of fish, crustaceans and molluscs is expressed in live weight that is the nominal weight of the aquatic organisms at the time of capture Quantities are given in tonnes. *includes all FAOSTAT group excluded aquatic animals nei, aquatic plants, aquatic mammals. ", "license": "proprietary" }, + { + "id": "geodata_2126", + "title": "Fishery Production - Inland Waters", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1960-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848333-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848333-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2126", + "description": "TOTAL PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. * includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals ", + "license": "proprietary" + }, { "id": "geodata_2127", "title": "Cadmium (Cd) Production", @@ -204658,6 +216332,45 @@ "description": "World metal consumption", "license": "proprietary" }, + { + "id": "geodata_2129", + "title": "Lead (Pb) Production", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-01-01", + "end_date": "2006-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847966-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847966-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2129", + "description": "Lead production refers to World mine production (metal content).", + "license": "proprietary" + }, + { + "id": "geodata_2130", + "title": "Lead (Pb) Consumption", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-01-01", + "end_date": "2006-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848445-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848445-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2130", + "description": "Lead Consumption refers to World refined lead consumption", + "license": "proprietary" + }, + { + "id": "geodata_2131", + "title": "Mercury (Hg) Production", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-01-01", + "end_date": "2006-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848487-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848487-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2131", + "description": "World metal production (primary metal)", + "license": "proprietary" + }, { "id": "geodata_2134", "title": "Agricultural Area Irrigated", @@ -204684,6 +216397,19 @@ "description": "Country area, area of the country including area under inland water bodies, but excluding offshore territorial waters. Possible variations in the data may be due to updating and revisions of the country data and not necessarily to any change of area.", "license": "proprietary" }, + { + "id": "geodata_2136", + "title": "Forest Area", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848786-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848786-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2136", + "description": "Forest area is the land spanning more than 0.5 hectares with trees higher than 5 metres and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use. Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 metres (m) in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate. Includes: areas with bamboo and palms provided that height and canopy cover criteria are met; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry or protective purposes, such as: rubber-wood plantations and cork, oak stands. Excludes: tree stands in agricultural production systems, for example in fruit plantations and agroforestry systems. The term also excludes trees in urban parks and gardens. ", + "license": "proprietary" + }, { "id": "geodata_2169", "title": "Consumption of Ozone-Depleting Substances - All", @@ -204736,6 +216462,45 @@ "description": "Consumption of Ozone-Depleting CFCs is the sum of the consumption of the weighted tons of the individual substances in the group\u2014metric tons of the individual substance (defined in the Montreal Protocol on Substances That Deplete the Ozone Layer) multiplied by its ozone-depleting potential. Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. The consumption of CFCs is the national production plus imports, minus exports, minus destroyed quantities, minus feedstock uses of individual CFCs. National annual consumption of CFCs is the sum of the weighted tons (consumption in metric tons multiplied by the estimated ozone-depleting potential) of the individual CFCs. ", "license": "proprietary" }, + { + "id": "geodata_2173", + "title": "Emissions of CO2 per GDP (PPP)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1980-01-01", + "end_date": "2006-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847194-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847194-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2173", + "description": "PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2005 international dollars. Source: World Bank, International Comparison Program database. ", + "license": "proprietary" + }, + { + "id": "geodata_2195", + "title": "Large Marine Ecosystem (LME)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-01-01", + "end_date": "2002-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849215-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849215-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2195", + "description": "LMEs are natural regions of ocean space encompassing coastal waters from river basins and estuaries to the seaward boundary of continental shelves and the outer margins of coastal currents. They are relatively large regions of 200,000 km2 or greater, the natural boundaries of which are based on four ecological criteria: bathymetry, hydrography, productivity, and trophically related populations. ", + "license": "proprietary" + }, + { + "id": "geodata_2197", + "title": "Improved Sanitation Coverage - Total Population with Shared Sanitation", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849360-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849360-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2197", + "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. Improved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine. ", + "license": "proprietary" + }, { "id": "geodata_2199", "title": "Carbon Stock in Living Forest Biomass", @@ -204749,6 +216514,84 @@ "description": "CARBON STOCK The quantity of carbon in a \u201cpool\u201d, meaning a reservoir or system which has the capacity to accumulate or release carbon. Examples of carbon pools are Living biomass (including Above and below-ground biomass); Dead organic matter (including dead wood and litter); Soils (soils organic matter). The units are mass. ", "license": "proprietary" }, + { + "id": "geodata_2200", + "title": "Forest Primary Designated Function - Production", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2010-01-01", + "end_date": "2010-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847443-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847443-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2200", + "description": "DESIGNATED FUNCTIONS (of Forest and Other wooded land) the designated function refers to the function or purpose assigned to a piece of land either by legal prescriptions or by decision of the land owner/manager. It applies to land classified as \u201cForest\u201d and as \u201cOther wooded land\u201d. Conservation of biodiversity: Forest/Other wooded land designated for conservation of biological diversity. It includes, but is not limited to, Protected Areas. Production: Forest/Other wooded land designated for production and extraction of forest goods, including both wood and non-wood forest products.", + "license": "proprietary" + }, + { + "id": "geodata_2201", + "title": "Forest Primary Designated Function - Conservation of Biodiversity", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2010-01-01", + "end_date": "2010-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847285-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847285-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2201", + "description": "DESIGNATED FUNCTIONS (of Forest and Other wooded land) the designated function refers to the function or purpose assigned to a piece of land either by legal prescriptions or by decision of the land owner/manager. It applies to land classified as \u201cForest\u201d and as \u201cOther wooded land\u201d. Conservation of biodiversity: Forest/Other wooded land designated for conservation of biological diversity. It includes, but is not limited to, Protected Areas. Production: Forest/Other wooded land designated for production and extraction of forest goods, including both wood and non-wood forest products.", + "license": "proprietary" + }, + { + "id": "geodata_2202", + "title": "Forest within Protected Areas", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2010-01-01", + "end_date": "2010-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847496-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847496-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2202", + "description": "As part of FRA 2010, countries were asked to provide information on the area of forest contained in protected areas systems. This is not an easy task where spatially explicit information is missing or outdated since not all protected areas are fully forested. However, most of the large, forest-rich countries did provide this information for all four reporting years. ", + "license": "proprietary" + }, + { + "id": "geodata_2203", + "title": "Forest Revenue", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2005-01-01", + "end_date": "2005-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847552-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847552-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2203", + "description": "Public expenditure and revenue collection from forestry are measures of the financial flows between government and the forestry sector. In FRA 2010 forest revenue was defined to include all taxes, fees, charges and royalties collected specifically from the domestic production and trade of forest products, but it excluded general taxes collected from all sectors of the economy (e.g. corporation tax and sales tax). ", + "license": "proprietary" + }, + { + "id": "geodata_2206", + "title": "Food Supply Quantity - Cereals", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1961-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846748-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846748-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2206", + "description": "Food: total calories Refers to the total amount of food available for human consumption expressed in kilocalories (kcal). Caloric content is derived by applying the appropriate food composition factors to the quantities of the commodities and shown in million units.", + "license": "proprietary" + }, + { + "id": "geodata_2207", + "title": "Livestock Production Index Base 1999-2001 - Total", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1961-01-01", + "end_date": "2009-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846571-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232846571-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2207", + "description": "The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 1999-2001. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. ", + "license": "proprietary" + }, { "id": "geodata_2208", "title": "Cereals - Area Harvested", @@ -204762,6 +216605,32 @@ "description": "Cereals also includes other cereals such as mixed grains and buckwheat. Crop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption). When the production data available refers to a production period falling into two successive calendar years and it is not possible to allocate the relative production to each of them, it is usual to refer production data to that year into which the bulk of the production falls. Crop production data are stored in tonnes (T). ", "license": "proprietary" }, + { + "id": "geodata_2215", + "title": "Hazardous Pesticides - Exports", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2007-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847283-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847283-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2215", + "description": "Refers to the value of the type of pesticide (put up in forms or packings for retail sale or as preparations or articles), provided to (exports) or received (imported) from the rest of the world. Differences between figures given for total exports and total imports at the world level may be due to several factors, e.g. the time lag between the dispatch of goods from exporting country and their arrival in the importing country; the use of different classification of the same product by different countries; or the fact that some countries supply data on general trade while others give data on special trade.", + "license": "proprietary" + }, + { + "id": "geodata_2216", + "title": "Hazardous Pesticides - Imports", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2007-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847501-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847501-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2216", + "description": "Refers to the value of the type of pesticide (put up in forms or packings for retail sale or as preparations or articles), provided to (exports) or received (imported) from the rest of the world. Differences between figures given for total exports and total imports at the world level may be due to several factors, e.g. the time lag between the dispatch of goods from exporting country and their arrival in the importing country; the use of different classification of the same product by different countries; or the fact that some countries supply data on general trade while others give data on special trade.", + "license": "proprietary" + }, { "id": "geodata_2217", "title": "Agricultural Area Certified Organic", @@ -204788,6 +216657,149 @@ "description": "Rivers maintain unique biotic resources and provide critical water supplies to people. The Earth's limited supplies of fresh water and irreplaceable biodiversity are vulnerable to human mismanagement of watersheds and waterways. Multiple environmental stressors, such as agricultural runoff, pollution and invasive species, threaten rivers that serve 80 percent of the world\u2019s population. These same stressors endanger the biodiversity of 65 percent of the world\u2019s river habitats putting thousands of aquatic wildlife species at risk. Efforts to abate fresh water degradation through highly engineered solutions are effective at reducing the impact of threats but at a cost that can be an economic burden and often out of reach for developing nations.", "license": "proprietary" }, + { + "id": "geodata_2223", + "title": "Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2000", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2000-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849273-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849273-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2223", + "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", + "license": "proprietary" + }, + { + "id": "geodata_2224", + "title": "Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2001", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2001-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849194-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849194-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2224", + "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. ", + "license": "proprietary" + }, + { + "id": "geodata_2225", + "title": "Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2003", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-01-01", + "end_date": "2003-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849086-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849086-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2225", + "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", + "license": "proprietary" + }, + { + "id": "geodata_2226", + "title": "Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2004", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2004-01-01", + "end_date": "2004-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848916-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848916-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2226", + "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", + "license": "proprietary" + }, + { + "id": "geodata_2227", + "title": "Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2005", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2005-01-01", + "end_date": "2005-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849045-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849045-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2227", + "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", + "license": "proprietary" + }, + { + "id": "geodata_2228", + "title": "Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2006", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2006-01-01", + "end_date": "2006-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847468-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847468-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2228", + "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", + "license": "proprietary" + }, + { + "id": "geodata_2229", + "title": "Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2007", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2007-01-01", + "end_date": "2007-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847271-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232847271-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2229", + "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", + "license": "proprietary" + }, + { + "id": "geodata_2230", + "title": "Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2008", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2008-01-01", + "end_date": "2008-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849203-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849203-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2230", + "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. ", + "license": "proprietary" + }, + { + "id": "geodata_2231", + "title": "Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2009", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2009-01-01", + "end_date": "2009-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849060-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849060-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2231", + "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", + "license": "proprietary" + }, + { + "id": "geodata_2232", + "title": "Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2002", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-01-01", + "end_date": "2002-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848919-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848919-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2232", + "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. ", + "license": "proprietary" + }, + { + "id": "geodata_2237", + "title": "Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2010", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2010-01-01", + "end_date": "2010-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849274-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849274-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2237", + "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", + "license": "proprietary" + }, { "id": "geodata_2240", "title": "Arsenic in Groundwater - Probability of Occurrence", @@ -204801,6 +216813,71 @@ "description": "Global assessment of the probability of occurrence of excessive Arsenic concentrations", "license": "proprietary" }, + { + "id": "geodata_2244", + "title": "Mineral Resources Outside the United States", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2006-01-01", + "end_date": "2010-01-01", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848393-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848393-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2244", + "description": "Mineral facilities and operations outside the United States compiled by the National Minerals Information Center of the USGS. This representation combines source data from five previous publications. National Minerals Information Center (NMIC) makes available a wide variety of commodity statistics and other mineral resource supply and production information both within the United States and internationally. These databases complement aggregate commodity statistics collected by the NMIC.", + "license": "proprietary" + }, + { + "id": "geodata_2245", + "title": "Mineral Resources Data System (MRDS)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848248-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848248-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2245", + "description": "Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. This product is a digest in which the fields chosen are those most likely to contain valid information. This digest of the complex mineral resources database is intended for use as reference material supporting mineral resource and environmental assessments on local to regional scale worldwide. ", + "license": "proprietary" + }, + { + "id": "geodata_2246", + "title": "Forests Certified by PEFC Chain of Custody - Number of Certifications", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-01-01", + "end_date": "2011-01-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848423-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848423-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2246", + "description": "PEFC's Chain of Custody certification is a mechanism for tracking certified material from the forest to the final product to ensure that the wood, wood fibre or non-wood forest produce contained in the product or product line can be traced back to certified forests. It is an essential part of the PEFC system which ensures that claims about products originating in sustainably managed forests are credible and verifiable throughout the whole supply chain. It is used to certify entities all along the value-chain of forest-based products", + "license": "proprietary" + }, + { + "id": "geodata_2247", + "title": "Forests Certified by PEFC Chain of Custody - Area", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2000-01-01", + "end_date": "2011-01-01", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848522-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848522-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2247", + "description": "PEFC's Chain of Custody certification is a mechanism for tracking certified material from the forest to the final product to ensure that the wood, wood fibre or non-wood forest produce contained in the product or product line can be traced back to certified forests. It is an essential part of the PEFC system which ensures that claims about products originating in sustainably managed forests are credible and verifiable throughout the whole supply chain. It is used to certify entities all along the value-chain of forest-based products", + "license": "proprietary" + }, + { + "id": "geodata_2251", + "title": "Green Water Footprint of National Production", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "2005-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848254-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848254-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2251", + "description": "Green water footprint :Volume of rainwater consumed during the production process. This is particularly relevant for agricultural and forestry products (products based on crops or wood), where it refers to the total rainwater evapotranspiration (from fields and plantations) plus the water incorporated into the harvested crop or wood. Water footprint of national production ? Another term for the ?water footprint within a nation?: Water footprint within a nation Is defined as the total freshwater volume consumed or polluted within the territory of the nation.", + "license": "proprietary" + }, { "id": "geodata_2252", "title": "Blue Water Footprint of National Production", @@ -204814,6 +216891,19 @@ "description": "Blue water footprint ? Volume of surface and groundwater consumed as a result of the production of a good or service. Consumption refers to the volume of freshwater used and then evaporated or incorporated into a product. It also includes water abstracted from surface or groundwater in a catchment and returned to another catchment or the sea. It is the amount of water abstracted from ground- or surface water that does not return to the catchment from which it was withdrawn. Water footprint of national production ? Another term for the ?water footprint within a nation?: Water footprint within a nation Is defined as the total freshwater volume consumed or polluted within the territory of the nation.", "license": "proprietary" }, + { + "id": "geodata_2253", + "title": "Grey Water Footprint of National Production", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-01-01", + "end_date": "2005-12-31", + "bbox": "-180, -90, 180, -60.5033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848523-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232848523-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/geodata_2253", + "description": "Grey water footprint \u2013 The grey water footprint of a product is an indicator of freshwater pollution that can be associated with the production of a product over its full supply chain. It is defined as the volume of freshwater that is required to assimilate the load of pollutants based on existing ambient water quality standards. It is calculated as the volume of water that is required to dilute pollutants to such an extent that the quality of the water remains above agreed water quality standards. Water footprint of national consumption \u2013 Is defined as the total amount of fresh water that is used to produce the goods and services consumed by the inhabitants of the nation. The water footprint of national consumption can be assessed in two ways. The bottom-up approach is to consider the sum of all products consumed multiplied with their respective product water footprint. In the top-down approach, the water footprint of national consumption is calculated as the total use of domestic water resources plus the gross virtual-water import minus the gross virtual-water export. Water footprint of national production \u2013 Another term for the \u2018water footprint within a nation\u2019: Water footprint within a nation Is defined as the total freshwater volume consumed or polluted within the territory of the nation.", + "license": "proprietary" + }, { "id": "geoecology_R1_656_1", "title": "Geoecology: County-Level Environmental Data for the United States, 1941-1981", @@ -222663,6 +234753,19 @@ "description": "This is the source code of the Inishell-2.0.4 flexible Graphical User Interface. It is configured through an XML file for applications that themselves need to be configured via ini-files. It allows to set constraints regarding the sections, keys and values that may be present in the ini-files that are produced by the end user. It is released under the GPL-v3 or later license. Precompiled binaries are available at https://models.slf.ch/p/inishell-ng/downloads/ while the development takes place at https://code.wsl.ch/snow-models/inishell (gitlab forge).", "license": "proprietary" }, + { + "id": "inpe_CPTEC_GLOBAl_FORECAST", + "title": "Global Meteorological Model Analysis and Forecast Images (INPE/CPTEC)", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-120, -60, 0, 30", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456094-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2227456094-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/inpe_CPTEC_GLOBAl_FORECAST", + "description": "CPTEC offers global model analysis and forecast images for twelve meteorological parameters. Forecast time steps range from the initial analysis each day out to six days. The user may choose forecasts from the most recent forecast run back to the previous 36 hours. Parameters Forecasted: Mean Sea Level Pressure Temperature at 1000 hPa Relative Humidity at 925 hPa, 850 hPa Vertical p_Velocity at 850 hPa, 500 hPa, 200 hPa Velocity Potential at 925 hPa, 200 hPa Stream Function at 925 hPa, 200 hPa 500/1000 hPa Thickness Advection of Temperature at 925 hPa, 850 hPa, 500 hPa Advection of Vorticity at 925 hPa, 850 hPa, 500 hPa Convergence of Humidity Flux at 925 hPa, 850 hPa Streamlines and Wind Speed at 925 hPa, 850 hPa, 200 hPa Total Precipitation Last 24 Hours All forecast images can be obtained via World Wide Web from the CPTEC Home Page. Link to: \"http://www.cptec.inpe.br/\"", + "license": "proprietary" + }, { "id": "input-data-for-break-point-detection-of-swiss-snow-depth-time-series_1.0", "title": "Input data for break point detection of Swiss snow depth time series", @@ -222702,6 +234805,19 @@ "description": "One of the most basic needs for inventorying, exploiting and monitoring the changes in the insect diversity of Africa is a complete list of species which are already know to occur in Africa. Surprisingly, such a basic list does not exist, despite some 250 years of formal scientific description of life on earth. The International Centre of Insect Physiology and Ecology (ICIPE), along with the National Museum of Natural History, is therefore sponsoring the production of the list, which will provide a reliable platform of 'standard' names for species on which many other projects depend. This list, or authority file, will greatly enhance communication both among scientists and between scientists and users of scientific data. The African list will also be a major contribution toward the proposed list of world species (e.g. the Global Biodiversity Information Facility (GBIF) and Species 2000 initiative of DIVERSITAS). A demonstration database is provided for the species of the orders Odonata (dragonflies and damselflies), Ephemeroptera (mayflies), Plecoptera (stoneflies), Megaloptera (alderflies), Hemiptera-Heteroptera (true bugs), Homoptera (cicadas, leafhoppers, planthoppers, scales, and others), and Trichoptera (caddisflies). Invitation to collaboration: Compilation of the checklist is being coordinated by Nearctica (formerly Entomological Information Specialists), because of their experience with Nomina Insecta Nearctica. We are attempting to collaborate with known specialists as contributors and reviewers, but we welcome additional suggestions of collaborators. Inquires can be directed to Scott Miller (miller.scott@nmnh.si.edu). Information was obtained from \"http://entomology.si.edu/\".", "license": "proprietary" }, + { + "id": "instm_trawl", + "title": "National Institute of Marine Sciences and Technologies - Trawling Surveys", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1983-04-16", + "end_date": "2006-11-03", + "bbox": "5.14, 17.1, 13.37, 38.1", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477692-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477692-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/instm_trawl", + "description": "The National Institute of Marine Sciences and Technologies (INSTM) fo Tunisia has four laboratories. Regular trawl surveys are done by the Laboratory of Marine Living Resources to assess the exploitable resource stocks. This dataset consists of 7664 records of 90 families.", + "license": "proprietary" + }, { "id": "intercomparison-of-photogrammetric-platforms_1.0", "title": "Photogrammetric snow depth maps from satellite-, airplane-, UAS and terrestrial platforms from the Davos region (Switzerland)", @@ -222806,6 +234922,45 @@ "description": "The Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Backgrounds dataset was collected by the LIS instrument mounted on the ISS and are used to detect the distribution and variability of total lightning occurring in the Earth\u2019s tropical and subtropical regions. This dataset consists of quality controlled science data. This data collection can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. The data are available in both HDF-4 and netCDF-4 formats.", "license": "proprietary" }, + { + "id": "iziko_Crustaceans", + "title": "iziko South African Museum - Crustacean Collection", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1883-01-01", + "end_date": "2003-12-31", + "bbox": "-97.22, -74.58, 172.7, 34.62", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477683-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477683-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/iziko_Crustaceans", + "description": "The iziko South African Museum houses the most important crustacean (crabs, lobsters, shrimps, barnacles) collection in South Africa. Significant past contributions were made by K.H. Barnard, J.R. Grindley and B.F. Kensley (Crustacea). It currently contains 5101 records of 274 families.", + "license": "proprietary" + }, + { + "id": "iziko_molluscs", + "title": "iziko South African Museum - Mollusc Collection", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1881-01-01", + "end_date": "2000-12-31", + "bbox": "-159.56, -59.45, 165.95, 50.6", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477686-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477686-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/iziko_molluscs", + "description": "The iziko South African Museum's mollusc collection of southern African species is the second largest mollusc collection in southern Africa. Significant additions were made in the past by K.H. Barnard. It currently contains 6078 records. The families were not provided.", + "license": "proprietary" + }, + { + "id": "iziko_sharks", + "title": "iziko South African Museum - Shark Collection", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1828-04-01", + "end_date": "2005-12-31", + "bbox": "179.73, 62, 179.25, 73.3", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477682-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477682-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/iziko_sharks", + "description": "This collection has global holdings. It includes numerous representatives of eight of the shark groups, most representatives of the Batoids and Chimaeras, including rare species. Significant material is being acquired from, fisheries research and tooth fish long-lining and fishing company by-catches.", + "license": "proprietary" + }, { "id": "jetty_sat_1", "title": "Jetty Peninsula Satellite Image Map 1:500 000", @@ -223079,6 +235234,19 @@ "description": "The KENX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "license": "proprietary" }, + { + "id": "kenya_marine", + "title": "Kenya Marine and Fisheries Research Institute - Marine Species", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "37.90619, -4.71, 41.74052, -0.0235591", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477677-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477677-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/kenya_marine", + "description": "Kenya Marine and Fisheries Research Institute (KMFRI) is a State Corporation in the Ministry of Fisheries Development of the Government of Kenya. It is mandated to conduct aquatic research covering all the Kenyan waters and the corresponding riparian areas including the Kenyan's EEZ in the Indian Ocean waters. This collection was compiled from publications, and it currently consists of 3080 records of 533 families. ", + "license": "proprietary" + }, { "id": "kerg_ant_bathy_1", "title": "Bathymetric Grid for the region 60E to 90E, 48S to 70S", @@ -225770,6 +237938,58 @@ "description": "This GIS dataset represents walking tracks on Macquarie Island and was compiled by the Australian Antarctic Data Centre from surveys and other sources. This data is displayed in a pair of A3 1:50000 maps of Macquarie Island (see a Related URL).", "license": "proprietary" }, + { + "id": "madagascar_diatoms", + "title": "MADAGASCAR National Oceanographic Data Centre - Diatoms", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2003-10-01", + "end_date": "2004-10-31", + "bbox": "43.61, -23.38, 43.68, -23.35", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477687-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477687-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/madagascar_diatoms", + "description": "The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar\u2019s northeast coast. This dataset of diatoms has been collected at three stations in Toliara Bay, and it currently consists of 2754 records of 19 families.", + "license": "proprietary" + }, + { + "id": "madagascar_dinoflagelles", + "title": "MADAGASCAR National Oceanographic Data Centre - Dinoflagellates", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2002-12-01", + "end_date": "2003-12-31", + "bbox": "43.61, -23.38, 43.68, -23.35", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477667-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477667-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/madagascar_dinoflagelles", + "description": "The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar\u2019s northeast coast. This dataset of dinoflagellates has been collected at three stations in Toliara Bay, and it currently consists of 1297 records of 15 families.", + "license": "proprietary" + }, + { + "id": "madagascar_fish", + "title": "MADAGASCAR National Oceanographic Data Centre - Fish", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-07-29", + "end_date": "2001-08-18", + "bbox": "43.58, -23.38, 43.58, -23.38", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477670-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477670-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/madagascar_fish", + "description": "The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar\u2019s northeast coast. This dataset has been collected in Toliara Bay, and includes bony fish, cartilagenous fish, mammals and reptiles. It currently consists of 721 records of 49 families.", + "license": "proprietary" + }, + { + "id": "madagascar_invertebrates", + "title": "MADAGASCAR National Oceanographic Data Centre - Invertebrates", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "2001-07-29", + "end_date": "2001-08-18", + "bbox": "43.58, -23.38, 43.58, -23.38", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477672-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477672-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/madagascar_invertebrates", + "description": "The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar\u2019s northeast coast. This dataset has been collected in Toliara Bay, and includes mollusks, echinoderms, crustaceans, sponges and annelids. It currently consists of 230 records of 7 phylums.", + "license": "proprietary" + }, { "id": "madcrypto-bryophyte-and-macrolichen-diversity-in-laurel-forests-of-madeira_1.0", "title": "MadCrypto \u2013 Bryophyte and macrolichen diversity in laurel forests of Madeira", @@ -226121,6 +238341,19 @@ "description": "This dataset consists of: 1 GeoEye-1 stereo imagery of an area of approximately 100 square kilometres including McDonald Island, captured 19 May 2012 2 A Digital Elevation Model (DEM) derived from the GeoEye-1 stereo imagery; and 3 Image products derived from the most vertical dataset of the stereo imagery and orthorectified using the DEM. 4 Contours generated from the DEM. The DEM was produced at a 1 metre pixel size and is available in ESRI grid, ESRI ascii and BIL formats. The DEM and image products are stored in a Universal Transverse Mercator zone 43 south projection, based on the WGS84 datum. The image products are geotiffs as follows. McDonald_Island_BGRN.tif: GeoEye-1 4-band multispectral (vis blue, green, red and Near Infrared), 2 metre resolution. McDonald_Island_PAN.tif: GeoEye-1 panchromatic, 0.5 metre resolution. McDonald_Island_PS_BGRN.tif: GeoEye-1 pansharpened, 4-band multispectral (vis blue, green, red and Near Infrared), 0.5 metre resolution. McDonald_Island_RGB.tif: GeoEye-1 pansharpened, natural colour enhancement, 0.5 metre resolution.", "license": "proprietary" }, + { + "id": "mcm_seals", + "title": "Marine and Coastal Management (MCM) - Seal Surveys", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1974-04-08", + "end_date": "2001-06-01", + "bbox": "11.68, -34.98, 26.11, -17.47", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477678-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477678-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/mcm_seals", + "description": "Marine and Coastal Management (MCM) is one of four branches of the Department of Environmental Affairs and Tourism. It is the regulatory authority responsible for managing all marine and coastal activities. The seal data set is a collection of seals shot at-sea cruises, and has been collected from cruises around the South African Coast, and currently contains 2440 records of 1 family (Otariidae).", + "license": "proprietary" + }, { "id": "mean-insect-occupancy-1970-2020_1.0", "title": "Mean insect occupancy 1970\u20132020", @@ -227694,6 +239927,19 @@ "description": "Cities are socio-ecological systems that filter and select species, thus establishing unique species assemblages and biotic interactions. Urban ecosystems can host richer wild bee communities than highly intensified agricultural areas, specifically in resource-rich urban green spaces such as allotment and family gardens. At the same time, urban beekeeping has boomed in many European cities, raising concerns that the fast addition of a large number of managed bees could deplete the existing floral resources, triggering competition between wild bees and honeybees. The data has been used to investigated the interplay between resource availability and the number of honeybees at local and landscape scales and how this relationship influences wild bee diversity. This dataset contains the raw and processed data supporting the findings from the paper: \"Low resource availability drives feeding niche partitioning between wild bees and honeybees in a European city\". The data contains: 1. Bee trait measurements at the species and individual-level of five functional traits. 2. The values of the feeding niche partitioning (functional dissimilarity to honeybees) 3. The predictors of resource availability and beekeeping intensity at local and landscape scales used in the modelling of the paper for the 23 experimental sites.", "license": "proprietary" }, + { + "id": "nigeria_marine", + "title": "Nigerian Institute for Oceanography and Marine Research - Marine Species", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-5, 4.27417, 5.89333, 6.39722", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477671-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232477671-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/nigeria_marine", + "description": "The Nigerian Institute for Oceanography and Marine Research (NIOMR), was created from the Marine Research Division of the Federal Department of Fisheries. The Aquaculture department is mandated to research into the development of Aquaculture, including improvement of transportation devices for juveniles to reduce mortality. This collection was compiled from publications, and it currently consists of 556 records of 106 families.", + "license": "proprietary" + }, { "id": "nitrogen_deposition_730_1", "title": "Nitrogen Deposition onto the United States and Western Europe", @@ -231529,6 +243775,19 @@ "description": "The Swiss Federal Research Institute WSL has extensive experience with surrogate bedload transport measurements. The first measuring site was established in the Erlenbach stream, a small (first-order) catchment in the pre-alpine valley Alptal in central Switzerland. Continuous bedload transport measurements were started in 1986, using first piezoelectric sensors (1986 to 1999) and then geophone sensors (from 2002 onwards) underneath a steel plate and mounted flush with the streambed. In the meantime, the so-called Swiss plate geophone (SPG) system has been installed at more than 20 field sites, primarily in smaller and steeper streams in Switzerland, Austria, and Italy but also in a few larger rivers and in some other streams worldwide (Israel, USA, Japan). Sediment transport observations in Switzerland with the SPG system concern the following streams: Erlenbach near Brunni (Alptal valley), Albula at Tiefencastel, Navisence at Zinal, Avan\u00e7on de Nant near Pont de Nant (see map). The data in this repository primarily refer to calibration measurements with the SPG system. The publications listed here discuss primarily the performance of the measuring system but also process-based aspects of bedload transport.", "license": "proprietary" }, + { + "id": "sediments_gom", + "title": "Gulf of Maine Contaminated Sediments Database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-71.4661, 40.6306, -67.2693, 44.6999", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553179-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553179-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/sediments_gom", + "description": "The overall objective of this project is to create a database of existing data on contaminants in sediment for the Gulf of Maine region that will be useful to persons throughout the region for scientific and management purposes. This task involves identification of data sources, entry of data into the database format, validation or scientific editing of the database, some analysis and synthesis of the compiled data, and publication of the database and associated bibliographies. The tasks of locating and entering data are being shared among the principle investigators in this project because they require a thorough knowledge of the geographic regions under consideration, an understanding of the types of data identified, and familiarity with active research in these regions. This cooperative approach insures that a more thorough identification and collection of data occurs than could take place from one institution. It also insures that the compiled database will be used by all the participants and their colleagues in the future. Objectives of the work: 1) Develop a comprehensive inventory (database) of available information on sediment contaminants, both inorganic and organic, for the Gulf of Maine 2) Encourage the cooperation and active participation of multiple agencies and organizations in locating, incorporating, and utilizing the data. 3) Place these and ancillary data in interactive, user-friendly, and readily exchangeable forms (such as desktop computer, FTP, and CD-ROM). 4) Map and analyze sediment contaminant distributions in order to provide the best assemblage of information possible for use in determining contaminant baselines 5) Utilize the database to address specific scientific questions about transport and fate of contaminants in the GOM system. 6) Provide guidance for other agencies and organizations to further the usefulness of the data in research, resource management, and public policy decisions. 7) Provide guidance on where to sample and how to analyze samples in the future to make more effective use of limited resources", + "license": "proprietary" + }, { "id": "seilaplan-herunterladen-des-dhm-mit-swissgeodownloader_1.0", "title": "Seilaplan Tutorial: DTM download with SwissGeoDownloader", @@ -235546,6 +247805,19 @@ "description": "Average spectral reflectance measurements of the ground surface of BOREAS flux tower sites. Measurements made along a transect with the instrument held at approximately one meter above the ground.", "license": "proprietary" }, + { + "id": "unep_marineturtle", + "title": "Marine Turtle Nesting Database", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "20, -39, 165, 39", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849059-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2232849059-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/unep_marineturtle", + "description": "Distribution of marine turtles in the Indian Ocean. Information was obtained from published and unpublished literature, and through liaison with turtle fieldworkers. It was intended that the database would be of use to a wide audience, including biologists, coastal planners and those concerned with emergency response to oil spills. Assessing the level of demand for these data, and the feasibility of maintaining data to reflect best available information.", + "license": "proprietary" + }, { "id": "urals-latitudinal-decline-in-treeline-biomass-and-productivity_1.0", "title": "Urals: latitudinal decline in treeline biomass and productivity", @@ -237054,6 +249326,19 @@ "description": "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to \"develop a uniform hierarchical vegetation methodology\" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (April, 1996). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to \"develop, test, refine, and finalize the standards and protocols\" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Congaree Swamp National Monument was designated as one of the prototype parks. Congaree Swamp National Monument, established in 1976, was designated as one of the prototypes within the National Park System. The park contains approximately 22,200 acres (34 square miles). Congaree Swamp National Monument is located approximately 15 miles southeast of Columbia, the state capitol of South Carolina. The Congaree River, draining over 8,000 square miles of Piedmont land to the northwest, forms the southern border. On June 30, 1983, Congaree Swamp National Monument became an International Biosphere Reserve. Congaree is noted for containing one of the last significant stands of old growth bottomland hardwood forest, over 11,000 acres in all. The Monument contains over 90 species of trees, 16 of which hold state records for size. Included in this list of records is a national record sweet gum with a basal circumference of nearly 20 feet. Congaree Swamp National Monument is located approximately 15 miles southeast of Columbia, the state capitol of South Carolina. Old Bluff Highway (old Highway 48) lies just north of the Monument boundary. The eastern boundary is located just northwest of the confluence of the Congaree and Wateree Rivers. The Monument extends west to where Cedar Creek and Myers Creek join. The normal process in vegetation mapping is to conduct an initial field reconnaissance, map the vegetation units through photointerpretation, and then conduct a field verification. The field reconnaissance visit serves two major functions. First, the photointerpreter keys the signature on the aerial photos to the vegetation on the ground at each signature site. Second, the photointerpreter becomes familiar with the flora, vegetation communities and local ecology that occur in the study area. Park and/or TNC field biologists that are familiar with the local vegetation and ecology of the park are present to help the photointerpreter understand these elements and their relationship with the geography of the park. Upon completion of the field reconnaissance, photo interpreters delineate vegetation units on mylar that overlay the 9x9 aerial photos. This effort is conducted in accordance with the TNC vegetation classification and criteria for defining each community or alliance. The initial mapping is then followed by a field verification session, whose purpose is to verify that the vegetation units were mapped correctly. Any PI related questions are also addressed during the visit. The vegetation mapping at Congaree Swamp National Monument in general followed the normal mapping procedure as described in the above paragraph with two major exceptions: 1) Preliminary delineations for most of the park, including a set of Focused Transect overlays that were labeled with an initial PI signature commenced prior to the field reconnaissance visit. 2) A TNC classification did not exist at the time the initial delineations began. TNC ecologist and AIS photo interpreters worked together to develop an interim signature key which addressed what was known at the time. At that time, no comprehensive study containing plot data was available to create an interim classification. From the onset of the Vegetation Inventory and Mapping Program, a standardized program-wide mapping criteria has been used. The mapping criteria contains a set of documented working decision rules used to facilitate the maintenance of accuracy and consistency of the photointerpretation. This criteria assists the user in understanding the characteristics, definition and context for each vegetation community. The mapping criteria for Congaree Swamp National Monument was composed of four parts: The standardized program-wide general mapping criteria A park specific mapping criteria A working photo signature key The TNC classification, key and descriptions The following sections detail the mapping criteria used during the photointerpretation of Congaree Swamp. General Mapping Criteria The mapping criteria at Congaree Swamp are a modified version from previously mapped parks. The criteria differs primarily in that the height and density variables were not mapped at Congaree Swamp. Instead, two additional variables were addressed: pre-hurricane Hugo community types and areas of pine that have been logged since the time of the 1976 aerial photography. These two categories will be addressed in the Park Specific Mapping Criteria section of this report. Since forest densities within the Monument are nearly always greater than 60%, it served little or no purpose in addressing this element as a separate attribute in the database. In addition it was also determined that height categories are extremely difficult to map in the Monument due to variability of the tree emergent layer, and lack of any significant reference points that help in determining canopy heights. Alliance / Community Associations The assignment of alliance and community association to the vegetation is based on criteria formulated by the field effort and classification development. In the case of Congaree Swamp National Monument, TNC provided AIS with a tentative community classification in April 1998. A final vegetation classification, key, and descriptions of each alliance and community, was provided in October 1998. In addition, TNC provided AIS with detailed plot data showing how the communities were developed in the Monument. The information for the metadata came from \"http://biology.usgs.gov/npsveg/cosw/metacoswspatial.html\" and was converted to the NASA Directory Interchange Format.", "license": "proprietary" }, + { + "id": "usgs_nps_d_microbialcontam", + "title": "Microbial Contamination of Water Resources in the Chatahoochee River National Recreation Area, Georgia", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1999-03-01", + "end_date": "2000-04-01", + "bbox": "-86, 30, -81, 35", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549590-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549590-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_d_microbialcontam", + "description": "The study area is the watershed for the Chattahoochee River from Buford Dam to just downstream of the mouth of Peachtree Creek. This study area includes the entire Chattahoochee River National Recreation Area, much of Metropolitan Atlanta, and extends downstream of two major wastewater treatment plant outfalls for the City of Atlanta and Cobb County. The 2-year study is for fiscal years 1999 and 2000. There are six months of microbial sampling in each fiscal year spanning from April 1, 1999 through March 30, 2000. This study measures fecal-indicator bacteria (fecal coliform, E. coli, and enterococci) every five days from April 1, 1999 to September 30, 1999 and every 8 days from October 1, 1999 to March 30, 2000 at three main stem Chattahoochee River sites. The five-day and eight-day sampling intervals will ensure mid week and weekend flow conditions are sampled. Indicator bacteria samples will also be collected during one 26-hour period to look at diel fluctuations. Another indicator bacteria (Clostridium perfringens), F-specific coliphages, somatic coliphages, and chemical sewage tracers will be measured as part of several synoptic surveys at 3 fixed sites and 9 synoptic sites. The 2-year project investigates the existence, severity, and extent of microbial contamination in the Chattahoochee River and 8 major tributaries within the Chattahoochee River National Recreation Area (CRNRA). High levels of fecal-indicator bacteria are the principal basis for impairment of streams in the CRNRA. Three data-collection activities include: 1.Fixed interval: Sample fecal-indicator bacteria and predictor variables (stream stage, stream flow, turbidity, and field water-quality parameters) every 5 days from April 1 to September 30, 1999 and every 8 days from October 1, 1999 to March 30, 2000 at 3 Chattahoochee River sites. (view map) 2.Synoptic surveys: Sample fecal-indicator bacteria, Clostridium perfringens, viruses, predictor variables, and chemical sewage tracers at 4 Chattahoochee River sites and 8 tributary sites during critical seasons and hydrologic conditions. 3.Diel samples: Sample fecal-indicator bacteria and predictor variables every 2 hours for one 26-hour period (August 4-5, 1999) at the Chattahoochee River at Atlanta, which is downstream of the CRNRA. Four proposed main stem sampling sites in downstream order on the Chattahoochee River include: 1.Chattahoochee River at Settles Bridge Road near Suwanee 2.Chattahoochee River at Johnsons Ferry Road near Atlanta 3.Chattahoochee River at Atlanta (Paces Ferry Road; downstream from Palisades Unit) 4.Chattahoochee River at State Highway 280, near Atlanta (Synoptic site only; downstream from all of the CRNRA, much of Metropolitan Atlanta, and 2 major wastewater treatment outfalls for the City of Atlanta and Cobb County; will provide microbial data for a Chattahoochee River site directly affected by point sources of wastewater effluent) Eight proposed tributary sampling sites within the CRNRA watershed in downstream order include: 1.James Creek near Cumming (James Burgess Road) 2.Suwanee Creek near Suwanee (at US Route 23, Buford Hwy) 3.Johns Creek near Warsaw (Buice Road) 4.Crooked Creek near Norcross (Spalding Road) 5.Big Creek near Roswell (below Water Works intake) 6.Willeo Creek near Roswell (State Route 120) 7.Sope Creek near Marietta (Lower Roswell Road) 8.Rottenwood Creek near Smyrna (Interstate Parkway North) In general, fecal-indicator bacteria are used to assess the public-health acceptability of water. The concentration of indicator bacteria is a measure of water safety for body-contact recreation or for consumption (Myers and Sylvester, 1997). Indicator bacteria do not typically cause diseases (pathogenic), but they indicate the possible presence of pathogenic organisms. Escherichia coli (E. coli) and enterococci are currently the preferred fecal indicators for recreational freshwaters because they are superior to fecal coliforms and fecal streptococci as predictors of swimming-associated gastroenteritis (Cabelli, 1977; Dufour, 1984); however fecal coliforms are still used by many states including Georgia to monitor recreational waters. Most historical indicator bacteria data for surface water within the CRNRA are fecal coliform counts collected once a month on a mid-weekday during normal working hours. This study proposes to measure fecal coliform using the membrane filter technique (preferred over the broth technique used by Georgia EPD),E. coli, and enterococci every five days during the recreation season at three main stem sites. The five-day cycle will ensure mid week and weekend flow conditions are sampled. All samples will be collected using USGS protocols for bacteria and equal width interval (EWI) sampling. Clostridium perfringens (C. perfringens) is another indicator bacteria that is present in large numbers in human and animal wastes, and its spores are more resistant to disinfection and environmental stresses than are most other bacteria. It is also a sensitive indicator of microorganisms that enter streams from point sources (Sorenson and others, 1989). It must be analyzed under anaerobic conditions in a laboratory and is best attempted by a biologist or highly trained technician. This study proposes to measure C. perfringens at 4 main stem and 8 tributary sites as part of synoptic surveys during critical seasons and hydrologic conditions. Because monitoring of enteric viruses is recognized as being difficult,time consuming, and expensive, some researchers advocate the use of coliphage for routine viral monitoring. Coliphages are bacteriophages that infect and replicate in coliform bacteria. Although somatic and Fecal-Specific coliphages are not consistently found in feces, they are found in high numbers in sewage and are thought to be reliable indicators of the sewage contamination of waters (International Association on Water Pollution Research and Control, 1991). Coliphage is also recognized to be representative of the survival transport of viruses in the environment. However, to date, they have not been found to correlate with the presence of pathogenic viruses. This study proposes to measure enteric viruses at 4 main stem and 8 tributary sites as part of synoptic surveys during critical seasons and hydrologic conditions.", + "license": "proprietary" + }, { "id": "usgs_nps_devilstower", "title": "Devils Tower National Monument Field Plots Data Base for Vegetation Mapping", @@ -237080,6 +249365,97 @@ "description": "The National Park Service (NPS), in conjunction with the Biological Resources Division BRD) of the U.S. Geological Survey (USGS), has implemented a program to \"develop a uniform hierarchical vegetation methodology\" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation at Devils Tower National Monument was mapped using 1:16,000 scale U.S. Forest Service Color Aerial Photography acquired July 29, 1993. The mapping classification used two separate classification systems. All natural vegetation used the National Vegetation Classification System (NVCS) as a base. The vegetation classification was created after extensive on site sampling and numerical analysis. The vegetation map units were derived from the vegetation classification. Other non-natural or cultural mapping units used the Anderson Level II classification system. The mapped area includes a buffer around the Monument boundary. This mapping effort originates from a long-term vegetation monitoring program that is part of a larger Inventory and Monitoring (I&M) program started by the National Park Service (NPS). I&M goals are, among others, to map the vegetation of all national parks and monuments and provide a baseline inventory of vegetation. The I&M program currently works in close cooperation with the Biological Resources Division (BRD) of the United States Geological Survey (USGS). The USGS/BRD continues overall management and oversight of all ongoing mapping efforts in close cooperation with the NPS. The purposes of the mapping effort are varied and include the following: Provides support for NPS Resources Management. Promotes vegetation-related research for both NPS and USGS/BRD. Provides support for NPS Planning and Compliance. Adds to the information base for NPS Interpretation. Assists in NPS Operations. The geographic extent of the data set is Devils Tower National Monument and about a 2 mile environs around Monument Boundaries - Black Hills, Wyoming, USA. Information was obtained from \"http://biology.usgs.gov/npsveg/deto/metadetospatial.html\" and converted to NASA Directory Interchange Format.", "license": "proprietary" }, + { + "id": "usgs_nps_fortlaramie", + "title": "Fort Laramie National Historic Site, Field Plots Data Base for Vegetation Mapping", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-06-21", + "end_date": "1997-06-29", + "bbox": "-104.34382, 42.11212, -104.13276, 42.13276", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552621-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552621-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_fortlaramie", + "description": "Vegetation field plots at Fort Laramie NHS were visited, described, and documented in a digital database. The database consists of two parts - (1) Physical Descriptive and Stratum Data, and (2) Species Listings. The purpose of the field plots is to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. The dataset is of the Fort Laramie National Historic Site and surroundings. Fort Laramie is located in Goshen County, Wyoming. Field sampling using releve plots. Information for this metadata was obtained from \"http://biology.usgs.gov/npsveg/fola/metafolafield.html\" and put into NASA Directory Interchange Format.", + "license": "proprietary" + }, + { + "id": "usgs_nps_fortlaramiespatial", + "title": "Fort Laramie National Historic Site, Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-07-15", + "end_date": "1995-07-15", + "bbox": "-104.5729, 41.18889, -104.5269, 42.225", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548993-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548993-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_fortlaramiespatial", + "description": "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to \"develop a uniform hierarchical vegetation methodology\" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (July, 1995). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to \"develop, test, refine, and finalize the standards and protocols\" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Fort Laramie National Historic Site was designated as one of the prototype parks. The monument is located in the high Great Plains. It contains prairie, hill, and riverine environments, with vegetation types that include upland woodland, prairie grassland, riverine woodland, and wetlands. Fort Laramie National Historic Site was created by the National Park Service on July 16, 1938. The park occupies 833 acres of land on the Laramie River, west of its confluence with the North Platte River in western Wyoming. Bureau of Land Management land south of the park (referred to as Plot 3) and northwest of the park (referred to as Plots 1 and 5) are also within the mapping study area. The park is primarily preserved as an historic site. The fort site was occupied first as a fur trading center, then subsequently as a military outpost. It further served as a way station for trappers, traders, and emigrants on the Oregon Trail. The old fort site, located in the western end of the park, contains a complex of restored buildings and ruins, dating from mid and late 19th century, surrounding a lawn quadrangle. The remainder of the park contains disturbed prairie and floodplains. The park itself lies mainly on the floodplain terrace of the Laramie River, with a portion on the North Platte River floodplain terrace just west of their confluence. A small portion of the northwest corner of the park lies above the terrace. Plot 3 lies directly south of the park, across the Fort Laramie Canal. It is an area of rolling hills. Plots 1 and 5 lie 1/4 mile northwest of the park, also in rolling hills. The park is surrounded by rolling hills that are used for grazing and some agricultural cultivation. The city of Fort Laramie is located 3 miles to the northeast of the park. The sampling approach used in this mapping effort was typical of small park sampling, where all polygons within the park boundary are sampled. Two levels of field data gathering were conducted in this park; plots and observations. Plots represented the most intensive sampling of the landscape and used TNC's 'Plot Form'. Observations consisted of brief descriptions and were designed to obtain a quick overview of the landscape without spending a large amount of time at each sample site. Observation points used the 'Observation Form' data sheet. Examples of both 'Plot' and 'Observation' forms are included in the companion report by TNC. Initially, plots were used to describe the vegetation of the park. A total of 49 plots were obtained from July through August 1996. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in July of 1997 to assess the initial mapping effort and to refine map class. The data can also be obtained from \"ftp://ftp.cbi.usgs.gov/pub/vegmapping/fola/fola.exe\". Information for this metadata was obtained from \"http://biology.usgs.gov/npsveg/fola/metafolaspatial.html\" and put into NASA Directory Interchange Format.", + "license": "proprietary" + }, + { + "id": "usgs_nps_isleroyale", + "title": "Isle Royale National Park, Field Plots Data Base for Vegetation Mapping", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-06-01", + "end_date": "1997-09-01", + "bbox": "-89.125, 47.8, -88.4, 48.2", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553838-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553838-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_isleroyale", + "description": "Vegetation field plots at Isle Royale National Park were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data and (2) Species Listings. The vegetation plots were used to describe the vegetation in and around Wind Cave National Park and to assist in developing a final mapping classification system. The purpose of the vegetation plots was to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data are collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the Park's vegetation types. Isle Royale National Park was authorized on March 3, 1931; it was formally established in 1940, and officially dedicated in 1946. Most of the park's land area (98%) was designated as a Wilderness area in October 1976, and later additions increased the total Wilderness to 99% of the park. The park was designated an International Biosphere Reserve in 1980. Field sampling was performed using releve plots. Information for this metadata was obtained from the site http://biology.usgs.gov/npsveg/isro/metaisrofield.html and converted to NASA Directory Interchange Format.", + "license": "proprietary" + }, + { + "id": "usgs_nps_isleroyalespatial", + "title": "Isle Royale National Park Spatial Vegetation Data; Cover Type / Association level of the National Vegetation Classification System", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-08-26", + "end_date": "1996-04-25", + "bbox": "-89.125, 47.8, -88.4, 48.2", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550981-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550981-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_isleroyalespatial", + "description": "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to \"develop a uniform hierarchical vegetation methodology\" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (spring - 1996 and fall - 1994). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to wisely manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to \"develop, test, refine, and finalize the standards and protocols\" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Isle Royale National Park was initially identified as one of the prototypes within the National Park System for the USGS-NPS Vegetation Mapping Program. Isle Royale National Park was established March 3, 1931 and was also designated as an International Biosphere Reserve in 1980. The park contains approximately 571,790 acres of land and water (893 square miles) of which 133,782 acres is land and the rest is open water of Lake Superior as well as inland lakes and ponds. Isle Royale National Park is an archipelago of islands located in the northwestern region of Lake Superior close to the United States-Canada border. The main island, Isle Royale, consists of a series of ridges and valleys running the length of the island and is surrounded by approximately 200 smaller islands. The primary methods of transportation on the island are hiking and boating. Isle Royale National Park was authorized on March 3, 1931; it was formally established in 1940, and officially dedicated in 1946. Most of the park's land area (98%) was designated as a Wilderness area in October 1976, and later additions increased the total Wilderness to 99% of the park. The park was designated an International Biosphere Reserve in 1980. Isle Royale National Park is an archipelago of islands located in the northwestern region of Lake Superior close to the United States-Canada border. The park is located about 60 miles northwest of Michigan.s Keweenaw Peninsula, about 22 miles east of Grand Portage, Minnesota, and about 35 miles southeast of Thunder Bay, Ontario. Information for this metadata was obtained from the site \"http://biology.usgs.gov/npsveg/isro/metaisrospatial.html\" and converted to NASA Directory Interchange Format.", + "license": "proprietary" + }, + { + "id": "usgs_nps_jewelcave", + "title": "Jewel Cave National Monument, Field Plots Data Base for Vegetation Mapping", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1996-07-01", + "end_date": "1996-08-01", + "bbox": "-103.87, 43.62, -103.75, 43.77", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553594-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553594-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_jewelcave", + "description": "Vegetation field plots at Jewel Cave NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listing. The purpose is to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. Field sampling was conducted using releve plots. Information for this metadata was obtained from the site \"http://biology.usgs.gov/npsveg/jeca/metajecafield.html\" and put into NASA Directory Interchange Format.", + "license": "proprietary" + }, + { + "id": "usgs_nps_jewelcavespatial", + "title": "Jewel Cave National Monument Spatial Vegetation Data;Cover Type / Association level of the National Vegetation Classification System", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-09-12", + "end_date": "1995-09-12", + "bbox": "-103.87, 43.62, -103.75, 43.77", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548897-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548897-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_jewelcavespatial", + "description": "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to \"develop a uniform hierarchical vegetation methodology\" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation at Jewel Cave National Monument was mapped using 1:16,000 scale U.S. Forest Service Color Aerial Photography acquired August 22, 1993. The mapping classification used two separate classification systems. All natural vegetation used the National Vegetation Classification System (NVCS) as a base. The vegetation classification was created after extensive on site sampling and numerical analysis. The vegetation map units were derived from the vegetation classification. Other non-natural or cultural mapping units used the Anderson Level II classification system. The mapped area includes a buffer around the Monument boundary. This mapping effort originates from a long-term vegetation monitoring program that is part of a larger Inventory and Monitoring (I&M) program started by the National Park Service (NPS). I&M goals are, among others, to map the vegetation of all national parks and monuments and provide a baseline inventory of vegetation. The I&M program currently works in close cooperation with the Biological Resources Division (BRD) of the United States Geological Survey (USGS). The USGS/BRD continues overall management and oversight of all ongoing mapping efforts in close cooperation with the NPS. The purposes of the mapping effort are varied and include the following: Provides support for NPS Resources Management. Promotes vegetation-related research for both NPS and USGS/BRD. Provides support for NPS Planning and Compliance. Adds to the information base for NPS Interpretation. Assists in NPS Operations. The location of the mapping is Jewel Cave National Monument and about a 2 mile environs around Monument Boundaries - Black Hills, South Dakota. Jewel Cave National Monument was responsible for obtaining permission from adjacent land owners for property access for sampling purposes. Most of the private lands were under some form of grazing or farming. Consequently, sampling on these lands was not necessary. The remainder of the lands within the mapping area are U.S. Forest Service Lands so permission was not necessary. To reduce duplicating previous work and to help in our effort we collected existing vegetation reports and maps from the staff at Jewel Cave National Monument. These materials were referenced during the mapping process and the information contained in them was incorporated where it was deemed useful. Because soils also affect the distribution of vegetation, soil maps and soil descriptions were also obtained as reference. These were not converted to a digital file. Digital elevation models (DEM) were obtained to create slope and aspect maps that helped in determining vegetation community distribution. The sampling approach used in this mapping effort was typical of small park sampling, where all polygons within the park boundary are sampled. Two levels of field data gathering were conducted in this park; plots and observations. Plots represented the most intensive sampling of the landscape and used TNC's 'Plot Form'. Observations consisted of brief descriptions and were designed to obtain a quick overview of the landscape without spending a large amount of time at each sample site. Observation points used the 'Observation Form' data sheet. Examples of both 'Plot' and 'Observation' forms are included in the companion report by TNC. Initially, plots were used to describe the vegetation of the park. A total of 28 plots were obtained from July 29 through August 1, 1996. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in May of 1997 to assess the initial mapping effort and to refine map classes. Information for this metadata was obtained from the site \"http://biology.usgs.gov/npsveg/jeca/metajecaspatial.html\" and put into NASA Directory Interchange Format.", + "license": "proprietary" + }, + { + "id": "usgs_nps_mountrushmore", + "title": "Mount Rushmore National Monument, Field Plots Data Base for Vegetation Mapping", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1997-06-01", + "end_date": "1997-08-01", + "bbox": "-103.5, 43.8, -103.4, 43.9", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549070-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549070-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_nps_mountrushmore", + "description": "Vegetation field plots at Mount Rushmore NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The purpose of the data plots were to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. Field sampling was conducted using releve plots. Information for this metadata was obtained from the site \"http://biology.usgs.gov/npsveg/moru/metamorufield.html\" and put into NASA Directory Interchange Format.", + "license": "proprietary" + }, { "id": "usgs_npwrc_acutetoxicity_Version 06JUL2000", "title": "Acute Toxicity of Fire-Control Chemicals, Nitrogenous Chemicals, and Surfactants to Rainbow Trout", @@ -237106,6 +249482,19 @@ "description": "The prevailing view of a wolf (Canis lupus) pack is that of a group of individuals ever vying for dominance but held in check by the \"alpha\" pair, the alpha male and the alpha female. Most research on the social dynamics of wolf packs, however, has been conducted on non-natural assortments of captive wolves. Here I describe the wolf-pack social order as it occurs in nature, discuss the alpha concept and social dominance and submission, and present data on the precise relationships among members in free-living packs based on a literature review and 13 summers of observations of wolves on Ellesmere Island, Northwest Territories, Canada. I conclude that the typical wolf pack is a family, with the adult parents guiding the activities of the group in a division-of-labor system in which the female predominates primarily in such activities as pup care and defense and the male primarily during foraging and food-provisioning and the travels associated with them.", "license": "proprietary" }, + { + "id": "usgs_npwrc_canvasbacks_Version 13NOV2001", + "title": "Influence of Age and Selected Environmental Factors on Reproductive Performance of Canvasbacks", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1974-01-01", + "end_date": "1980-01-01", + "bbox": "-102.5, 48, -95, 60", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549601-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549601-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_npwrc_canvasbacks_Version%2013NOV2001", + "description": "Age, productivity, and other factors affecting breeding performance of canvasbacks (Aythya valisineria) are poorly understood. Consequently, we tested whether reproductive performance of female canvasbacks varied with age and selected environmental factors in southwestern Manitoba from 1974 to 1980. Neither clutch size, nest parasitism, nest success, nor the number of ducklings/brood varied with age. Return rates, nest initiation dates, renesting, and hen success were age-related. Return rates averaged 21% for second-year (SY) and 69% for after-second-year (ASY) females (58% for third-year and 79% for after-third-year females). Additionally, water conditions and spring temperatures influenced chronology of arrival, timing of nesting, and reproductive success. Nest initiation by birds of all ages was affected by minimum April temperatures. Clutch size was higher in nests initiated earlier. Interspecific nest parasitism did not affect clutch size, nest success, hen success, or hatching success. Nest success was lower in dry years (17%) than in moderately wet (54%) or wet (60%) years. Nests per female were highest during wet years. No nests of SY females were found in dry years. In years of moderate to good wetland conditions, females of all ages nested. Predation was the primary factor influencing nest success. Hen success averaged 58% over all years. The number of ducklings surviving 20 days averaged 4.7/brood. Because SY females have lower return rates and hen success than ASY females, especially during drier years, management to increase canvasback populations might best be directed to increasing first year recruitment (no. of females returning to breed) and to increasing overall breeding success by reducing predation and enhancing local habitat conditions during nesting.", + "license": "proprietary" + }, { "id": "usgs_npwrc_ducks_Version 07JAN98", "title": "Assessing Breeding Populations of Ducks by Ground Counts.", @@ -237132,6 +249521,19 @@ "description": "We evaluated the accuracy and precision of tooth wear for aging gray wolves (Canis lupus) from Alaska, Minnesota, and Ontario based on 47 known-age or known-minimum-age skulls. Estimates of age using tooth wear and a commercial cementum annuli-aging service were useful for wolves up to 14 years old. The precision of estimates from cementum annuli was greater than estimates from tooth wear, but tooth wear estimates are more applicable in the field. We tended to overestimate age by 1-2 years and occasionally by 3 or 4 years. The commercial service aged young wolves with cementum annuli to within year of actual age, but under estimated ages of wolves 9 years old by 1-3 years. No differences were detected in tooth wear patterns for wild wolves from Alaska, Minnesota, and Ontario, nor between captive and wild wolves. Tooth wear was not appropriate for aging wolves with an underbite that prevented normal wear or severely broken and missing teeth.", "license": "proprietary" }, + { + "id": "usgs_npwrc_incidentalmarinecatc_Version 11APR2001", + "title": "Incidental Catch of Marine Birds in the North Pacific High Seas Driftnet Fisheries in 1990.", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1990-01-01", + "end_date": "1990-01-01", + "bbox": "-140, 20, 140, 50", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553439-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553439-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_npwrc_incidentalmarinecatc_Version%2011APR2001", + "description": "The incidental take of marine birds was estimated for the following North Pacific driftnet fisheries in 1990: Japanese squid, Japanese large-mesh, Korean squid, and Taiwanese squid and large-mesh combined. The take was estimated by assuming that the data represented a random sample from an unstratified population of all driftnet fisheries in the North Pacific. Estimates for 13 species or species groups are presented, along with some discussion of inadequacies of the design. About 416,000 marine birds were estimated to be taken incidentally during the 1990 season; 80 % of these were in the Japanese squid fishery. Sooty Shearwaters, Short-tailed Shearwaters, and Laysan Albatrosses were the most common species in the bycatch. Regression models were also developed to explore the relations between bycatch rate of three groups Black-footed Albatross, Laysan Albatross, and \"dark\" shearwatersand various explanatory variables, such as latitude, longitude, month, vessel, sea surface temperature, and net soak time (length of time nets were in the water). This was done for only the Japanese squid fishery, for which the most complete information was available. For modeling purposes, fishing operations for each vessel were grouped into 5-degree blocks of latitude and longitude. Results of model building indicated that vessel had a significant influence on bycatch rates of all three groups. This finding emphasizes the importance of the sample of vessels being representative of the entire fleet. In addition, bycatch rates of all three groups varied spatially and temporally. Bycatch rates for Laysan Albatrosses tended to decline during the fishing season, whereas those for Black-footed Albatrosses and dark shearwaters tended to increase as the season progressed. Bycatch rates were positively related to net soak time for Laysan Albatrosses and dark shearwaters. Bycatch rates of dark shearwaters were lower for higher sea surface temperatures.", + "license": "proprietary" + }, { "id": "usgs_npwrc_manitobaspiders_Version 16JUL97", "title": "A Checklist of Manitoba Spiders (Araneae) with Notes on Geographic Relationships", @@ -237145,6 +249547,32 @@ "description": "An annotated list of spider species is compiled from museum collections and several personal collections. This list includes 483 species in 20 families; 139 species are new provincial records. The spider fauna of Manitoba is compared with that of British Columbia, Quebec, and Newfoundland. Manitoba's spider fauna is composed of northern elements (arctic or subarctic species), boreal elements (holarctic or nearctic), and eastern elements (mainly species of the eastern deciduous forest), and a few that are regarded as introductions from abroad. Forty-three species reach the limits of their ranges here. This relatively small province (6.5% of the total land mass of Canada) contains 59% of the Canadian spider families and 37% of the Canadian species.", "license": "proprietary" }, + { + "id": "usgs_npwrc_muskoxen_Version 31MAY2000", + "title": "Lack of Reproduction in Muskoxen and Arctic Hares Caused by Early Winter", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1998-07-01", + "end_date": "1998-07-11", + "bbox": "-86.1, 79.5, -85.9, 80.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549051-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549051-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_npwrc_muskoxen_Version%2031MAY2000", + "description": "A lack of young muskoxen (Ovibos moschatus) and arctic hares (Lepus arcticus) in the Eureka area of Ellesmere Island, Northwest Territories (now Nunavut), Canada, was observed during summer 1998, in contrast to most other years since 1986. Evidence of malnourished muskoxen was also found. Early winter weather and a consequent 50% reduction of the 1997 summer replenishment period appeared to be the most likely cause, giving rise to a new hypothesis about conditions that might cause adverse demographic effects in arctic herbivores. The study area included a 150 km2 region of the Fosheim Peninsula in a 180o arc north of Eureka, Ellesmere Island, Nunavut, Canada (all within about 9 km of 80oN, 86oW). The area, extending from Eureka Sound to Remus Creek and from Slidre Fiord to Eastwind Lake, included shoreline, hills, lowlands, creek bottoms, and the west side of Blacktop Ridge. An associate, Layne Adams, and I spent 1-11 July 1998 in this area on all-terrain vehicles, following a pair of wolves Canis lupus (Mech, 1994). Adams and I also surveyed the surrounding area with binoculars for prey animals, in much the same manner that my assistants and I have practiced for one to six weeks each summer in the same area since 1986 (Mech, 1995, 1997). Because both muskoxen and arctic hares were common residents of the area during most years and were not the focus of our studies, no standardized counts were made. However, general field notes were sufficient to document that during most summers both species and their young were present.", + "license": "proprietary" + }, + { + "id": "usgs_npwrc_nestingsuccess_Version 26MAR2001", + "title": "Importance of Individual Species of Predators on Nesting Success of Ducks in the Canadian Prairie Pothole Region", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-145.27, 37.3, -48.11, 87.61", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551032-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551032-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_npwrc_nestingsuccess_Version%2026MAR2001", + "description": "We followed 3094 upland nests of several species of ducks. Clutches in most nests were lost to predation. We related daily nest predation rates to indices of activity of eight egg-eating predators, precipitation during the nesting season, and measures of wetland conditions. Activity indices of red fox (Vulpes vulpes), striped skunk (Mephitis mephitis), and raccoon (Procyon lotor) activity were positively correlated, as were activity indices of coyote (Canis latrans), Franklin's ground squirrel (Spermophilus franklinii), and black-billed magpie (Pica pica). Indices of fox and coyote activity were strongly negatively correlated (r = early-season nests were lower in areas and years in which larger fractions of seasonal wetlands contained water. For late-season nests, a similar relationship held involving semipermanent wetlands. We suspect that the wetland measures, which reflect precipitation during some previous period, also indicate vegetation growth and the abundance of buffer prey, factors that may influence nest predation rates.", + "license": "proprietary" + }, { "id": "usgs_npwrc_purpleloostrife_Version 04JUN99", "title": "Avian Use of Purple Loosestrife Dominated Habitat Relative to Other Vegetation Types in a Lake Huron Wetland Complex", @@ -237158,6 +249586,19 @@ "description": "Purple loosestrife (Lythrum salicaria), a native of Eurasia, is an introduced perennial plant in North American wetlands that displaces other wetland plants. Although not well studied, purple loosestrife is widely believed to have little value as habitat for birds. To examine the value of purple loosestrife as avian breeding habitat, we conducted early, mid-, and late season bird surveys during two years (1994 and 1995) at 258 18-m (0.1 ha) fixed-radius plots in coastal wetlands of Saginaw Bay, Lake Huron. We found that loosestrife-dominated habitats had higher avian densities, but lower avian diversities than other vegetation types. The six most commonly observed bird species in all habitats combined were Sedge Wren (Cistothorus platensis), Marsh Wren (C. palustris), Yellow Warbler (Dendroica petechia), Common Yellowthroat (Geothylpis trichas), Swamp Sparrow (Melospiza georgiana), and Red-winged Blackbird (Agelaius phoeniceus). Swamp Sparrow densities were highest and Marsh Wren densities were lowest in loosestrife dominated habitats. We observed ten breeding species in loosestrife dominated habitats. We conclude that avian use of loosestrife warrants further quantitative investigation because avian use may be higher than is commonly believed. Received 27 May 1998, accepted 26 Aug. 1998.", "license": "proprietary" }, + { + "id": "usgs_npwrc_saltmam", + "title": "Mammal Checklists of the United States - Salton Sea National Wildlife Refuge", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-177.1, 13.71, -61.48, 76.63", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552573-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552573-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgs_npwrc_saltmam", + "description": "Wildlife species in this brochure have been grouped into four categories: Birds, Mammals, Reptiles and Amphibians, and Fish. All mammals listed are considered resident species with the exception of the bats which migrate on a seasonal basis like many of the birds. Families follow that of A Field Guide to the Mammals by Burt and Grossenheider.", + "license": "proprietary" + }, { "id": "usgsbrdasc00000004", "title": "Air quality monitoring protocol - Denali National Park and Preserve", @@ -237171,6 +249612,32 @@ "description": "Ambient air quality monitoring is important in Denali, to document baseline conditions and to track long term trends. Denali National Park and Preserve is the only National Park in Alaska designated as class I under the Clean Air Act. Geographic Description: Specific coordinates in the Denali National Park and Preserve, Alaska. Denali National Park and Preserve is located in the central Alaska Range, approximately 210 km southwest of Fairbanks, Alaska. Methodology: Denali currently participates in three nationwide air quality monitoring networks: National Atmospheric Deposition Program (NADP), Interagency Monitoring of Protected Visual Environments (IMPROVE), National Park Service Gaseous Pollutant Monitoring Network (ozone monitoring). Air quality monitoring protocols have been written for each network, and approved by the respective network steering committees. Since there is no local control over methodology, the network manuals are the park's guiding documents. This is a compilation of network protocols.", "license": "proprietary" }, + { + "id": "usgsbrdfcsc_d_seagrass", + "title": "Mapping and Characterizing Seagrass Areas Important to Manatees in Puerto", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1994-10-01", + "end_date": "1995-06-01", + "bbox": "-65.75, 18.15, -65.5, 18.3", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549769-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231549769-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgsbrdfcsc_d_seagrass", + "description": "The population of manatees in Puerto Rico is the only group of Antillean manatees (Trichechus manatus manatus) managed and protected by the United States. The Manatee Recovery Plan for the Puerto Rico Population of West Indian Manatees includes requirements to identify and manage habitats and develop criteria and biological information important to its recovery. To this end, the Sirenia Project initiated telemetry studies of manatees in Puerto Rico at the U.S. Naval Station Roosevelt Roads (RRNS) in 1992. Concurrently, the Project began gathering information on habitats critical to manatee in eastern Puerto Rico. Computer aided mapping based on the interpretation of aerial photographs and field groundtruthing was used in the current project to define these habitats and map their distribution in the area of high manatee use. Benthic habitats along approximately 32 miles (52 kilometers of RRNS shoreline were mapped. Field assessment and characterization of important seagrass habitats was conducted as a means of identifying seagrass and macroalgae communities, especially in areas with known manatee feeding sites. The purpose of this dataset is to identify and manage manatee habitats and to develop biological information important to the manatees' recovery. Data was obtained during ground truthing in October, 1994 and June, 1995. One hundred and twenty-five sites, many representing questions raised during preliminary habitat delineations were visited, along with sites with characteristic signatures useful for broader interpretations. Transects were made over several areas with rapidly changing benthic communities and confusing signatures. Data recorded at each site included depth (range 0.5-7.1 m), classification, dominant community, subdominant community, and pertinent comments. the locations of all groundtruth sites were plotted onto one Arc Cad layer of mapping information. Groundtruthing was used to field verify and correct the initial delineations made. Improvements were made to the draft classification scheme based on field observations. Sites of questionable draft delineations were located on the water and confirmed or corrected. Known manatee use of the area for resting or feeding was noted. These sites were accurately located on the overlay for inclusion on the maps. Site location (latitude and longitude) was determined with a Garmin 45 GPS and water depth (tape), temperature (hand-held mercury thermometer), and salinity (hand-held temperature compensated refractometer) recorded. In addition, salinity measurements were made at select nearshore locations to assess the influence of drainage creeks and ditches on nearshore water salinity. Underwater video photography and 35 mm photography were used to document observations. A review of vertical images of waters of RRNS was taken on December 17, 1993, for the United States Navy, along with other collateral information, was used to develop a benthic habitat classification system useful for mapping benthic communities in the area. The system developed for this project was similar to that developed for Geographic Information System (GIS) mapping of benthic communities in the Florida Keys National Marine Sanctuary. Clear acetate overlays were placed over the 9\" x 9\" aerial prints and the polygon method of delineation used to outline habitats on the overlays. Computer aided design methods (PC Arc Cad) were used to create a shoreline base map from navigational charts for this region of Puerto Rico. Habitat polygons extending as far from shore as allowed by the resolution of the images were digitized onto the base map. A minimum mapping unit of 0.5 acres was applied based on the scale and quality of the images. Once finalized, maps were printed in both color and black-line. The information for this metadata was partially taken from the document Mapping and Characterizing Seagrass Areas Important to Manatees in Puerto Rico - Benthic Communities Mapping and Assessment. Prepared for the U.S. Department of Interior, National Biological Service, Sirenia Project. Prepared by Curtis Kruer, Senior Biologist, Caribbean Fisheries Consultants, Inc.", + "license": "proprietary" + }, + { + "id": "usgsbrdfcsc_d_vieques", + "title": "Mapping and Characterization of Nearshore Benthic Habitats around Vieques Island, Puerto Rico", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1995-09-01", + "end_date": "", + "bbox": "-65.75, 18.15, -65.5, 18.3", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553026-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553026-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgsbrdfcsc_d_vieques", + "description": "The Vieques Island Mapping Project was initiated in September 1995 as a cooperative effort between NSRR and the Sirenia Project (Military Interdepartmental Purchase Request no. NOO38995MP00012). Caribbean Fisheries Consultants, Inc. was contracted by the Sirenia Project to help produce the desired information in conjunction with Sirenia Project biologists. Products include maps delineating Vieques' benthic habitat and coastal wetlands, an electronic georeferenced habitat map (UTM coordinate system) in a format compatible with ARC/INFO (Environmental Systems Research Institute, Inc.) and a report describing methods used, the classification scheme, and the relationship of these habitats to manatee use of Vieques Island. These map products complement the Navy's Vieques Land Use Management Plan by identifying marine resources targeted for protection in the plan. Objectives include producing maps of the coastal seagrass beds and other bottom habitat (including coral reefs) surrounding the island of Vieques and characterizing the species composition and density of seagrasses in areas frequented by manatees near Vieques. Ground truthing by boat around Vieques Island was conducted from May 14 to May 19 1996 and from October 4 through October 9 1996. The ground truthing was conducted to verify the interpretation of benthic habitat visible in the images, verify accuracy of the shoreline limits, and refine the habitat classification scheme used for the Vieques maps. Three hundred and thirty-two ground truth stations were established around Vieques Island, located on the aerial image overlays, and digitized. These sites are plotted as a layer on the habitat map. The listing of ground truth sites includes site identifier, latitude and longitude, community classification, depteh, dominant community elements, less dominant elements, and other pertinent information. Latitude and longitude were obtained for each station in the field using a Garmin 45 GPS unit. Water depth for each station was determined from a Hummingbird LCR - 400 Video Fathometer with transom mounted transducer. Underwater Hi-8 video and 35 mm photography were used to document observations at selected sites. The habitat classification scheme used is similar to that used by Kruer and others in southern Forida seagrass beds and other benthic habitats in the Florida Keys National Marine Sanctuary and Biscayne National Park. This scheme, also used for benthic habitat mapping at NSRR in 1994/1995 (Kruer 1995), was refined for the Vieques Island mapping project by adding the category \"sand bottom with rock\". Also, mangroves were mapped in interior areas. The information for this metadata was partially taken from the report - Mapping and characterization of Nearshore Benthic Habitats around Vieques Island, Puerto Rico.", + "license": "proprietary" + }, { "id": "usgsbrdnpwrc_d_birds_checklists_Version 12MAY03", "title": "Birds Checklists of the United States", @@ -237184,6 +249651,19 @@ "description": "This resource is known as Bird Checklists of the United States. Bird Checklists of the United States. For years, people and groups have developed listings or checklists of birds that occur in a particular region. Information on the distribution or seasonal occurrence of birds in an area, however, can change over time. Bird checklists often are outdated in only a few years after printing, but budget and time constraints prohibit regular updates. The Internet provides new opportunities for the compilation and dissemination of current information on bird distribution. Here we offer bird checklists developed by others that indicate the seasonal occurrence of birds in state, federal, and private management areas, nature preserves, and other areas of special interest in the United States. Bird checklists exist for Great Plains States: Colorado, Iowa, Kansas, Minnesota, Missouri, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Wyoming; East of Great Plains states: Alabama, Arkansas, Connecticut, Delaware, Florida, Georgia, Illinois, Indiana, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Mississippi, New Hampshire, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina, Tennessee, Vermont, Virginia, West Virginia, Wisconsin; and West of Great Plains: Arizona, California, Idaho, Nevada, Oregon, Utah, Washington. It is hoped that these checklists will serve several purposes. First, we hope the checklists will help bird enthusiasts decide where to visit. A visit to these unique areas can be a rewarding experience for both the amateur and expert birdwatcher. Second, we hope that these checklists will provide potential visitors with a guide to birds that might occur in a region during a particular season. The checklists were kept simple to facilitate printing so they can be easily carried into the field. And third, we hope that these checklists will stimulate and encourage visitors to these areas to help improve the accuracy and completeness of the checklists. The information in some checklists already has been updated; these checklists contain more current information than the printed versions. Sightings of birds and other wildlife are an important part of monitoring wildlife use. Visitors are encouraged to share their observations of rare, aberrant, or occasional birds with the staff at these areas. With each checklist, we have included an address for visitors to send information on rare birds so that checklists can be updated. To assist in establishing standards in observation and reporting, we also provide a Record Documentation Form to document supporting details of rare bird observations. The efforts and dedication of the many birders, birding groups, biologists, and resource managers who developed these checklists are acknowledged. The information for this metadata was partially taken from the Northern Prairie Wildlife Research Center website at http://www.npwrc.usgs.gov/resource/birds/chekbird/index.htm", "license": "proprietary" }, + { + "id": "usgsbrdnpwrc_d_ndfleas_Version 16JUL97", + "title": "Fleas of North Dakota", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-104, 46, -96.5, 49", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553614-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553614-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgsbrdnpwrc_d_ndfleas_Version%2016JUL97", + "description": "The dataset contains distribution maps for the following species of fleas: Aetheca wagneri, Amaradix euphorbi, Amphipsylla sibirica pollionis, Callistopsyllus terinus campestris, Cediopsylla inaequalis inaequalis, Ceratophyllus (Ceratophyllus) idius, Corrodopsylla curvata curvata, Chaetopsylla lotoris, Ctenocephalides canis, Epitedia faceta, Epitedia wenmanni, Euhoplopsyllus glacialis affinis, Eumolpianus eumolpi eumolpi, Foxella ignota albertensis, Hystrichopsylla dippiei dippiei, Megabothris (Megabothris) asio megacolpus, Megabothris (Amegabothris) lucifer, Meringis jamesoni, Myodopsylla insignis, Nearctopsylla genalis hygini, Neopsylla inopina, Nosopsyllus fasciatus, Oropsylla (Oropsylla) arctomys, Opisodasys (Opisodasys) pseudarctomys, Orchopeas caedens, Orchopeas howardi, Peromyscopsylla hamifer, Pleochaetis exilis, Pules irritans, Rhadinopsylla (Actenophthalmus) fraterna, Thrassis bacchi bacchi. The information for this metadata was partially taken from the Northern Prairie Wildlife Research Center website at http://www.npwrc.usgs.gov/resource/insects/ndfleas/", + "license": "proprietary" + }, { "id": "usgsbrdnpwrcb00000013_Version 30SEP2002", "title": "A Bibliography of Fisheries Biology in North and South Dakota", @@ -237210,6 +249690,19 @@ "description": "The success of vegetation management programs for waterfowl is dependent on knowing the physical and physiological requirements of the target species. Lakes and riverine impoundments that contain an abundance of the American wildcelery plant (Vallisneria americana) have traditionally been favored by canvasback ducks (Aythya valisineria) and other waterfowl species as feeding areas during migration. Information on the ecology of V. americana is summarized to serve as a guide for potential wetland restoration projects. Because of the geographic diversity and wetland conditions in which V. americana is found, we have avoided making hard-and-fast conclusions about the requirements of the plant. Rather, we present as much general information as possible and provide the sources of more specific information. Vallisneria americana is a submersed aquatic plant that has management potential. Techniques are described for transplanting winter buds from one location to another. Management programs that employ these techniques should define objectives clearly and evaluate the water regime carefully before initiating a major effort.", "license": "proprietary" }, + { + "id": "usgsbrdnpwrcd00000002_Version 02MAR98", + "title": "Ecological Effects of Fire Retardant Chemicals and Fire Suppressant Foams", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1993-01-01", + "end_date": "1998-01-01", + "bbox": "-98, 47, -98, 47", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550534-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550534-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgsbrdnpwrcd00000002_Version%2002MAR98", + "description": "Laboratory studies with algae, aquatic invertebrates, and fish. Short-term toxicity tests showed that both fire-retardant and foam-suppressant chemicals were very toxic to aquatic organisms including algae, aquatic invertebrates, and fish. Foam-suppressant are more toxic than fire-retardant chemicals. The primary toxicant in fire-retardants is the ammonia component, whereas the nitrite and nitrate components do not seem to contribute much to the toxicity of the formulations. In foam suppressants the primary toxicant is the surfactant component. The most sensitive life-stage for fish is the swim-up stage. Accidental spills of fire-fighting chemicals in streams could cause substantial fish kills depending on the stream size and flow rate. For example, the retardant Fire-Trol GTS-R is prepared for field use by mixing 1.66 pounds per gallon of water to produce 1.1 gallons of slurry, which is equivalent to 198,930 mg/liter. Comparing the concentration of FT GTS-R field mixture to the acute toxicity values for the most sensitive life stage for rainbow trout gives a ratio of 853 in soft water and 1474 in hard water. Applying a safety factor of 100 to this ratio suggests a dilution of 85, 300 in soft water and 147,400 in hard water is needed to lower the chemical concentration in a receiving water to limit adverse effects, i.e., fish kill, in a stream. For rainbow trout, other dilution factors would be 52,100 for Fire-Trol LCG-R, 85,600 for Phos-Chek D75-F, 71,400 for Phos-Chek WD-881, and 50,000 for Silv-ex. Fire-fighting chemicals are very toxic in aquatic environments and fire control managers need to consider protection of aquatic resources, especially if endangered species are present.", + "license": "proprietary" + }, { "id": "usgsbrdnpwrcd00000012_Version 31JUL97", "title": "Changes in Breeding Bird Populations in North Dakota: 1967 to 1992-93.", @@ -237236,6 +249729,19 @@ "description": "The invasion of exotic plants is becoming a problem in many ecosystems including some areas in Rocky Mountain National Park (RMNP) (Rocky Mountain National Park Resource Management Reports #1 and #13). Some exotic species, such as leafy spurge and spotted knapweed, are capable of rapidly colonizing areas, altering community composition, and even displacing native species (Belcher and Wilson 1989, Tyser and Key 1988). In many cases, the processes of invasion are poorly documented, and little information is available on an area's past history. However, there is a large amount of information available in the literature which relates to the life history traits of exotic species and the distribution of exotic species. This information can be used to help predict the potential distribution and threat of exotic species to ecosystems. Exotic plants can be thought of as those plants which did not originally occur in the ecosystem, and have since been introduced to the area. The National Park Service (NPS) defines an exotic species as, \"those that occur in a given place as a result of direct or indirect, deliberate, or accidental actions by humans.\" This somewhat conservative definition of exotic species is necessary to insure that natural resources in national parks are preserved. NPS policy generally prohibits the introduction of exotic species into natural areas of national parks. Exotic species which threaten park resources or public health are to be managed or eliminated if possible. In addition, the NPS recently signed a memorandum of understanding with 10 other federal and state agencies in the state of Colorado. This agreement states that all paid management agencies will work with private and county entities to manage exotic plants and, in particular, \"noxious weeds.\" RMNP is currently working with Estes Park in exotic plant control as part of this agreement.", "license": "proprietary" }, + { + "id": "usgsbrdnpwrcd0000003_Version 16JUL97", + "title": "Human Disturbances of Waterfowl: An Annotated Bibliography.", + "catalog": "CEOS_EXTRA STAC Catalog", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-177.1, 13.71, -61.48, 76.63", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551896-CEOS_EXTRA.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231551896-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/usgsbrdnpwrcd0000003_Version%2016JUL97", + "description": "The expansion of outdoor recreational activities has increased greatly the interaction between the public and waterfowl and waterfowl habitat. The effects of these interactions on waterfowl habitats are more visible and obvious, whereas the effects of interactions which disrupt the normal behavior of waterfowl are more subtle and often overlooked, but perhaps no less of a problem than destruction of habitat. This bibliography contains excerpts or annotations from 211 articles that contained information about effects of human disturbances on waterfowl. Indices are provided for subject/keywords, geographic locations, species of waterfowl, and authors used in this bibliography.", + "license": "proprietary" + }, { "id": "usgsbrdnpwrcs0000004_Version 12MAY03", "title": "Collecting and Analyzing Data from Duck Nesting Studies", diff --git a/nasa_cmr_catalog.tsv b/nasa_cmr_catalog.tsv index d41fedb04e..2bbe91e801 100644 --- a/nasa_cmr_catalog.tsv +++ b/nasa_cmr_catalog.tsv @@ -8,6 +8,7 @@ id title catalog state_date end_date bbox url description license 065f6040ef08485db989cbd89d536167_NA ESA Fire Climate Change Initiative (Fire_cci): Small Fire Dataset (SFD) Burned Area pixel product for Sub-Saharan Africa, version 1.1 FEDEO STAC Catalog 2016-01-01 2016-12-31 -20, -35, 55, 25 https://cmr.earthdata.nasa.gov/search/concepts/C2548142692-FEDEO.umm_json The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Dataset (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from MODIS MOD14MD Collection 6 active fire products.This dataset is part of v1.1 of the Small Fire Dataset (also known as FireCCISFD11), which covers Sub-Saharan Africa for the year 2016. Data is available here at pixel resolution (0.00017966259 degrees, corresponding to approximately 20m at the Equator). Gridded data products are also available in a separate dataset. proprietary 07eeca6888c645d89a7ef91de0290eca_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 4.2 FEDEO STAC Catalog 1997-09-03 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142626-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 4.2 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 4.2 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection). proprietary 0944645 Age and Composition of the East Antarctic Shield SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214605206-SCIOPS.umm_json We completed a field season in Antarctica in 2010-11 with a 5-person field party. Ten sampling sites along the Transantarctic Mountains from the Convoy Range to Hatcher Bluffs were visited by helicopter or fixed-wing aircraft, where rock samples were collected. All samples were returned to the University of Minnesota-Duluth, where they were prepared for laboratory study. Laboratory work includes examination of polished thin sections by optical microscope and scanning electron microscope to determine textures, mineral assemblages, and mineral compositions. Samples of igneous and metamorphic rock clasts were crushed in order to isolate the mineral zircon; zircon from these samples was analyzed by U-Pb, O and Hf isotopic analysis in order to determine their ages and isotopic character. Monazite was identified in selected samples for U-Pb age dating in polished thin section. A suite of Ross Orogen granitoids was also prepared for zircon separation and for whole-rock geochemical analysis. Petrographic study is complete for over 300 samples of igneous and metamorphic rock clasts collected from glacial moraines on the ‘backside’ of the Transantarctic Mountains, mainly between the inlets to the Byrd through Shackleton Glaciers. We U-Pb, O and Hf analyses of zircon and monazite in igneous and metamorphic clasts, and in samples of TAM granitoids. proprietary +0ac98747-eb94-4c9f-aef8-56f9d3a04740 Earthquake Risk-Annual Average Losses CEOS_EXTRA STAC Catalog 2012-01-01 2013-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848506-CEOS_EXTRA.umm_json The map (risk map) presents the results of earthquake annual average losses (AAL) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (AAL-absolute value) and millar (AAL/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL. proprietary 0b23b3c771db4fff8958196432d978cb_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Margin from ERS-2 for winter 1995-1996, v1.1 (June 2016 release) FEDEO STAC Catalog 1995-09-02 1996-03-29 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143114-FEDEO.umm_json This dataset contains ice velocities for the Greenland margin for winter 1995-1996, which have been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The data were derived from intensity-tracking of ERS-2 data acquired between 03-09-1995 and 29-03-1996. It provides components of the ice velocity and the magnitude of the velocity.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that previous versions of this product provided the horizontal velocities as true East and North velocities.Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by DTU Space - Microwaves and Remote Sensing. For further information please see the product user guide.Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product. proprietary 0d2260ad4e2c42b6b14fe5b3308f5eaa_NA ESA Ozone Climate Change Initiative (Ozone CCI): Level 3 Total Ozone Merged Data Product, version 01 FEDEO STAC Catalog 1996-03-31 2011-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143081-FEDEO.umm_json This dataset is a monthly mean gridded total ozone data record (level 3) produced by the ESA Ozone Climate Change Initiative project (Ozone CCI). The dataset is a prototype of a merged harmonised ozone data record combining ozone data from the GOME instrument on ERS-2, the SCIAMACHY instrument on ENVISAT and the GOME-2 instrument on METOP-A, and covers the period between April 1996 to June 2011. proprietary 0e289294f2c141bca545cd9d7fcb62d0_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Helheim Glacier for 2015-2017 from Sentinel-1 data, v1.1 FEDEO STAC Catalog 2015-06-09 2017-03-21 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143491-FEDEO.umm_json This dataset contains a time series of ice velocities for the Helheim Glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between between June 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid. proprietary @@ -165,6 +166,7 @@ id title catalog state_date end_date bbox url description license 10.7289/v5zs2tt5_Not Applicable Carbon dioxide, hydrographic and chemical data collected from profile discrete samples during the R/V Knorr cruise (EXPOCODE 316N20071003) in Davis Strait from 2007-10-03 to 2007-10-21 (NCEI Accession 0173248) NOAA_NCEI STAC Catalog 2007-10-03 2007-10-21 -63.3, 61.9, -52.2, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089378563-NOAA_NCEI.umm_json This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, CTD oxygen, nutrients, dissolved inorganic carbon, total alkalinity other measurements obtained during the R/V Knorr cruise (EXPOCODE 316N20071003) in Davis Strait from 2007-10-03 to 2007-10-21. proprietary 10Be-Law-Dome-10-year-composite_1 High Resolution ice core 10Be records from Law Dome, Antarctica: 10-year composite (revised dating) AU_AADC STAC Catalog 1999-12-01 2009-12-31 112.8, -66.77, 112.8, -66.77 https://cmr.earthdata.nasa.gov/search/concepts/C1214305617-AU_AADC.umm_json This record comprises composite 10Be concentrations from three Law Dome ice cores (DSS0506-core, DSS0809-core and DSS0910-core). Sample dating is revised from that presented in Pedro et al., clim. Past 7, 707-721, 2011 by accounting for sub-seasonal variability in snow accumulation. The accumulation record was derived from the European Center for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim). See Appendix 1 of Pedro et al., J. Geophys. Res. 116, D23120, 2011 for details of method. proprietary 1162_4_IPEV_FR Adult integument colour - MDO Alaska SCIOPS STAC Catalog 2004-05-01 146.333, 59.452, 146.333, 59.452 https://cmr.earthdata.nasa.gov/search/concepts/C1214598260-SCIOPS.umm_json - Spectrograms or pictures of gape, tongue, eye-ring and bill of each adult that was caught on the tower in Middleton island. - Colour data obtained from those spectrograms and pictures. proprietary +118cc853-52a2-46e2-a5be-40e1f58ab46d_1 HUMAN POPULATION AND ADMINISTRATIVE BOUNDARIES DATABASE FOR THE RUSSIAN FED. CEOS_EXTRA STAC Catalog 1970-01-01 -36, 41.19658, 180, 81.85193 https://cmr.earthdata.nasa.gov/search/concepts/C2232847474-CEOS_EXTRA.umm_json The Russian administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. The database was prepared at UNEP/GRID-Geneva in collaboration with Denis Eckert (Centre National de la Recherche Scientifique / France) and the Centre Universitaire d'Ecologie Humaine/Université de Genève. BOUNDARY AND POPULATION DATA 2486 third-level administrative units (1863 raions, 325 gorods, 298 gorsoviets) digitized by D. Eckert have been matched to the national boundaries of the widely used Digital Chart of the World. 649 cities have been digitized from various sources and adjusted to the administrative map. Population figures of the administrative units refer to the 1993 official figures whereas urban data was compiled from heterogeneous sources and projected to the year 1995. INTERPOLATION The interpolation of the vectorial data to a population density raster grid at a resolution of 2.5 arc-minutes was carried out according to a model developed by Uwe Deichman for a previous work on Asia (UNEP/GRID-Geneva). The basic assumption upon which the model is based is that population densities are strongly correlated with accessibility. A complex transport network (more than 170'000 arcs) was used to distribute populations and densities to a raster grid. OUTPUTS - a vector dataset containing the 2846 administrative units fitted to the Digital Chart of the World used as template. Population figures of the 1993 official figures and subsequent projections to year 1995 are stored in the polygon attribute file. - a raster dataset of the interpolated and distributed population totals at a resolution of 2.5 arc-minutes - a raster dataset of the interpolated and distributed population densities at a resolution of 2.5 arc-minutes GIF images for display on the Web proprietary 11c5f6df1abc41968d0b28fe36393c9d_NA ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 3 aerosol products from MERIS (ALAMO algorithm), Version 2.2 FEDEO STAC Catalog 2008-01-01 2008-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143004-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 3 aerosol daily and monthly gridded products from MERIS for 2008, using the ALAMO algorithm, version 2.2. The data have been provided by Hygeos.For further details about these data products please see the linked documentation. proprietary 12-hourly_interpolated_surface_position_from_buoys 12-Hourly Interpolated Surface Position from Buoys SCIOPS STAC Catalog 1979-01-01 2009-12-01 -180, 60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600619-SCIOPS.umm_json This data set contains Arctic Ocean daily buoy positions interpolated to hours 0Z and 12Z. proprietary 12-hourly_interpolated_surface_velocity_from_buoys 12-Hourly Interpolated Surface Velocity from Buoys SCIOPS STAC Catalog 1979-01-01 2009-12-02 -180, 74, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600621-SCIOPS.umm_json This data set contains 12-hourly interpolated surface velocity data from buoys. Point grid: Latitude 74N to 90N - 4 degree increment Longitude 0E to 320E - 20 and 40 degree increment. proprietary @@ -375,8 +377,10 @@ id title catalog state_date end_date bbox url description license 2785ee1ec6274be39d11e7e7ce51b381_NA ESA Sea Level Climate Change Initiative (Sea_Level_cci): Fundamental Climate Data Records of sea level anomalies and altimeter standards, Version 2.0 FEDEO STAC Catalog 1993-01-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142554-FEDEO.umm_json As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) Project, Fundamental Climate Data Records (FCDRs) have been computed for all the altimeter missions used within the project. These FCDR's consist of along track values of sea level anomalies and altimeter standards for the period between 1993 and 2015. This version of the product is v2.0.The FCDR's are mono-mission products, derived from the respective altimeter level-2 products. They have been produced along the tracks of the different altimeters, with a resolution of 1Hz, corresponding to a ground distance close to 6km. The dataset is separated by altimeter mission, and divided into files by altimetric cycle corresponding to the repetivity of the mission. When using or referring to the Sea Level cci products, please mention the associated DOIs and also use the following citation where a detailed description of the Sea Level_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these projects please email: info-sealevel@esa-sealevel-cci.org proprietary 27fc79c6e65f4302a18ec9788605c246_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Hagen glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1 FEDEO STAC Catalog 1991-08-25 2010-05-07 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142954-FEDEO.umm_json This dataset contains a time series of ice velocities for the Hagen glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 26/08/1991 and 7/5/2010. It provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 6 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland). proprietary 28458e44db959dd2b1e920457964665327a333f6 3 year daily average solar exposure map Mali 3Km GRAS December 2008-2011 SCIOPS STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603938-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for December. proprietary +2940cda8-cf01-490a-a7ab-688bd54fb56a Earthquake Risk-Probable Maximum Losses CEOS_EXTRA STAC Catalog 2012-01-01 2013-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848573-CEOS_EXTRA.umm_json The map (risk map) presents the results of earthquake probable maximum loss (PML) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (PML-absolute value) and percentage (PML/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL. proprietary 294b4075ddbc4464bb06742816813bdc_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the BESD algorithm (CO2_SCI_BESD), v02.01.02 FEDEO STAC Catalog 2003-01-08 2012-03-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142508-FEDEO.umm_json The CO2_SCI_BESD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (CO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on board the European Space Agency's (ESA's) environmental research satellite ENVISAT. It has been produced using the Bremen Optimal Estimation DOAS (BESD) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.The Bremen Optimal Estimation DOAS (BESD) algorithm is a full physics algorithm which uses measurements in the O2-A absorption band to retrieve scattering information about clouds and aerosols. This is the Greenhouse Gases CCI baseline algorithm for deriving SCIAMACHY XCO2 data. A product has also been generated from the SCIAMACHY data using an alternative algorithm: the WFMD algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this BESD product. For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.For further information on the product, including details of the BESD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents. proprietary 296f4386-4af1-4a73-866c-d9192ec18685_NA MERIS - Water Parameters - North Sea, 10-Day FEDEO STAC Catalog 2006-01-01 2010-03-10 -6.10393, 49.9616, 11.4301, 61.9523 https://cmr.earthdata.nasa.gov/search/concepts/C2207458047-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-day maps. proprietary +2bb39206-6988-4127-89e5-85a0430e20cc Earthquakes frequency for MMI categories higher than 9 1973-2007 CEOS_EXTRA STAC Catalog 1973-01-01 2007-12-31 -180, -58, 180, 85.03594 https://cmr.earthdata.nasa.gov/search/concepts/C2232848457-CEOS_EXTRA.umm_json This dataset includes an estimate of earthquake frequency of MMI categories higher than 9 over the period 1973-2007. It is based on Modified Mercalli Intensity map available in the Shakemap Atlas from USGS. Unit is expected average number of events per 1000 years. This product was compiled by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing Shakemap Atlas from USGS, compilation and global hazard distribution UNEP/GRID-Europe. proprietary 2dimpacts_1 Two-Dimensional Video Disdrometer (2DVD) IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-15 2020-02-28 -75.4912, 37.9194, -75.4462, 37.9543 https://cmr.earthdata.nasa.gov/search/concepts/C1995564612-GHRC_DAAC.umm_json The Two-Dimensional Video Disdrometer (2DVD) IMPACTS data were collected in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. The IMPACTS field campaign addressed providing observations critical to understanding the mechanisms of snowband formation, organization, and evolution, examining how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands, and improving snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. These data consist of the size, equivalent diameter, fall speed, oblateness, cross-sectional area of raindrops, particle concentration, total number of drops, total drop concentration, liquid water content, rain rate, reflectivity, and rain event characteristics. Data files are available from January 15, 2020 through February 28, 2020 in ASCII format. proprietary 2e54b40f184b44c797db36e192d2b679_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier from COSMO-SkyMed for 2012-2014, v1.0 FEDEO STAC Catalog 2012-06-01 2014-12-25 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142500-FEDEO.umm_json This dataset contains ice velocity time series of then Jakobshavn glacier in Greenland, derived from intensity-tracking of COSMO-SkyMed data acquired between 2/6/2012 and 25/12/2014. The ice velocity data is derived using 4-day COSMO-SkyMed offset-tracking pairs. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 250m grid spacing. Image pairs with a repeat cycle of 4 days have been used.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by DTU Space. For further details, please consult the document:T. Nagler, et al., Product User Guide (PUG) for the Greenland_Ice_Sheet_cci project of ESA's Climate Change Initiative, version 2.0. proprietary 2e656d34d016414c8d6bced18634772c_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from the Multi-Sensor UV Absorbing Aerosol Index (MS UVAI) algorithm, Version 1.7 FEDEO STAC Catalog 1978-11-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142580-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 Absorbing Aerosol Index (AAI) products, using the Multi-Sensor UVAI algorithm, Version 1.7. L3 products are provided as daily and monthly gridded products as well as a monthly climatology. For further details about these data products please see the linked documentation. proprietary @@ -545,8 +549,10 @@ id title catalog state_date end_date bbox url description license 42ad984d-a92e-41c2-af23-f28ecd22018d_1 AFRICA CITIES POPULATION DATABASE (ACPD) CEOS_EXTRA STAC Catalog 1990-10-26 1990-10-26 -20, -38, 54, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2232847815-CEOS_EXTRA.umm_json "The African Cities Population Database (ACPD) has been produced by the Birkbeck College of the University of London in 1990 at the request of the United Nations Environment Programme (UNEP) in Nairobi, Kenya. The database contains head counts for 479 cities in Africa which either have a population of over 20,000 or are capitals of their nation state. Listed are the geographical location of the cities and their population sizes. The material is primarily derived from a 1988 report of the Economic Commission for Africa (ECA) and several issues of the United Nations Demographic Yearbook (1973-81). Severe problems were found with several countries such as Togo, Ghana and South Africa. For South Africa, the data were derived from the United Nations Demographic Yearbook 1987. WCPD is an Arc/Info point coverage. It has no projection, as the cities are located on the basis of their latitude and longitude. Coordinates were assigned on the basis of gazetteers or African maps. Each record in the data base contains details of the city name, country name, latitude and longitude of the city, and its population at a defined time. The Arc/Info attribute table contains the following fields: AREA Arc/Info item PERIMETER Arc/Info item ACPD# Arc/Info item ACPD-ID Arc/Info item ID-NUM Unique number for each city CITY City name COUNTRY Country name CITY-POP Population of city proper YEAR Latest available year of collection ACPD comes as an Arc/Info EXPORT file originally called ""ACPD.E00"" and contains 67 Kb of data. The file has a record length of 80 and a block size of 8000 (blocking factor = 100). The file can be read from tape using Arc/Info's TAPEREAD command or any other generic copy utility. If distributed on a diskette it can be read using the ordinary DOS 'COPY' command. The file has to be converted to Arc/Info internal format using its IMPORT command. References to the WCPD data set can be found in: - SERLL News, Issue No. 1, January 1991, Birkbeck College, London, UK. - D. Rhind. ""Cartographically-related research at Birkbeck College 1987-91"" in: The Cartographic Journal, Vol. 28, June 1991, pp. 63-66. The source of the WCPD data set as held by GRID is Birkbeck College, University of London, Department of Geography, London, UK." proprietary 42f7230ab55641cdac1bba84eabd446a_NA ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Uncollated (L3U) Climate Data Record, version 2.1 FEDEO STAC Catalog 1981-08-23 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142559-FEDEO.umm_json This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) level 3 uncollated data (L3U) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L3U product provides these SST data on a 0.05 regular latitude-longitude grid with with a single orbit per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x proprietary 43d73291472444e6b9c2d2420dbad7d6_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.1 FEDEO STAC Catalog 1978-11-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143086-FEDEO.umm_json The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001 proprietary +43f81a9f-f903-43d4-8333-dcda52b2bc63 Global estimated risk index for flood hazard CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2232847571-CEOS_EXTRA.umm_json This dataset includes an estimate of the global risk induced by flood hazard. Unit is estimated risk index from 1 (low) to 5 (extreme). This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: UNEP/GRID-Europe. proprietary 466b48b8-78c4-4009-97a2-c8d70f9075bf_NA MERIS - Water Parameters - Baltic Sea, 10-Day FEDEO STAC Catalog 2006-01-01 2012-04-08 6.98888, 52.1246, 34.1429, 66.7187 https://cmr.earthdata.nasa.gov/search/concepts/C2207458045-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-day maps. proprietary 46d136149d0a4f1cb8de7efbe8abf4b2_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRFP (RemoTeC) Full Physics algorithm (CH4_GOS_SRFP), version 2.3.8 FEDEO STAC Catalog 2009-03-31 2015-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142763-FEDEO.umm_json The CH4_GOS_SRFP dataset is comprised of level 2, column-averaged mole fractiona (mixing ratioa) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra onboard the Japanese Greenhouse gases Observing Satellite (GOSAT) using the SRFP (RemoTec) algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the dataset is v2.3.8 and forms part of the Climate Research Data Package 4.The RemoTeC SRFP baseline algorithm is a Full Physics algorithm. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key products with and without bias correction. Information relevant for the use of the data is also included in the data file, such as the vertical layering and averaging kernels. Additionally, the parameters retrieved simultaneously with XCH4 are included (e.g. surface albedo), as well as retrieval diagnostics like retrieval errors and the quality of the fit. For further information on the product, including the RemoTeC Full Physics algorithm and the TANSO-FTS instrument please see the Product User Guide (PUG) or the Algorithm Theoretical Basis Document. proprietary +47211801-72f3-4064-8c01-715cd2b7dc71_1 Matthews' global vegetation DB for climate studies CEOS_EXTRA STAC Catalog 1984-05-01 1984-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232847392-CEOS_EXTRA.umm_json "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes. Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice. The proper reference to these data sets is ""Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487."" The Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes." proprietary 48fc3d1e8ada405c8486ada522dae9e8_NA ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0 FEDEO STAC Catalog 2010-11-01 2017-04-30 -180, -90, 180, -16 https://cmr.earthdata.nasa.gov/search/concepts/C2548143597-FEDEO.umm_json This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data gridded on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2017. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information. proprietary 4afb736dc395442aa9b327c11f0d704b_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ensemble product), Version 2.6 FEDEO STAC Catalog 1995-08-01 2002-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142590-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 1995 to 2002. For further details about these data products please see the documentation. proprietary 4b0773a84e8142c688a628c9ce62d4ec_NA ESA Fire Climate Change Initiative (Fire_cci): Small Fire Database (SFD) Burned Area grid product for Sub-Saharan Africa, version 1.1 FEDEO STAC Catalog 2016-01-01 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142938-FEDEO.umm_json The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Database (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from MODIS MOD14MD Collection 6 active fire products.This gridded dataset has been derived from the Small Fire Database (SFD) Burned Area pixel product for Sub-Saharan Africa, v1.1 (also available), which covers Sub-Saharan Africa for the year 2016, by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution. proprietary @@ -566,7 +572,9 @@ id title catalog state_date end_date bbox url description license 57b4201d-5bf0-4a4a-ab88-5674c7af02ca_NA METOP GOME-2 - Bromine Monoxide (BrO) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207457994-FEDEO.umm_json The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational BrO (Bromine monoxide) total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. For more details please refer to https://atmos.eoc.dlr.de/app/missions/gome2 proprietary 584d4028633a4b7e9fa36da72dbd91c7_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection, Version 3.1 FEDEO STAC Catalog 1997-09-03 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143369-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 3.1 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary 58f00d8814064b79a0c49662ad3af537_NA ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1 FEDEO STAC Catalog 2001-01-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143166-FEDEO.umm_json The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. These MODIS Fire_cci v5.1 pixel products are distributed as 6 continental tiles and are based upon data from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001-2020. This product supersedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has later been periodically extended to include 2018 to 2020. The Fire_cci v5.1 Pixel product described here includes maps at 0.00224573-degrees (approx. 250m) resolution. Burned area(BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Land_Cover_cci v2.0.7 product.Files are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carrée projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation. proprietary +5940d3fb-860d-4f3e-bc3a-4022639c272a_1 Matthews' global cultivation intensity (land use) CEOS_EXTRA STAC Catalog 1984-05-01 1984-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848577-CEOS_EXTRA.umm_json "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes. Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice. The proper reference to these data sets is ""Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487."" The Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes. " proprietary 5970b33c92ef444793fb6d7e54d1230e_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by TU Dresden, v1.3 FEDEO STAC Catalog 2002-03-31 2016-08-31 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142627-FEDEO.umm_json This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by TU Dresden. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to August 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.3) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. This GMB product has been produced by TU Dresden for comparison with the existing GMB product derived by DTU Space.Please cite the dataset as follows: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065 proprietary +598f86dc-01da-49e3-824e-c8b8f1089a0e_1 Matthews' seasonal integrated surface albedo CEOS_EXTRA STAC Catalog 1984-05-01 1984-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848146-CEOS_EXTRA.umm_json "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes. Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice. The proper reference to these data sets is ""Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487."" The Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes. " proprietary 5990f1bd-9fc6-4a22-bbb5-c269312fec06_NA IRS-1D - Wide Field Sensor Images (WiFS) - Europe FEDEO STAC Catalog 1999-01-28 2005-01-27 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458035-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. proprietary 5a168a35-8cd2-4960-a134-2f319bb06760_NA METOP GOME-2 - Ozone (O3) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207457992-FEDEO.umm_json "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The operational ozone total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The new improved DOAS-style (Differential Optical Absorption Spectroscopy) algorithm called GDOAS, was selected as the basis for GDP version 4.0 in the framework of an ESA ITT. GDP 4.x performs a DOAS fit for ozone slant column and effective temperature followed by an iterative AMF / VCD computation using a single wavelength. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/" proprietary 5ab5267b17254152bcdbc055747faa02_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 5.0 FEDEO STAC Catalog 1997-09-04 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142826-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 5.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection). proprietary @@ -1945,17 +1953,28 @@ ARISE_MetNav_AircraftInSitu_C130_Data_1 ARISE 2014 C-130 In-Situ Meteorological ARISE_Radiation_AircraftInSitu_C130_Data_1 ARISE 2014 C-130 In-Situ Radiation Data LARC_ASDC STAC Catalog 2014-08-30 2014-10-05 14.25, -37.93, -170, 82 https://cmr.earthdata.nasa.gov/search/concepts/C1968754505-LARC_ASDC.umm_json ARISE_Radiation_AircraftInSitu_C130_Data_1 is the Arctic Radiation - IceBridge Sea & Ice Experiment (ARISE) 2014 in-situ cloud data product. This product is a result of a joint effort of the Radiation Sciences, Cryospheric Sciences and Airborne Sciences programs of the Earth Science Division in NASA's Science Mission Directorate in Washington. Data were collected via the Solar Spectral Flux Radiometer (SSFR), BroadBand Radiometer (BBR), and Spectrometers for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR). Data collection is complete. ARISE was NASA's first Arctic airborne campaign designed to take simultaneous measurements of ice, clouds and the levels of incoming and outgoing radiation, the balance of which determined the degree of climate warming. Over the past few decades, an increase in global temperatures led to decreased Arctic summer sea ice. Typically, Arctic sea ice reflects sunlight from the Earth. However, a loss of sea ice means there is more open water to absorb heat from the sun, enhancing warming in the region. More open water can also cause the release of more moisture into the atmosphere. This additional moisture could affect cloud formation and the exchange of heat from Earth’s surface to space. Conducted during the peak of summer ice melt (August 28, 2014-October 1, 2014), ARISE was designed to study and collect data on thinning sea ice, measure cloud and atmospheric properties in the Arctic, and to address questions about the relationship between retreating sea ice and the Arctic climate. During the campaign, instruments on NASA’s C-130 aircraft conducted measurements of spectral and broadband radiative flux profiles, quantified surface characteristics, cloud properties, and atmospheric state parameters under a variety of Arctic atmospheric and surface conditions (e.g. open water, sea ice, and land ice). When possible, C-130 flights were coordinated to fly under satellite overpasses. The primary aerial focus of ARISE was over Arctic sea ice and open water, with minor coverage over Greenland land ice. Through these efforts, the ARISE field campaign helped improve cloud and sea ice computer modeling in the Arctic. proprietary ARK_0 Optical measurements in the Arctic Ocean during 2002 and 2003 OB_DAAC STAC Catalog 2002-05-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360111-OB_DAAC.umm_json Measurements taken in the Arctic Ocean, east of Greenland and north of Scandinavia in 2002 and 2003. proprietary ARME_898_1 Pre-LBA Amazonian Region Micrometeorological Experiment (ARME) Data ORNL_CLOUD STAC Catalog 1983-09-01 1986-09-01 -60, -3, -59, -2 https://cmr.earthdata.nasa.gov/search/concepts/C2777403867-ORNL_CLOUD.umm_json The Amazonian Region Micrometeorological Experiment (ARME) data contain micrometeorological data (climate, interception of precipitation, mircometeorology and soil moisture) on the elements of the energy balance and evapotranspiration for the Amazonian forest. ASCII text data files for each of the four data types have been zipped toghether. One of the many scientific findings of this experiment was that tropical forest does not experience water stress due to the lack of precipitation, during periods when evapotranspiration is at the potential rate (Shuttleworth, 1988). ARME data types include climate (meteorological), interception of precipitation, micrometeorology, and soil moisture. These data are described in the Data Description section below. proprietary +ARNd0001_103 Global CO2 Emission CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232997868-CEOS_EXTRA.umm_json Based on fossil fuel statistics from OECD, UNEP and the World bank. (SAF, Centre for Applied Res., Norw. School of Economy & Buisiness Administration, Oslo.)SAF Working Paper no. 59/89 included Members informations: Attached Vector(s): MemberID: 1 Vector Name: CO2 Emission database - 2 floppy disks Vector CO2 Emission database on 2 floppy disks in Lotus 2.01 format. SAF Working Paper no. 59/89. proprietary +ARNd0002_103 Global vegetation regions and climate change effects - The biome model CEOS_EXTRA STAC Catalog 1970-01-01 3.88, 56.69, 32.56, 81.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232847832-CEOS_EXTRA.umm_json "Global vegetation change due to climate change modelled by using climate change models from Global Fluid Dynamics Laboratory (GFD, Princeton), Goddard Inst. for Space Studies (GISS, NASA) and Oregon State University combined with the by the Biomes classification system (Cramer & Prentice 1990). Vegetation regions are defined by parameters. University of Trondheim, Dep. of Geography, Norway Programme toidrisi to convert from tabular data. Programme found in /global/themes/cl_clima/inform Attached Raster(s): Member_ID: 1 Raster Name: GFDL Coldest month in degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 2 Raster Name: GIS Coldest month in degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 3 Raster Name: GIS Warmest moth in degrees celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 4 Raster Name: GFDL Warmest month in degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 5 Raster Name: GFD Actual evotranspiration/potenial evotranspiration Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 6 Raster Name: GISS Actual evotranspiration/potenial evotranspiration Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 7 Raster Name: GIS Growing degree days baseline 0 degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 8 Raster Name: GIS Growing degree days baseline 5 degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 9 Raster Name: GFDL Growing degree days baseline 5 degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 10 Raster Name: GFDL Growing degree days baseline 0 degrees Celsius Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 11 Raster Name: Global DTM from Cramer 30 mm lat/long grid from ETOPO5 Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 12 Raster Name: Global 3"" lat/long grid, % sunshine hours of possible Raster 12 images from January through to December. Global 30 min lat/lon raster model; digital terrain model derived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 13 Raster Name: Global 30"" lat/long grid, modelled precipitation in mm Raster 12 images from January through to December. Global 30 min lat/lon raster model; digital terrain model derived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 14 Raster Name: Global 3"" lat/long grid, modelled normal monthly temperature Raster 12 images from January through to December. Global 30 min lat/lon raster model; digital terrain model derived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 15 Raster Name: Biome 1.1 - normal climate (reclassified) Raster Categories include: ice, tundra, boreal forest, temp. deciduous, temp. evergreen, steppe, savannah, semi-desert, desert, trop. seasonal, trop. evergreen, cool desert. Attached Raster(s): Member_ID: 16 Raster Name: Biome 1.1 - OSU climate (reclassified) Raster Categories include: ice, tundra, boreal forest, temp. deciduous, temp. evergreen, steppe, savannah, semi-desert, desert, trop. seasonal, trop. evergreen, cool desert. Attached Raster(s): Member_ID: 17 Raster Name: Shift of the Boreal Zone - OSU climate Raster Categories include: decreasing, stable, available. Attached Raster(s): Member_ID: 18 Raster Name: Shift of the Boreal Zone - OSU climate - larger grid Raster Categories include: decreasing, stable, available. Attached Raster(s): Member_ID: 19 Raster Name: Shift of the Boreal Zone in Europe - OSU climate Raster Categories include: decreasing, stable, available. Attached Raster(s): Member_ID: 20 Raster Name: Percent change in temperature sum > 5 degrees Celsius (OSU) Raster Catergories include: 0-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80-90, 90-100, 100-150, 150-200, >200. Attached Raster(s): Member_ID: 21 Raster Name: Absolute change of the vegetative period >5 degrees Celsius (OSU) Raster Categories include: no change, <7 days, 7-14, 14-21, 21-28, 28-35, 35-42, 42-49, 49-56, 56-63, 63-70, >70. Members informations: Attached Vector(s): MemberID: 22 Vector Name: Original data files Vector Original data files for global vegetation regions and climate change effects. Files include: bic30gfd.dta, bic30gis.dta, bic30nor.dta, bic30osu.dta, bic30ukm.dta, clo30.gdd, prc30.gdd, tmp30.gdd." proprietary ARNd0003_103 Cramer global climate database CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232847968-CEOS_EXTRA.umm_json Climate model based on climate stations and parameters from national weather monitoring stations, and digital terrain model. Interpolation method used: Partial thin-plate spline smoothing (Huthcinson). Attached Raster(s): Member_ID: 1 Raster Name: Modeled percent sunshine hours of possible .Global 30 min latitude/longitude Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 2 Raster Name: Modeled precipitation in mm. Global 30 min latitude/longitude Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. Attached Raster(s): Member_ID: 3 Raster Name: Modeled monthly temperature in degrrees Celsius Global 30 min latitude/longitude Raster Global 30 min lat/lon raster model; digital terrain model drived from ETOPO5. Cell value origo is in lower left/SW corner: Cell value is in SW lat/lon corner, not in centre. proprietary +ARNd0012_103 Digital terrain model Norway CEOS_EXTRA STAC Catalog 1970-01-01 3.88, 56.69, 32.56, 81.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232847831-CEOS_EXTRA.umm_json Contourlines with ekv. 300 meters in the scale of 1:1mill. Gridded data extracted from the same source are also available. Generated on basis of data from Norwegian Mapping Authorities, H?nefoss, Norway proprietary +ARNd0013_103 Digital terrain model continental shelf Norway CEOS_EXTRA STAC Catalog 1970-01-01 3.88, 56.69, 32.56, 81.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232847974-CEOS_EXTRA.umm_json Extract from the ETOPO5 for the continental shelf of Norway. Raster 0.5 * 0.5 degree**2. Depthlines from (NSKV) Mapping authority of Norway are also available. Digital terrain model continental shelf Norway proprietary ARNd0015_103 Arctic Base map CEOS_EXTRA STAC Catalog 1970-01-01 -180, -89.24, 180, 89.24 https://cmr.earthdata.nasa.gov/search/concepts/C2232848960-CEOS_EXTRA.umm_json Coastline, islands and iceshelf in the Arctic area. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo export file of the Arctic coastlines Projection: geographic Projection_desc: lat/long Projection_meas: decimal degrees Feature_type: arcs Vector ArcInfo export file of the Arctic coastlines derived from data received from the Norwegian Polar Institute. proprietary ARNd0016_103 Arctic areas under Environmental protection CEOS_EXTRA STAC Catalog 1970-01-01 3.88, 56.69, 32.56, 81.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232848437-CEOS_EXTRA.umm_json Arctic areas under Environmental protection Protected areas in the Arctic region. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo polygon coverage of protected areas in Norway Feature_type: poly Vector ArcInfo polygon coverage of protected areas in Norway Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo point coverage of protected areas in Norway Feature_type: point Vector ArcInfo point coverage of protected areas in Norway Members informations: Attached Vector(s): MemberID: 3 Vector Name: ArcInfo polygon coverage of protected areas in Svalbard Feature_type: polygon Vector ArcInfo polygon coverage of protected areas in Svalbard Members informations: Attached Vector(s): MemberID: 4 Vector Name: ArcInfo point coverage of bird sanctuaries on Svalbard Feature_type: points Vector ArcInfo point coverage of bird sanctuaries on Svalbard. Members informations: Attached Vector(s): MemberID: 5 Vector Name: Six ArcInfo coverage of protected areas on Svalbard Projection: geographic/lambert Projection_meas: decimal degrees/metres Feature_type: polyarcpt Vector Six ArcInfo coverage used for the study of protected areas on Svalbard. Coverage includes: svalco, svalco1, svalco2, sval_eco, sval_veg and svern1. proprietary ARNd0033_103 Bathymetry - the Baltic Sea, Kattegat and Skagerat CEOS_EXTRA STAC Catalog 1970-01-01 10, 53, 35, 67 https://cmr.earthdata.nasa.gov/search/concepts/C2232849161-CEOS_EXTRA.umm_json Image file of Baltic sea, Kattegat and Skagerat A time dependent budget model for nutrients in the Baltic Sea. Attached Raster(s): Member_ID: 1 Raster Name: Bathymtry of the Baltic sea original raster data file Raster ERDAS files containing information about the bathymetry of the Baltic sea, Kattegat and Skagerak Files include .gis and .lan files plus ERDAS image files and a .pcx files proprietary ARNd0040_103 Bedrock geology for Norway CEOS_EXTRA STAC Catalog 1970-01-01 3.88, 56.69, 32.56, 81.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232849266-CEOS_EXTRA.umm_json Various coverages representing bedrock geology in Norway. One aml for producing eps files. Members informations: Attached Vector(s): MemberID: 1 Vector Name: Coverage showing bedrock geology in Norway Vector Description to be added Members informations: Attached Vector(s): MemberID: 2 Vector Name: Line coverage displaying geological faults in Norway Vector Description to be added Members informations: Attached Vector(s): MemberID: 3 Vector Name: Coverage displaying trust/reversed faults Vector Description to be added proprietary +ARNd0071_103 Latvian Drainage network CEOS_EXTRA STAC Catalog 1970-01-01 20.76, 55.32, 28.76, 58.44 https://cmr.earthdata.nasa.gov/search/concepts/C2232848019-CEOS_EXTRA.umm_json "Digitised from paper maps by Lativan Environment Data Centre. Converted from DXF files to ARC/INFO coverage (GRID-Arendal). Details start-end date ask Sindre To get precise coordinates use ARC/INFO command ""describe"" on member 1 Members informations: Attached Vector(s): MemberID: 1 Vector Name: Rivers of Latvia Projection: UTM Projection_desc: Zone 35 Projection_meas: Metres Feature_type: lline Vector Source map is Russian topographic map, scale supposed to be 1:500 000." proprietary +ARNd0073_103 Ice classes - Barents Sea CEOS_EXTRA STAC Catalog 1970-01-01 -180, -89.24, 180, 89.24 https://cmr.earthdata.nasa.gov/search/concepts/C2232846647-CEOS_EXTRA.umm_json The polar regions hold large masses of water in the form of ice, and this ice has a modifying effect on temperature variations. An increase in the mean temperature of the Arctic, as has been predicted, may result in an intensified melting of the sea ice. While a compact ice cover absorbs 15-50% of the incoming solar radiation, an ice free ocean absorbs about 90%. The absorbed radiation causes warming and evaporation. A change in the ice cover may therefore drastically effect the heat budget of the sea surface. This illustrates an important self-magnifying effect of the increased heat absorption causing the acceleration of ice melting. An increased greenhouse effect due to changes in the gas composition of the atmosphere could therefore be monitored by studying the changes in the total mass of sea ice in the Arctic. Ice charts of the Barents Sea , with the ice separated in ten ice classes, weekly, from 1966 to 1989. The classes are from open water to dense pack ice. Attached Raster(s): Member_ID: 1 Raster Name: *.BPIX files - internal format of Norwegian Polar Institute Raster Raster files received from the Norwegian Polar Institute in their own internal format. Attached Raster(s): Member_ID: 2 Raster Name: ArcInfo grids representing sea ice in the barents region Raster ArcInfo grids representing the barents sea ice cover from 1966 to 1989. Attached Raster(s): Member_ID: 3 Raster Name: ArcInfo grids for january 1989. Raster Five ArcInfo grids representing ice coverage in January 1989. Attached Raster(s): Member_ID: 4 Raster Name: Arcinfo grid of sea ice for February 1989. Raster ArcInfo grid representing sea ice cover for February 1989. Attached Raster(s): Member_ID: 5 Raster Name: ArcInfo grid of sea ice for January - year unknown. Raster ArcInfo grid representing sea ice cover in January - year unknown. Attached Raster(s): Member_ID: 6 Raster Name: Arcinfo grid of sea ice Raster ArcInfo grid representing sea ice - date unknown. Need better documentation for this grid - need to know the coverage date. Attached Raster(s): Member_ID: 7 Raster Name: ArcInfo grid of sea ice for July and August Raster ArcInfo grid representing sea ice cover for July and August, year unknown. Attached Raster(s): Member_ID: 8 Raster Name: ArcInfo grid of minimum sea ice extent for 1989. Raster Arcinfo grid representing minimum sea ice extent for 1989. Members informations: Attached Vector(s): MemberID: 9 Vector Name: Vector coverages of ice extent Feature_type: polygons Vector Vector coverages representing ice extent for the years 1966 through to 1989. Members informations: Attached Vector(s): MemberID: 10 Vector Name: Vector ArcInfo coverages of sea ice extent Feature_type: polygons Vector Vector coverages representing sea ice extent for the years 1966 through to 1989. Members informations: Attached Vector(s): MemberID: 11 Vector Name: Clipping area for the barents sea region Feature_type: polygon Vector ArcInfo clipping vector coverage of the Barents sea region. Members informations: Attached Vector(s): MemberID: 12 Vector Name: ArcInfo coverage of July-August ice extent Feature_type: polygon Vector ArcInfo vector coverage representing the ice extent for July and August. Year unknown proprietary ARNd0075_103 Antarctic coastline CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C2232848591-CEOS_EXTRA.umm_json The antarctic coastline. Map with geographical coordinates. Coastline, islands and iceshelf in the Antarctic area. Members informations: Attached Vector(s): MemberID: 1 Vector Name: Arcinfo coverage of The Antarctic incl South America & Southern most island Projection: geographic Projection_desc: Lat/long Projection_meas: decimal degrees Feature_type: arcs Vector Arcinfo coverage representing the Antarctic coastline. The surrounding areas are also included in this coverage such as the southern coast of South America and the southern most islands. Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo export file of The Antarctic coastline. Projection: geographic Projection_desc: lat/long Projection_meas: decimal degrees Feature_type: arcs Vector Arcinfo export coverage of The Antarctic coastline not including surrounding areas. Members informations: Attached Vector(s): MemberID: 3 Vector Name: Arcinfo coverage of southern coastline of South America Projection: geographic Projection_desc: lat/long Projection_meas: decimal degrees Feature_type: arcs Vector ArcInfo coverage representing the southern coast of South America to be used together with The Antarctic coastline. Members informations: Attached Vector(s): MemberID: 4 Vector Name: ArcInfo coverage of southern most islands surrounding The Antarctic. Projection: geographic Projection_desc: lat/long Projection_meas: decimal degrees Feature_type: arcs Vector Arcinfo coverage representing the southern most islands found around The Antarctic. To be used together with The Antarctic coastline. proprietary ARNd0076_103 Arctic basemap CEOS_EXTRA STAC Catalog 1970-01-01 -180, 65, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232849275-CEOS_EXTRA.umm_json A circumpolar basemap of The Arctic. Members informations: Attached Vector(s): MemberID: 1 Vector Name: Circumpolar ArcInfo coverage of The Arctic Source Map Name: ArcWorld on CD-ROM Source Map Scale: 25000000 Projection: Lambert Azimuthal Projection_desc: Lat of centre: 90 0 0 Projection_meas: metres Feature_type: arcs/polys Vector Circumpolar ArcInfo coverage of The Arctic derived from ESRI's ArcWorld 1:25M basemap of the world. Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo coverage of latitude line 50 degrees Projection: Azimuthal Projection_desc: lat of centre: 90 0 0 Projection_meas: metres Feature_type: arcs/polys Vector Circumpolar ArcInfo coverage of the latitude line 50 degrees. proprietary ARNd0078_103 Bear Island basemap CEOS_EXTRA STAC Catalog 1970-01-01 3.88, 56.69, 32.56, 81.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232848177-CEOS_EXTRA.umm_json Coastline of Bear Island. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo coverage of the coastline of Bear Island Vector ArcInfo coverage of Bear Island as dervied from data provided by the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo coverage of the coastline of Bear Island in UTM coordinates Vector ArcInfo coverage of the coastline of Bear Island as derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 3 Vector Name: ArcInfo generate file of the coastline of Bear Island Vector ArcInfo generate file representing the coastline of Bear Island derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 4 Vector Name: Norwegian Polar Institute internal format file of the coastline of Bear Island Vector Norwegian Polar Institute internal format file representing the coastline of Bear Island as received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 5 Vector Name: Line-koordinate file of the coastline of Bear Island Vector Line-koordinate file representing the coastline of Bear Island derived from data received from the Norwegian Polar Institute. proprietary +ARNd0079_103 Franz Josef Land basemap CEOS_EXTRA STAC Catalog 1970-01-01 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2232847672-CEOS_EXTRA.umm_json Franz Josef Land coastline information. Members informations: Attached Vector(s): MemberID: 1 Vector Name: Six ArcInfo coverages of the coastline of Franz Josef Land Projection: geographic Projection_desc: lat/long Projection_meas: decimal degrees Feature_type: arcs Vector Six ArcInfo coverages representing the coastline of Franz Josef land. FRAJO1.....FRAJO5 represent only parts of the whole coverage FRAJO. These coverages are derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo coverage of the coastline fo Franz Josef Land in UTM coordinates Projection: UTM Projection_desc: zone 33 Projection_meas: metres Feature_type: arcs/polys Vector ArcInfo coverage of the coastline of Franz Josef Land as derived from data received from the Norwegian Polar Institute. Attached Raster(s): Member_ID: 3 Raster Name: Projection file, geographic to utm and utm to geographic. Raster Projection files used to project coverages in geographic coordinates to UTM coordinates, zone 33 and vise versa. Members informations: Attached Vector(s): MemberID: 4 Vector Name: ArcInfo generate files of the coastline of Franz Josef Land Vector Five ArcInfo generate files representing the coastline of Franz Josef Land derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 5 Vector Name: Norwegian Polar Institute internal format files of the coast of Franz Josef Land Vector Five Norwegian Polar Institute internal format files of the coastline of Franz Josef Land as received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 6 Vector Name: Line-koordinate files (.lik) of the coastline of Franz Josef Land Vector Five line-koordinate files (.lik) representing the coastline of Franz Josef Land derived from data received from the Norwegian Polar Institute. proprietary +ARNd0082_103 Jan Mayen Island basemap CEOS_EXTRA STAC Catalog 1970-01-01 3.88, 56.69, 32.56, 81.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232849393-CEOS_EXTRA.umm_json Jan Mayen Island coastline. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate file of the coastline of Jan Mayen Island Feature_type: arcs Vector ArcInfo generate file representing the coastline of Jan Mayen Island derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal format file of the Jan Mayen coastline Vector Norwegian Polar institute file representing the coastline of Jan Mayen island as received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 3 Vector Name: Several ArcInfo coverages representing basemap information for Jan Mayen Island Source Map Name: DCW Source Map Scale: 1000000 Projection: geographic Projection_desc: lat/long Projection_meas: metres Feature_type: polyarcpt Vector Several ArcInfo coverages representing basemap information for Jan Mayen Island extract from the Digital Chart fo the World CD-ROM. Layers include cultural point features (clpoint), drainage network (dnnet), data quality layer (dqnet), supplemental drainage points (dspoint), hyposography supplemental lines (hsline), hypsography supplemental points (hspoint), hypsography network (hynet), hypsography points (hypoint), political and oceanic boundaries (ponet). Members informations: Attached Vector(s): MemberID: 4 Vector Name: Several ArcInfo coverages representing basemap info for Jan Mayen Island in UTM Source Map Name: DCW Source Map Scale: 1000000 Projection: UTM Projection_desc: zone 29 Projection_meas: metres Feature_type: polyarcpt Vector Several ArcInfo coverages representing basemap information for Jan Mayen Island extract from the Digital Chart fo the World CD-ROM. Layers include drainage network (dnnet), hyposography supplemental lines (hsline), hypsography network (hynet), political and oceanic boundaries (ponet). Associated projection file used is included (geo-utm). proprietary +ARNd0083_103 Iceland basemap CEOS_EXTRA STAC Catalog 1970-01-01 -24.55, 62.81, -12.79, 67.01 https://cmr.earthdata.nasa.gov/search/concepts/C2232849025-CEOS_EXTRA.umm_json Basemap of Iceland Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate file of Iceland's coastline Vector ArcInfo generate file representing the coastline of Iceland derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal fromat files of the Icelandic coastline Vector Norwegian Polar Institute internal format files of the Icelandic coastline as received from the Norwegian Polar institute. proprietary +ARNd0084_103 Greenland basemap CEOS_EXTRA STAC Catalog 1970-01-01 -75.34, 56.78, -9.36, 86.6 https://cmr.earthdata.nasa.gov/search/concepts/C2232847703-CEOS_EXTRA.umm_json Basemap information of Greenland Incorrect bounding box Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate files of Greenlands coastline Vector Thirteen ArcInfo generate files representing the coastline of Greenland derived from data received from the Norwegian Polar Insitute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal format files of the Greenland coastline Vector Norwegian Polar Institute internal format files representing the coastline of Greenland as received from the norwegian Polar Institute. proprietary ARNd0086_103 Alaska basemap CEOS_EXTRA STAC Catalog 1970-01-01 -170, 51, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2232847525-CEOS_EXTRA.umm_json Basemap of Alaska. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate file of Alaskan coastline Vector ArcInfo generate file representing the coastline of Alaska derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal format file of Alaskan coastline Vector Norwegian Polar Institute internal format file representing the coastline of Alaska as received from the Norwegian Polar Institute. proprietary ARNd0098_103 Basemap - Nordic countries CEOS_EXTRA STAC Catalog 1970-01-01 -15, 35, 45, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2232848873-CEOS_EXTRA.umm_json Nordisk Kartdatabas/Nordic Cartographic Database. Details of the internal boundaries of the Nordic countries co-ordinated by the National Land Survey, Sweden. Area not strictly Scandinavia but Nordic. Members informations: Attached Vector(s): MemberID: 1 Vector Name: Export file of the fylke/lan/district boundaries of the Nordic countries Projection: geographic Projection_desc: lat/long Projection_meas: decimal degrees Feature_type: arcs Vector Export file of the fylke/lan/district boundaries of the Nordic countries. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Export file of the kommune/county boundaries of the Nordic countries Projection: geographic Projection_desc: lat/long Projection_meas: decimal degrees Feature_type: arcs Vector Export file of the kommune/county boundaries of the Nordic countries. Members informations: Attached Vector(s): MemberID: 3 Vector Name: ArcInfo coverage of the coastline/borders of Norway, Sweden and Finland Feature_type: polyarcpt Vector ArcInfo coverage of the coastline/borders of Norway, Sweden and Finland. Members informations: Attached Vector(s): MemberID: 4 Vector Name: ArcInfo coverage of the coastline of Norway and border with Sweden Feature_type: polyarcpt Vector ArcInfo coverage of the coastline of Norway and border with Sweden. Members informations: Attached Vector(s): MemberID: 5 Vector Name: Clipped ver of ArcInfo coverage of coastline/border of Norway, Sweden & Finland Feature_type: polyarcpt Vector Clipped version of ArcInfo coverage of coastline/border of Norway, Sweden and Finland. proprietary ARNd0105_103 Climate in Norway CEOS_EXTRA STAC Catalog 1970-01-01 3.88, 56.69, 32.56, 81.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232848243-CEOS_EXTRA.umm_json Climatic zones in Norway at different times of the year. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo coverages of climatic zones of Norway at different times of the year Feature_type: polys/arcs Vector ArcInfo coverages of climatic zones of Norway at different times of the year. Coverages include: kl623, klima613, klima622, klima623, klima626, klima626b, klima632, klima632b, klima653, klima653b, klima656, klima656b. Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo coverage of temperature isolines for January. Feature_type: polyarcpt Vector ArcInfo coverage of temperature isolines for January, including Svalbard. Members informations: Attached Vector(s): MemberID: 3 Vector Name: ArcInfo coverage of temperature isolines for July. Feature_type: polyarcpt Vector ArcInfo coverage of temperature isolines for July, including Svalbard. proprietary +ARNd0117_103 Economic regions of Europe CEOS_EXTRA STAC Catalog 1970-01-01 -15, 35, 45, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2232849218-CEOS_EXTRA.umm_json Economic boundaries within Europe. This shows which countries belong to the E?S, the agreement between countries belonging to EFTA (European Trade Agreement) and the EU. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo coverage showing countries belonging to the E?S Source Map Name: WDBII Projection: geographic Projection_meas: decimal degrees Feature_type: polyarcpt Vector ArcInfo coverage showing countries belonging to the E?S, the agreement between countries belonging to EFTA (European Trade Agreement) and the EU. This coverage was taken from the World Databank II data set. Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo coverage showing countries belonging to the E?S in a polar projection Projection: polar Projection_desc: long 10 0 0/lat 60 0 0 Projection_meas: metres Feature_type: polyarcpt Vector ArcInfo coverage showing countries belonging to the E?S (in a polar projection), the agreement between countries belonging to EFTA (European Trade Agreement) and the EU. This coverage was taken from the World Databank II data set. proprietary ARNd0132_103 Conservation of Arctic Flora & Fauna (CAFF) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -89.24, 180, 89.24 https://cmr.earthdata.nasa.gov/search/concepts/C2232848701-CEOS_EXTRA.umm_json When the Ministers of the Arctic countries adapted the Arctic Environmental Protection Strategy (AEPS) in 1991, they signalled out habitat conservation as an area for special attention. Consequently when the International Working Group for the Conservation of Arctic Flora and Fauna (CAFF) met at its inaugural meeting in 1992, it included Arctic habitat conservation as a priority in its Work Plan. Norway, on behalf of the eight Arctic countries, accepted the task of examining the current status of habitat protection within the Arctic countries as the first phase of CAFF's long-term strategy on habitat. This datasets holds data concerning existing protected areas in the Arctic. proprietary ARPANSA_BIO_12 Daily broad-band ultra-violet radiation observations using biologically effective UVR detectors AU_AADC STAC Catalog 1996-07-23 62.84, -68.66, 158.95844, -54.47642 https://cmr.earthdata.nasa.gov/search/concepts/C1214305714-AU_AADC.umm_json This dataset also forms part of the set of State of the Environment (SOE) indicators. INDICATOR DEFINITION Daily measurements of solar Ultra-Violet radiation at Casey and Davis stations, reported in units of standard erythemal dose (SED). TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system. This indicator is one of: CONDITION and PRESSURE RATIONALE FOR INDICATOR SELECTION Stratospheric ozone depletion began in the mid-1970's and is likely to persist until mid this century or beyond. Ozone depletion allows more short wavelength, biologically damaging, UVB radiation (280-320 nm) to reach the Earth's surface. Thus, organisms living beneath depleted ozone are likely to be impacted by enhanced UVB irradiances. Enhanced UVB irradiances can increase the incidence of skin cancer, cataract eye disease and even immune system suppression in humans. It can also reduce the growth, productivity and survival of marine organisms and can cause changes in the structure and function of Antarctic marine communities. This indicator provides a direct measure of the extent and magnitude to which UV irradiances are enhanced and provides vital data against which biological responses to UV exposure can be normalised. Living organisms are sensitive to UV radiation because vital biological molecules such as DNA, lipids and proteins absorb strongly in these wavelengths. DNA, with a peak absorption at 260 nm, is particularly sensitive, and is liable to mutation. DNA damage has been extensively studied in microbial and mammalian systems where UV-induced damage produces two distinct effects, mutagenesis and toxicity. In humans the impact of DNA damage manifests mainly as skin cancer. DNA damage in plants has been the subject of relatively few studies (Britt, 1999; Taylor et al, 1996; Vornarx et al, 1998) with most research examining impacts of UV-B on growth or photosynthesis, predominantly using crop plants. Terrestrial plants are potentially very vulnerable to UV-B induced DNA damage. Firstly the levels of UV-B are higher on land than in water. In addition plants rely on light for photosynthesis and are therefore adapted to absorb high levels of solar radiation (and the associated, harmful UV-B). Defence mechanisms to protect against damaging high energy UV radiation are also found in plants. Compounds such as flavonoids, and carotenoids absorb UV radiation and act as sun-screens, reducing the levels of UV-B at the molecular level. Research has been limited in Antarctic plants but there are clear differences in protective pigment levels in 3 Antarctic mosses with Grimmia antarctici (an endemic species) showing low levels of these pigments compared to other cosmopolitan species (Robinson et al 2001). This suggests that the endemic species may be more vulnerable to UV-B damage. Studies have recently commenced to investigate DNA-damage in these plants. Work by Skotnicki and coworkers (Skotnicki et al 2000) which shows high levels of somatic mutation could also be a result of UV-B exposure. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial Scale: The Australian Radiation Protection and Nuclear Safety Agency take broadband in situ observations at Antarctic mainland stations (Casey, Davis and Mawson) and at Macquarie Island. Frequency: Continuous measurements Measurement Technique: Broad band UV radiometry (use of biometer or biologically effective UVR detector). Total UVR measurements are also made using an Eppley TUV radiometer (responds across 290 to 400 nm wavelength range). Spectral measurements have also been made at Davis station. Readings are taken every ten minutes and the total SED's calculated for the day. RESEARCH ISSUES A need exists for a comprehensive monitoring network of broadband measurements, complemented by a small baseline network of precision spectral measurements across the nation. Such a network is being planned by the Bureau of Meteorology to link directly with the basic national meteorological observations. Validation of satellite data with surface based measurements (ARPANSA) over Australia for the period 1979-1992 has been carried out (Udelhofen et al 1999) and a follow up is planned for 1992-2000. Validation of satellite data and surface UVR measurements over the Antarctic and sub-Antarctic is planned between the Antarctic Division, ARPANSA and Dan Lubin at UCLA. LINKS TO OTHER INDICATORS SOE Indicator 9 - Daily records of total column ozone at Macquarie Island DATA DESCRIPTION 10 minute averages of weighted UVR (CIE 1987 spectral effectiveness). The data in the files is : Date, time, total solar radiation (counts), gain 1, Total UVR (counts), gain 2, UVB(counts), gain 3, biometer , temperature. Main Detector is Solar Light UVBiometers (SL501) Detector 1 - Eppley total solar radiation pyranometer. Detector 2 - Eppley total UVR (TUV) radiometer - covers wavelength range 290 to 400 nm. Detector 3 - International Light UVB radiometer - covers wavelength range 290 to 315 nm. Detector 4 - Solar Light UVBiometer (SL501) - approximates CIE erythemal spectral effectiveness. The 2nd last column is the biometer in MEDs/hr (1 MED is 200 J/m2 effective weighted with the CIE (1987) erythemal response) and the last column is temperature inside the detector. The 3 other detectors, with outputs in counts, are the total solar, Total UVR (TUV) and the UVB. Data are stored as zipped up .dat files, and in excel spreadsheets. Last data were added in 2020. The fields in this dataset are: Date Time Total Solar Radiation (counts) Gain 1 Total UVR (counts) Gain 2 UVB(counts) Gain 3 Biometer Temperature proprietary ASAC2100_1 DMS in the Southern Ocean AU_AADC STAC Catalog 1997-10-06 1998-11-23 63, -70, 160, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1214313058-AU_AADC.umm_json From 1991 to 2000 14 voyages have been completed in the Southern Ocean. Measurements of DMS (Dimethylsulfide) and DMSP (Dimethylsulfoniopropionate) have been carried out on surface and subsurface waters together with physical and biological measurements, with a view to understanding the main processes that affect DMS in the Southern Ocean. The first flux measurements have been carried out for DMS (see Curran and Jones 2000) in the last 3 years a concerted study has been carried out in the seasonal ice zone this study aims to identify the major phytoplankton assemblages responsible for DMS and DMSP production in the sea ice zone. It is thought that the sea ice zone also contributes to DMS in the atmosphere. This is being quantified. The fields in this dataset are: Site Date Time (local) Latitude Longitude Snow Cover (metres) Core Length (metres) DMSPt (nano Mols) Chlorophyl a (micrograms per litre) Sea Ice depth (metres) Pigments Fucoxanthin (micrograms per litre) Peridinin (micrograms per litre) 19' hexanoyloxyfucoxanthin (micrograms per litre) Salinity (ppt) Nitrate (micro Mols) Nitrite (micro Mols) Silicate (micro Mols) Phosphate (micro Mols) proprietary @@ -2402,10 +2421,10 @@ AST_L1B_003 ASTER L1B Registered Radiance at the Sensor V003 LPDAAC_ECS STAC Cat AST_L1T_003 ASTER Level 1 precision terrain corrected registered at-sensor radiance V003 LPDAAC_ECS STAC Catalog 2000-03-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000320-LPDAAC_ECS.umm_json The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B (AST_L1B) (https://doi.org/10.5067/ASTER/AST_L1B.003), that has been geometrically corrected, and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A (AST_L1A) (https://doi.org/10.5067/ASTER/AST_L1A.003) product. The bands available in the AST_L1T depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T dataset does not include the aft-looking VNIR band 3. The precision terrain correction process incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover). For daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution in both as a text file and a single band browse images with the valid GCPs overlaid. This multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depends on the bands available in the corresponding (AST_L1A) (https://doi.org/10.5067/ASTER/AST_L1A.003) dataset. proprietary AST_L1T_031 ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance V031 LPDAAC_ECS STAC Catalog 2000-03-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2052604735-LPDAAC_ECS.umm_json The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) Version 3.1 data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B AST_L1B (https://doi.org/10.5067/ASTER/AST_L1B.003), that has been geometrically corrected and rotated to a north-up UTM projection. The AST_L1T V3.1 is created from a single resampling of the corresponding ASTER L1A AST_L1A (https://doi.org/10.5067/ASTER/AST_L1A.003) product. Radiometric calibration coefficients Version 5 (RCC V5) are applied to this product to improve the degradation curve derived from vicarious and lunar calibrations. The bands available in the AST_L1T V3.1 depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T V3.1 dataset does not include the aft-looking VNIR band 3. The 3.1 version uses a precision terrain correction process that incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover). For daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution in both a text file and a single band browse image with the valid GCPs overlaid. This multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depend on the bands available in the corresponding AST_L1A dataset. The AST_L1T V3.1 data product is only available through NASA’s Earthdata Search. The ASTER L1T V3.1 Order Instructions provide step-by-step directions for ordering this product. proprietary ATCS_0 The A-Train Cloud Segmentation Dataset OB_DAAC STAC Catalog 2007-11-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2172083412-OB_DAAC.umm_json ATCS is a dataset designed to train deep learning models to volumetrically segment clouds from multi-angle satellite imagery. The dataset consists of spatiotemporally aligned patches of multi-angle polarimetry from the POLDER sensor aboard the PARASOL mission and vertical cloud profiles from the 2B-CLDCLASS product using the cloud profiling radar (CPR) aboard CloudSat. proprietary -ATL02_006 ATLAS/ICESat-2 L1B Converted Telemetry Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2547589158-NSIDC_CPRD.umm_json This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations. proprietary ATL02_006 ATLAS/ICESat-2 L1B Converted Telemetry Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.umm_json This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations. proprietary -ATL03_006 ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.umm_json This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. proprietary +ATL02_006 ATLAS/ICESat-2 L1B Converted Telemetry Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2547589158-NSIDC_CPRD.umm_json This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations. proprietary ATL03_006 ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.umm_json This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. proprietary +ATL03_006 ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.umm_json This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. proprietary ATL03_ANC_MASKS_1 ATLAS/ICESat-2 ATL03 Ancillary Masks, Version 1 NSIDCV0 STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2278879612-NSIDCV0.umm_json This ancillary ICESat-2 data set contains four static surface masks (land ice, sea ice, land, and ocean) provided by ATL03 to reduce the volume of data that each surface-specific along-track data product is required to process. For example, the land ice surface mask directs the ATL06 land ice algorithm to consider data from only those areas of interest to the land ice community. Similarly, the sea ice, land, and ocean masks direct ATL07, ATL08, and ATL12 algorithms, respectively. A detailed description of all four masks can be found in section 4 of the Algorithm Theoretical Basis Document (ATBD) for ATL03 linked under technical references. proprietary ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.umm_json ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary @@ -2418,15 +2437,15 @@ ATL08QL_006 ATLAS/ICESat-2 L3A Land and Vegetation Height Quick Look V006 NSIDC_ ATL08_006 ATLAS/ICESat-2 L3A Land and Vegetation Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.umm_json This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL08_006 ATLAS/ICESat-2 L3A Land and Vegetation Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565090645-NSIDC_ECS.umm_json This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL09QL_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2551528419-NSIDC_ECS.umm_json ATL09QL is the quick look version of ATL09. Once final ATL09 files are available the corresponding ATL09QL files will be removed. ATL09 contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary -ATL09_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.umm_json This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL09_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.umm_json This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary +ATL09_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.umm_json This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL10QL_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2551529078-NSIDC_ECS.umm_json ATL10QL is the quick look version of ATL10. Once final ATL10 files are available the corresponding ATL10QL files will be removed. ATL10 contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL10_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2567856357-NSIDC_ECS.umm_json This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL10_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553243-NSIDC_CPRD.umm_json This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary -ATL11_006 ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.umm_json This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations. proprietary ATL11_006 ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.umm_json This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations. proprietary -ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary +ATL11_006 ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.umm_json This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations. proprietary ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary +ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL13QL_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650092501-NSIDC_ECS.umm_json ATL13QL is the quick look version of ATL13. Once final ATL13 files are available the corresponding ATL13QL files will be removed. ATL13 contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7 km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary ATL13_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.umm_json This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary ATL13_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.umm_json This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary @@ -2434,18 +2453,18 @@ ATL14_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003 N ATL14_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776895337-NSIDC_CPRD.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary ATL14_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004 NSIDC_CPRD STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3162179692-NSIDC_CPRD.umm_json This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary ATL14_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004 NSIDC_ECS STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3159684163-NSIDC_ECS.umm_json This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary -ATL15_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464171-NSIDC_ECS.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary ATL15_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776895930-NSIDC_CPRD.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary +ATL15_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464171-NSIDC_ECS.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary ATL15_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004 NSIDC_CPRD STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3162334027-NSIDC_CPRD.umm_json This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary ATL15_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004 NSIDC_ECS STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3159684532-NSIDC_ECS.umm_json This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary -ATL16_005 ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.umm_json This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary ATL16_005 ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.umm_json This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary -ATL17_005 ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.umm_json This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary +ATL16_005 ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.umm_json This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary ATL17_005 ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.umm_json This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary -ATL19_003 ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003 NSIDC_ECS STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.umm_json This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography. proprietary +ATL17_005 ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.umm_json This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary ATL19_003 ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography. proprietary -ATL20_004 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.umm_json ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection. proprietary +ATL19_003 ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003 NSIDC_ECS STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.umm_json This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography. proprietary ATL20_004 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.umm_json ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection. proprietary +ATL20_004 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.umm_json ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection. proprietary ATL21_003 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Polar Sea Surface Height Anomaly V003 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2737912334-NSIDC_ECS.umm_json ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids. proprietary ATL21_003 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Polar Sea Surface Height Anomaly V003 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2753316241-NSIDC_CPRD.umm_json ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids. proprietary ATL22_003 ATLAS/ICESat-2 L3B Mean Inland Surface Water Data V003 NSIDC_ECS STAC Catalog 2018-10-14 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2738530540-NSIDC_ECS.umm_json ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers. proprietary @@ -2830,29 +2849,137 @@ B09_0 Measurements near Santa Barbara, California in 2009 OB_DAAC STAC Catalog 2 BAHAMAS2004_0 Measurements from the Bahamas in 2004 OB_DAAC STAC Catalog 2004-03-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360136-OB_DAAC.umm_json Measurements from the Bahamas in 2004. proprietary BANGSS_Ocean_1 Aurora Australis Southern Ocean oceanographic (CTD) data, cruise 1994/95 V6 (BANGSS) AU_AADC STAC Catalog 1995-03-09 1995-04-02 64, -67.5, 82, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214313155-AU_AADC.umm_json This dataset contains CTD (conductivity, temperature, depth) data obtained from the Big ANtarctic Geological and Seismic Survey (BANGSS) 94/95 cruise of the Aurora Australis, during Feb - Apr 1995. 24 CTD casts were taken in the Prydz Bay region, as a supplement to the geology research program. This dataset is a subset of the whole cruise data. The fields in this dataset are: Pressure Temperature Sigma-T Salinity Geopotential ANomaly Specific volume Anomaly samples deviation conduction proprietary BANZARE_logs_1 BANZARE ship logs and station lists AU_AADC STAC Catalog 1929-10-21 1931-03-24 18, -68, 178, -33 https://cmr.earthdata.nasa.gov/search/concepts/C1214313167-AU_AADC.umm_json The British Australian (and) New Zealand Antarctic Research Expedition (BANZARE) was a research expedition into Antarctica between 1929 and 1931, involving two voyages over consecutive Austral summers. This document describes the ship's log and station list taken from Biological Organisation and Station List by T. Harvey Johnston, BANZARE Reports, Series B, Vol I, Part 1, pages 1-48 Data are stored in an Access database. The 5 tables are banzare_noon_log_1929_1930 and banzare_noon_log_1930_1931 noon positions from page 46-47 - assumed log_date is local noon, latitude and longitude in decimals. banzare_stations_1929_1930 and banzare_stations_1930_1931 odate is station date (no time is given) depth is echo depth (metres) latg and long is refined positions using Google Earth and Kerguelen map on page 14 full_speed_nets_1930_1931 log of full sped nets - see pages 40-44; time is possibly UTC distance is travel of ship when net is deployed depth is possible depth of net in fathoms tow_speed is ship speed in knots proprietary +BANd0005_113 NOAA Weekly Global Vegetation Index (GVI) - Asia - 1982 to 1989 CEOS_EXTRA STAC Catalog 1970-01-01 20, -10, -170, 79 https://cmr.earthdata.nasa.gov/search/concepts/C2232848172-CEOS_EXTRA.umm_json NOAA Weekly Global Vegetation Index (GVI maxima) dataset for Asia. The dataset represents the period from April 1982 to December 1989 proprietary +BANd0009_113 NOAA AVHRR GAC Images of South East Asia - November 8-9, 1989 CEOS_EXTRA STAC Catalog 1970-01-01 90, 4, 112, 30 https://cmr.earthdata.nasa.gov/search/concepts/C2232848446-CEOS_EXTRA.umm_json Global Area Coverage (GAC) 5 band data for South East Asia derived from the NOAA Satellite AVHRR sensor Global Area Coverage (GAC) proprietary BANd0016_113 AVHRR False Color Composition, Kuwait region, January 21-March 1, 1991 CEOS_EXTRA STAC Catalog 1970-01-01 46.62, 28.34, 48.74, 30 https://cmr.earthdata.nasa.gov/search/concepts/C2232847477-CEOS_EXTRA.umm_json NOAA-11 AVHRR 3 band False Color Composites of the Kuwait region from January 21, 1991 to March 1, 1991. Dataset includes 17 false color composites from imageries acquired over this period proprietary +BANd0018_113 District Boundaries of India dataset CEOS_EXTRA STAC Catalog 1970-01-01 66.79, 6.58, 99.01, 36.96 https://cmr.earthdata.nasa.gov/search/concepts/C2232848622-CEOS_EXTRA.umm_json District boundaries of India dataset prepared for FAO by Department of Energy and natural resources, University of Illinois. Includes coastlines, national-subnational boundaries, lakes, and islands proprietary +BANd0019_113 India and Pakistan Boundaries CEOS_EXTRA STAC Catalog 1970-01-01 60.18, 22.94, 78.66, 37.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848452-CEOS_EXTRA.umm_json District boundaries of India & Pakistan (SATERT5G) with census data on maize, wheat, rice in INDATDBF.dbf and State boundaries of India & Pakistan (STATE08G) proprietary +BANd0020_113 National and Provincial Boundaries map of Burma from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 91.41, 9.22, 102.13, 29.34 https://cmr.earthdata.nasa.gov/search/concepts/C2232847480-CEOS_EXTRA.umm_json Digital map of National and Provincial boundaries of Burma compiled from the World Boundary Database II (WBD II) proprietary +BANd0021_113 Hydrology (Rivers and Lakes) map pf Burma from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 91.41, 9.22, 102.13, 29.34 https://cmr.earthdata.nasa.gov/search/concepts/C2232846580-CEOS_EXTRA.umm_json Digital map of Hydrology consisting Rivers and Lakes of Burma compiled from the World Boundary Database II. proprietary +BANd0023_113 Elevation map of Burma from Global Elevation dataset ETOP05 CEOS_EXTRA STAC Catalog 1970-01-01 91.41, 9.22, 102.13, 29.34 https://cmr.earthdata.nasa.gov/search/concepts/C2232847954-CEOS_EXTRA.umm_json Digital Elevation Contour map of Burma at 100 meters contour interval produced from the Global Elevation ETOP05 dataset proprietary +BANd0024_113 Landset-5 TM Image of Tak, Burma (29 January 1990) CEOS_EXTRA STAC Catalog 1970-01-01 91.41, 9.22, 102.13, 29.34 https://cmr.earthdata.nasa.gov/search/concepts/C2232849357-CEOS_EXTRA.umm_json Landset-5 TM 7 band Image of Tak, Burma on 29 January 1990. Scene ID # 52160 - 031125, Path 131, Row 48 proprietary +BANd0025_113 Forest Classification Map of Myanmar (89-90) CEOS_EXTRA STAC Catalog 1970-01-01 91.41, 9.22, 102.13, 29.34 https://cmr.earthdata.nasa.gov/search/concepts/C2232849001-CEOS_EXTRA.umm_json Classified Digital Forest Map of Myanmar together with National boundaries proprietary +BANd0038_113 National and Provincial Boundaries map of Bangladesh from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 87.95, 20.75, 93.07, 26.62 https://cmr.earthdata.nasa.gov/search/concepts/C2232847142-CEOS_EXTRA.umm_json Digital map of National and Provincial boundaries of Bangladesh compiled from the World Boundary Database II (WBD II) proprietary +BANd0039_113 Hydrology (Rivers and Lakes) map of Bangladesh from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 87.95, 20.75, 93.07, 26.62 https://cmr.earthdata.nasa.gov/search/concepts/C2232847325-CEOS_EXTRA.umm_json Digital map of Hydrology consisting Rivers and Lakes of Bangladesh compiled from the World Boundary Database II (WBD II) proprietary +BANd0041_113 Elevation map of Bangladesh from Global Elevation data ETOPO5 CEOS_EXTRA STAC Catalog 1970-01-01 87.95, 20.75, 93.07, 26.62 https://cmr.earthdata.nasa.gov/search/concepts/C2232847886-CEOS_EXTRA.umm_json Digital Elevation Contour map of Bangladesh at 100 meters contour interval produced from the Global Elevation ETOPO5 dataset proprietary +BANd0049_113 Landset-5 TM Image of Nepal (18 Dec 1989) CEOS_EXTRA STAC Catalog 1970-01-01 79.9, 26, 88.84, 30.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232847623-CEOS_EXTRA.umm_json Landset-5 TM 7 band Image of Nepal on 18 December 1989. Scene ID# 52118 - 041144, Path 141, Raw 41 proprietary +BANd0050_113 Nepal National Datasets of 11 parameters - Geology, River etc. CEOS_EXTRA STAC Catalog 1970-01-01 79.9, 26, 88.84, 30.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232847736-CEOS_EXTRA.umm_json National Datasets of Nepal. Dataset consists of 11 parameters; Geology, Major Rivers, Roads, Elevation, Boundaries, Regions, Zones, District, Protected areas, Precipitation, and Towns proprietary BANd0051_113 Datasets of Sindhupalchok District, Nepal - 14 parameters CEOS_EXTRA STAC Catalog 1970-01-01 79.9, 26, 88.84, 30.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232847904-CEOS_EXTRA.umm_json Dataset of Sindhupalchol District, Nepal consisting 14 parameters District/Panchayat (Old and New) boundaries, Settlements, Villages, Roads, Bridges, Rivers, Elevation, Land Utilization/Capability proprietary +BANd0052_113 Elevation Contour Map of Bagmati Zone, Nepal CEOS_EXTRA STAC Catalog 1970-01-01 79.9, 26, 88.84, 30.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232847527-CEOS_EXTRA.umm_json Digital Elevation Contours of Bagmati Zone, Nepal from GRID-Geneva proprietary +BANd0053_113 Land Utilization and Land Capability Maps of Bagmati Zone, Nepal CEOS_EXTRA STAC Catalog 1970-01-01 79.9, 26, 88.84, 30.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232846603-CEOS_EXTRA.umm_json Land Utilization and Land Capability Maps of Bagmati Zone, Nepal compiled by GRID-Bangkok. The data is in several files (uncombined) corresponding to various sectons in the Zone proprietary +BANd0054_113 Nepal Land Capability CEOS_EXTRA STAC Catalog 1970-01-01 79.9, 26, 88.84, 30.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232848458-CEOS_EXTRA.umm_json Land Capability splited to 2 zones; 44, 45 in 100 Meters resolution proprietary +BANd0056_113 Nepal Land Utilization CEOS_EXTRA STAC Catalog 1970-01-01 79.9, 26, 88.84, 30.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232849376-CEOS_EXTRA.umm_json Land Utilization splited to 2 zones; 44, 45 in 100 Meters resolution proprietary +BANd0062_113 National and Provincial Boundaries Map of Laos from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232848289-CEOS_EXTRA.umm_json Digital Map of National and Provincial boundaries of Laos compiled from the World Bandary Database II (WBD II). proprietary +BANd0063_113 Hydrology (Rivers and Lakes) Map of Laos from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232848771-CEOS_EXTRA.umm_json Digital Map of Hydrology consisting Rivers and Lakes of Laos compiled from the World Boundary Database II (WBD II). proprietary +BANd0065_113 Elevation Map of Laos from Global Elevation dataset ETOPO5 CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232847202-CEOS_EXTRA.umm_json Digital Elevation Contour Map of Laos at 100 meters contour interval produced from the Global Elevation ETOPO5 dataset. proprietary BANd0066_113 Digital Basemap of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232847684-CEOS_EXTRA.umm_json Digital Basemap of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0067_113 Digital Geology Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232848237-CEOS_EXTRA.umm_json Digital Geology Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0068_113 Digital Surface Configuration Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232848965-CEOS_EXTRA.umm_json Digital Surface Configuration Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0069_113 Digital Engineering Geology Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232849328-CEOS_EXTRA.umm_json Digital Engineering Geology Mao of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0071_113 Digital Soil Moisture Regimes Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232849077-CEOS_EXTRA.umm_json Digital Soil Moisture Regimes Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin at 1:2 milion scale. proprietary +BANd0072_113 Digital Hydrogeology Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232848283-CEOS_EXTRA.umm_json Digital Hydrogeology Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0073_113 Digital Engineering Soils Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232848775-CEOS_EXTRA.umm_json Digital Engineering Soils Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0074_113 Digital Precipitation Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232847356-CEOS_EXTRA.umm_json Digital Precipitation Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong at 1:2 million scale. proprietary BANd0075_113 Digital Climatic Zones Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232847189-CEOS_EXTRA.umm_json Digital Climatic Zones Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0076_113 Digital Roads, Railroads Map of Laos from the Operational Navigation Chart J-10 and K-10 CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232847682-CEOS_EXTRA.umm_json Digital Roads, Railroads Map of Laos from the Operational Navigation Chart J-10 and K-10 at 1:1 million scale. proprietary +BANd0077_113 Digital Rivers, Lakes, Islands Map of Laos from the Operational Navigation Chart J-10 and K-10 CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232848240-CEOS_EXTRA.umm_json Digital Rivers, Lakes, Islands Map of Laos from the Operational Navigation Chart J-10 and K-10 at 1:1 million scale. proprietary +BANd0078_113 Digital Provincial Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232848971-CEOS_EXTRA.umm_json Digital Provincial Map of Laos from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0080_113 Landuse Map of Laos CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232847343-CEOS_EXTRA.umm_json Landuse Map of Laos with national boundary from WBD II. proprietary +BANd0081_113 Forest Classification Map of Laos CEOS_EXTRA STAC Catalog 1970-01-01 99.77, 13.47, 108.1, 22.98 https://cmr.earthdata.nasa.gov/search/concepts/C2232847008-CEOS_EXTRA.umm_json Forest Classification Map of Laos with national boundary. proprietary +BANd0084_113 Ecosystem Dataset of Western Samoa CEOS_EXTRA STAC Catalog 1970-01-01 -176, -18, -165, -9 https://cmr.earthdata.nasa.gov/search/concepts/C2232849244-CEOS_EXTRA.umm_json Ecosystem Map of Western Samoa digitized by GRID-Bangkok from maps obtained from Western Samoa proprietary +BANd0085_113 Landsat-5 TM Image of Bhutan - Path 137 Row 41 CEOS_EXTRA STAC Catalog 1970-01-01 88.8, 26.54, 92.37, 28.46 https://cmr.earthdata.nasa.gov/search/concepts/C2232849204-CEOS_EXTRA.umm_json Landsat-5 TM 4 bands (band 2-5) Image of Bhutan on 2nd January 1988. proprietary +BANd0086_113 Landsat-5 TM Image of Bhutan - Path 138 Row 41 CEOS_EXTRA STAC Catalog 1970-01-01 88.8, 26.54, 92.37, 28.46 https://cmr.earthdata.nasa.gov/search/concepts/C2232848317-CEOS_EXTRA.umm_json Landsat-5TM 4 bands (band 2-5) Image of Bhutan on 28 February 1989. proprietary +BANd0087_113 National and Provincial Boundaries Map of Malaysia from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 99.4, -0.2, 120.19, 7.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848850-CEOS_EXTRA.umm_json Digital Map of National and Provincial boundaries of Malaysia compiled from the World Boundary Database II (WBD II). proprietary +BANd0088_113 Hydrology (Rivers) Map of Malaysia from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 99.4, -0.2, 120.19, 7.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232846572-CEOS_EXTRA.umm_json Digital Map of Hydrology consisting Rivers of Malaysia compiled from the World Boundary Database II (WBD II). proprietary +BANd0090_113 Elevation Map of Malaysia from Global Elevation dataset ETOPO5 CEOS_EXTRA STAC Catalog 1970-01-01 99.4, -0.2, 120.19, 7.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847345-CEOS_EXTRA.umm_json Digital Elevation Contour Map of Malaysia at 100 meters contour interval produced from the Global Elevation ETOPO5. proprietary +BANd0095_113 Hydrology (Rivers and Lakes) Map of Philippines from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 20, 10, 65, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2232849199-CEOS_EXTRA.umm_json Digital Map of Hydrology consisting Rivers and Lakes of Philippines compiled from the World Boundary Database II (WBD II). proprietary +BANd0098_113 Hydrology (Rivers and Lakes) Map of Indonesia from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 93.11, -12.65, 143.45, 7.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232846574-CEOS_EXTRA.umm_json Digital Map of Hydrology consisting Rivers and Lakes of Indonesia compiled from the World Boundary Database II (WBD II). proprietary +BANd0100_113 Elevation Map of Indonesia from Global Elevation Dataset ETOPO5 CEOS_EXTRA STAC Catalog 1970-01-01 93.11, -12.65, 143.45, 7.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232848969-CEOS_EXTRA.umm_json Digital Elevation Contour Map of Indonesia at 100 meters contour interval produced from the Global Elevation ETOPO5 dataset. proprietary +BANd0101_113 National & Provincial Boundaries map of Cambodia from WBDII CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232849327-CEOS_EXTRA.umm_json Digital map of National and Provincial boundaries of Cambodia compiled from World Boundary Database II (WBDII). proprietary +BANd0102_113 Hydrology (Rivers & Lakes) map of Cambodia from WBDII CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848530-CEOS_EXTRA.umm_json Digital map of Hydrology consisting Rivers and Lakes of Cambodia compiled from the World Boundary Database II - WBDII. proprietary +BANd0104_113 Landsat-5 MSS Image of Phnom Penh, Cambodia (25 Nov 1984) CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232846754-CEOS_EXTRA.umm_json Landsat-5 MSS 5 band Image of Phnom Penh area, Central Cambodia on 25 Nov 1984. Scene ID 85026902495, Path 126, Row 052. proprietary +BANd0105_113 Landsat-5 MSS Image of Phnom Penh, Cambodia (27 Dec 1984) CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847542-CEOS_EXTRA.umm_json Landsat-5 MSS 5 band Image of Phnom Penh area, Central Cambodia on 27 Dec 1984. Scene ID 85030102501, Path 126, Row 052. proprietary +BANd0107_113 Elevation Map of Cambodia from Global Elevation dataset ETOPO5 CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847836-CEOS_EXTRA.umm_json Digital Elevation Contour map of Cambodia at 100 meters contour interval produced from the Global Elevation ETOPO5 dataset. proprietary BANd0108_113 Digital Basemap of Cambodia from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232849213-CEOS_EXTRA.umm_json Digital Basemap of Cambodia from the Atlas of Physical, economic and social resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0109_113 Digital Geology map of Cambodia from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232849069-CEOS_EXTRA.umm_json Digital Geology map of Cambodia from the Atlas of physical, economic and social resources of the lower Mekong Basin at 1:2million scale. proprietary +BANd0110_113 Digital Surface Configuration map of Cambodia from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848963-CEOS_EXTRA.umm_json Digital Surface Configuration map of Cambodia from the Atlas of physical, economic and social resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0111_113 Digital Engineering Geology map of Cambodia from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232849332-CEOS_EXTRA.umm_json Digital Engineering Geology map of Cambodia from the Atlas of physical, economic and social resources of the lower Mekong Basin at 1:2 million scale proprietary +BANd0112_113 Digital Soil Moisture Regimes map of Cambodia from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848533-CEOS_EXTRA.umm_json Digital Soil Moisture Regimes map of Cambodia from the Atlas of physical, economic and social resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0113_113 Digital Hydrology map of Cambodia from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848430-CEOS_EXTRA.umm_json Digital Hydrology map of Cambodia from the Atlas of physical, economic and social resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0114_113 Digital Engineering Soil map of Cambodia from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232846746-CEOS_EXTRA.umm_json Digital Engineering Soil map of Cambodia from the Atlas of physical, economic and social resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0115_113 Digital Precipitation map of Cambodia from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847546-CEOS_EXTRA.umm_json Digital Precipitation map of Cambodia from the Atlas of physical, economic and social resources of the lower Mekong Basin at 1:2 million scale. proprietary BANd0116_113 Digital Climatic Zone map of Cambodia from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847986-CEOS_EXTRA.umm_json Digital Climatic Zone map of Cambodia from the Atlas of physical, economic and social resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0117_113 Digital Roads, Railroads map of Cambodia from the Operational Navigation Chart J-10 and K-10 CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847834-CEOS_EXTRA.umm_json Digital Roads, Railroads map of Cambodia from the Operational Navigation Chart J-10 and K-10 at 1:1million scale. proprietary +BANd0118_113 Digital Rivers, Lakes, Islands map of Cambodia from the Operational Navigation Chart J-10 and K-10 CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232849212-CEOS_EXTRA.umm_json Digital Rivers, Lakes, Islands map of Cambodia from the Operational Navigation Chart J-10 and K-10 at 1:1 million scale. proprietary +BANd0119_113 Digital Provincial map of Cambodia from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232849080-CEOS_EXTRA.umm_json Digital Provincial map of Cambodia from the Atlas of physical, economic and social resources of the lower Mekong Basin at 1:2 million scale. proprietary BANd0120_113 Digital Drainage (Flooded area) map of Cambodia from the Atlas of Physical, Economic and Social Resources of the lower Mekong Basin CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848130-CEOS_EXTRA.umm_json Digital Drainage (Flooded area) map of Cambodia from the Atlas of physical, economic and social resources of the lower Mekong Basin at 1:2 million scale. proprietary +BANd0121_113 District Centers map of Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847624-CEOS_EXTRA.umm_json Digital District Centers map of Cambodia from TOPO map sheet at 1:500,000 scale. proprietary +BANd0125_113 National Boundaries map of Cambodia from USAID CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848369-CEOS_EXTRA.umm_json Digital map of National Boundariesof Cambodia compiled from the USAID map. proprietary BANd0127_113 Administrative Districts map of Thmarpouk province, Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232849233-CEOS_EXTRA.umm_json Digital Administrative Districts map of Thmarpouk province, Cambodia compiled from the USAID map. proprietary BANd0128_113 Agricultural Soils map of Thmarpouk province, Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847733-CEOS_EXTRA.umm_json Agricultural Soils map of Thmarpouk province, Cambodia compiled from the Atlas of the Lower Mekong Basin. proprietary BANd0129_113 Dams and Reservoirs map of Thmarpouk province, Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847913-CEOS_EXTRA.umm_json Dams and Reservoirs map of Thmarpouk province, Cambodia compiled from the USAID map. proprietary +BANd0130_113 Hydrology (Rivers) map of Thmarpouk province, Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848139-CEOS_EXTRA.umm_json Hydrology (Rivers) map of Thmarpouk province, Cambodia compiled from the USAID map. proprietary +BANd0131_113 Infrastructure (Roads) map of Thmarpouk province, Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847619-CEOS_EXTRA.umm_json Infrastructure (roads, railroads, bridges) map of Thmarpouk province, Cambodia compiled from USAID map. proprietary +BANd0132_113 Landuse map of Thmarpouk province, Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232846760-CEOS_EXTRA.umm_json Landuse map of Thmarpouk province, Cambodia from the Remote Sensing and Mapping Unit, Mekong Secretariat. proprietary +BANd0133_113 Mine fields map of Thmarpouk province, Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847306-CEOS_EXTRA.umm_json Mine fields map of Thmarpouk province, Cambodia compiled from the USAID map. proprietary +BANd0134_113 Map of proposed new villages of Thmarpouk province, Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848913-CEOS_EXTRA.umm_json Map showing the proposed new villages of Thmarpouk province, Cambodia compiled from the USAID map. proprietary +BANd0135_113 Map showing settlement of Thmarpouk province, Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848365-CEOS_EXTRA.umm_json Map showing settlements of Thmarpouk province, Cambodia compiled from the USAID map. proprietary +BANd0136_113 Landsat-5 TM Image of Cambodia (25 January 1992) CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232849142-CEOS_EXTRA.umm_json Landsat-5 TM 7 band Images of Cambodia on 25 January 1992. Scene ID # 52886 - 023746, Path 125, Row 51. proprietary +BANd0138_113 Digital map of road system in Phnom Penh CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847741-CEOS_EXTRA.umm_json Digital map of road system in Phnom Penh proprietary +BANd0139_113 Digital map of buildings in Phnom Penh CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847897-CEOS_EXTRA.umm_json Digital map of building in Phnom Penh. proprietary +BANd0146_113 National & Provincial Boundaries map of Vietnam from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848801-CEOS_EXTRA.umm_json Digital map of National and Provincial boundaries of Vietnam compiled from the World Boundary Database II (WBD II). proprietary +BANd0147_113 Hydrology (Rivers) map of Vietnam from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232848341-CEOS_EXTRA.umm_json Digital map of Hydrology consisting Rivers of Vietnam compiled from the World Boundary Database II (WBD II). proprietary +BANd0149_113 Elevation Map of Vietnam from Global Elevation dataset ETOPO5 CEOS_EXTRA STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232846579-CEOS_EXTRA.umm_json Digital Elevation Contour map of Vietnam at 100 meters contour interval produced from the Global Elevation ETOPO5 dataset. proprietary +BANd0150_113 Forest Classification Map of Vietnam (85-86) CEOS_EXTRA STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232847098-CEOS_EXTRA.umm_json Forest Classification Map of Vietnam with national boundary. proprietary +BANd0151_113 Forest Classfication Map of Vietnam (92-93) CEOS_EXTRA STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232847329-CEOS_EXTRA.umm_json Forest Classfication Map of Vietnam with national boundary. proprietary +BANd0155_113 Historical Landsat MSS datasets - Chiang Mai deforestration CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232849268-CEOS_EXTRA.umm_json Forest Non-forest classification of Landsat MSS images (1975, 79, 84, 85) of Chiang Mai Province in Northern Thailand illustrating the deforestration problem. proprietary BANd0156_113 Demonstration images of Mae Klang Watershed, Thailand CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848806-CEOS_EXTRA.umm_json Demonstration Images of Mae Klang Watershed developed as part of the GRID-Thailand case study on deforestration & soil erosion illustrating the use of Universal Soil Loss Equation (USLE), proprietary +BANd0157_113 Landsat MSS Image (14 Feb 1973) of Thailand - Path 141 Row 47 CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848329-CEOS_EXTRA.umm_json Landsat-1 MSS Image of Thailand on 14 February 1973. Scene ID 1206 - 03230, Path 141, Row 47. proprietary +BANd0158_113 Landsat MSS Image (18 May 1979) of Thailand - Path 141 Row 47 CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232847479-CEOS_EXTRA.umm_json Landsat-2 MSS Image of Thailand on 18 May 1979. Scene ID 21516-03003, Path 141, Row 47. proprietary +BANd0159_113 Landsat MSS Image (27 Oct. 1984) of Thailand - Path 131 Row 47 CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232846589-CEOS_EXTRA.umm_json Landsat-5 MSS Image of Thailand on 27 October 1984. Scene ID 50240 - 03185, path 131, Row 47. proprietary +BANd0160_113 Landsat MSS Image (5 April 1985) of Thailand - Path 131 Row 47 CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848617-CEOS_EXTRA.umm_json Landsat-5 MSS Image of Thailand on 5 April 1985. Scene ID 50400 - 03191, Path 131, Row 47. proprietary +BANd0161_113 Landsat MSS Image (25 Dec. 1985) of Thailand - Path 131 Row 47 CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848453-CEOS_EXTRA.umm_json Landsat-4 MSS Image of Thailand on 25 December 1985. Scene ID 41258 - 03090, Path 131, Row 47. proprietary BANd0163_113 DEM of Nakhon Si Thammarat, Thailand from SPOT XS (9 Feb 89) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232849269-CEOS_EXTRA.umm_json Digital Elevation Model (DEM) of Amphoe Pipun, Nakhon Si Thammarat, Thailand derived from the SPOT XS data of 9 February 1989. proprietary +BANd0164_113 Landsat-5 TM Image of Bangkok (9 Dec 87) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848005-CEOS_EXTRA.umm_json Landsat-5 TM 7 channel raw Image of Bangkok on 9 December 1987. Scene ID 51378-030624, Path 129, Row 51. proprietary +BANd0165_113 Landsat-5 TM Image of Thailand - Burma border (14 Feb 1990) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232847860-CEOS_EXTRA.umm_json Landsat-5 TM 7 band Image of Thailand-Burman border on 14 February 1990. Scene ID 52176-031004, Path 131, Row 46. proprietary +BANd0166_113 Landsat-5 TM 7 band Image of Thailand (1 June 1990) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232846689-CEOS_EXTRA.umm_json Landsat-5 TM 7 band Image of Thailand on 1 June 1990. Scene ID 52283-025345, Path 128, Row 55. proprietary +BANd0167_113 Landsat-5 TM Image of Thailand - Burma border (25 May 1990) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232847538-CEOS_EXTRA.umm_json Landsat-5 TM 7 band Image of Mae Hong Son area, Thailand - Burma border on 25 March 1990. Scene ID 52215-031521, Path 132, Row 47. proprietary +BANd0168_113 Landsat-5 TM Image of Thailand - Burma border (3 Apr 1990) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848256-CEOS_EXTRA.umm_json Landsat-5 TM 7 band Image of Mae Sot area, Thailand - Burma border on 3 April 1990. Scene ID 52224-030921, Path 131, Row 48. proprietary +BANd0169_113 Landsat-5 TM Image of Thailand - Burma border (3 Apr 90 - No. 2) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848763-CEOS_EXTRA.umm_json Landsat-5 TM 7 band Image of Moul Mein area, Thailand - Burma border on 3 April 1990. Scene ID 52224-030945, Path 131, Row 49. proprietary +BANd0170_113 Landsat MSS Image of Songkhla region, Thailand (28 Feb 1973) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848623-CEOS_EXTRA.umm_json Landsat MSS 5 band Image of Songkhla region, Thailand on 28 February 1973 obtained from the MSS Historical Archive at EROS Data Center, USA. Scene ID 8122003033500, Path 137, Row 55. proprietary +BANd0171_113 Landsat MSS Image of Songkhla region, Thailand (27 June 1991) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848448-CEOS_EXTRA.umm_json Landsat MSS 5 band Image of Songkhla region, Thailand on 27 June 1991 obtained from the MSS Historical Archive at EROS Data Center, USA. Scene ID unknown, Path 138, Row 55. proprietary BANd0173_113 Classified Landsat MSS Image (30 March 1976) of Lake Songkhla CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232849270-CEOS_EXTRA.umm_json Land classification of Songkhla lake region using Landsat MSS Image of 30 March 1976 used in the ONEB-ILEC-GRID project on the study of Lake Songkhla region. proprietary BANd0174_113 Classified Landsat TM Image (20 Sept. 1991) of Lake Songkhla CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848011-CEOS_EXTRA.umm_json Land classification of Songkhla lake region using Landsat TM Image of 20 September 1991 used in the ONEB-ILEC-GRID project on the study of Lake Songkhla region. proprietary +BANd0175_113 Forest Classification Map of Thailand CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232847856-CEOS_EXTRA.umm_json Classified Digital Forest Map of Thailand together with National and Provincial boundaries. proprietary +BANd0176_113 National & Provincial Boundaries map of Thailand from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232846669-CEOS_EXTRA.umm_json Digital map of National and Provincial boundaries of Thailand compiled from the World Boudary Database II (WBD II). Updated to 76 provinces from Royal Thai Survey Department Map. proprietary +BANd0177_113 Hydrology (Rivers and Lakes) map of Thailand from WBD II CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232847541-CEOS_EXTRA.umm_json Digital map of Hydrology consisting Rivers and Lakes of Thailand compiled from the World Boundary Database II (WBD II). proprietary +BANd0179_113 Elevation map of Thailand from Global Elevation dataset ETOPO5 CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848758-CEOS_EXTRA.umm_json Digital Elevation Contour map of Thailand at 100 meters contour interval produced from the Global Elevation ETOPO5 dataset. proprietary +BANd0181_113 Landsat-5 TM Image of Suratthani, Thailand (4 March 1990) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232847717-CEOS_EXTRA.umm_json Landsat-5 TM 7 band Image of Suratthani region, Thailand on 4 March 1990. Scene ID 52194 - 030019, Path 129, Row 54. proprietary +BANd0182_113 Landsat-5 TM Image of Thailand-Cambodia border (27 March 92) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232846608-CEOS_EXTRA.umm_json Landsat-5 TM 7 band Image of Thailand-Cambodia border on 27 March 1992. Scene ID 52948-025018, Path 127, Row 52. proprietary +BANd0183_113 Forest-Nonforest of Thailand 1991 CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232847520-CEOS_EXTRA.umm_json Forest Area of Thailand 1991 proprietary +BANd0184_113 Landuse Map of Thailand CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848628-CEOS_EXTRA.umm_json Landuse Map of Thailand 197 classes proprietary BANd0185_113 Asia Biomass Dataset 1980 (5 files) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848484-CEOS_EXTRA.umm_json This is the modelled biomass density incorporating climatic / edhaphic / geomorphological factors and the biomass density in the absence of human disturbance proprietary BANd0188_113 Classified Landsat TM image (4 March 1990) of South Thailand CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232847608-CEOS_EXTRA.umm_json Classified image of the peninsular resgion of Thailand; Surat Thani, Nakhon Si Thammarat and Krabi. Under the contract of TREES project with JRC (Ispra) Italy. Path 129 Row 54 proprietary BANd0189_113 Classified Landsat TM image (14 Feb '91) of North Thailand CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848155-CEOS_EXTRA.umm_json Classified image of the Northern Highland of Thailand, Myanmar and Laos (Golden Triangle). Under the contract of TREES project with JRC (Ispra) Italy. Path 131 Row 46 proprietary +BANd0193_113 Landsat-5 TM Image of Huay Kha Kang, Thailand (17 Mar '98) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232847522-CEOS_EXTRA.umm_json Landsat-5 TM 7 bands Image of Thailand on 17 Mar 1998. Path 130, Row 49 Quad 9. proprietary +BANd0194_113 Landsat-5 TM Image of Huay Kha Kang, Thailand (6 Mar '94) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848643-CEOS_EXTRA.umm_json Landsat-5 TM 7 bands Image of Thailand on 6 Mar 1994. Path 130, Row 50 Quad 6 proprietary +BANd0195_113 Landsat-5 TM Image of Huay Kha Kang, Thailand (9 Mar '95) CEOS_EXTRA STAC Catalog 1970-01-01 96.83, 4.8, 106.42, 21.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848479-CEOS_EXTRA.umm_json Landsat-5 TM 7 bands Image of Thailand on 9 Mar 1995. Path 130, Row 50 Quad 6 proprietary BANd0198_113 Classified Landsat TM image (25 Jan '92) of East Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847613-CEOS_EXTRA.umm_json Classified image of the Eastern part of Cambodia; Kratie and Stung Treng. Under the contract of TREES project with JRC (Ispra) Italy. Path 125 Row 51. proprietary BANd0199_113 Classified Landsat TM Image (27 Mar '92) of West Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848154-CEOS_EXTRA.umm_json Classified image of the Western part of Cambodia. Under the contract of TREES project with JRC (Ispra) Italy. Path 127 Row 52 proprietary +BANd0201_113 Nature Reserves in the Coastal Zone of Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847530-CEOS_EXTRA.umm_json Nature Reserves in the Coastal Zone of Cambodia with names proprietary BANd0202_113 Depleted Mangrove Forest in the Coastal Zone of Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848015-CEOS_EXTRA.umm_json Depleted Mangrove Forest in Koh Kong of Cambodia. It is classified into 2 classes; 1= Mangrove forest and 2 = Shrimp farm proprietary BANd0203_113 Coral Reef of Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232847870-CEOS_EXTRA.umm_json Coral Reef of Cambodia classified into 4 classes proprietary +BANd0204_113 Fishing Ground in the Coastal Zone of Cambodia CEOS_EXTRA STAC Catalog 1970-01-01 102.28, 10.07, 107.98, 14.86 https://cmr.earthdata.nasa.gov/search/concepts/C2232848945-CEOS_EXTRA.umm_json It shows the marine fish exploitation caught by the villagers of the coastal provinces and actual yield in 1994 - 95 proprietary +BANd0206_113 Landsat-5 TM Image of Nepal (25 Feb '97) CEOS_EXTRA STAC Catalog 1970-01-01 79.9, 26, 88.84, 30.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232848574-CEOS_EXTRA.umm_json Landsat-5 TM 7 bands Image of Nepal on 25 Feb 1997. Path 139, Row 42 proprietary +BANd0207_113 NOAA AVHRR LAC Data for the Philippines (11 Feb '85) CEOS_EXTRA STAC Catalog 1970-01-01 116.68, 4.85, 127.23, 19.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232848442-CEOS_EXTRA.umm_json NOAA AVHRR LAC Data (11 Feb '85) for the Philippines supplied by STAR program of AIT. proprietary +BANd0209_113 Elevation map of Philippines from Global Elevation data ETOPO5 CEOS_EXTRA STAC Catalog 1970-01-01 116.68, 4.85, 127.23, 19.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232847211-CEOS_EXTRA.umm_json Digital Elevation Contour map of Philippines at 100 meters contour interval produced from the global Elevation ETOPO5 dataset proprietary +BANd0210_113 Hydrology (Rivers and Lakes) map of Philippines from WBDII CEOS_EXTRA STAC Catalog 1970-01-01 116.68, 4.85, 127.23, 19.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232846656-CEOS_EXTRA.umm_json Digital map of Hydrology consisting Rivers and Lakes of Philippines compiled from the World Boundary Database II (WBDII) proprietary +BANd0213_113 Nature Reserves in the Coastal Zone of China CEOS_EXTRA STAC Catalog 1970-01-01 70.83, 15.06, 137.97, 56.58 https://cmr.earthdata.nasa.gov/search/concepts/C2232847866-CEOS_EXTRA.umm_json Nature Reserves in the Coastal Zone of China with general sites information proprietary BANd0214_113 Coral Reef of China CEOS_EXTRA STAC Catalog 1970-01-01 70.83, 15.06, 137.97, 56.58 https://cmr.earthdata.nasa.gov/search/concepts/C2232848933-CEOS_EXTRA.umm_json Coral Reef of China with names proprietary BANd0216_113 Administrative map of Vietnam CEOS_EXTRA STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232848581-CEOS_EXTRA.umm_json District boundaries of Vietnam proprietary +BANd0217_113 Geological map of Vietnam CEOS_EXTRA STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232848441-CEOS_EXTRA.umm_json Geological complex of Vietnam proprietary +BANd0218_113 Main rivers of Vietnam CEOS_EXTRA STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232847445-CEOS_EXTRA.umm_json Main rivers of Vietnam proprietary +BANd0219_113 Main roads of Vietnam CEOS_EXTRA STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232847235-CEOS_EXTRA.umm_json Main roads of Vietnam proprietary BANd0221_113 Asia Pacific Mosaic Poster 1993 CEOS_EXTRA STAC Catalog 1970-01-01 20, -10, 170, 79 https://cmr.earthdata.nasa.gov/search/concepts/C2232848343-CEOS_EXTRA.umm_json The poster covers Asia Pacific region from Mongolia to New Zealand and from Cook Islands in the east to Iran in the west. Some cartographic detail added. proprietary BAROCLINIC_HRET14_14 Harmonic Constants for Baroclinic Tide Prediction POCLOUD STAC Catalog 1993-01-01 2021-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2935657850-POCLOUD.umm_json This dataset of Harmonic Constants for Baroclinic Tide Prediction was produced by Edward Zaron (Oregon State University) and Shane Elipot (University of Miami). It provides sea surface height and ocean surface currents associated with the predictable astronomical tide at the M2, S2, N2, K1, and O1 frequencies. The tidal harmonic constants, in-phase and quadrature with respect to the equilibrium potential, are provided on a latitude/longitude at 1/20-deg resolution. Using the software available at the Github repository, the dataset can be used to predict baroclinic tidal sea surface height and surface ocean currents at arbitrary time and location throughout the world oceans.
The harmonic constants were estimated within the time period from 1993 to 2021 and incorporate roughly 30 years of multi-satellite altimeter data and 20 years of data from drifting buoys. The observations were combined with a kinematic wave model and the internal wave polarization relations to prepare uniformly gridded estimates from the sparse and irregular data sampling. These files may be used by the altimeter community to compute corrections intended to remove baroclinic tidal variability from sea level anomaly observations. Researchers with an interest in ocean surface currents may also use these data to predict baroclinic tidal surface currents. Such information may be used to plan observational campaigns or optimize the design of future surface current mapping satellite missions.
This dataset is funded by NASA SWOT Science Team award #80NSSC21K0346 and NSF Physical Oceanography Program award #1850961. The software to make baroclinic tidal calculations using this dataset is regularly updated at the provided Github link, and an archived snapshot of the software is also provided in the documentation. The harmonic constants and prediction software may be updated every few years as additional data for mapping the tides becomes available. proprietary BASIN_TCP_963_1 BASIN TCP Stable Isotope Composition of CO2 in Terrestrial Ecosystems ORNL_CLOUD STAC Catalog 2001-06-05 2005-01-02 -68.73, 45.2, -68.73, 45.2 https://cmr.earthdata.nasa.gov/search/concepts/C2762903260-ORNL_CLOUD.umm_json This data set reports stable isotope ratio data of CO2 (13C/12C and 18O/16O) associated with photosynthetic and respiratory exchanges across the biosphere-atmosphere boundary. Measurements were made at selected AmeriFlux sites including Harvard Forest, Howland Forest, Rannells Flint Hills Prairie, Niwot Ridge Forest, and the Wind River Canopy Crane Site, which span the dominant ecosystem types of the United States. These data were collected periodically from 2001 through 2004 and are available as an ASCII comma separated file.The goal of this Terrestrial Carbon Processes (TCP) project is to better capture isotopic effects of ecosystem-atmosphere interaction at diurnal, seasonal and interannual time scales by long-term monitoring 13C of CO2 exchange with the atmosphere at weekly intervals. Photosynthesis and respiration in terrestrial ecosystems have opposite effects on diurnal and seasonal patterns on atmospheric CO2 concentration and isotope ratios. This isotopic variation contains information about the functioning of different terrestrial ecosystems. proprietary @@ -2915,6 +3042,8 @@ BOREAS_RSS-03_Snapshots_289_2 BOREAS RSS-03 Imagery and Snapshots from a Helicop BOREAS_SLICER_508_2 BOREAS Scanning Lidar Imager of Canopies by Echo Recovery (SLICER): Level-3 Data ORNL_CLOUD STAC Catalog 1996-07-18 1996-07-30 -106.2, 53.63, -98.03, 55.93 https://cmr.earthdata.nasa.gov/search/concepts/C2761666619-ORNL_CLOUD.umm_json Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) data were acquired in support of BOReal Ecosystem-Atmosphere Study (BOREAS) at all of the Tower Flux (TF) sites in the Southern and Northern Study Areas (SSA and NSA, respectively) and along transects between the study areas. Data were acquired on 5 days between 18 and 30 July 1996. Each coverage of a tower site is typically 40 km in length, with a minimum of 3 and a maximum of 10 lines across each tower oriented in a variety of azimuths. The SLICER data were acquired simultaneously with Advanced Solid-State Array Spectroradiometer (ASAS) hyperspectral, multiview angle images. The SLICER Level 3 products consist of binary files for each flight line with a data record for each laser shot composed of 13 parameters and a 600-byte waveform that is the raw record of the back scatter laser energy reflected from Earth's surface. proprietary BOUSSOLE_0 BOUSSOLE project OB_DAAC STAC Catalog 2001-07-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360165-OB_DAAC.umm_json The purpose of the BOUSSOLE project is to establish a time series of optical properties in oceanic waters, in support to bio-optics research, to calibration of ocean color satellite observations, and to validation of the products derived from these observations. The bio-optics research as well as the match-up analyses and vicarious calibration experiments are performed based on the data set that is being built from the permanent marine optical buoy and monthly cruises. The site where the mooring is deployed and where the cruises are carried out is located in the Ligurian sea, one of the sub-basins of the Western Mediterranean sea. BOUSSOLE is a joint effort by multiple organizations and is funded and supported by the following agencies and academic or governmental institutes European Space Agency (ESA), Centre National d'Etudes Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences de l'Univers (INSU), National Aeronautics and Space Administration (NASA), University Pierre et Marie Curie (UPMC), and Observatoire Oceanologique de Villefranche-sur-Mer. proprietary BRAZIL_0 Measurements made in the Amazon River outflow region in 2002 OB_DAAC STAC Catalog 2002-04-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360167-OB_DAAC.umm_json Measurements made in the Amazon River outflow region of the Atlantic Ocean off the coast of Brazil in 2002. proprietary +BRD_LSC002 Index of Biotic Integrity for Fish Communities in the DE River Basin CEOS_EXTRA STAC Catalog 1995-06-01 1996-08-01 -76, 41, -75, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2232411633-CEOS_EXTRA.umm_json Two years of field work have been completed in the development of an index of biotic integrity (IBI) for fish communities in the middle-to-upper Delaware River basin (Delaware Water Gap to Callicoon, New York). Fish were collected in riffles and pools on 200 m segments of eight tributaries. Collections were made concurrently in the Delaware River mainstem within 0.5 mile (downstream) of the same tributary mouths, in three habitat types: riffles, deep pools, and inshore submerged vegetation zones. Quality control on species identity, as well as length/weight/disease determination, has been completed on all specimens. A total of 15,673 fish were collected (7,655 in tributaries and 8,018 in mainstem habitats) representing 44 species (36 in tributaries and 36 in mainstem habitats). Fish community data (species richness, trophic composition, and population/health data) will be related to both water quality and land use data to develop IBIs. Water quality data, including a dozen or more physical, chemical, and biological parameters taken during the same seasons/years by Delaware River Basin Commission personnel, are currently being indexed for use in the models. Land use data in four categories (22 subcategories), obtained from the Anderson Level 2 database using GIS techniques, have been summarized for use in the models. Current work involves examination of the variance associated with traditional fish metrics and the identification of alternative metrics that may better explain the covariance with water quality and land use. The Research and Development Laboratory-Wellsboro (RDL-W) is located on 55 acres near Wellsboro, Pennsylvania (Tioga County). Laboratory facilities include 3 modern buildings, 8x200-foot concrete raceways, 3 production wells, and support equipment. The RDL-W conducts research for restoration of depleted fisheries and other aquatic biological resources. A diversified research program in ecology, conservation technology, genetics, and physiology emphases the integration of laboratory and field studies to develop scientifically sound approaches to the management of aquatic ecosystems. Research is directed primarily towards development of information and technology to increase understanding of aquatic ecosystems in the northeastern United States and to assist client agencies to better manage these ecosystems and their biota. Technical assistance is provided to clients throughout the nation. proprietary +BRD_LSC003_MAHA MAHA Stream Order Fish Community Study CEOS_EXTRA STAC Catalog 1993-10-01 1997-09-01 -81, 40, -75, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231548545-CEOS_EXTRA.umm_json The research establishes a national repository for fishieries data and management practices to improve technical understanding of the status, trends, causes, and effects of changes in native fish populations and their habitats and 2) increase investigations of fish contaminant impacts, habitat losses, and control of exotics to restore depleted or endangered fishes. proprietary BRD_LSC_AMERSHAD001 American Shad Riverine Habitat Requirements CEOS_EXTRA STAC Catalog 1990-01-01 1992-01-01 -76, 41, -75, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231548710-CEOS_EXTRA.umm_json Field evaluations of existing habitat suitability index (HSI) models for spawning adults, eggs, and larvae of American shad (Alosa sapidissima) were conducted in 1990-1992; initial models for juveniles in nursery habitats were developed. Fish abundance in various habitats of the upper Delaware River was quantified by (1) observation of adult spawning activity, (2) collection of eggs and larvae with metered plankton and drift nets, and (3) enumeration of juveniles by underwater observation and seining techniques. Regression analysis, principal component analysis, and range analysis were used to relate abundance to an array of physical habitat variables potentially influencing fish distributions. No HSI model was previously developed for juvenile American shad in riverine habitats. Four physical habitat variables were correlated with juvenile abundance: water temperature, dissolved oxygen (covariates), river depth, and turbidity. Regression analysis, principal component analysis, and range analysis were used to relate abundance to an array of physical habitat variables potentially influencing fish distributions. The Research and Development Laboratory-Wellsboro (RDL-W) is located on 55 acres near Wellsboro, Pennsylvania (Tioga County). Laboratory facilities include 3 modern buildings, 8x200-foot concrete raceways, 3 production wells, and support equipment. Core Capabilities The RDL-W conducts research for restoration of depleted fisheries and other aquatic biological resources. A diversified research program in ecology, conservation technology, genetics, and physiology emphases the integration of laboratory and field studies to develop scientifically sound approaches to the management of aquatic ecosystems. Research is directed primarily towards development of information and technology to increase understanding of aquatic ecosystems in the northeastern United States and to assist client agencies to better manage these ecosystems and their biota. Technical assistance is provided to clients throughout the nation. proprietary BRMCR2_1 Pre-IceBridge MCoRDS L2 Ice Thickness V001 NSIDC_ECS STAC Catalog 1993-06-23 2007-09-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1726730204-NSIDC_ECS.umm_json This data set contains depth sounder measurements of ice elevation, ice surface, ice bottom, and ice thickness over Greenland and Antarctica, acquired by the Multichannel Coherent Radar Depth Sounder (MCoRDS). proprietary BROKE-WEST_12kHZ_Bathy_Data_1 Bathymetry Data from the 12KHZ sounder on the BROKE-West voyage of the Aurora Australis, 2006 AU_AADC STAC Catalog 2006-01-02 2006-03-12 30, -70, 80, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214313366-AU_AADC.umm_json Readme - Bathymetry Files Data for BROKE-WEST 2006 1) Zipped folder contains .csv files created from each acoustics ev file for Transects 1 to 11. 2) These files contain subsections of each transect of variable length (usually between 50 and 100 km). 3) No data exists for files; Transect01_01 and 01_02 as the sea floor was greater than 5000m deep in these areas and was below the range set for the sounder. 4) Each file contains 11 columns of data; Ping_date, Ping_time, Ping_milliseconds, Latitude, Longitude, Position_status, Depth, Line_Status, Ping_status, Altitude, GPS_UTC time. 5) For practical purposes, the columns of interest will be Ping_date, Ping_time, Latitude, Longitude and Depth. Other columns are ancillary acoustics information and can be ignored. Line status should be 1 (meaning good) as sea floor was only picked when it could be easily defined. If the sea floor could not be visually defined or was deemed to uncertain, it was not picked in the echogram. Hence sea floor may not be totally contiguous. 6) Depth of the sea floor was only defined for those areas deemed to be 'on transect', i.e. straight transects for acoustics survey purposes. Deviations from the transect, i.e. to pick up moorings, conduct target or routine trawls or visit nice looking bergs were deemed 'off transect' and were excluded from the analysis. 7) Sea floor depth was primarly defined for the purposes of the acoustics analysis, i.e. exclusion from the echograms. Hence the values in the files are for the 'sea floor exclusion line' that is set above the true sea floor in order to exclude noise from the analysis. This means the sea floor depths in these files are likely to be an underestimate of the true depth. The uncertainty is likely to be of the order of 2 to 10m. 8) Another source of error is that depth was calculated with values of absorption coefficient and sound speed set to default values derived from pre-cruise hydrographic data. One value for each parameter was applied to the whole data set. These values were; 0.028 dB/m (120 KhZ), 0.010 dB/m (38kHz), 0.041 dB/m (200 kHz), 0.0017 dB/m (12kHz - bathy sounder) for absorption coefficient and 1456 m/s for sound speed. 9) These values will be recalculated from the oceanographic data derived during the voyage and applied to the data set during post-processing (forthcoming analyses for May-June 2006). Revision of these parameters may cause a slight shift in the calculated depths, although this is likely to be small. 10) Reprocessing of the data may also result in more accurate bottom detection. This data should be available post June 2006 and will be sent to interested parties as soon as it is completed. 11) Dataset was created by Esmee van Wijk. proprietary @@ -3098,10 +3227,14 @@ CAL_WFC_L1_1Km-ValStage1-V3-01_V3-01 CALIPSO Wide Field Camera Level 1B 1 km Nat CAL_WFC_L1_1Km-ValStage1-V3-02_V3-02 CALIPSO Wide Field Camera Level 1B 1 km Native Science data, Validated Stage 1 V3-02 LARC_ASDC STAC Catalog 2011-11-01 2020-04-10 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C7427596-LARC_ASDC.umm_json CAL_WFC_L1_1Km-ValStage1-V3-02 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC), Level 1B 1 km Native Science data, Validated Stage 1 Version 3-02. Data collection for this product is ongoing. Version 3.02 represents a transition of the Lidar, Imaging Infrared Radiometer (IIR), and WFC processing and browse code to a new cluster computing system. No algorithm changes were introduced, and minor changes were observed between V3.01 and V3.02 due to the compiler and computer architecture differences. The primary WFC Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During regular operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The WFC Level 1B 1 km Native Science grid covers the 61 km swath centered on the Lidar track. CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), IIR, and WFC. CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales). proprietary CAL_WFC_L1_IIR-ValStage1-V3-01_V3-01 CALIPSO Wide Field Camera Level 1B 1 km Registered Science data, Validated Stage 1 V3-01 LARC_ASDC STAC Catalog 2006-06-13 2011-11-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C5862867-LARC_ASDC.umm_json CAL_WFC_L1_IIR-ValStage1-V3-01 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera Level 1B 1 km Registered Science data. Version 3.01 includes new metadata parameters and corrections to several minor software bugs. Specifically, the Orbit Number and Path Number metadata parameters are now included to facilitate improved subsetting capabilities. The primary Wide Field Camera Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The Wide Field Camera Level 1B 1 IIR Registered Science grid provides WFC data on the identical grid as the CALIPSO IIR data and is produced to facilitate the use of the WFC data in the IIR retrievals. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales). proprietary CAL_WFC_L1_IIR-ValStage1-V3-02_V3-02 CALIPSO Wide Field Camera Level 1B 1 km Registered Science data, Validated Stage 1 V3-02 LARC_ASDC STAC Catalog 2011-11-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C7441430-LARC_ASDC.umm_json CAL_WFC_L1_IIR-ValStage1-V3-02 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera Level 1B 1 km Registered Science data. Version 3.02 represents a transition of the Lidar, IIR, and WFC processing and browse code to a new cluster computing system. No algorithm changes were introduced and very minor changes were observed between V3.01 and V3.02 as a result of the compiler and computer architecture differences. The primary Wide Field Camera Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During the normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The Wide Field Camera Level 1B 1 IIR Registered Science grid provides WFC data on the identical grid as the CALIPSO IIR data and is produced to facilitate the use of the WFC data in the IIR retrievals. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. proprietary +CAM5K30CFCLIM_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Coefficient Climatology Monthly Global 0.05Deg V003 LPCLOUD STAC Catalog 2003-01-01 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3274449168-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global coefficient climatology product (CAM5K30CFCLIM). The product is provided at 0.05 degree (~5 kilometer) resolution. The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product. They are congruent to the temporally equivalent CAM5K30EM emissivity data product. The HSR emissivity spectra for the same month each year and each unique combination of lab dataset version and number of Principal Components (PC)s are first computed independently and then combined via a weighted average. The weighted average over 2003 through 2021 (19 years) defines the weights by the number of samples from each unique combination. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Provided in the CAM5K30CFCLIM product are variables for PCA coefficients, the weights and sample numbers of the climatology coefficients used in the average calculation, sets of the number of PCA coefficients, laboratory version numbers, latitude, longitude, and land flag information. PCA coefficients depend on the lab PC data version and the number of PCs used. proprietary CAM5K30CF_002 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Coefficient Monthly Global 0.05Deg V002 LPCLOUD STAC Catalog 2000-04-01 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266335-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly coefficients at 0.05 degree (~5 kilometer) resolution (CAM5K30CF). The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product and are congruent to the temporally equivalent CAM5K30EM emissivity data product. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Provided in the CAM5K30CF product are layers for PCA coefficients, number of PCA coefficients, laboratory version, snow fraction derived from MODIS Snow Cover data (MOD10), latitude, longitude, and the CAMEL quality information. PCA coefficients are dependent on the version of lab PC data and number of PCs used. proprietary CAM5K30CF_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Coefficient Monthly Global 0.05Deg V003 LPDAAC_ECS STAC Catalog 2000-03-01 2024-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2600365285-LPDAAC_ECS.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly coefficients at 0.05 degree (~5 kilometer) resolution (CAM5K30CF). The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product and are congruent to the temporally equivalent CAM5K30EM (https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) emissivity data product. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Provided in the CAM5K30CF product are layers for PCA coefficients, number of PCA coefficients, laboratory version, snow fraction derived from MODIS Snow Cover data (MOD10), latitude, longitude, and the CAMEL quality information. PCA coefficients are dependent on the version of lab Principal Component (PC) data and the number of PCs used. proprietary +CAM5K30COVCLIM_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Covariances Climatology Monthly Global 0.25Deg V003 LPCLOUD STAC Catalog 2003-01-01 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3274450252-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global covariances climatology product (CAM5K30COVCLIM). The product is provided at 0.25 degree (~25 kilometer) resolution. The CAMEL covariance product includes the mean and variance of the covariance matrixes created for each month from 2003 through 2021 (19 years) on a 0.25 x 0.25 degree grid of 416 spectral points from the V003 CAMEL Emissivity product (CAM5K30EM). Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Provided in the CAM5K30COVCLIM product are variables for the mean and variance of the emissivity, latitude, longitude, spectral frequencies, and number of observations. proprietary +CAM5K30EMCLIM_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Emissivity Climatology Monthly Global 0.05Deg V003 LPCLOUD STAC Catalog 2003-01-01 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3274448375-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global emissivity climatology product (CAM5K30EMCLIM). This 0.05 degree (~5 kilometer) resolution product represents the mean emissivity from 2003 through 2021 (19 years). Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Variables provided in the CAM5K30EMCLIM product include latitude, longitude, wavelength, number of samples used to calculate climatology, CAMEL quality flag, snow fraction derived from MODIS (MOD10), and CAMEL Emissivity. proprietary CAM5K30EM_002 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Emissivity Monthly Global 0.05Deg V002 LPCLOUD STAC Catalog 2000-04-01 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266338-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Infrared Emissivity dataset (UWIREMIS) and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include: adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) 5 kilometer resolution, merging of the 5 ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and Best Fit Emissivity (BFE) quality information. proprietary CAM5K30EM_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Emissivity Monthly Global 0.05Deg V003 LPDAAC_ECS STAC Catalog 2000-03-01 2024-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2600365286-LPDAAC_ECS.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) emissivity database and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the UWBF 5 kilometer resolution, merging of the five ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and the UW Baseline Fit (UWBF) Emissivity quality information. proprietary +CAM5K30UCCLIM_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Climatology Monthly Global 0.05Deg V003 LPCLOUD STAC Catalog 2003-01-01 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3274446180-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global uncertainty climatology product (CAM5K30UCCLIM). The product is provided at 0.05 degree (~5 kilometer) resolution. The 13 hinge-point uncertainty climatology is computed by taking an average over each available month from 2003 through 2021 (19 years) and includes three independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty climatology is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Corresponding emissivity values can be found in the CAM5K30EMCLIM data product. Provided in the CAM5K30UCCLIM product are variables for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, and total uncertainty quality flag information. proprietary CAM5K30UC_002 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Monthly Global 0.05Deg V002 LPCLOUD STAC Catalog 2000-04-01 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266343-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty, comprising 3 independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Corresponding emissivity values can be found in the CAM5K30EM data product. Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information. proprietary CAM5K30UC_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Monthly Global 0.05Deg V003 LPDAAC_ECS STAC Catalog 2000-03-01 2024-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2600365287-LPDAAC_ECS.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty comprising three independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Corresponding emissivity values can be found in the CAM5K30EM (https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) data product. Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information. proprietary CAMEX4_ER2_MAS_1 Fourth Convection and Moisture Experiment ER2 MODIS Airborne Simulator LARC_ASDC STAC Catalog 2001-08-13 2001-09-26 -121.13, 16.43, -61.8, 39.62 https://cmr.earthdata.nasa.gov/search/concepts/C1535862443-LARC_ASDC.umm_json The Convection And Moisture EXperiment (CAMEX) 4 focused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using NASA-fundend aircraft and surface remote sensing instrumentation. These aircraft flew over, through, and around selected hurricanes as they approached landfall in the Caribbean, Gulf of Mexico, and along the East Coast of the United States. This study yields high spatial and temporal information of hurricane structure, dynamics, and motion. The data set contains the measurements collected by the MAS instrument onboard the ER2 aircraft. The MODIS Airborne Simulator (MAS) is an airborne scanning spectrometer that acquires high spatial resolution imagery of cloud and surface features from its vantage point on-board a NASA ER-2 high-altitude research aircraft. The MAS spectrometer acquires high spatial resolution imagery in the range of 0.55 to 14.3 microns. A total of 50 spectral bands are available in this range. A 50-channel digitizer which records all 50 spectral bands at 12 bit resolution became operational in January 1995. The MAS spectrometer is mated to a scanner sub-assembly which collects image data with an IFOV of 2.5 mrad, giving a ground resolution of 50 meters from 20000 meters altitude, and a cross track scan width of 85.92 degrees. proprietary @@ -4192,6 +4325,11 @@ CNDA-ESP_ANT94-0905_LIQ_05 Adaptive strategies of lichen species to cold environ CNDP_HES_20230103_CHALLENGE_ALS_1.0 Algae sampling of the project CHALLENGE-2 SCIOPS STAC Catalog 2023-01-03 2023-02-28 -70.1938725, -68.1163134, -56.8344988, -61.085064 https://cmr.earthdata.nasa.gov/search/concepts/C2723265658-SCIOPS.umm_json The objective of this sampling is to know the biodiversity of the Antarctic algae communities (macroalgae and microalgae) and their temporal changes along the South Shetland Islands and the Antarctic Peninsula. Another objective of the sampling is to know the molecular biology of certain species of the red algae group and its nuclear patterns. For all this, it is necessary to carry out sampling both in the intertidal zone and in the sublitoral zone. For this study, a total of 54 stations have been sampled. For intertidal communities, 25 x 25cm squares were taken with three replicates per community and a sample was obtained for each different species found throughout the season. For diatoms in the intertidal zone, three sediment falcon tubes were taken from the beach break area. Samples for each species were also collected within the sublitoral zone and in addition to the target species for the molecular study. Samples for each different species were also obtained in the sublitoral area and sampled in addition to the target species for molecular study. Diatoms were obtained by extracting sediment during diving or using multicore and gravity core, in which the first centimetres of sediment were obtained in a falcon tube. On the other hand, samples of epiphyte diatoms, found on benthic animals such as starfish or tunicates, were taken by scraping and later preserved in 70% alcohol. A total of 218 samples of diatoms have been obtained and frozen at -20ºC for preservation. Those taken from sediment and animals have been kept in the refrigerator at 4ºC. A total of 39 samples from squares have been taken. These samples have been classified by taxa at species level and weighed in wet weight. Qualitative biodiversity samples have been 351. These have been stored in zip bags at -20ºC. The samples for molecular studies have been 37 and preserved in three ways each sample; frozen, in Silica gel and in Carnoy (Solution of Ethanol and Glacial Acetic). Analyses and calculations of these results will be carried out later in the Antarctic campaign. proprietary CNDP_JCI_20220103_EPOLAAR_CAM_1.0 All Sky Camera Images, Livingston Island SCIOPS STAC Catalog 2022-01-03 2022-01-29 -60.3904851, -62.6637967, -60.3813871, -62.6617865 https://cmr.earthdata.nasa.gov/search/concepts/C2566384413-SCIOPS.umm_json Images provided by an All Sky Camera installed at the SAS Juan Carlos I on Livingston Island in 2022 proprietary CNDP_JCI_20240101_TRIPOLI_CAM_1.0 All Sky Camera Images, Livingston Island (2023) SCIOPS STAC Catalog 2024-01-01 -60.3904851, -62.6637967, -60.3813871, -62.6617865 https://cmr.earthdata.nasa.gov/search/concepts/C3069335901-SCIOPS.umm_json Images provided by an All Sky Camera installed at the SAS Juan Carlos I on Livingston Island since 2023 proprietary +CNES_http__cnes.fr_ark_68059_0b222c7948a7ffa8035b3053b4b3ad30_IDN_1.4 JASON 3 experiment: Satellite information CEOS_EXTRA STAC Catalog 2016-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2226555601-CEOS_EXTRA.umm_json By succeeding Topex/Poseidon, Jason-1 and Jason-2, Jason-3 extends the high-precision ocean altimetry data record to support climate monitoring, operational oceanography and seasonal forecasting. Jason-3 is the result of a joint effort by Cnes, NASA, EUMETSAT and NOAA, pursuing a heritage that has been keeping the oceans under close watch for more than 20 years. Partnership is as for Jason-2, but the operational agencies (NOAA and EUMETSAT) take the lead; Cnes serves as the system coordinator and all partners -including NASA- support science team activities. By continuing long-term operational oceanography observations, Jason-3 is a key element of the constellation of altimetry satellites in the years ahead. Succeeding Jason-2, it boasts a number of enhancements to its systems and in processing of the data it delivers. From June 2016, Jason-3 is the reference altimetry mission.The satellite is built around a PROTEUS bus carrying a typical suite of altimetry mission instruments that acquires highly accurate measurements of ocean surface height to extend the data record compiled by Topex/Poseidon, Jason-1 and Jason-2. The orbit is the traditional T/P-Jason orbit -non-sun-synchronous, 1336 km, 66° inclination on a Proteus platform. By succeeding Topex/Poseidon, Jason-1 and Jason-2, Jason-3 extends the high-precision ocean altimetry data record to support climate monitoring, operational oceanography and seasonal forecasting. Jason-3 is the result of a joint effort by CNES, NASA, EUMETSAT and NOAA, pursuing a heritage that has been keeping the oceans under close watch for more than 20 years. Partnership is as for Jason-2, but the operational agencies (NOAA and EUMETSAT) take the lead; CNES serves as the system coordinator and all partners -including NASA- support science team activities. By continuing long-term operational oceanography observations, Jason-3 is a key element of the constellation of altimetry satellites in the years ahead. From June 2016, Jason-3 is the reference mission for the altimetry constellation. Succeeding Jason-2, it boasts a number of enhancements to its systems and in processing of the data it will deliver. The satellite is built around a PROTEUS bus carrying a typical suite of altimetry mission instruments that acquires highly accurate measurements of ocean surface height to extend the data record compiled by Topex/Poseidon, Jason-1 and Jason-2. The orbit is the traditional T/P-Jason orbit -non-sun-synchronous, 1336 km, 66° inclination on a Proteus platform. [http://www.aviso.altimetry.fr/en/missions/future-missions/jason-3.html] [http://www.aviso.altimetry.fr/en/missions/future-missions/jason-3.html] proprietary +CNES_http__cnes.fr_ark_68059_0b7a761c3e62fd4332cd4f66eff0c845_IDN_1.2 HYDROWEB experiment: RIVER PRODUCT CEOS_EXTRA STAC Catalog 1992-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2226555501-CEOS_EXTRA.umm_json The products offered by the Hydroweb project consist of continuous, long-duration time-series of the levels of large lakes with surface areas over 100 km2, reservoirs and the 20 biggest rivers in the world.The operational measurement series are updated no later than 1.5 days after a new altimetry measurement becomes available. They cover about 80 large lakes and 300 measurement points along about 20 major rivers.The research measurement series are updated at regular intervals according to the progress made with processing by the LEGOS laboratory. They cover about 150 large lakes and 1,000 measurement points along about 20 major rivers.Continental waters account for only 0.65% of the total amount of water on Earth, 97% being stored in the oceans and 2.15% in the cryosphere. However, these waters have a significant impact on life on Earth and household needs. They also play a major role in climate variability. Water on Earth is continually recycled through precipitation, evaporation and run-off towards the sea. The increasingly accurate characterisation of the water cycle on land surfaces enables more accurate forecasting of the climate and more careful control of global water resources (human consumption and activities such as agriculture, urbanisation and the production of hydroelectric power, for example). Radar echoes over land surfaces are hampered by interfering reflections due to water, vegetation and topography. As a consequence, waveforms (e.g., the power distribution of the radar echo within the range window) may not have the simple broad peaked shape seen over ocean surfaces, but can be complex, multi-peaked, preventing from precise determination of the altimetric height. If the surface is flat, problems may arise from interference between the vegetation canopy and water from wetlands, floodplains, tributaries and main river. In other cases, elevated topography sometimes prevents the altimeter to lock on the water surface, leading to less valid data than over flat areas. The time series available in Hydroweb are constructed using Jason-2 and Saral GDRs. The basic data used for rivers are the 20 or 40Hz data(“high rate” data).To construct river water level time series, we need to define virtual stations corresponding to the intersection of the satellite track with the river. For that purpose, we select for each cycle a rectangular “window” taking into account all available along track high rate altimetry data over the river area. The coordinate of the virtual station is defined as the barycenter of the selected data within the “window”. After rigourous data editing, all available high rate data of a given cycle are geographically averaged. At least two high rate data are needed for averaging otherwise no mean height is provided. Scattering of high rate data with respect to the mean height defines the uncertainty associated with the mean height.The water level and volume time series is operationally updated less than 1.5 working days after the availability of the input altimetry data, for some virtual stations on rivers. Other virtual stations are monitored on a research mode basis. [https://www.theia-land.fr/fr/projets/hydroweb] proprietary +CNES_http__cnes.fr_ark_68059_3bf5d54adb12f57809057d19c2ea4f25_IDN_1.2 HYDROWEB experiment: LAKE PRODUCT CEOS_EXTRA STAC Catalog 1992-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2226555523-CEOS_EXTRA.umm_json The products offered by the Hydroweb project consist of continuous, long-duration time-series of the levels of large lakes with surface areas over 100 km2, reservoirs and the 20 biggest rivers in the world.The operational measurement series are updated no later than 1.5 days after a new altimetry measurement becomes available. They cover about 80 large lakes and 300 measurement points along about 20 major rivers.The research measurement series are updated at regular intervals according to the progress made with processing by the LEGOS laboratory. They cover about 150 large lakes and 1,000 measurement points along about 20 major rivers.Continental waters account for only 0.65% of the total amount of water on Earth, 97% being stored in the oceans and 2.15% in the cryosphere. However, these waters have a significant impact on life on Earth and household needs. They also play a major role in climate variability. Water on Earth is continually recycled through precipitation, evaporation and run-off towards the sea. The increasingly accurate characterisation of the water cycle on land surfaces enables more accurate forecasting of the climate and more careful control of global water resources (human consumption and activities such as agriculture, urbanisation and the production of hydroelectric power, for example). Altimetry missions used are repetitive, i.e. the satellite overflow the same point at a given time interval (10, 17 or 35 days depending on the satellite). The satellite does not deviate from more than +/-1 km across its track. A given lake can be overflown by several satellites, with potentially several passes. The water level and volume time series is operationally updated less than 1.5 working days after the availability of the input altimetry data, for some lakes. Other lakes are also monitored on a research mode basis. [https://www.theia-land.fr/fr/projets/hydroweb] proprietary +CNES_http__cnes.fr_ark_68059_467a7851f76cf868d9568f8d85bd664a_IDN_1.6 JASON 2 experiment: Geophysical products CEOS_EXTRA STAC Catalog 2008-06-20 -180, -66, 180, 66 https://cmr.earthdata.nasa.gov/search/concepts/C2226555596-CEOS_EXTRA.umm_json "The JASON-2 project is a response to the international demand for programs to study and observe oceans and the climate, through a worldwide ocean observation system. It is a continuation to the TOPEX/POSEIDON and JASON-1 altimetry missions developed by CNES and NASA. Altimetry, i.e. the precise measurement of ocean surface topography, has indeed become since 1992 (launch of TOPEX/POSEIDON) an essential tool for the study of oceans on a global scale.JASON-2 is part of cooperation between CNES, EUMETSAT, NASA and NOAA. Space and ground segments of the JASON-2 mission strongly inherit from the JASON-1 mission.Onboard the JASON-2 satellite, which uses a PROTEUS platform, the payload is composed of a Poseidon-3 radar altimeter supplied by CNES, an Advanced Microwave Radiometer (AMR) supplied by NASA/JPL, and a triple system for precise orbit determination: the DORIS instrument (CNES), GPS receiver and a Laser Retroflector Array (LRA) (NASA). Three further onboard instruments (T2L2, LPT, CARMEN-2) will also be included.In order to ensure continuity and optimal inter-calibration of observations over the long term, JASON-2 flies the same orbit as JASON-1 and TOPEX/POSEIDON. Moreover, data processing is integrated into the CNES ground segment ""SALP"" (altimetry and precise positioning system), which already operates the altimetry missions TOPEX/POSEIDON, JASON-1, ENVISAT, GFO, whose data is distributed on the AVISO website.The level 2 data stored at CNES are those addressed in this description. The JASON-2 project is a response to the international demand for programs to study and observe oceans and the climate, through a worldwide ocean observation system. It is a continuation to the TOPEX/POSEIDON and JASON-1 altimetry missions developed by CNES and NASA. Altimetry, i.e. the precise measurement of ocean surface topography, has indeed become since 1992 (launch of TOPEX/POSEIDON) an essential tool for the study of oceans on a global scale.JASON-2 is part of cooperation between CNES, EUMETSAT, NASA and NOAA. Space and ground segments of the JASON-2 mission strongly inherit from the JASON-1 mission.Onboard the JASON-2 satellite, which uses a PROTEUS platform, the payload is composed of a Poseidon-3 radar altimeter supplied by CNES, an Advanced Microwave Radiometer (AMR) supplied by NASA/JPL, and a triple system for precise orbit determination: the DORIS instrument (CNES), GPS receiver and a Laser Retroflector Array (LRA) (NASA). Three further onboard instruments (T2L2, LPT, CARMEN-2) will also be included.In order to ensure continuity and optimal inter-calibration of observations over the long term, JASON-2 will fly the same orbit as JASON-1 and TOPEX/POSEIDON. Moreover, data processing will be integrated into the CNES ground segment ""SALP"" (altimetry and precise positioning system), which already operates the altimetry missions TOPEX/POSEIDON, JASON-1, ENVISAT, GFO, whose data is distributed on the AVISO website.The level 2 data stored at CNES are those addressed in this description. The data described here are part of the European Directive INSPIRE. [http://smsc.cnes.fr/JASON2/] [http://smsc.cnes.fr/JASON2/]" proprietary +CNES_http__cnes.fr_ark_68059_54736f5916e9134386cb9725dbbe67ae_IDN_1.5 JASON 1 experiment: Geophysical products CEOS_EXTRA STAC Catalog 2001-12-07 2013-07-03 -180, -66, 180, 66 https://cmr.earthdata.nasa.gov/search/concepts/C2226555543-CEOS_EXTRA.umm_json "JASON-1 is the follow-on to Topex/Poseidon, whose main features it has inherited (orbit, instruments, measurement accuracy, etc.). JASON-1 is the result of close international cooperation between space agencies (CNES and NASA), industry and data users working to accomplish a benchmark mission in terms of data quality and science and economic return. JASON-1 flies on the same orbit as TOPEX/POSEIDON to ensure a continuity and an optimal inter comparison for long term observations. The data processing is integrated to the CNES ""SALP"" (Systeme d'Altimetrie et de Localisation Precise) Ground Segment, which operates many other missions (TOPEX/POSEIDON, ENVISAT, GFO altimetry missions, JASON-2, SARAL...) whose data are distributed on AVISO web site. The level 2 data stored at CNES are those addressed in this description. JASON-1 is the follow-on to Topex/Poseidon, whose main features it has inherited (orbit, instruments, measurement accuracy, etc.). JASON-1 is the result of close international cooperation between space agencies (CNES and NASA), industry and data users working to accomplish a benchmark mission in terms of data quality and science and economic return. JASON-1 flies on the same orbit as TOPEX/POSEIDON to ensure a continuity and an optimal inter comparison for long term observations. The data processing is integrated to the CNES ""SALP"" (Systeme d'Altimetrie et de Localisation Precise) Ground Segment, which operates many other missions (TOPEX/POSEIDON, ENVISAT, GFO altimetry missions, JASON-2, SARAL...) whose data are distributed on AVISO web site.The level 2 data stored at CNES are those addressed in this description. [http://smsc.cnes.fr/JASON/index.htm] [http://smsc.cnes.fr/JASON/index.htm]" proprietary CNNADC_1999_ARCTIC_MAP 1:5000000 map of Arctic Ocean area SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214587206-SCIOPS.umm_json This dataset is maps of Arctic Ocean area,their scales are 1:5000000,1:10000000 and 1:40000000. proprietary CNNADC_2006_ZhongshanStation_Antarctica 2006 Zhongshan station earth tide data SCIOPS STAC Catalog 2006-04-01 2006-11-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214587196-SCIOPS.umm_json This is Laseaman hills earth tide data from March to November 2006 by using Lacoste ET gravimeter. proprietary CNNADC_2006_ZhongshanStation_Antarctica_2006 2006 Zhongshan station earth tide data - CNNADC_2006_ZhongshanStation_Antarctica_2006 SCIOPS STAC Catalog 2006-04-01 2006-11-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1221420502-SCIOPS.umm_json This is Laseaman hill's earth tide data from March to November 2006 by using Lacoste ET gravimeter. proprietary @@ -4505,6 +4643,7 @@ DSCOVR_EPIC_L2_CLOUD_03 DSCOVR EPIC Level 2 Cloud Version 03 LARC_ASDC STAC Cata DSCOVR_EPIC_L2_COMPOSITE_01 EPIC-view satellite composites for DSCOVR, Version 1 LARC_ASDC STAC Catalog 2015-06-12 2017-12-31 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1576365803-LARC_ASDC.umm_json In DSCOVR_EPIC_L2_composite_01, cloud property retrievals from multiple imagers on low Earth orbit (LEO) satellites (including MODIS, VIIRS, and AVHRR) and geostationary (GEO) satellites (including GOES-13 and -15, METEOSAT-7 and -10, MTSAT-2, and Himawari-8) are used to generate the composite. Based on the Ceres cloud detection and retrieval system, all cloud properties were determined using a standard set of algorithms, the Satellite ClOud and Radiation Property Retrieval System (SatCORPS). Cloud properties from these LEO/GEO imagers are optimally merged together to provide a seamless global composite product at 5-km resolution by using an aggregated rating that considers five parameters (nominal satellite resolution, pixel time relative to the Earth Polychromatic Imaging Camera (EPIC) observation time, viewing zenith angle, distance from day/night terminator, and sun glint factor) and selects the best observation at the time nearest to the EPIC measurements. About 72% of the LEO/GEO satellite overpass times are within one hour of the EPIC measurements, while 92% are within two hours of the EPIC measurements. The global composite data are then remapped into the EPIC Field of View (FOV) by convolving the high-resolution cloud properties with the EPIC point spread function (PSF) defined with a half-pixel accuracy to produce the EPIC composite. PSF-weighted radiances and cloud properties averages are computed separately for each cloud phase. Ancillary data (i.e., surface type, snow and ice map, skin temperature, precipitable water, etc.) needed for anisotropic factor selections are also included in the composite. These composite images are produced for each observation time of the EPIC instrument (typically 300 to 600 composites per month). proprietary DSCOVR_EPIC_L2_COMPOSITE_02 GEO/LEO based cloud property composites for DSCOVR EPIC view, Version 2 LARC_ASDC STAC Catalog 2015-06-13 2021-07-31 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231134699-LARC_ASDC.umm_json In DSCOVR_EPIC_L2_composite_02, cloud property retrievals from multiple imagers on low Earth orbit (LEO) satellites (including MODIS, VIIRS, and AVHRR) and geostationary (GEO) satellites (including GOES-13 and -15, METEOSAT-7 and -10, MTSAT-2, and Himawari-8) are used to generate the composite. Based on the CERES cloud detection and retrieval system, all cloud properties were determined using a standard set of algorithms, the Satellite ClOud and Radiation Property Retrieval System (SatCORPS). Cloud properties from these LEO/GEO imagers are optimally merged to provide a seamless global composite product at 5-km resolution by using an aggregated rating that considers five parameters (nominal satellite resolution, pixel time relative to the EPIC observation time, viewing zenith angle, distance from day/night terminator, and sun glint factor) and selects the best observation at the time nearest to the EPIC measurements. About 72% of the LEO/GEO satellite overpass times are within one hour of the EPIC measurements, while 92% are within two hours of the EPIC measurements. The global composite data are then remapped into the EPIC FOV by convolving the high-resolution cloud properties with the EPIC point spread function (PSF) defined with a half-pixel accuracy to produce the EPIC composite. PSF-weighted radiances and cloud properties averages are computed separately for each cloud phase. Ancillary data (i.e., surface type, snow and ice map, skin temperature, precipitable water, etc.) needed for anisotropic factor selections are also included in the composite. These composite images are produced for each observation time of the EPIC instrument (typically 300 to 600 composites per month). proprietary DSCOVR_EPIC_L2_GLINT_01 DSCOVR EPIC Level 2 GLINT LARC_ASDC STAC Catalog 2015-06-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2091727220-LARC_ASDC.umm_json DSCOVR_EPIC_L2_GLINT_01 is Version 1 of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 2 glint data product. This product indicates the presence of glint caused by the single scattering specular reflection of sunlight either from horizontally oriented ice crystals floating in clouds or from smooth, highly reflective water surfaces. Such glints can prevent accurate retrievals of atmospheric and surface properties using existing algorithms but can also be used to learn more about the glint-causing objects. The glint detection algorithm relies on EPIC taking images at different wavelengths at slightly different times. For example, red images are taken about 4 minutes after blue images. During these few minutes, the Earth's rotation changes the scene's orientation by one degree, affecting whether EPIC observations at a specific wavelength will capture or miss the narrowly focused specular reflection from ice clouds or smooth water surfaces. As a result, sharp brightness differences between EPIC images taken a few minutes apart can identify glint signals. The glint product includes three parameters for each pixel in the part of EPIC images where the alignment of solar and viewing directions is suitable for sun glint observations: (1) The surface type flag shows whether the area of a pixel is covered mainly by water, desert, or non-desert land; (2) The glint angle—the angle between the actual EPIC view direction and the direction of looking straight into the specular reflection from a perfectly horizontal surface—tells how favorable the EPIC view direction is for glint detection and can help in estimating the distribution of ice crystal orientation; (3) The glint mask indicates whether or not glint has been detected. proprietary +DSCOVR_EPIC_L2_MAIAC-DAILY_01 MAIAC Daily V01 LARC_ASDC STAC Catalog 2015-06-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3264309299-LARC_ASDC.umm_json DSOCVR EPIC_L2 MAIAC-Daily_01 contains plots of data generated from DSCOVR_EPIC_L2_MAIAC_03, the DSCOVR EPIC L2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) Version 03 data product. Data collection for this product is ongoing. The datasets visualized include Aerosol Layer Height (ALH), Aerosol Optical Depth, and Single Scattering Albedo at 340nm, 388nm, 443nm, 551 nm, 680nm, and 780nm. Level 2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) provides an interdisciplinary suite of products for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC). The current version 3 reports the following products: a) Atmosphere: cloud mask, global aerosol optical depth at 443nm and 551nm, fine mode fraction (over the ocean), aerosol layer height (ALH) globally, and spectral aerosol absorption for detected biomass burning or mineral dust aerosols. The absorption information includes single scattering albedo at 340-780nm range, imaginary refractive index at 680nm (k0), and Spectral Absorption Exponent (SAE) characterizing spectral increase of imaginary refractive index from Red towards UV wavelengths. The aerosol optical properties {AOD, ALH, k0, SAE} are retrieved simultaneously by matching EPIC measurements in the UV-NIR range, including atmospheric oxygen A- and B-bands. b) Land: atmospherically corrected spectral bidirectional reflectance factors (BRF) along with Lambertian surface reflectance and bidirectional reflectance distribution function (BRDF) for the backscattering view geometries of EPIC. The BRDF is represented by three parameters of the Ross-Thick Li-Sparse model. c) Ocean: Water leaving reflectance (non-dimensional) at Ultraviolet-Visible (UV-Vis) bands. The parameters are provided at 10 km resolution on a zonal sinusoidal grid with a 1—to 2-hour temporal frequency. MAIAC version 03 also provides gap-filled global composite products for the Normalized Difference Vegetation Index (NDVI) over land and water, leaving reflectance in 5 UV-Vis bands over the global ocean. The composite products represent a weighted running average where the weight of the latest observation is maximized towards the local noon and low aerosol conditions. proprietary DSCOVR_EPIC_L2_MAIAC_02 DSCOVR EPIC L2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) Version 02 LARC_ASDC STAC Catalog 2015-06-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1969999465-LARC_ASDC.umm_json DSCOVR_EPIC_L2_MAIAC_02 is the DSCOVR EPIC L2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) Version 02 data product. Data collection for this product is ongoing. Level 2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) provides an interdisciplinary suite of products for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC). The current version 2 reports the following products: a) Atmosphere: cloud mask, global aerosol optical depth at 443nm and 551nm, fine mode fraction (over ocean) and spectral aerosol absorption for detected biomass burning or mineral dust aerosols. The absorption information includes single scattering albedo at 443nm, imaginary refractive index at 680nm, and Absorption Angstrom Exponent (AAE) characterizing spectral increase of imaginary refractive index at Vis-UV wavelengths. The absorption information is provided for two effective aerosol layer heights of 1km and 4km generally representing boundary layer and transport mode. b) Land: atmospherically corrected spectral bidirectional reflectance factors (BRF) along with Lambertian surface reflectance, and bidirectional reflectance distribution function (BRDF) for the backscattering view geometries of EPIC. The BRDF is represented by 3 parameters of the Ross-Thick Li-Sparse model. c) Ocean: Water leaving reflectance (non-dimensional) at Ultraviolet-Visible (UV-Vis) bands. The parameters are distributed at 10 km rotated sinusoidal grid and 1 to 2-hour temporal frequency. MAIAC version 02 also provides gap-filled global composite products for Normalized Difference Vegetation Index (NDVI) over land, and water leaving reflectance in 5 UV-Vis bands over global ocean. The composite products represent a weighted running average where the weight of the latest observation is maximized towards the local noon and low aerosol conditions. proprietary DSCOVR_EPIC_L2_O3SO2AI_02 DSCOVR EPIC L2 Ozone (O3), Sulfur Dioxide (SO2) Aerosol Index (AI) with Epic L1B V03 Input, Version 2 LARC_ASDC STAC Catalog 2015-06-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1715718433-LARC_ASDC.umm_json Robust cloud products are critical for the Deep Space Climate Observatory (DSCOVR) to contribute significantly to climate studies. Building on our team’s track record in cloud detection, cloud property retrieval, oxygen band exploitation, and DSCOVR-related studies, we propose to develop a suite of algorithms for generating the operational Earth Polychromatic Imaging Camera (EPIC) cloud mask, cloud height, and cloud optical thickness products. Multichannel observations will be used for cloud masking; the cloud height will be developed with information from the oxygen A- and B- band pairs (780 nm vs. 779.5 nm and 680 nm vs. 687.75 nm); for the cloud optical thickness retrieval, we propose an approach that combines the EPIC 680 nm observations and numerical weather model outputs. Preliminary results from radiative transfer modeling and proxy data applications show that the proposed algorithms are viable. Product validation will be conducted by comparing EPIC observations/retrievals with counterparts from coexisting Low Earth Orbit (LEO) and Geosynchronous Earth Orbit (GEO) satellites. The proposed work will include a rigorous uncertainty analysis based on theoretical and computational radiative transfer modeling that complements standard validation activities with physics-based diagnostics. We also plan to evaluate and improve the calibration of the EPIC O2 A- and B-band absorption channels by tracking the instrument performance over known targets, such as cloud-free ocean and ice sheet surfaces. The deliverables for the proposed work include an Algorithm Theoretical Basis Document (ATBD) for peer review, products generated with the proposed algorithms, and supporting research articles. The data products, archived at the Atmospheric Science Data Center (ASDC) at the NASA Langley Research Center, will provide essential inputs needed for the community to apply EPIC observations to climate research and better interpret The National Institute of Standards and Technology Advanced Radiometer (NISTAR) observations. The proposed work directly responds to the solicitation to “develop and implement the necessary algorithms and processes to enable various data products from EPIC sunrise to sunset observations once on orbit” and improve “the calibration of EPIC based on in-flight data.” proprietary DSCOVR_EPIC_L2_O3SO2AI_03 DSCOVR EPIC Level 2 O3SO2AI LARC_ASDC STAC Catalog 2015-06-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2229467436-LARC_ASDC.umm_json Robust cloud products are critical for Deep Space Climate Observatory (DSCOVR) to contribute to climate studies significantly. Building on our team’s track record in cloud detection, cloud property retrieval, oxygen band exploitation, and DSCOVR-related studies, we propose to develop a suite of algorithms for generating the operational Earth Polychromatic Imaging Camera (EPIC) cloud mask, cloud height, and cloud optical thickness products. Multichannel observations will be used for cloud masking; the cloud height will be developed with information from the oxygen A- and B- band pairs (780 nm vs. 779.5 nm and 680 nm vs. 687.75 nm); for the cloud optical thickness retrieval, we propose an approach that combines the EPIC 680 nm observations and numerical weather model outputs. Preliminary results from radiative transfer modeling and proxy data applications show that the proposed algorithms are viable. Product validation will be conducted by comparing EPIC observations/retrievals with counterparts from coexisting Low Earth Orbit (LEO) and Geosynchronous Earth Orbit (GEO) satellites. The proposed work will include a rigorous uncertainty analysis based on theoretical and computational radiative transfer modeling that complements standard validation activities with physics-based diagnostics. We also plan to evaluate and improve the calibration of the EPIC O2 A- and B-band absorption channels by tracking the instrument performance over known targets, such as cloud-free ocean and ice sheet surfaces. The deliverables for the proposed work include an Algorithm Theoretical Basis Document (ATBD) for peer review, products generated with the proposed algorithms, and supporting research articles. The data products, which will be archived at the Atmospheric Science Data Center (ASDC) at the NASA Langley Research Center, will provide essential inputs needed for the community to apply EPIC observations to climate research and to interpret better The National Institute of Standards and Technology Advanced Radiometer (NISTAR) observations. The proposed work directly responds to the solicitation to “develop and implement the necessary algorithms and processes to enable various data products from EPIC sunrise to sunset observations once on orbit” and improve “the calibration of EPIC based on in-flight data.” proprietary @@ -4625,22 +4764,38 @@ E06_OCM_LAC_STGO00GND_1.0 EOS-06 OCM Local Area Coverage (LAC) - 366m Resolution EANET Acid Deposition Monitoring Network in East Asia Data (EANET) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232847617-CEOS_EXTRA.umm_json "Rapid industrialization in the East Asian countries has helped in achieving economic growth. Along with industrialization, primary energy consumption has also rapidly increased in East Asia. In 2002, total primary energy consumption in East Asia was 2.5 billion tons (oil equivalent). The major energy source in East Asia is coal, accounting for 38% of the total in 2002. Oil and natural gas follow at a rate of 33% and 8.7% respectively. The combustion of these fossil fuels is the main source of air pollutants such as sulfur dioxide and nitrogen oxides released into the atmosphere. East Asiafs total primary energy consumption in 2030 is estimated to be 4.7 billion tons (oil equivalent), twice large than in 2002 (international Energy Agency (IEA), World Energy Outlook 2004). If there is no efficient control, the emission of air pollutants will also increase. Sulfur and nitric acids are recognized as major causes of atmospheric acidification. Sulfur dioxide and nitrogen oxides emitted from the burning of coal and oil react in the atmosphere to form sulfuric acid and nitric acid that are deposited on the earth. Sulfuric acid is one of the most important components used to evaluate acid deposition. In some major cities in East Asia the annual deposition of sulfate amounts to more that 100 kg/ha. Sulfuric acid is not only deposited with precipitation in the cities but also transported together with sulfur dioxide and sulfate as well as other acids to surrounding areas and may affect our natural ecosystems. Acid deposition can cause various effects on the ecosystems through acidification of soil and waters as well as damage to buildings and cultural heritage through corrosion of metals, concrete and stone. In order to assess the adverse effects on the ecosystem, it is necessary to identify dose-effect relationship of acid and eutrophic substances in environment. It is also important to quantify the effects on ecosystems, estimate the necessary amount of reduction of emission, and consider the most cost-effective policy options. Determination of emission reduction target may require the identification of the threshold level of acidic and eutrophic substances that do not cause any adverse effect on ecosystems. Acid deposition is not limited by national boundaries and therefore cooperation at the regional and international level is required to effectively address this problem. In Europe, it was successfully achieved through the activities under the Convention on Long-Range Transboundary Air Pollution (CLRTAP). As pointed out in Agenda 21 adopted by the United Nations Conference on Environment and Development in June 1992, ""the programs (in Europe and North America) need to be continued and enhanced, and their experience needs to be shared with other regions of the world"". The Acid Deposition Monitoring Network in East Asia (EANET) was established as a regional cooperative initiative to promote efforts for environmental sustainability and protection of human health in the East Asian region." proprietary EARTH_CRUST_AEDD_PAC_MAR_GEOL1 Branch of Pacific Marine Geology Sample-oriented Digital Data Base, USGS, Alaska CEOS_EXTRA STAC Catalog 1964-01-01 -180, 53, -130, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2231555378-CEOS_EXTRA.umm_json Summary level describes each U.S. Geological Survey, Office of Pacific Marine Geology cruise. Inventory level describes each individual sampling activity. Sample log details tests on each sample. Data level contains results of tests and assessments (geologic, engineering, biological). Data is from the Pacific Ocean and Arctic Ocean basins. Data base is mostly complete for region offshore of central and northern California. Interactive access from outside the facility is limited at present. proprietary EARTH_CRUST_AK_PETROGRAPH_THIN1 Alaskan Rocks - Petrographic Thin Sections; USGS, Anchorage CEOS_EXTRA STAC Catalog 1891-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231550025-CEOS_EXTRA.umm_json A collection of petrographic thin sections made from rock samples collected by USGS field geologists in Alaska. Many of the sections have corresponding descriptions cards; thin sections number 30,000. Written requests or appointment only. Access to materials restricted to on-site use for non-USGS employees. proprietary +EARTH_CRUST_AUS_BMR_Min_Loc_DB Mineral Occurrence Location Data Base; BMR, Australia CEOS_EXTRA STAC Catalog 1989-08-01 110, -45, 155, -10 https://cmr.earthdata.nasa.gov/search/concepts/C2231957528-CEOS_EXTRA.umm_json The Australian Mineral Occurrence Location Database provides information on mineral occurrence and deposit locations in Australia. Currently, data covers 52% of Australia by area. The data base contains the name of mineral occurrences with the geographical coordinates of each occurrence. The spatial resolution: varies from 10m to 10 km, mainly about 500m. Sources of the data are named, such as maps, bibliographies, or correspondence. Commodities associated with the occurrence are delineated. The data is about 32 Megabytes and is rectified to standard coordinates. There are charges for the data. Order form and information about the data set are available from B. Elliott, Project Manager, Mineral Databases (telephone 06-2499502) or BMR Publication Sales (fax 06-2499982). proprietary EARTH_CRUST_USGS_AK_NOTEBOOKS1 Alaskan Geologic Field Notebooks; USGS, Anchorage CEOS_EXTRA STAC Catalog 1891-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231549582-CEOS_EXTRA.umm_json These notebooks are the original field records of geologic observations made by USGS geologists working in Alaska. They contain field stations, sample numbers, rock types and descriptions, terrain conditions and outcrop sketches. Some also report topographic measurements, daily weather, and camp life. A few personal diaries of the early explorers are preserved. The notebooks may be viewed by written requests or appointment only. Access to certain materials may be restricted for non-government employees. Microfilm for these records is stored in Menlo Park, California. The data consists of 3,600 notebooks. proprietary +EARTH_CRUST_USGS_COAL_NCRDS_DB National Coal Resources Data System; USGS CEOS_EXTRA STAC Catalog 1966-01-01 -125, 25, -66, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231554417-CEOS_EXTRA.umm_json The basic National Coal Resources Data System (NCRDS) allows users to interactively retrieve information on coal quantity and quality and to build new resource data from ongoing research on the geology of coal by the U.S. Geological Survey and state agencies. The NCRDS is an automated system. Data bases accessed contain some proprietary data. Access to non-U.S. Geological Survey users is limited to nonproprietary data. NCRDS is a user-oriented computerized storage, retrieval, and display system devised by the U.S. Geological Survey to assess the quantity and quality of national coal resources. The U.S. Geological Survey has initiated a 5- to 10-year program to provide point-source coverage for the coal-bearing rocks in the U.S. Cooperative projects with many state geologic agencies have been funded to supplement U.S. Geological Survey work and to amass the volume of data required to assess U.S. coal resources. Currently, files containing summary areal coal tonnage estimates and proximate/ultimate chemical analyses and point-located major, minor, and trace-element analyses are available. The point-source files are used to calculate resource estimates and to depict trends in the occurrence and chemical characteristics of coal. Primary data for measurements, coal-bed outcrop patterns, burned, channeled, and mined-out areas, and geochemical analyses. System software can calculate coal resource estimates, generate overburden or interburden distribution, and delineate areas of coal with selected quality parameters (for example, >1 percent sulfur, <50 ppm Zinc) within specified boundaries, (for example, county, quadrangle, or lease tract). Data may be displayed descriptively by preformated tables, user-determined listings, and selected statistics or graphically by two-and three-dimensional diagrams, trend surfaces, isoline maps, and stratigraphic sections. The system runs on a SUN Network of servers and workstations located in Reston, VA. and Denver, CO. proprietary +EARTH_CRUST_USGS_GeoNames Geologic Names of the U.S., Territories and Possessions, USGS CEOS_EXTRA STAC Catalog 1800-01-01 -125, 25, -66, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231552995-CEOS_EXTRA.umm_json The data base contains an annotated index lexicon of formal geologic nomenclature of the United States, territories, and possessions, with data on location, geologic age, U.S. Geological Survey usage, lithology, geologic province, thickness at type section, location of type section, and naming reference for each geologic unit. proprietary +EARTH_CRUST_USGS_NPRA_GEOCHEM1 National Petroleum Reserve-Alaska Geochemical Data; USGS CEOS_EXTRA STAC Catalog 1977-01-01 1978-12-31 -162, 60, -152, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2231554279-CEOS_EXTRA.umm_json The National Petroleum Reserve in Alaska (NPRA) is located in the primitive wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began some geological surveying in this area in the early 1900's, and the U.S. Navy began geological and geophysical surveys and drilling in 1945 to appraise the petroleum potential of the Reserve. GEOCHEMICAL DATA Includes microfilm reels of Phase I, II, and III geochemical analyses of well cores. See also the NPPRA legacy data archive:http://energy.cr.usgs.gov/ proprietary +EARTH_CRUST_USGS_NPRA_GEO_RPTS1 National Petroleum Reserve-Alaska Geophysical & Geological Data Reports; USGS CEOS_EXTRA STAC Catalog 1977-01-01 -162, 60, -152, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2231553277-CEOS_EXTRA.umm_json The National Petroleum Reserve in Alaska (NPRA) is located in the primitive wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began some geological surveying in this area in the early 1900's, and the U.S. Navy began geological and geophysical surveys and drilling in 1945 to appraise the petroleum potential of the Reserve. GEOPHYSICAL AND GEOLOGICAL REPORTS A variety of reports are available from the USGS summarizing and interpreting geophysical and geological data about the NPRA. See the NPRA Legacy Data Archive: http://energy.cr.usgs.gov/ proprietary +EARTH_CRUST_USGS_NPRA_SEISMIC1 National Petroleum Reserve (NPR) Alaska Seismic Reflection Data; USGS CEOS_EXTRA STAC Catalog 1972-01-01 1981-12-31 -162, 60, -152, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2231555099-CEOS_EXTRA.umm_json The National Petroleum Reserve in Alaska (NPRA) is located in the primitive wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began some geological surveying in this area in the early 1900's, and the U.S. Navy began geological and geophysical surveys and drilling in 1945 to appraise the petroleum potential of the Reserve. Information on surveys prior to 1955 may be obtained from the Branch of Alaskan Geology at: Alaska Technical Data Unit Mail Stop 48 U.S. Geological Survey 345 Middlefield Road Menlo Park, CA 94025. SEISMIC DATA: Common Depth Point (CDP) seismic reflection data and documentation covering about 13,000 miles for 1972-81 are available from USGS. Full-scale (5 in./sec) sections are available for most of the lines, which were shot at either 6-, 12-, or 24-fold multiplicity. Data sets include index maps, shot-point location maps, seismic sections and velocity analyses. MAJOR DATA SETS: CDP seismic reflection data Reprocessed seismic sections Seismic reflection field tapes Processed field tapes Miscellaneous Geophysical data tapes Barrow data Stacking velocities Anciliary Data for the Seismic Data: TIME, VELOCITY, DEPTH DATA This file contains time, velocity and depth (TVD) information for up to 25 identified seismic horizons. Position information includes line number, shot- point number, latitude and longitude for most of 1972-1981. These data were generated by Petroleum Information for 1981. The data are preliminary and are from Terra Tech (contractor). SHOT-POINT LOCATION DATA This file contains position information for shot locations during 1972-1981. The file was created by National Geophysical Data Center (NGDC) from TVD data and other shot-point tapes. ELEVATION DATA Elevation data for the National Petroleum Reserve in Alaska includes elevation, northing and easting information for 1972-1979. This file was created by Tetra Tech (contractor for USGS) and contains position information, including line number, shot point, latitude and longitude. proprietary +EARTH_CRUST_USGS_NPRA_WELL_LOGS National Petroleum Reserve-Alaska Well Log Data for 1946 to 1981; USGS CEOS_EXTRA STAC Catalog 1946-01-01 1981-12-31 -162, 60, -152, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2231550414-CEOS_EXTRA.umm_json The National Petroleum Reserve in Alaska (NPRA) is located in the primitive wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began some geological surveying in this area in the early 1900's, and the U.S. Navy began geological and geophysical surveys and drilling in 1945 to appraise the petroleum potential of the Reserve. Information on surveys prior to 1955 may be obtained from the Branch of Alaskan Geology, Alaska Technical Data Unit, Mail Stop 48, U.S. Geological Survey, 345 Middlefield Road, Menlo Park, CA 94025. WELL LOG DATA Well logs and associated information are available from USGS. These data deal primarily with NPRA exploration and development since the drilling of the South Barrow No. 5 Well in 1955. Well log formats include Schlumberger LISLOG, DRESSER, and DATOUT. Included with some well log data sets are auxiliary information such as drilling history of the wells and velocity check-shot surveys. Well core analyses include porosity, permeability and fluid saturation measurements. MAJOR DATA SETS: Well logs; Digitized well logs; Well core analyses; Seismic velocity surveys; Synthetic seismograms; Palynology/Micropaleontology reports. proprietary EARTH_INT_AUS_BMR_AIR_MAG_GAMMA Digital Airborne Magnetic and Gamma Ray Geophysical Data; BMR, Australia CEOS_EXTRA STAC Catalog 1951-01-01 110, -45, 155, -10 https://cmr.earthdata.nasa.gov/search/concepts/C2231957517-CEOS_EXTRA.umm_json The Australian Digital Airborne Geophysical Data set consists of reconnaissance measurements of magnetic field anomalies and land surface emission of gamma rays in the range 0.3-3.0 MeV. Stacked total magnetic intensity profiles with each profile base the least squares straight line fit to the navigation. Radiometric Profiles are stacked profiles of radiometric total count and total count radiometric contours. Potassium, Uranium and Thorium Profiles are stacked profiles of radiometric K, U, and Th counts. TMI Contours are Total magnetic intensity contours. The Radiometric contours usually are at a scale of 1:250000. The Gamma-ray spectrometer contours are total count contours. The Magnetic digital data is position located processed magnetic digital data. The Gamma-ray spectrometer digital data is position located processed gamma-ray digital data for K, U, Th, and total count channels. Gridded magnetic digital data is available in processed form. The data is available in ASCII format. The positional accuracy varies from 1 metre to 100 metres. To order stacked profiles and maps, contact BMR Copy Service. For digital data, contact the Airborne Section (telephone 06-2499223 or fax 06-2499986). proprietary EARTH_INT_AUS_BMR_EARTHQUAKE_DB BMR-ASC World Earthquake Database; BMR, Australia CEOS_EXTRA STAC Catalog 1891-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231957515-CEOS_EXTRA.umm_json BMR-ASC World Earthquake Database is maintained by the Australian Bureau of Mineral Resources (BMR). It lists the Hypocentres of world earthquakes from 1904-1989 and Australian earthquakes from 1873-1991 where the magnitude is 4 or greater. Data is also obtained from the International Seismological Centre, UK and the USGS. Aside from date and time and the hypocenter parameters, the data base provides focal depth, magnitude, and describes its effects. To order data by FAX, Fax to 06-2499969. proprietary EARTH_INT_AUS_BMR_GEOCHRON1 Australian Geochronology Data Base; BMR, Australia CEOS_EXTRA STAC Catalog 1989-07-01 110, -45, 155, -10 https://cmr.earthdata.nasa.gov/search/concepts/C2231957510-CEOS_EXTRA.umm_json The National Database of Australian Geochronology includes Main tables on SAMPLES, K_AR, AR40_39, RB_SR, SM_ND, ZIRCON, SHRIMP, REFERENCES and STOREBOXES. The table SAMPLES includes attributes for sample no., stratigraphic unit, rock type, country, state, region, AMG reference and decimal latitude and longitude. K-Ar, Ar-Ar, Rb-Sr, Sm-Nd, Pb-Pb zircon and Pb-Pb ion microprobe (SHRIMP, mostly zircon) tables contain dating results. Table REFERENCES contains bibliographic references relevant to samples &/or results. The data is Australia-wide, mainly from hardrock areas. In the future, possibly Antarctica and PNG samples will be added. The Relational Database is on Magnetic Disk. The interchange format is ASCII. The entire data base is about 10 Megabytes. proprietary EARTH_INT_AUS_BMR_GRAVITY_DB1 Australian National Gravity Database CEOS_EXTRA STAC Catalog 1937-01-01 110, -45, 155, -10 https://cmr.earthdata.nasa.gov/search/concepts/C2231957522-CEOS_EXTRA.umm_json The Australian National Gravity Database contains point located observed gravity and height values over Australia and its continental shelf. Here, observed gravity is the acceleration due to gravity measured at the location measured on an 11 km grid or finer. Ground elevation is the elevation of the observation point on the Australian Height Datum measured on an 11 km grid or finer. The whole data base is about 100 Megabytes. Some of the data have been provided by State Government Departments, universities and private exploration companies. Copies of the database on magnetic tapes are available for $7500. Copies of the database on 1:1M sheet on disc are available for $350. A Bouguer anomaly map dyeline is $25. Order with a written request to BMR or fax (06)2488420 for data and fax (06)2472728 for maps. proprietary +EARTH_INT_USGS_NPRA_GAMMA_MAG1 National Petroleum Reserve Alaska: Aerial Gamma Ray and Magnetic Survey data; USGS CEOS_EXTRA STAC Catalog 1977-01-01 1978-12-31 -162, 60, -152, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2231550010-CEOS_EXTRA.umm_json "The National Petroleum Reserve in Alaska (NPRA) is located in the primitive wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began some geological surveying in this area in the eary 1900's, and the U.S. Navy began geological and geophysical surveys and drilling in 1945 to appraise the petroleum potential of the Reserve. Information on surveys prior to 1955 may be obtained from the Branch of Alaskan Geology, Alaska Technical Data Unit, Mail Stop 48, U.S. Geological Survey, 345 Middlefield Road, Menlo Park, CA 94025. AERIAL GAMMA RAY AND MAGNETIC DATA Radiometric and magnetic profiles from 1977 are available from USGS. Aerial data were recorded at 1-sec intgervals from a helicopter about 800 feet above the terrain with average ground speed of 100m/hr. Included with the data set are 5 index maps, 2 record location maps, 2 residual total magnetic-field profile maps, and an interpreted depth-to-basement map. These files are available as Open-File Report 95-835. ""http://pubs.usgs.gov/of/1995/ofr-95-0835/""" proprietary EARTH_INT_USGS_S_AK_EARTHQUAKES Catalog of Earthquake Hypocenters at Alaskan Volcanoes: January 1,1994 through December 31, 1999; USGS CEOS_EXTRA STAC Catalog 1971-01-01 170, 51, -130, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2231550074-CEOS_EXTRA.umm_json Between 1994 and 1999, the Alaska Volcano Observatory (AVO) seismic monitoring program underwent significant changes with networks added at new volcanoes during each summer from 1995 through 1999. The existing network at Katmai ?Valley of Ten Thousand Smokes (VTTS) was repaired in 1995, and new networks were installed at Makushin (1996), Akutan (1996), Pavlof (1996), Katmai - south (1996), Aniakchak (1997), Shishaldin (1997), Katmai - north (1998), Westdahl, (1998), Great Sitkin (1999) and Kanaga (1999). These networks added to AVO's existing seismograph networks in the Cook Inlet area and increased the number of AVO seismograph stations from 46 sites and 57 components in 1994 to 121 sites and 155 components in 1999. The 1995?1999 seismic network expansion increased the number of volcanoes monitored in real-time from 4 to 22, including Mount Spurr, Redoubt Volcano, Iliamna Volcano, Augustine Volcano, Mount Snowy, Mount Griggs, Mount Katmai, Novarupta, Trident Volcano, Mount Mageik, Mount Martin, Aniakchak Crater, Pavlof Volcano, Mount Dutton, Isanotski volcano, Shisaldin Volcano, Fisher Caldera, Westdahl volcano, Akutan volcano, Makushin Volcano, Great Sitkin volcano, and Kanaga Volcano (see Figures 1-15). The network expansion also increased the number of earthquakes located from about 600 per year in 1994 and 1995 to about 3000 per year between 1997 and 1999. Highlights of the catalog period include: 1) a large volcanogenic seismic swarm at Akutan volcano in March and April 1996 (Lu and others, 2000); 2) an eruption at Pavlof Volcano in fall 1996 (Garces and others, 2000; McNutt and others, 2000); 3) an earthquake swarm at Iliamna volcano between September and December 1996; 4) an earthquake swarm at Mount Mageik in October 1996 (Jolly and McNutt, 1999); 5) an earthquake swarm located at shallow depth near Strandline Lake; 6) a strong swarm of earthquakes near Becharof Lake; 7) precursory seismicity and an eruption at Shishaldin Volcano in April 1999 that included a 5.2 ML earthquake and aftershock sequence (Moran and others, in press; Thompson and others, in press). The 1996 calendar year is also notable as the seismicity rate was very high, especially in the fall when 3 separate areas (Strandline Lake, Iliamna Volcano, and several of the Katmai volcanoes) experienced high rates of located earthquakes. This catalog covers the period from January 1, 1994, through December 31,1999, and includes: 1) earthquake origin times, hypocenters, and magnitudes with summary statistics describing the earthquake location quality; 2) a description of instruments deployed in the field and their locations and magnifications; 3) a description of earthquake detection, recording, analysis, and data archival; 4) velocity models used for earthquake locations; 5) phase arrival times recorded at individual stations; and 6) a summary of daily station usage from throughout the report period. We have made calculated hypocenters, station locations, system magnifications, velocity models, and phase arrival information available for download via computer network as a compressed Unix tar file. proprietary +EARTH_LAND_NBS_GLACIER_TERMINUS Glacier National Park Glacier Terminus Positions Data from the National Biological Service (NBS) CEOS_EXTRA STAC Catalog 1887-01-01 -115, 48, -113, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231554369-CEOS_EXTRA.umm_json Glacier terminus positions data are provided by the project on glacier dynamics in relation to climate. The terminus positions are surveyed by field crews or derived from aerial and oblique photos of glaciers from 1887 to the present in Glacier National Park, Montana, USA. proprietary EARTH_LAND_UAK_GI_Permafrost1 Alaska Permafrost Drillhole Temperature Logs (PTDAK); U. Alaska Geophysical Institute SCIOPS STAC Catalog 1977-01-01 170, 51, -130, 73 https://cmr.earthdata.nasa.gov/search/concepts/C1214584976-SCIOPS.umm_json This data set (PTDAK) includes handwritten temperature logs from drill holes in permafrost. The time series consists of temperatures versus time for active layer and permafrost. Data are stored on ERROM's. Transect from Prudoe Bay to Glenallen, ANWR and other sites in Alaska. proprietary EARTH_LAND_USFWS_AK_Wildlife1 Arctic National Wildlife Refuge (ANWR) Data Base, U.S. Fish and Wildlife Service, Alaska CEOS_EXTRA STAC Catalog 1956-01-01 -180, 53, -130, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2231553077-CEOS_EXTRA.umm_json "The Arctic National Wildlife Refuge (ANWR) is the most northern and one of the largest Refuges within America's National Wildlife Refuge System. The Arctic Refuge is managed by the U.S. Fish and Wildlife Service, U.S. Department of the Interior. The USFWS ANWR site contains information and data on wildlife, habitat, and people in the Arctic Refuge. The Refuge is home to more than 160 bird species, 36 kinds of land mammals, nine marine mammal species, and 36 types of fish. The ANWR geological and land surface databases are available from the Alaska Geospatial Data Clearinghouse (AGDC) at ""http://agdc.usgs.gov/data/projects/anwr/webhtml/"" The data consists of political boundaries and coastlines, land cover and vegetation maps, rivers, streams and wetlands, elevation contours, satellite images (Landsat-MSS and Landsat-TM), surficial geology, and coastal bathymetry as well as Alaska statewide land characterization and geospatial data." proprietary EARTH_LAND_USGS_AK_B_Landcover1 Beechey Point, Alaska Vegetation and Land Cover, USGS, Alaska CEOS_EXTRA STAC Catalog 1979-01-01 -168, 66, -141, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2231551996-CEOS_EXTRA.umm_json Vegetation and land cover for Beechey Point 1:250,000-scale topographic quadrangle. Derived from a single Landsat MSS scene. Printed map: AK Vegetation and Land Cover Series l-0211. Digital raster data set also available. Seven categories. Data stored as 50 meter UTM-referenced pixels. Information available as map or digital data set. Map unit coverage within each township printed on back of map. Map printed using Scitex laser printer. Documentation - Walker, D.A. and W. Acevedo, 1987, Vegetation and a Landsat-derived Land Cover Map of the Beechey Point Quadrangle, Arctic Coastal Plain, Alaska, U.S. Army Cold Regions Research and Engineering Laboratory, CRREL Report 87-5, 63 p. proprietary EARTH_LAND_USGS_AK_Bristol_Bay Bristol Bay Region, Alaska, Vegetation Cover Classification, USGS, Alaska CEOS_EXTRA STAC Catalog 1982-12-31 1982-12-31 -163, 55, -153, 61 https://cmr.earthdata.nasa.gov/search/concepts/C2231552606-CEOS_EXTRA.umm_json This digital data set includes vegetation cover classification derived from Landsat MSS data and can be keyed on a 1:250,000 quadrangle basis. Spatial referencing is by 50 meter grid cell size. The data source is Landsat MSS data (73 records), storage required varies by storage medium and selected area. The file structure is sequential. Data are available on: 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, fixed record length tape and 8' floppy disk. Subsets and custom formats are available; documentation is also available. The data is organized by 7 1/2 ' or 15 ' quads. General area covered: Bristol Bay region in Alaska. proprietary EARTH_LAND_USGS_AK_Colville1 Colville River Delta Landcover Data; USGS, Alaska CEOS_EXTRA STAC Catalog 1986-01-01 -152, 70, -150, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2231554544-CEOS_EXTRA.umm_json Vector and digital data from the Colville River area in Alaska. Data contains land cover classifications derived from Landsat MSS data, aerial photography, and National Wetlands Inventory (no DEM data). Data can be keyed on a U.S. Geological Survey quadrangle basis. Spatial referencing is by 50 meter grid cells. Data source is Landsat MSS data (4 records). Storage required varies by storage medium and selected area. The file structure is sequential. Subsets and custom formats as well as limited documentation are available. The data is organized by 7 1/2 ' or 15 ' quads. covering the area from 70 degrees 15' North to 70 degrees 30' North and from 150 degrees 15' west to 151 degrees 30' west. proprietary EARTH_LAND_USGS_AK_HI_ALT_PHOT Alaska High Altitude Aerial Photography (AHAP) Program SCIOPS STAC Catalog 1978-01-01 1986-12-31 -180, 53, -130, 74 https://cmr.earthdata.nasa.gov/search/concepts/C1214585044-SCIOPS.umm_json "[From GeoData Center Home Page descriptions, ""http://www.gi.alaska.edu/alaska-satellite-facility/geodata-center""] The GeoData Center is the browse facility for the state copy of the AHAP collection, which covers approximately 95% of the State of Alaska in 1:60,000 color infrared (CIR) and 1:120,000 black and white (B&W) photography. The data reside in 10"" film format. Approximately 70,000 frames of photography were acquired between 1978 and 1986." proprietary +EARTH_LAND_USGS_AK_Iditarod1 Iditarod/George Resource Management Area; USGS, Alaska CEOS_EXTRA STAC Catalog 1980-01-01 2000-02-01 -161, 61, -156, 63 https://cmr.earthdata.nasa.gov/search/concepts/C2231548832-CEOS_EXTRA.umm_json Since the early 1980's the EROS Alaska Field Office (AFO) has been involved in the acquisition, classification, and analysis of digital land cover data over the State of Alaska, and to a limited extent, northwestern Canada and Wrangle Island, Russia. The digital data currently covers approximately 77% of the land and water within the boundaries of the State of Alaska. These data are currently being made available via the AFO web site to land managers and researchers who may be interested in land cover conditions over various portions of Alaska. The land cover maps as a result of digital analysis of Landsat multispectral scanner, Landsat thematic mapper, and SPOT multispectral scanner satellite data. Some data however, are missing from the database due to data degradation on storage media or loss of data tapes, although a limited number of hard copy map products may be in existence, for example, U.S. Forest Service land cover data for southeast Alaska. The land cover data are stored online in a series of directories and are available via the web. Each directory name is indicative of the area for which the land cover data exists. Some directories connote a U.S. Geological Survey 1:250,000 quadrangle (for example, anchorage_int), while others indicate a particular project area (for example, anwr represents Arctic National Wildlife Refuge) or the agency responsible for producing the data. For example, nowitna.fws indicates that the data were produced for the U.S. Fish and Wildlife Service. Directories with an '_int' suffix denote that the data correspond to the Interim Landcover Mapping project. Each directory contains five files with the following extensions: .bil, .blw, .hdr, .prj, and .stx. As a group, one or more of the files can be read by most image analysis and GIS software packages. The .bil file contains the binary raster land cover data and the others are ASCII files. The .bil file does not contain any header or footer records, and can be easily imported into image processing software by using the information contained in the .blw and .hdr files. The .blw file provides pixel size information as well as the upper left corner coordinate. The .hdr contains number of rows and columns of the data set, as well as band format (band interleave). The .prj file gives projection information, and the .stx file gives information on minimum/maximum/ mean values of the data. Projection parameters for the data sets are either in Universal Transverse Projection (UTM) or Alaska Albers Equal Area Conic. proprietary +EARTH_LAND_USGS_AK_Innoko1 Innoko National Wildlife Refuge Landcover and Topography; USGS, Alaska CEOS_EXTRA STAC Catalog 1986-12-31 1986-12-31 -160, 62, -155, 64 https://cmr.earthdata.nasa.gov/search/concepts/C2231550865-CEOS_EXTRA.umm_json The Innoko National Wildlife Refuge digital data sets contain land cover classifications derived from Landsat MSS data, and elevation, slope and aspect data derived from DEM data. Data can be keyed on a U.S. Geological Survey 1:250,000 quadrangle basis. Spatial referencing is from 50 meter grid cells and data source is Landsat MSS data and Digital Elevation Model (DEM) data. This data set contains 113 records. The amount of storage required varies by storage medium and selected area. The file structure is sequential. Data are available online. The data is organized by 7 1/2 ' or 15 ' quads. General area covered: 62 to 64 north to 155 to 45' to 160 to 15' west. proprietary +EARTH_LAND_USGS_AK_Koyukuk1 Koyukuk National Wildlife Refuge Landcover, Topography, Etc.; USGS, Alaska CEOS_EXTRA STAC Catalog 1956-01-01 1985-12-31 -157, 63, -152, 65 https://cmr.earthdata.nasa.gov/search/concepts/C2231548673-CEOS_EXTRA.umm_json Digital data is given on the Koyukuk National Wildlife Refuge in west-central Alaska. The data set contains information on land status, lake and streams digitized at 1:63,360, and wildlife ditribution digitized at 1:250,000. Also included are digitized land cover, slope, a scale of elevation, and aspect from EROS Data Center; data are digitized from U.S. Geological Survey quadrangle maps and updated periodically. The amount of storage required is unknown. Data are available on 9-track, 800 bpi, 1600 bpi, unlabeled, AMS variable block, or DLG 3 fixed block in binary or ASCII, variable record length tape, 5 1/4 inch floppy disk, and cassette. Subsets and custom formats are available. The data dictionary has been completed for this record. The data is organized by 7 1/2 ' or 15 ' quads. proprietary +EARTH_LAND_USGS_AK_NOAA_AVHRR NOAA Digital AVHRR Satellite Data; USGS, Alaska CEOS_EXTRA STAC Catalog 1984-01-01 170, 52, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231553312-CEOS_EXTRA.umm_json This digital data set contains selected NOAA 6, 7, 8 and 9 Advanced Very High Resolution Radiometer (AVHRR) imagery of Alaska; AVHRR is carried on NOAA's polar orbiting satellites. Spatial referencing is 1.1 km at nadir. Data source is National Oceanic and Atmospheric Administration (NOAA). The data set includes 47 records with estimated growth rate of 100 records per year. Storage required varies by storage medium and selected scene. The file structure is sequential. Data are available on 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, BCD, fixed record length tape. Subsets and customs formats are available. Limited documentation is available. Data is organized by 7 1/2 ' or 15 ' quads. Uses include fuel mapping, vegetation monitoring, large area mosaic, and monitoring of ice/snow dynamics. proprietary +EARTH_LAND_USGS_AK_NPRA_veg1 National Petroleum Reserve in Alaska (NPRA) - Vegetation Map, USGS CEOS_EXTRA STAC Catalog 1975-01-01 1977-12-31 -180, 53, -132, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2231553282-CEOS_EXTRA.umm_json A vegetation/land cover raster digital data set for the entire National Petroleum Reserve in Alaska (NPR-A) was generated from Landsat multispectral data sets. Included are eleven categories of vegetation and land cover which are derived from all or portions of 10 Landsat MSS scenes. The data set covers all or part of thirteen 1:250,000-scale topographic quadrangles. Data are stored in 50 meter pixels and registered to a UTM base. A full NPR-A mosaic as well as the 1:250,000 topographic series. Data are available in two forms: a digital mosaic of (1) the entire NPR-A coverage, split into two pieces each and registered to a separate UTM zone, or (2) for each 1:250,000-scale topo quadrangle area within the NPR-A. This file is too large to remain online. It is stored on magnetic tape at Moffett Field, CA. proprietary EARTH_LAND_USGS_AK_Wildlif_Ref1 Arctic National Wildlife Refuge Data; USGS, Alaska CEOS_EXTRA STAC Catalog 1976-08-27 1981-08-05 -151.5, 65.5, -140.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552054-CEOS_EXTRA.umm_json Digital land cover and terrain data of the Arctic National Wildlife Refuge (ANWR) were prduced by the U.S. Geological Survey (USGS) Earth Resources Observation Systems Field Office, Anchorage, Alaska for the U.S. Fish and Wildlife Service (USFWS). These and other environmental data, were incorporated into the USFWS geographic information system to prepare a comprehensive conservation plan for the ANWR and an environmental impact statement which addresses oil and gas development in the Arctic Coastal Plain, and to assist research of the Porcupine Caribou herd. The data set contains land cover classification derived from Landsat MSS data, and elevation, slope and aspect data derived from DEM data. Data can be keyed on a U.S. Geological Survey 1:250,000 quadrangle basis. Spatial referencing is by 50 meter grid cells. Data source is Landsat MSS data, Digital Elevation Model (DEM) data, containing 299 records and the storage required varies by storage medium and selected area; file structure is sequential. Limited documentation and users guide are available. The data is organized by 7 1/2 ' or 15 ' quads. proprietary EARTH_LAND_USGS_ALASKA_FOSSILS1 Alaskan Fossil Identification File CEOS_EXTRA STAC Catalog 1898-01-01 -180, 53, -130, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2231550059-CEOS_EXTRA.umm_json The data base consists of a compilation of reports made by the U.S. Geological Survey Branch of Paleontology and Stratigraphy concerning the identification of fossils collected in Alaska. Data includes fossil type and age, sample locality, collector, author, and date of report. Reports are grouped together by year, but are not indexed. Written requests or appointment only. Permission for access by non-U.S. Geological Survey employees must be obtained in writing from the U.S. Geological Survey Branch of Paleontology and Stratigraphy. Data consists of 65 notebooks. proprietary EARTH_LAND_USGS_ALASKA_GEODETIC Alaska Geodetic Control Files; USGS CEOS_EXTRA STAC Catalog 1890-01-01 -180, 53, -130, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2231552547-CEOS_EXTRA.umm_json Positional (horizontal) central data and elevational (vertical) control data for the state of Alaska. Data may include description of control points and 'to-reach' information. It is issued in 1/2 ' or 15 ' quads, states, and the 1:250,000 topographic series. These maps are not yet available in digital form. proprietary EARTH_LAND_USGS_AMES_AIR_PHOTOS Aerial Photographs (from AMES Pilot Land Data System); USGS EDC, Sioux Falls USGS_LTA STAC Catalog 1970-01-01 -180, 20, -60, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1220566371-USGS_LTA.umm_json "The aerial photography inventoried by the Pilot Land Data System (PLDS) at NASA AMES Research Center has been transferred to the USGS EROS Data Center. The photos were obtained from cameras mounted on high and medium altitude aircraft based at the NASA Ames Research Center. Several cameras with varying focal lengths, lenses and film formats are used, but the Wild RC-10 camera with a focal length of 152 millimeters and a 9 by 9 inch film format is most common. The positive transparencies are typically used for ancillary ground checks in conjunctions with digital processing for the same sites. The aircraft flights, specifically requested by scientists performing approved research, often simultaneously collect data using other sensors on board (e.g. Thematic Mapper Simulators (TMS) and Thermal Infrared Multispectral Scanners). High altitude color infrared photography is used regularly by government agencies for such applications as crop yield forecasting, timber inventory and defoliation assessment, water resource management, land use surveys, water pollution monitoring, and natural disaster assessment. To order, specify the latitude and longitude of interest. You will then be given a list of photos available for that location. In some cases, ""flight books"" are available at EDC that describe the nature of the mission during which the photos were taken and other attribute information. The customer service personnel have access to these books for those photo sets for which the books exist." proprietary +EARTH_LAND_USGS_DEM_AK1 Digital Terrain Data Sets for Alaska, USGS CEOS_EXTRA STAC Catalog 1982-01-01 -180, 54, -135, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231550167-CEOS_EXTRA.umm_json This data set contains up to nine types of digital elevation data: 1-1 degree blocks, 2-1 degree x 3 degree mosaic of elevation (latitude/longitude coordinate system), 3-1 degree x 3 degree mosaic of slope, 4-1 degree x 3 degree mosaic of aspect (latitude/longitude coordinate system), 5-1 degree x 3 degree mosaic of filtered elevation (5 x 5 filter), 6-1 degree x 3 degree mosaic of elevation (UTM registered), 7-1 degree x 3 degree mosaic of slope (UTM registered), 8-1 degree x 3 degree mosaic of aspect (UTM registered), 9-1 degree x 3 degree mosaic of shaded relief (latitude/longitude coordinate system). Data coverage is from 1982 to present with work ongoing. Data source is 1:250,000 scale Defense Mapping Agency Digital Terrain Series. The data set currently contains 966 records with estimated growth of 5-15 records per year. Storage required varies by selection on area size. Data are available on: 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, or BCD tape. Subsets on the main file and custom formats as well as limited documentation is available. Data is organized by 7 1/2 ' or 15 ' quads. This data is intended to be used for land cover analysis, wildlife refuge studies, drainage analysis, and land use planning. proprietary +EARTH_LAND_USGS_EDC_AK_Landsat Landsat 1-5 dataset from Alaska Field Office's Dbase; USGS, Alaska CEOS_EXTRA STAC Catalog 1972-01-01 170, 52, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231551714-CEOS_EXTRA.umm_json This data set contains raw unregistered Landsat digital data covering most of Alaska. Data obtained from EROS Data Center in Sioux Falls, South Dakota. Data acquired from 1980 and is ongoing. Some Landsat scenes date back to 1972. The data set currently has 585 records with a growth chart at 5-10 records per year. The amount of storage required varies by medium used or full scene or subscene selection; the file structure is sequential. Spatial referencing of data is by 57 x 59 meter grid cell size-MSS data. Data are available on 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, BCD, fixed record length tape. Subsets and custom formats are available. Limited documentation is available. The data is organized in 7 1/2 ' or 15 ' quads. Data is used for false color composites, land cover analysis, geologic analysis, hydrogeologic analysis, land use planning, basis for update of topographic maps, production of image maps. proprietary EARTH_LAND_USGS_Water_PKFIL Annual Peak Discharge and Stage of US Surface Water; USGS CEOS_EXTRA STAC Catalog 1900-01-01 -125, 25, -66, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231549666-CEOS_EXTRA.umm_json Originally available as hard copy publication, the Annual Peak Discharge and Stage of US Surface Water data will be made available to the public via the World Wide Web. The URL of the data set is to be announced. For more information, please contact the U.S. Geological Survey, Water Resources Division. The Peak Flow File (PKFIL) of the U.S. Geological Survey/Water Resources Division contains data on Annual maximum (peak) streamflow (discharge) and gage height (stage) values at surface water sites in the U.S. These data are published annually on a state basis in water resources data reports. proprietary ECA011 Air-Water flux of organochlorine pesticides along the Western Antarctic Peninsula SCIOPS STAC Catalog 2001-10-07 2002-03-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595136-SCIOPS.umm_json Hexachlorobenzene (HCB), heptachlor, α- and γ- HCH and heptachlor epoxide were identified in air, seawater, sea ice, and snow. Samples were collected during the austral winter (September-October 2001) and summer (January-February 2002) along a transect in the Western Antarctic Peninsula. By comparison with previous studie they concluded HCB and HCH levels declined over the past 20 years, with a half-life of 3 28 years in Antarctic air. However, they observed that heptachlor epoxide levels did not decrease in Antarctic air over the past decade, possibly due to continued use of heptachlor in the southern hemisphere. They detected peak heptachlor concentrations in air coincident with air masses moving into the region from lower latitudes. Levels of lindane were 1.2-200 times higher in annual sea ice and snow compared to α HCH, likely due to greater atmospheric input of γ-HCH. On the basis of the ratio of α/γ-HCH <1 in Antarctic air, sea ice and snow they concluded that there is a predominance of influx of lindane versus technical HCH to the regional environment. However, they also observed that the α/γ-HCH in seawater was >1, likely due to more rapid microbial degradation of γ- versus α-HCH. Also this study concluded that the water/air fugacity ratios for HCHs demonstrate continued atmospheric influx of HCHs to coastal Antarctic seas, particularly during late summer proprietary ECA012 Air-Water Gas Exchange of Hexachlorocycloheane Enamtiomers in the South Atlantic Ocean and Antarctica SCIOPS STAC Catalog 1997-11-30 1998-02-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595146-SCIOPS.umm_json The spatial distribution of α-HCH and the net direction of air/water gas exchange were determined between November 1997 and February 1998. Air and water samples were collected between South Atlantic Ocean (South Africa) and Antarctica SANAE Base (70°S, 3°E). The α-HCH concentrations in air and surface water were much lower than in Arctic regions, consistent with the historically lower usage of technical HCH in the Southern Hemisphere. The water/air fugacity ratios of α-HCH were lower than or equal to 1.0, indicating steady state or net deposition conditions. One analysis of the enantiomeric fractionation was also made The results showed that the α-HCH in water was enantioselectively metabolized and that the two isomers [(-)α-HCH and (+)α-HCH] in the air boundary layer reflected those in surface water, showing the bidirectional nature of gas exchange. proprietary @@ -4740,6 +4895,9 @@ ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4_V4r4 ECCO Ocean Temperature and S ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4 ECCO Ocean Temperature and Salinity - Snapshot llc90 Grid (Version 4 Release 4) POCLOUD STAC Catalog 1992-01-01 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1991543757-POCLOUD.umm_json This dataset provides instantaneous ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00. proprietary ECMWF_DATA_ARCHIVE Catalogue of European Center for Medium Range Weather Forecasts (ECMWF) model archived data and products CEOS_EXTRA STAC Catalog 1978-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2230056974-CEOS_EXTRA.umm_json Catalogue of ECMWF model archived data and products This catalogue describes the ECMWF archive according to the daily operational production. It does not reflect the past archive structure in detail. The catalogue is also available in pdf format. - Operational atmospheric model daily - Operational atmospheric model monthly - Operational wave model daily - Operational wave model monthly - Ensemble Prediction System (EPS) atmospheric - Ensemble Prediction System (EPS) wave - Seasonal forecast - Re-analysis (ERA) atmospheric model daily - Re-analysis (ERA) atmospheric model monthly - Re-analysis (ERA) wave model daily - Observational Access the ECMWF Data Catalogue: http://www.ecmwf.int [Summary Extracted from the ECMWF Homepage] proprietary ECMWF_DEMETER Development of a European Multimodel Ensemble system for seasonal to inTERannual prediction (DEMETER) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2230056985-CEOS_EXTRA.umm_json [Excerpt from: Palmer, T.N., A. Alessandri, U. Andersen, P. Cantelaube, M. Davey, P. Délécluse, M. Déqué, E. Díez, F. J. Doblas-Reyes, H. Feddersen, R. Graham, S. Gualdi, J.-F. Guérémy, R. Hagedorn, M. Hoshen, N. Keenlyside, M. Latif, A. Lazar, E. Maisonnave, V. Marletto, A. P. Morse, B. Orfila, P. Rogel, J.-M. Terres and M. C. Thomson, Development of a European Multi-Model Ensemble System for Seasonal to Inter-Annual Prediction (DEMETER), ECMWF Technical Memorandum 434, 2004 ] The DEMETER project (Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction) was conceived, and funded under the European Union Vth Framework Environment Programme. The principal aim of DEMETER was to advance the concept of multi- model ensemble prediction by installing a number of state-of-the-art global coupled ocean-atmosphere models on a single supercomputer, and to produce a series of six-month multi-model ensemble hindcasts with common archiving and common diagnostic software. Such a strategy posed substantial technical problems, as well as more mundane but nevertheless important issues (e.g. on agreeing units in which model variables were archived). proprietary +ECMWF_OPERATIONAL_WAVE European Centre for Medium-Range Weather Forecasts (ECMWF) Operational Wave Data Sets CEOS_EXTRA STAC Catalog 1992-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2230056995-CEOS_EXTRA.umm_json These data sets contain data at the resolution of the data assimilation and forecast system in operational use at ECMWF. Since the resolution and internal representation of the archive may vary according to changes in ECMWF's operational practice, data services associated with these data sets include the provision of interpolation to requested resolutions and representation forms. Four data sets are separately supported: Analysis - Global wave analysis - Mediterranean wave analysis Forecast - Global wave forecast - Mediterranean wave forecast Access the ECMWF Wave Data Sets: http://apps.ecmwf.int/archive-catalogue/?class=od proprietary +ECMWF_OPER_EPS European Centre fro Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) Data Sets CEOS_EXTRA STAC Catalog 1994-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2230056988-CEOS_EXTRA.umm_json These data sets contain data at the resolution of the ensemble prediction forecast system in operational use at ECMWF. Since the resolution and internal representation of the archive may vary according to changes in ECMWF's operational practice, data services associated with these data sets include the provision of interpolation to requested resolutions and representation forms. Five data sets are separately supported: - Control Forecast + Surface ensemble + Pressure level ensemble + Model level ensemble + Wave model ensemble - Perturbed Forecast + Surface ensemble perturbed forecasts + Pressure level ensemble perturbed forecasts + Wave model ensemble [Summary Extracted from the ECMWF home page] proprietary +ECMWF_WCRP_TOGA European Centre for Medium-Range Weather Forecasts (ECMWF)/World Climate Research Program (WCRP) level III-A Global Atmospheric (TOGA) Data Sets CEOS_EXTRA STAC Catalog 1985-01-01 -180, -20, 180, 20 https://cmr.earthdata.nasa.gov/search/concepts/C2230056982-CEOS_EXTRA.umm_json ECMWF created and maintains an archive of level III-A atmospheric data in support of projects associated with the World Climate Research Program (WCRP). This archive is directly interpolated from the ECMWF operational, full resolution, surface and pressure level data. It is accommodated the 10 year period beginning 1 January 1985, fulfilling ECMWF's role as a Tropical Ocean and Global Atmosphere (TOGA) Level III Atmospheric Data Centre. The Level III-A archive is subdivided into three classes of data sets: * Basic Level III * Supplementary Fields * Extension The data sets are based on quantities analyzed or computed within the ECMWF data assimilation scheme or from forecasts based on these analyses. The Basic Data Set contain selected analysed values in a compact form at a resolution of 2.5 degree x 2.5degree. They are particularly suitable for users with limited data processing resources. Derived quantities (fluxes, etc.) are not included, but can in principle be calculated from the data provided in the data sets. The Supplementary Fields Data Set contains additional surface data, fluxes and net radiation data derived from short-range forecasts used as first-guess data for the analyses. Most of the fields in this data set contain values accumulated over the first 6 (or 12) hours of the forecast. The exceptions, total cloud cover fields, contain instantaneous 6 (or 12) hour forecast values. This is a subset of the operational first-guess surface data. The Extension Data Set contains additional surface data, fluxes, net radiation data and precipitation derived from 24-hour forecast values. All the fields in this data set contain values accumulated between time step 12 and time step 36 of the forecast. The archive is currently maintained using the WMO FM 92-IX Ext GRIB (grid in binary) form of data representation, with ECMWF local versions of GRIB Table 2. All fields of data are global within the archive. A full extraction service is supported, enabling users to obtain sub-areas of data and data at various resolutions on regular Gaussian or latitude/longitude grids, or as spherical harmonics with selected triangular truncation. All extracted data are delivered using the GRIB representation. [Summary Extracted from the ECMWF home page] proprietary ECO1BATT_001 ECOSTRESS Attitude Daily L1B Global 70m V001 LPDAAC_ECS STAC Catalog 2018-07-09 -180, -54, 180, 54 https://cmr.earthdata.nasa.gov/search/concepts/C1534582884-LPDAAC_ECS.umm_json The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website. The ECO1BATT Version 1 data product provides both corrected and uncorrected attitude quaternions and spacecraft ephemeris data obtained from the ISS. The data are provided in 1 second intervals by the ISS, and each product file contains vectors from the duration of the orbit. The time elements are copied from the ISS raw data. The ECO1BATT Version 1 data product contains layers of corrected and uncorrected attitude quaternions, spacecraft ephemeris data including Earth-centered inertial (ECI) position and velocity, and associated time elements. proprietary ECO1BGEO_001 ECOSTRESS Geolocation Daily L1B Global 70m V001 LPDAAC_ECS STAC Catalog 2018-07-09 -180, -54, 180, 54 https://cmr.earthdata.nasa.gov/search/concepts/C1534584923-LPDAAC_ECS.umm_json The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website. The ECO1BGEO Version 1 data product provides the geolocation information for the radiance values retrieved in the ECO1BRAD Version 1 data product. The ECO1BGEO data product should be used to georeference the ECO1BRAD, ECO2CLD, ECO2LSTE, ECO3ANCQA, ECO3ETPTJPL, ECO4ESIPTJPL, and ECO4WUE data products. The geolocation processing corrects the ISS-reported ephemeris and attitude data by image matching with a global ortho-base derived from Landsat data, and then assigns latitude and longitude values to each of the Level 1 radiance pixels. When image matching is successful, the data are geolocated to better than 50 meter (m) accuracy. The ECO1BGEO data product is provided as swath data. The ECO1BGEO data product contains data layers for latitude and longitude values, solar and view geometry information, surface height, and the fraction of pixel on land versus water. proprietary ECO1BMAPRAD_001 ECOSTRESS Resampled Radiance Daily L1B Global 70m V001 LPDAAC_ECS STAC Catalog 2018-07-09 -180, -54, 180, 54 https://cmr.earthdata.nasa.gov/search/concepts/C1545228916-LPDAAC_ECS.umm_json The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website. The ECO1BMAPRAD Version 1 data product combines the at-sensor calibrated radiance values retrieved for the ECO1BRAD data product and the geolocation information provided in the ECO1BGEO data product to produce a geotagged, resampled radiance product. The ECO1BMAPRAD data product is produced as a map registered product that is in a rotated geographic projection with a spatial resolution of 70 meters (m). The ECO1BMAPRAD data product accounts for the overlap and variable pixel size in the ECO1BRAD data product. The ECO1BMAPRAD Version 1 data product contains data layers including the radiance values for the five thermal infrared (TIR) bands, digital number (DN) values for the shortwave infrared (SWIR) band, associated data quality indicators, latitude and longitude values, solar and view geometry information, and surface height. proprietary @@ -4881,6 +5039,7 @@ EO:EUM:DAT:SENTINEL-3:SL_2_WST___NTC_2017-07-05 SLSTR Sea Surface Temperatures ( EOLE1_001 Eole 1 Raw Temperature, Pressure and Location Data Near 200 mbar (EOLE1) at GES DISC GES_DISC STAC Catalog 1971-08-27 1972-07-04 -180, -60, 180, -30 https://cmr.earthdata.nasa.gov/search/concepts/C3031691150-GES_DISC.umm_json The Eole 1 Raw Temperature, Pressure and Location Data Near 200 mbar product was obtained from the experimenter and originally consisted of a BCD tape generated on a CDC 6600 computer, subsequently converted to ASCII characters. The data are arranged sequentially by orbit. Data from each orbit are contained in a single record and consist of a heading giving the orbit number, the number of balloons contacted, and a control number. Following the heading, the balloon number, date of observation, location, and ambient temperature and pressure are listed. A maximum of 25 balloon contacts may appear in a single record. Empty records with no balloon contacts have been omitted. These data were obtained from balloons near 200 mbar and are for the region between 30 deg S and 60 deg S. The upper level wind speed and direction can be generated from these data by comparing individual balloon locations obtained from successive orbits. Eole, also known as the Cooperative Application Satellite (CAS-A), was the the second French experimental relay and meteorological satellite and the first launched by NASA under a cooperative agreement with the Centre National d'Etudes Spatiales (CNES). proprietary EOSWEBSTER_CLIMCALC_NE_US A Spatial Model of Atmospheric Deposition For the Northeastern U.S. SCIOPS STAC Catalog 1970-01-01 -77, 38, -66, 48 https://cmr.earthdata.nasa.gov/search/concepts/C1214584276-SCIOPS.umm_json CLIMCALC is a simple model of physical and chemical climate for the northeasten United States (New York and New England) that can be incorporated into a geographic information system (GIS) for integration with ecosystem models presented. The variables include average maximum and minimum daily temperature, precipitation, humidity, and solar radiation, all at a monthly time step, as well as annual wet and dry deposition of sulfur and nitrogen. Regressions on latitude, longitude, and elevation are fitted to regional data bases of these variables The equations are combined with a digital elevation model (DEM) of the region to generate GIS coverages of each variableresults are from a model of atmospheric deposition called CLIMCALC. Spatial patterns of atmospheric deposition across the northeastern United States were evaluated and summarized in a simple model as a function of elevation and geographic position within the region. For wet deposition, 3-11 yr of annual concentration data for the major ions in precipitation were obtained from the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) for 26 sites within the region. Concentration trends were evaluated by regression of annual mean concentrations against latitude and longitude. For nitrate, sulfate, and ammonium concentrations, a more than twofold linear decrease occurs from western New York and Pennsylvania to eastern Maine. These trends were combined with regional and elevational trends or precipitation amount, obtained from 30-yr records of annual precipitation at >300 weather stations, to provide long-term patterns of wet deposition. Regional trends of dry deposition of N and S compounds were determined using 2-3 yrs of particle and gas concentration data collected by the National Dry Deposition Network (NDDN) and several other sources, in combination with estimates of deposition velocities. Contrary to wet deposition trends, the dominant air concentration trends were steep decreases from south to north, creating regional decreases in total deposition (wet + dry) from the southwest to the northeast. This contrast between wet and dry deposition trends suggests that within the northeast the two deposition forms are received in different proportions from different source areas, wet deposited materials primarily from areas to the west and dry deposited materials primarily from urban areas along the southern edge of the region. The equations generated describing spatial patterns of wet and dry depositions within the region were entered into a geographic information system (GIS) containing a digital elevation model (DEM) in order to develop spatially explicit predictions of atmospheric deposition for the region. proprietary EOSWEBSTER_US_County_Data Agricultural, Geographic and Population data for Counties in the Contiguous United States SCIOPS STAC Catalog 1972-01-01 1998-12-31 -124, 26, -66, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1214608658-SCIOPS.umm_json Annual crop data from 1972 to 1998 are now available on EOS-WEBSTER. These data are county-based acreage, production, and yield estimates published by the National Agricultural Statistics Service. We also provide county level livestock, geography, agricultural management, and soil properties derived from datasets from the early 1990s. The National Agricultural Statistics Service (NASS), the statistical arm of the U.S. Department of Agriculture, publishes U.S., state, and county level agricultural statistics for many commodities and data series. In response to our users requests, EOS-WEBSTER now provides 27 years of crop statistics, which can be subset temporally and/or spatially. All data are at the county scale, and are only for the conterminous US (48 states + DC). There are 3111 counties in the database. The list includes 43 cities that are classified as counties: Baltimore City, MD; St. Louis City, MO; and 41 cities in Virginia. In addition, a collection of livestock, geography, agricultural practices, and soil properties variables for 1992 is available through EOS-WEBSTER. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF. The US County data has been divided into seven datasets. US County Data Datasets: 1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties proprietary +EPA0175 National Water Quality Assessment Program (NAWQA) Home Page CEOS_EXTRA STAC Catalog 1991-01-01 -125, 25, -67, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2232411681-CEOS_EXTRA.umm_json "The ""National Water Quality Assessment Program (NAWQA) Home Page"" is an Internet resource that provides information on research dealing with water quality in the United States. This home page provides links to NAWQA activities, selected publications, a bibliography, and summaries of current research projects. The NAWQA program is designed to assess historical, current, and future water-quality conditions in representative river basins and aquifers nationwide. One of the primary objectives of the program is to describe relations between natural factors, human activities, and water quality conditions and to define those factors that most affect water quality in different parts of the Nation. The linkage of water quality to environmental processes is of fundamental importance to water-resource managers, planners, and policy makers. It provides a strong and unbiased basis for better decision making by those responsible for making decisions that affect our water resources, including the United States Congress, Federal, State, and local agencies, environmental groups, and industry. Information from the NAWQA Program also will be useful for guiding research, monitoring, and regulatory activities in cost effective ways. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: National Water Quality Assessment Program Dissemination Media: Online File Format: Size: Memory Requirements: Operating System: Hardware Required: Software Required: Availability Status: On Request Documentation Available:" proprietary EPA_AQA Air Quality Atlas SCIOPS STAC Catalog 1970-01-01 -109.35, 25.19, -88.54, 37.43 https://cmr.earthdata.nasa.gov/search/concepts/C1214621333-SCIOPS.umm_json The Air Quality Atlas is a collection of maps prepared by the Air Quality Analysis Section in the Region 6 office of the U.S. Environmental Protection Agency (EPA). The atlas presents a spatial analysis of air quality in EPA Region 6 for 1996, focusing on the six criteria pollutants for which the EPA has set primary and secondary standards to protect public health and welfare. These standards, defined as the National Ambient Air Quality Standards (NAAQS), have been set for the following six pollutants: lead, nitrogen dioxide, carbon monoxide, sulfur dioxide, ozone, and small particles less than or equal to 10 microns in aerodynamic diameter (PM-10). The primary standards are set to protect public health, and the secondary standards are set to protect public welfare, such as buildings, forests, and agricultural crops. The primary and secondary standards are currently identical for all of the criteria pollutants except sulfur dioxide. The sulfur dioxide secondary standard is based on a three hour averaging time, while the primary standard is based on both 24-hour and annual averaging times. The maps show Region 6 air quality levels referenced against the standards set for the six criteria pollutants. The legend for each map, except for the two exceedance day maps, was constructed to show the following information: (1) The blue shade depicts levels less than 10% of the standard; (2) the green shade depicts levels between 10-50% of the standard; (3) the gray shade depicts levels between 50-90% of the standard; (4) the yellow shade depicts levels within 10% of the standard; and (5) the red shade depicts levels over the standard. Counties not shaded (white) either do not contain monitors, or their monitors did not achieve a data capture rate of at least 75% (exception - all ozone site data were reported). The data used to compose each map were obtained from the EPA's Aerometric Information Retrieval System (AIRS) data base. Analysis of the maps reveals that all Region 6 monitors recorded concentrations below the NAAQS set for lead, nitrogen dioxide, and sulfur dioxide. Indeed, a significant amount of areas in Region 6 recorded maximum concentrations well below these standards. Additional map analysis shows that one Region 6 county (El Paso) contained monitors recording measurements above the carbon monoxide 8-hour standard, that two Region 6 counties (El Paso and Dona Ana) contained monitors recording measurements above the PM-10 standards, and that every state except Arkansas had at least one monitor with values above the ozone standard. Following each map displaying the 1996 Region 6 status of particulate and ozone air quality is a map showing the number of days per county in which a monitor recorded concentrations above the PM-10 or ozone standards. [Summary provided by the EPA.] proprietary EPEA_0 ANTARES monitoring station at the EPEA Station on the Argentina shelf OB_DAAC STAC Catalog 2012-07-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360228-OB_DAAC.umm_json The EPEA (Estación Permanente de Estudios Ambientales) time series station was started in 2000 and since 2003 belongs to ANTARES (www.antares.ws), a network of Latin American time series stations whose main goal is the study of long-term changes in coastal ecosystems to distinguish those due to natural variability from those due to external perurbations (anthropogenic effects).Different research groups at the INIDEP (the National Institute of Fisheries Research and Development of Argentina) sample at the EPEA station, monitoring chemical, environmental and bio-optical variables as well as the bacterioplankton, phytoplankton, zooplankton, and the icthyoplankton communities. EPEA station is located on the Argentine shelf (38°28'S, 57°41'W), 27.0 nautical miles from Mar del Plata city and 13.5 nautical miles from the coast and has a depth of 50m. EPEA is characterized by a temperate regime, with annual sea surface temperatures between 10°C and 21°C and salinity values ranging between 33.5 and 34.1. Occasionally the site can receive less salty waters coming from the North, influenced by the La Plata River, driving salinity values to less than 31.0. Its oceanographic regime is described as the transition between high salinity coastal waters to the medium shelf (Guerrero et al., 1997). proprietary ERBE_S10N_WFOV_NF_Edition2 Earth Radiation Budget Experiment (ERBE) S-10N (Nonscanner-only) Wide Field of View (WFOV) Numerical Filter (NF) Radiant Flux and Albedo Edition 2 in Native Format LARC_ASDC STAC Catalog 1984-11-01 1999-09-30 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000800-LARC_ASDC.umm_json ERBE_S10N_WFOV_NF_Edition2 is the Earth Radiation Budget Experiment (ERBE) S-10N (Nonscanner-only) Wide Field of View (WFOV) Numerical Filter (NF) Radiant Flux and Albedo Edition 2 in Native Format data product. Data collection for this product is complete. The reprocessed ERBE S10N_WFOV ERBS Edition2 data product contains temporally and spatially averaged shortwave (SW) and longwave (LW) top-of-the-atmosphere (TOA) fluxes derived from one month of Earth Radiation Budget Experiment non-scanning wide field-of-view instruments aboard the Earth Radiation Budget Satellite. Instantaneous TOA fluxes from the ERBE/ERBS S7 product were spatially averaged on a 5° and 10° equal-angle grid using numerical filter and shape factor techniques, respectively. ERBE scanner-independent temporal interpolation algorithms were applied to produce daily, monthly-hourly, and monthly mean fluxes from the instantaneous gridded data. The S10N_WFOV files contain both temporally averaged and instantaneous gridded mean values of TOA total-sky LW flux, total-sky SW flux, and total-sky albedo for each 5° and 10° region observed during the month. The major differences between Edition2 and the original release are in the monthly mean fluxes with (1) the incorporation of stochastic quality assurance algorithms for filtering out monthly-mean data with excessive temporal sample errors and (2) a self-consistent usage of the WFOV data in selecting scene-dependent directional models for temporal interpolation of the ERBE WFOV instantaneous gridded data. proprietary @@ -4985,6 +5144,12 @@ Eurobis_2_24 Feb 2004 (Version 2.1) AlgaeBase (EUROBIS) SCIOPS STAC Catalog 1970 Eurobis_505_1 A comparison of benthic biodiversity in the North Sea, English Channel and Celtic Seas (EUROBIS) SCIOPS STAC Catalog 1992-05-12 1996-07-09 -7.99, 48.5, 8.39, 58 https://cmr.earthdata.nasa.gov/search/concepts/C1214586057-SCIOPS.umm_json "Data which produced the publications: Rees, H. L. et al. (1999) and Rees, H. L. et al. (2000). See references below. Size reference: 69 stations sampled, 2735 distribution records [Source: The information provided in the summary was extracted from the MarBEF Data System at ""http://www.marbef.org/data/eurobisproviders.php""]" proprietary Eurobis_618_1 70 samples data of Kiel Bay (EUROBIS) SCIOPS STAC Catalog 1995-05-29 10.3944, 54.3814, 10.3944, 54.3814 https://cmr.earthdata.nasa.gov/search/concepts/C1214586110-SCIOPS.umm_json "Marine Benthic data on benthos at station 014 in Kiel Bay representing 1,144 distribution records of 56 taxa from 1 station in Kiel Bay. [Source: The information provided in the summary was extracted from the MarBEF Data System at ""http://www.marbef.org/data/eurobisproviders.php""]" proprietary Eyes on the Ground Image Data_1 Eyes on the Ground Image Data MLHUB STAC Catalog 2020-01-01 2023-01-01 33.9095879, -2.9922132, 38.7868576, 1.15124 https://cmr.earthdata.nasa.gov/search/concepts/C2781412330-MLHUB.umm_json The 'Eyes on the Ground' project ([lacunafund.org](https://lacunafund.org/ag2020awards/)) is a collaboration between ACRE Africa, the International Food Policy Research Institute (IFPRI), and the Lacuna Fund, to create a large machine learning (ML) dataset of smallholder farmer's fields based upon previous work within the Picture Based Insurance framework (Ceballos, Kramer and Robles, 2019, [https://doi.org/10.1016/j.deveng.2019.100042](https://doi.org/10.1016/j.deveng.2019.100042)). This is a unique dataset of georeferenced crop images along with labels on input use, crop management, phenology, crop damage, and yields, collected across 8 counties in Kenya.The research leading to this dataset was undertaken as part of the CGIAR research program on Policies, Institutions and Markets (PIM) proprietary +FAO_AGL FAO/AGL World River Sediment Yields Database CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2232283741-CEOS_EXTRA.umm_json "Food and Agricultural Organization of the United Nations (FAO)/AGL World River Sediment Yields Database The World River Sediment Yields database contains data on annual sediment yields in worldwide rivers and reservoirs, searchable by river, country and continent. The database was compiled from different sources by HR Wallingford, UK, on behalf of the FAO Land and Water Development Division. It is currently in a test phase. The database allows its user to enter the name of the river, the country, or the continent for which they would like to see summary sedimentation data. From this data, you can discover explanations of the data and complete sedimentation records Data URL: ""http://www.fao.org/ag/AGL/aglw/sediment/default.asp"" Information taken from ""http://www.fao.org/ag/AGL/aglw/sediment/default.asp""" proprietary +FAO_FIGIS FAO Fisheries Global Information System CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232284263-CEOS_EXTRA.umm_json FAO's major program on Fisheries aims to promote sustainable development of responsible fisheries and contribute to food security. To implement this major program, the Fisheries Department focuses its activities, through programs in Fishery Resources, Fishery Policy, Fishery Industries and Fishery Information on three medium-term strategic objectives, including promotion of responsible fisheries sector management at the global, regional and national levels, promotion of increased contribution of responsible fisheries and aquaculture to world food supplies and food security, and global monitoring and strategic analysis of fisheries The FAO Fisheries Global Information System is a global network of integrated fisheries information. FIGIS is a work in progress - sections are currently under development. Valuable information can be accessed on topics such as aquatic species, marine resources, marine fisheries, and fishing technology. Soon you will be able to access databases on trade and marketing, aquaculture, inland fisheries, and fisheries issues. http://www.fao.org/fishery/figis proprietary +FAOd0008_148 FAO World Soil Resources CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232283938-CEOS_EXTRA.umm_json In 1990 a Map of World Soil Resources was completed at scale 1:25.000.000, generalized from the FAO/UNESCO Soil Map or the World at scale 1:5.000.000 (FAO, 1971 - 1981). The map was issued on the occasion of the 14th International Congress of Soil Science held in Kyoto, Japan in 1990. Since then new material has become available, the FAO/UNESCO Soil Map of the World has been partly updated under the SOTER Programme and the FAO legend has been replaced by the World Reference Base for Soil Resources (WRB). In 1998 the latter was adopted by the International Union of Soil Sciences as the standard for soil correlation and nomenclature. In the light of these new developments it was decided to prepare an updated version of the generalized Map of the World Soil Resources at 1:25.000.000. The updating exercise covered: - the switch from the original map projection to a Flat Polar Quartic projection - the conversion of the FAO legend into the WRB classification - the incorporation of additional soil data obtained from new or revised soil map sources - the matching, when possible of soil unit boundaries with major landforms proprietary +FAOd0018_148 Distribution of Major Soil Types CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232283838-CEOS_EXTRA.umm_json "This CD-ROM is released in conjunction with World Soil Resources Reports No. 94: ""Lecture Notes on the Major Soils of the World"". In addition to the complete (hyperlinked) text of the book, it contains many additional pictures, a slideshow with a virtual tour of soils and landscapes and a typical soil profile for each of the thirty reference soil groups of the World Reference Base for Soil Resources. In total more than 550 slides and pictures illustrate the lecture notes. ""http://www.fao.org/icatalog/search/dett.asp?aries_id=102985""" proprietary +FAOd0019_148 Digital Soil Map of the World and Derived Soil Properties. CEOS_EXTRA STAC Catalog 1970-01-01 6.11, 36.15, 19.33, 47.71 https://cmr.earthdata.nasa.gov/search/concepts/C2232283478-CEOS_EXTRA.umm_json "This CD-ROM contains the Digital Soil Map of the World in various formats, verctor as well as raster, supported by most GIS software. The base material is the FAO/UNESCO Soil Map of the World at an original scale of 1:5 million. Programs and data files give tabular country information on soil characteristics and derived soil properties from the map are included, such as pH, organic carbon content and soil moisture storage capability. In addition programs and data files are included that display derived soil properties. The revision included the adding of a number of user-friendly ArcView files allowing the display of dominant soils by continent and the inclusion of the update of the image of the WRB World Soil Resources Map. ""http://www.fao.org/icatalog/search/dett.asp?aries_id=103540""" proprietary +FAOd0020_148 Hydrological Basins of Africa CEOS_EXTRA STAC Catalog 1970-01-01 -17.3, -34.6, 51.1, 38.2 https://cmr.earthdata.nasa.gov/search/concepts/C2232283043-CEOS_EXTRA.umm_json Hydrological Basins of Africa, with major basins and sub basins, automatically derived from USGS topographic data with some manual corrections in flat areas. Current version completed March 2000. proprietary FAUNA_PENGUIN_COLONY_1 A census of penguin colony counts (provided to OBIS) from the year 1900 to 1996 in the Antarctic Region AU_AADC STAC Catalog 1901-01-01 1996-12-31 -180, -80, 180, -45 https://cmr.earthdata.nasa.gov/search/concepts/C1214313468-AU_AADC.umm_json This dataset is a census of penguin colony counts from the year 1900 in the Antarctic region. It forms part of the Inventory of Antarctic seabird breeding sites within the Antarctic and subantarctic islands. The Antarctic and subantarctic fauna database (seabirds) is a database detailing the distribution and abundance of breeding localities for Antarctic and Subantarctic seabirds. Each species' compilation was produced by members of the SCAR Bird Biology Subcommittee. This separate metadata record has been created beacause it represents only the penguin colony counts that have been published to OBIS. Note: The Year (not day or month) date is only relevent in this dataset. The positions that have been published to OBIS include latitude and longitude positions that were not included within the original dataset. The latitude and longitude positions that were not noted by the observer have been created from the locality given by the observer using the Antarctic Composite Gazetteer. Two spreadsheets are available for download, from the URL given below. The original, unmodified spreadsheet is available, as well as a corrected spreadsheet. In the corrected spreadsheet, the AADC has attempted to reconcile the poorly presented localities into a single column. It is possible that some of these localities may not be correct. The fields in this dataset are: SCAR Number Species Region Locality Longitude Latitude Number of Colonies Number of Pairs Type and accuracy of count Data Date References Remarks These data are further referenced in ANARE Research Notes 9 - see reference below. proprietary FEDMAC_AEROSOLS Aerosol Optical Thickness Measurements During the Forest Ecosystem Dynamics - Multisensor Aircraft Campaign SCIOPS STAC Catalog 1990-09-08 1990-09-15 -68, 45, -68, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214600425-SCIOPS.umm_json " Forest Ecosystem Dynamics Multisensor Airborne Campaign (FED MAC): Aerosol Optical Thickness The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem. Measurement of atmospheric attenuation and hence estimate of the aerosol optical thickness were made in the Northern Experimental Forest (NEF) in Howland, Maine, with sunphotometers. This parameter is useful in calibration and correction of other measurements made with remote sensing instruments at FED sites. Measurements were made with the eight channel sun-photometer named SXM-2 (440, 522, 613, 672, 781, 871 and 1030 nm with 10 nm FWHM) located on the ground. It tracks the sun automatically using a 4 quadrant detector. The detector is a silicon photodiode which is kept at a constant temperature. The instrument has a 1.5 degree field-of-view. The FED Home Page is at: ""https://forest.gsfc.nasa.gov/"". " proprietary FEDMAC_ALPS Airborne Laser Polarization Sensor (ALPS) Experiment During the Forest Ecosystem Dynamics - Multisensor Airborne Campaign SCIOPS STAC Catalog 1990-09-09 1990-09-11 -68, 45, -68, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214600409-SCIOPS.umm_json " Forest Ecosystem Dynamics Multisensor Airborne Campaign (FED MAC): Airborne Laser Polarization Experiment The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem. A new remote sensing instrument, the Airborne Laser Polarization Sensor (ALPS), mounted on a helicopter, was used to make multispectral radiometric and polarization measurements of the Earth's surface using a polarized laser light source. The ALPS system consists of a pulsed, polarized laser source, an optical receiver package, a video camera and recorder, and data acquisition and analysis hardware and software. The choice of laser wavelengths is limited to frequencies from the ultraviolet to the near-infrared by the photo-cathode response of the selected photo multiplier tube (PMT) detectors. Twelve PMTs were used corresponding to the 12 channels of data: Channels 1,2,3,4,9 & 10 have 1090 nm bandpass filters. The reminder are for 532 nm. Channels 9 and 11 have no polarization filters. For each wavelength, polarization filters are mounted in front of each PMT at angles relative to the transmitted polarization. A pulsed (7 ns) Nd:YAG laser is employed. It operates in the infrared at 1060 nm and the visible at 532 nm. The 532 nm green wavelength can be seen near the center of the TV screen as it hits the surface in most cases. This is used for ground truth correlation. The spot is about 20 cm in diameter from 100 meters altitude. In these data for ALPS Experiment for the FED MAC 90, the file tabulation refers to data files taken on September 9 and 11. A standard VHS video tape is available (the master tapes are recorded at the SP speed on Super-VHS). The first half of this tape is from a camera coaxial with the laser transmission. Time on the tape correspond to file times while oral comments on the tape supplement the general comments. The second half of the tape consists primarily of site descriptive narration on the ground and some pictures of the helicopter setup. The FED Home Page is at: ""https://forest.gsfc.nasa.gov/"". " proprietary @@ -5283,6 +5448,7 @@ GB-NERC-BAS-AEDC-00423 AFI 01/07_02 - Observations of Antarctic Precipitation pr GB-NERC-BAS-PDC-00499 ACES-FOCAS: Forcings from the Ocean, Clouds, Atmosphere and Sea-ice SCIOPS STAC Catalog 2008-01-01 2008-02-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214599974-SCIOPS.umm_json ACES will investigate the atmospheric and oceanic links that connect the climate of the Antarctic to that of lower latitudes, and their controlling mechanisms. Specific research topics will include the formation and properties of Antarctic clouds, the complexities of the atmospheric boundary layer, and the importance to the global ocean circulation of cold, dense water masses generated in the Antarctic. By quantifying the role of southern polar processes in the global climate system, ACES will help improve predictions of climate change. Our knowledge of the workings of the climate system is far from complete. We know that atmospheric and oceanic processes in the Antarctic and Southern Ocean influence and are influenced by global climate, but we are unsure of important details. Describing and quantifying the role of the southern polar regions in the global climate system is both important and timely. Delivering the Results ACES will carry out a comprehensive programme of oceanographic measurements from BAS ships in the Weddell and Bellingshausen Seas, and will use BAS's instrument-carrying Twin Otter aircraft to help us study cloud microphysics and air-sea-ice interaction. We will obtain an ice core from the southwestern Antarctic Peninsula to give us a 150-year record of the strength of the circumpolar westerly winds. We will use these observations to test and improve global climate models and a new regional atmosphere-ice-ocean model for the Antarctic. ACES will link with CACHE, GRADES, GEACEP, BIOFLAME, DISCOVERY 2010, and SEC. proprietary GB-NERC-BAS-PDC-00500 ACES-ACCENT: Antarctic Climate Change and Nonlinear Teleconnections SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600003-SCIOPS.umm_json ACES will investigate the atmospheric and oceanic links that connect the climate of the Antarctic to that of lower latitudes, and their controlling mechanisms. Specific research topics will include the formation and properties of Antarctic clouds, the complexities of the atmospheric boundary layer, and the importance to the global ocean circulation of cold, dense water masses generated in the Antarctic. By quantifying the role of southern polar processes in the global climate system, ACES will help improve predictions of climate change. Our knowledge of the workings of the climate system is far from complete. We know that atmospheric and oceanic processes in the Antarctic and Southern Ocean influence and are influenced by global climate, but we are unsure of important details. Describing and quantifying the role of the southern polar regions in the global climate system is both important and timely. Delivering the Results ACES will carry out a comprehensive programme of oceanographic measurements from BAS ships in the Weddell and Bellingshausen Seas, and will use BAS's instrument-carrying Twin Otter aircraft to help us study cloud microphysics and air-sea-ice interaction. We will obtain an ice core from the southwestern Antarctic Peninsula to give us a 150-year record of the strength of the circumpolar westerly winds. We will use these observations to test and improve global climate models and a new regional atmosphere-ice-ocean model for the Antarctic. ACES will link with CACHE, GRADES, GEACEP, BIOFLAME, DISCOVERY 2010, and SEC. proprietary GCAM_Land_Cover_2005-2095_1216_1 CMS: Land Cover Projections (5.6-km) from GCAM v3.1 for Conterminous USA, 2005-2095 ORNL_CLOUD STAC Catalog 2005-01-01 2095-12-31 -124.69, 25.25, -67.09, 49.35 https://cmr.earthdata.nasa.gov/search/concepts/C2395504063-ORNL_CLOUD.umm_json The data provided are annual land cover projections for years 2005 through 2095 generated by the Global Change Assessment Model (GCAM) Version 3.1. For the conterminous USA, the GCAM global gridded results were downscaled to ~5.6 km (0.05 degree) resolution. For each 5.6 x 5.6 km area, the annual land cover percentage comprised by each of the nineteen different land cover classes/plant functional types (PFTs) of the Community Land Model (CLM) (Table 1) are provided.Results are reported for GCAM runs of three scenarios of future human efforts towards climate mitigation as related to global carbon emissions, radiative forcing, and land cover change. Specific scenario conditions were 1) a reference scenario with no explicit climate mitigation efforts that reaches a radiative forcing level of over 7 W/m2 in 2100, 2) the 2.6 mitigation pathway (MP) scenario which is a very low emission scenario with a mid-century peak in radiative forcing at ~3 W/m2, declining to 2.6 W/m2 in 2100, and 3) the 4.5 MP scenario which stabilizes radiative forcing at 4.5 W/m2 (~ 650 ppm CO2-equivalent) before 2100.These downscaled land cover projections can be used to derive spatially explicit estimates of potential shifts in croplands, grasslands, shrub lands, and forest lands in each future climate scenario.Data are presented as three NetCDF v4 files (.nc4), one for each future climate scenario -- 2.6 MP, 4.5 MP, and GCAM reference). proprietary +GCIP-GREDS GCIP Reference Data Set (GREDS), U.S. Geological Survey Open-File Report 94-388 CEOS_EXTRA STAC Catalog 1951-01-01 1995-04-01 -125, 24, -66, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2231554529-CEOS_EXTRA.umm_json "The data sets on this compact disc are a compilation of several geographic reference data sets of interest to the global-change research community. The data sets were chosen with input from the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) Data Committee and the GCIP Hydrometeorology and Atmospheric Subpanels. The data sets include: locations and periods of record for stream gages, reservoir gages, and meteorological stations; a 500-meter-resolution digital elevation model; grid-node locations for the Eta numerical weather-prediction model; and digital map data sets of geology, land use, streams, large reservoirs, average annual runoff, average annual precipitation, average annual temperature, average annual heating and cooling degree days, hydrologic units, and state and county boundaries. Also included are digital index maps for LANDSAT scenes, and for the U.S. Geological Survey 1:250,000, 1:100,000, and 1:24,000-scale map series. Most of the data sets cover the conterminous United States; the digital elevation model also includes part of southern Canada. The stream and reservoir gage and meteorological station files cover all states having area within the Mississippi River Basin plus that part of the Mississippi River Basin lying within Canada. Several data-base retrievals were processed by state, therefore many sites outside the Mississippi River Basin are included. See: ""http://nsdi.usgs.gov"" for a complete desciption of metadata and browse images." proprietary GCOM-C_SGLI_L1A_SWI_and_TIR_1km_NA GCOM-C/SGLI L1A Shortwave Infrared Thermal Infrared (1km) JAXA STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128829-JAXA.umm_json GCOM-C/SGLI L1A Shortwave Infrared Thermal Infrared (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1A products are using the data from the satellite as inputs and the following processes are applied on the input data: determination scene range, deletion of duplicated packets and filling of missing data with dummy data, calculation of radiometric correction information, calculation of geometric information and creation of missing packet information and quality information. This product is Observation DN value observed by SGLI-IRS Radiometer (Short Wavelength Infrared (SWI: 1.05 micrometer to 2.21 micrometer, 4 channels) and Thermal Infrared (TIR: 10.8 micrometer, 12.0 micrometer, 2 channels)) are stored for each band as image data. The provided format is HDF5. The spatial resolution is 1 km also 250 m are available. The geometry is not corrected, and the observation position of ground of pixel is varied each band. Therefore, the latitude/longitude information of 10 pixels interval in each band is appended. However, there is no interval in AT direction of IRS. The stored geometric information is the center position of the pixel. The current version of the product is Version 2. proprietary GCOM-C_SGLI_L1A_SWI_and_TIR_250m_NA GCOM-C/SGLI L1A Shortwave Infrared Thermal Infrared (250m) JAXA STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130512-JAXA.umm_json GCOM-C/SGLI L1A Shortwave Infrared Thermal Infrared (250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1A products are using the data from the satellite as inputs and the following processes are applied on the input data: determination scene range, deletion of duplicated packets and filling of missing data with dummy data, calculation of radiometric correction information, calculation of geometric information and creation of missing packet information and quality information. This product is Observation DN value observed by SGLI-IRS Radiometer (Short Wavelength Infrared (SWI: 1.05 micrometer to 2.21 micrometer, 4 channels) and Thermal Infrared (TIR: 10.8 micrometer, 12.0 micrometer, 2 channels)) are stored for each band as image data. The provided format is HDF5. The spatial resolution is 250 m also 1 km is available. The geometry is not corrected, and the observation position of ground of pixel is varied each band. Therefore, the latitude/longitude information of 10 pixels interval in each band is appended. However, there is no interval in AT direction of IRS. The stored geometric information is the center position of the pixel.The current version of the product is Version 2. proprietary GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_NP_1km_NA GCOM-C/SGLI L1A Visible and Near Infrared (Non-Polarization) (1km) JAXA STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128832-JAXA.umm_json GCOM-C/SGLI L1A Visible and Near Infrared (Non-Polarization) (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1A products are using the data from the satellite as inputs and the following processes are applied on the input data: determination scene range, deletion of duplicated packets and filling of missing data with dummy data, calculation of radiometric correction information, calculation of geometric information and creation of missing packet information and quality information. This product is the intensity of reflected light scattered and absorbed by the atmosphere and the earth’s surface using Non Polarized observation observed by Visible and Near Infrared Radiometer. Observation DN value are stored for each band as image data. The provided format is HDF5. The spatial resolution is 1 km. 250 m is also available. The geometry is not corrected, and the observation position of ground of pixel is varied each band. Therefore, the latitude/longitude information of 10 pixels interval in each band is appended. However, there is no interval in AT direction of IRS. The stored geometric information is the center position of the pixel. The current version of the product is Version 2. proprietary @@ -5897,6 +6063,7 @@ GCOM-W_AMSR2_L3_WV_1day_0.1deg_NA GCOM-W/AMSR2 L3 Integrated Water Vapor (1-Day, GCOM-W_AMSR2_L3_WV_1day_0.25deg_NA GCOM-W/AMSR2 L3 Integrated Water Vapor (1-Day,0.25 deg) JAXA STAC Catalog 2012-07-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130448-JAXA.umm_json "GCOM-W/AMSR2 L3 Integrated Water Vapor (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the ""A-Train"" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth’s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Integrated Water Vapor (WV) overwritten by latest data, amount of vertically accumulated water vapor (H2O in gaseous state) in the atmosphere, and defined as amount of water per unit area. Coverage of the product is over the ocean only, and unit is [kg/m2]. Total precipitable water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and cloud liquid water. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global." proprietary GCOM-W_AMSR2_L3_WV_1month_0.1deg_NA GCOM-W/AMSR2 L3 Integrated Water Vapor (1-Month, 0.1 deg) JAXA STAC Catalog 2012-07-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130806-JAXA.umm_json "GCOM-W/AMSR2 L3 Integrated Water Vapor (1-Month, 0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the ""A-Train"" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth’s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes MonthMean Integrated Water Vapor (WV), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). WV is amount of vertically accumulated water vapor (H2O in gaseous state) in the atmosphere, and defined as amount of water per unit area. Coverage of the product is over the ocean only, and unit is [kg/m2]. Total precipitable water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and cloud liquid water. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine ""Geophysical Data"". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global." proprietary GCOM-W_AMSR2_L3_WV_1month_0.25deg_NA GCOM-W/AMSR2 L3 Integrated Water Vapor (1-Month, 0.25 deg) JAXA STAC Catalog 2012-07-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130272-JAXA.umm_json "GCOM-W/AMSR2 L3 Integrated Water Vapor (1-Month, 0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the ""A-Train"" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth’s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Integrated Water Vapor (WV), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). WV is amount of vertically accumulated water vapor (H2O in gaseous state) in the atmosphere, and defined as amount of water per unit area. Coverage of the product is over the ocean only, and unit is [kg/m2]. Total precipitable water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and cloud liquid water. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine ""Geophysical Data"". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global." proprietary +GCRP-DDS-10 Modern Average Global Sea-Surface Temperature, GCRP Subset CEOS_EXTRA STAC Catalog 1981-10-01 1989-12-31 -180, -66, 180, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231550138-CEOS_EXTRA.umm_json "Information about the Modern Average Global Sea-Surface Temperature (USGS data series DDS-10) Data Set archived at the USGS Global Change Research Program is available via FTP: ""ftp://geochange.er.usgs.gov in /pub/magsst"" or via World Wide Web: ""http://pubs.usgs.gov/dds/dds10/magsst.html"" Directions on how to obtain the CD-ROM or access it on-line are made available on the WWW site. The following information about the data set was provided by the data center contact: The purpose of this data set is to provide Paleoclimate researchers with a tool for estimating the average seasonal variation in sea- surface temperature (SST) through out the modern world ocean and for estimating the modern monthly and weekly sea-surface temperature at any given oceanic location. It is expected that these data will be compared with temperature estimates derived from geological proxy measures such as faunal census analyses and stable isotopic analyses. The results can then be used to constrain general circulation models of climate change. The data contained in this data set are derived from the NOAA Advanced Very High Resolution Radiometer Multichannel Sea Surface Temperature data (AVHRR MCSST), which are obtainable from the Distributed Active Archive Center at the Jet Propulsion Laboratory (JPL) in Pasadena, Calif. The JPL tapes contain weekly images of SST from October 1981 through December 1990 in nine regions of the world ocean: North Atlantic, Eastern North Atlantic, South Atlantic, Agulhas, Indian, Southeast Pacific, Southwest Pacific, Northeast Pacific, and Northwest Pacific. This data set represents the results of calculations carried out on the NOAA data and also contains the source code of the programs that made the calculations. The objective was to derive the average sea- surface temperature of each month and week throughout the whole 10-year series, meaning, for example, that data from January of each year would be averaged together. The result is 12 monthly and 52 weekly images for each of the oceanic regions. Averaging the images in this way tends to reduce the number of grid cells that lack valid data and to suppress interannual variability. As ancillary data, the ETOPO5 global gridded elevation and bathymetry data (Edwards, 1989) were interpolated to the resolution of the SST data; the interpolated topographic data are included. The images are provided in three formats: a modified form of run-length encoding (MRLE), Graphics Interchange Format (GIF), and Macintosh PICT format. Also included in the data set are programs that can retrieve seasonal temperature profiles at user-specified locations and that can decompress the data files. These nongraphical SST retrieval programs are provided in versions for UNIX, MS-DOS, and Macintosh computers. Graphical browse utilities are included for users of UNIX with the X Window System, 80386-based PC's, and Macintosh computers. " proprietary GCRW_DEM_2016_1793_1 Digital Elevation Models for the Global Change Research Wetland, Maryland, USA, 2016 ORNL_CLOUD STAC Catalog 2016-06-22 2016-08-15 -76.55, 38.87, -76.54, 38.88 https://cmr.earthdata.nasa.gov/search/concepts/C2408633818-ORNL_CLOUD.umm_json This dataset contains four alternative digital elevation models (DEMs) at 1 m resolution and model performance statistical metrics for the Global Change Research Wetland (GCReW) site on the Rhode River, a tributary of the Chesapeake Bay in Maryland, USA, for the year 2016. Three DEMs were created by using different strategies for correcting positive biases in Light Detection and Ranging (LiDAR)-based DEMs that are common in tidal wetlands. These included (1) applying a single average offset based on a literature review, (2) using the LiDAR Elevation Correction with NDVI (LEAN)-method, and (3) applying plant community-specific offsets using a local vegetation cover map. Existing LiDAR data at 1 m resolution collected in 2011 was the basis for these DEMs. The fourth DEM was created by using Empirical Bayesian Kriging to extrapolate between measured ground points. The elevation is provided in meters relative to the North American Vertical Datum of 1988 (NAVD 88). To calibrate the four approaches, the elevation of the entire marsh complex was surveyed at 20 m x 20 m resolution to document the distribution of elevation relative to tidal datums from a single year. Two Trimble R8 real-time kinematic (RTK) GPS receivers were used to survey 525 points over the complex from July 26, 2016, to August 15, 2016. Relative plant cover was also documented. Tidal datums were calculated from the nearby Annapolis, MD tidal gauge located 13 km from GCReW. proprietary GE01_MSI_L1B_1 GeoEye-1 Level 1B Multispectral 4-Band Satellite Imagery CSDA STAC Catalog 2009-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2471470251-CSDA.umm_json The GeoEye-1 Level 1B Multispectral 4-Band L1B Satellite Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the GeoEye-1 satellite using the GeoEye-1 Imaging System across the global land surface from September 2008 to the present. This satellite imagery is in the visible and near-infrared waveband range with data in the blue, green, red, and near-infrared wavelengths. The imagery has a spatial resolution of 1.84m at nadir (1.65m before summer 2013) and has a temporal resolution of approximately 3 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program. proprietary GE01_Pan_L1B_1 GeoEye-1 Level 1B Panchromatic Satellite Imagery CSDA STAC Catalog 2009-01-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497510652-CSDA.umm_json The GeoEye-1 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the GeoEye-1 satellite using the GeoEye-1 Imaging System across the global land surface from September 2008 to the present. This data product includes panchromatic imagery with a spatial resolution of 0.46m at nadir (0.41m before summer 2013) and a temporal resolution of approximately 3 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program. proprietary @@ -5961,6 +6128,7 @@ GFCC30SR_001 Global Forest Cover Change Surface Reflectance Estimates Multi-Year GFCC30TC_003 Global Forest Cover Change Tree Cover Multi-Year Global 30m V003 LPCLOUD STAC Catalog 2000-01-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266352-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Tree Cover Multi-Year Global dataset is available for four epochs centered on the years 2000, 2005, 2010, and 2015. The dataset is derived from the GFCC Surface Reflectance product (GFCC30SR) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30SR.001), which is based on enhanced Global Land Survey (GLS) datasets. The GLS datasets are composed of high-resolution Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. GFCC30TC provides tree canopy information and can be used to understand forest changes. Each tree cover product features four files associated with it; a tree cover layer with an embedded color map, a tree cover error (uncertainty) file, and an index (provenance) file, plus a list of path/rows that relate to the Surface Reflectance input files. Note that the index file and file list were not generated for the 2015 epoch. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). proprietary GFCC30WC_001 Global Forest Cover Change Water Cover 2000 Global 30m V001 LPCLOUD STAC Catalog 1999-06-29 2003-01-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261619-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Water Cover 2000 Global dataset provides surface-water information at 30 meter spatial resolution. This dataset was derived from waterbodies in the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) and Forest Cover Change (GFCC30FCC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30FCC.001) products based on a classification-tree model. Data are available for selected dates between June 1999 and January 2003. GFCC30WC follows the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). proprietary GFEI_CH4_1 Global Inventory of Methane Emissions from Fuel Exploitation V1 (GFEI_CH4) GES_DISC STAC Catalog 2016-01-01 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2143386827-GES_DISC.umm_json This is a global inventory of methane emissions from fuel exploitation (GFEI) created for the NASA Carbon Monitoring System (CMS). The emission sources represented in this dataset include fugitive emission sources from oil, gas, and coal exploitation following IPCC 2006 definitions and are estimated using bottom-up methods. The inventory emissions are based on individual country reports submitted in accordance with the United Nations Framework Convention on Climate Change (UNFCCC). For those countries that do not report, the emissions are estimated following IPCC 2006 methods. Emissions are allocated to infrastructure locations including mines, wells, pipelines, compressor stations, storage facilities, processing plants, and refineries. The purpose of the inventory is to be used as a prior estimate of fuel exploitation emissions in inverse modeling of atmospheric methane observations. GFEI only includes fugitive methane emissions from oil, gas, and coal exploitation activities and does not include any combustion emissions as defined in IPCC 2006 category 1A. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning. proprietary +GFOI_Boreno_Island GFOI_Borneo_Island CEOS_EXTRA STAC Catalog 1972-01-01 114, 1, 114, 1 https://cmr.earthdata.nasa.gov/search/concepts/C2231555055-CEOS_EXTRA.umm_json The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:foster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC). proprietary GFSAD1KCD_001 Global Food Security Support Analysis Data (GFSAD) Crop Dominance 2010 Global 1 km V001 LPCLOUD STAC Catalog 2007-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261621-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security Support Analysis Data (GFSAD) Crop Dominance Global 1 kilometer (km) dataset was created using multiple input data including: Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation, and Moderate Resolution Imaging Spectrometer (MODIS) remote sensing data; crop type data, secondary elevation data; 50-year precipitation and 20-year temperature data; reference sub-meter to 5 meter resolution ground data; and country statistic data. The GFSAD1KCD data were produced for nominal 2010 by overlaying the five dominant crops of the world produced by Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009) over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute (IWMI; Thenkabail et al., 2009a, 2009b, 2011, Biradar et al., 2009) to ultimately create eight classes of crop dominance. The GFSAD1KCD nominal 2010 product is based on data ranging from years 2007 through 2012. proprietary GFSAD1KCM_001 Global Food Security Support Analysis Data (GFSAD) Crop Mask 2010 Global 1 km V001 LPCLOUD STAC Catalog 2007-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261632-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security Support Analysis Data (GFSAD) Crop Mask Global 1 kilometer (km) dataset was created using multiple input data including: remote sensing such as Landsat, Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation and Moderate Resolution Imaging Spectrometer (MODIS); secondary elevation data; climate 50-year precipitation and 20-year temperature data; reference submeter to 5 meter resolution ground data and country statistics data. The GFSAD1KCM provides spatial distribution of a disaggregated five class global cropland extent map derived for nominal 2010 at 1 km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). The GFSAD1KCM nominal 2010 product is based on data ranging from years 2007 through 2012. proprietary GFSAD30AFCE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Africa 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-06-30 -37, -50.108476, 70.001218, 40.10861 https://cmr.earthdata.nasa.gov/search/concepts/C2763261633-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over the continent of Africa for nominal year 2015 at 30 meter resolution (GFSAD30AFCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AFCE data product uses two pixel-based supervised classifiers, Random Forest (RF) and Support Vector Machine (SVM), and one object-oriented classifier, Recursive Hierarchical Image Segmentation (RHSEG). The classifiers retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30AFCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary @@ -6004,28 +6172,28 @@ GISS-CMIP5_1 GISS ModelE2 contributions to the CMIP5 archive NCCS STAC Catalog 0 GIS_EastAngliaClimateMonthly_551_1 Global Monthly Climatology for the Twentieth Century (New et al.) ORNL_CLOUD STAC Catalog 1900-01-01 1998-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2780535151-ORNL_CLOUD.umm_json A 0.5 degree lat/lon data set of monthly surface climate over global land areas, excluding Antarctica. Primary variables are interpolated directly from station time-series: precipitation, mean temperature and diurnal temperature range. proprietary GLAH01_033 GLAS/ICESat L1A Global Altimetry Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000400-NSIDC_ECS.umm_json Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product. proprietary GLAH01_033 GLAS/ICESat L1A Global Altimetry Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547306-NSIDC_CPRD.umm_json Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product. proprietary -GLAH02_033 GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.umm_json GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product. proprietary GLAH02_033 GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.umm_json GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product. proprietary -GLAH03_033 GLAS/ICESat L1A Global Engineering Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.umm_json Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02. proprietary +GLAH02_033 GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.umm_json GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product. proprietary GLAH03_033 GLAS/ICESat L1A Global Engineering Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991863-NSIDC_ECS.umm_json Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02. proprietary -GLAH04_033 GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing. proprietary +GLAH03_033 GLAS/ICESat L1A Global Engineering Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.umm_json Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02. proprietary GLAH04_033 GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing. proprietary -GLAH05_034 GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product. proprietary +GLAH04_033 GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing. proprietary GLAH05_034 GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549166-NSIDC_CPRD.umm_json GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product. proprietary -GLAH06_034 GLAS/ICESat L1B Global Elevation Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2033638023-NSIDC_CPRD.umm_json GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product. proprietary +GLAH05_034 GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product. proprietary GLAH06_034 GLAS/ICESat L1B Global Elevation Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.umm_json GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product. proprietary -GLAH07_033 GLAS/ICESat L1B Global Backscatter Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product. proprietary +GLAH06_034 GLAS/ICESat L1B Global Elevation Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2033638023-NSIDC_CPRD.umm_json GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product. proprietary GLAH07_033 GLAS/ICESat L1B Global Backscatter Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product. proprietary +GLAH07_033 GLAS/ICESat L1B Global Backscatter Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product. proprietary GLAH08_033 GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.umm_json GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product. proprietary GLAH08_033 GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1631093696-NSIDC_ECS.umm_json GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product. proprietary GLAH09_033 GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.umm_json GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product. proprietary GLAH09_033 GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product. proprietary GLAH10_033 GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-09-25 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.umm_json GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product. proprietary GLAH10_033 GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-09-25 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.umm_json GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product. proprietary -GLAH11_033 GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549738-NSIDC_CPRD.umm_json GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product. proprietary GLAH11_033 GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.umm_json GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product. proprietary -GLAH12_034 GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary +GLAH11_033 GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549738-NSIDC_CPRD.umm_json GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product. proprietary GLAH12_034 GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary +GLAH12_034 GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary GLAH13_034 GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549910-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary GLAH13_034 GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary GLAH14_034 GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153551318-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary @@ -6067,12 +6235,14 @@ GLDTMK_001 G-LiHT Digital Terrain Model KML V001 LPCLOUD STAC Catalog 2011-06-30 GLDTMT_001 G-LiHT Digital Terrain Model V001 LPCLOUD STAC Catalog 2011-06-30 -170, 10, -50, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2763264722-LPCLOUD.umm_json Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission (https://gliht.gsfc.nasa.gov/) utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Digital Terrain Model data product (GLDTMT) is to provide LiDAR-derived bare earth elevation, aspect and slope on the EGM96 Geopotential Model. Scientists at NASA’s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and that the collection will continue to grow as aerial campaigns are flown and processed. GLDTMT data are processed as a raster data product (GeoTIFF) at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the digital terrain with a color map applied in JPEG format. proprietary GLE_Bibliography_1 Bibliography of Ground Level Enhancements - near-Earth cosmic radiation AU_AADC STAC Catalog 2001-01-01 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214308573-AU_AADC.umm_json This bibliography contains references to Ground Level Enhancements (GLE) - rare but powerful radiation storms from the sun. The compilers would appreciate lists of missed and new items for inclusion. These should be sent to the dataofficer at the Australian Antarctic Data Centre at the contact details listed below. The fields in this dataset are: year author title journal proprietary GLE_Database_1 Database of Ground Level Enhancements data - near-Earth cosmic radiation AU_AADC STAC Catalog 1942-01-01 1998-08-25 62.86, -67.61, 62.88, -67.59 https://cmr.earthdata.nasa.gov/search/concepts/C1214308574-AU_AADC.umm_json NOTE: This database has been taken offline, as it was no longer maintained. A copy of the data that was stored in the database is available for download from the provided URL. The database holds all known cosmic ray data from the worldwide network of observatories that observed Ground Level Enhancements. The Cosmic Ray program contributes to our understanding of the radiation environment in space near the Earth. Radiation constantly bombards the Earth from space and is measurable at and below the surface of the Earth. The high energy particles are detected at the Mawson cosmic ray laboratory with large detectors located on the surface and in an underground vault. This is the only system of its type in polar regions and gives a unique view of the radiation effects. Variations in the radiation are constantly monitored. The sun plays a major role in generating the changes. The radiation levels are important to spacecraft and crew and to high altitude aircraft flying on polar routes. There is also some evidence that the radiation may influence climate. proprietary +GLFC_FishHabitatDatabase Fish Habitat Database CEOS_EXTRA STAC Catalog 1970-01-01 -96, 44, -70, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231551991-CEOS_EXTRA.umm_json The Fish Habitat Database is a synthesis of extensive information on habitat requirements and characteristics of selected Great Lakes fish species. Sponsored by the Great Lakes Fishery Commission through its Habitat Advisory Board, the Fish Habitat Database was developed in response to a need for habitat information on Great Lakes fish species. This need was identified in part in the Joint Strategic Plan for Management of the Great Lakes Fisheries. In addition, natural resource managers from environmental agencies indicated a need for fish habitat data in order to develop and implement Fish Management Plans, Lakewide Management Plans, and Remedial Action Plans that recognize fish as an integral component of the Great Lakes ecosystem. The database potentially contains habitat information for 18 selected fish species at five stages of their life and in six bodies of water. This information was obtained primarily from the U. S. Fish and Wildlife Service's Habitat Suitability Index Models and other data where available. This version of the database is not the final product. In fact, information gaps are present in this version. The database is intended to be an ongoing effort which will need maintenance as new needs are identified and new information is discovered. The Great Lakes Fishery Commission is seeking an individual or agency that will be able to take over this role on behalf of other users in the Great Lakes basin. proprietary GLHYANC_001 G-LiHT Hyperspectral Ancillary V001 LPCLOUD STAC Catalog 2011-06-30 -170, 10, -50, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2763264728-LPCLOUD.umm_json Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Hyperspectral Ancillary data product (GLHYANC) is to provide information related to aircraft attitude and altitude, view and solar angles, and other ancillary reflectance and radiance data. GLHYANC data are processed as a zipped raster data product (GeoTIFF) with associated header file (.hdr) at 1 meter spatial resolution over locally defined areas. proprietary GLHYVI_001 G-LiHT Hyperspectral Vegetative Indices V001 LPCLOUD STAC Catalog 2011-06-30 -170, 10, -50, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2763264731-LPCLOUD.umm_json Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Hyperspectral Vegetative Indices data product (GLHYVI) is to provide vegetative, stress, and other index data in 44 science dataset layers. Included in the product are vegetative indices such as Normalized Difference Vegetation Index (NDVI), Triangular Vegetation Index (TVI), Renormalized Difference Vegetation Index (RDVI), Modified Triangular Vegetation Index (MTVI), and Difference Vegetation Index (DVI). Stress indices include, but are not limited to, Carter Stress, Gitelson and Merzlyac Stress, Maccioni Stress, and Vogelmann Stress. GLHYVI data are processed as a raster data product (GeoTIFF) at 1 meter spatial resolution over locally defined areas. A browse image displaying NDVI is also included. proprietary GLLIDARPC_001 G-LiHT Lidar Point Cloud V001 LPCLOUD STAC Catalog 2011-06-30 -170, 10, -50, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2763264735-LPCLOUD.umm_json Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the coterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s LiDAR Point Cloud data product (GLLIDARPC) is to provide high-density individual LiDAR return data, including 3D coordinates, classified ground returns, Above Ground Level (AGL) heights, and LiDAR apparent reflectance. GLLIDARPC data are processed as a LAS Version 1.1 binary format specified by the American Society for Photogrammetry and Remote Sensing (ASPRS). The point cloud includes a density of more than 10 points per square meter. A low resolution browse is also provided showing the LiDAR Point Cloud as an Inverse Data Weighted (IDW) interpolation in PNG format. proprietary GLMETRICS_001 G-LiHT Metrics V001 LPCLOUD STAC Catalog 2011-06-30 -170, 10, -50, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2763264737-LPCLOUD.umm_json Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Metrics data product (GLMETRICS) is to provide extensive lidar height and density metrics and return statistics in more than 80 science data set layers. Included in the product are mean, standard deviation, and percentile information for ground, tree, and shrub data. Some flights also contain Canopy Height Model (CHM) and Digital Terrain Model (DTM) returns. The total number of metrics layers varies by flight or campaign. GLMETRICS data are processed as a raster data product (GeoTIFF) at a 13 meter spatial resolution over locally defined areas. proprietary GLOBEC_0 Global Ocean Ecosystem Dynamics (GLOBEC) OB_DAAC STAC Catalog 1997-10-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360257-OB_DAAC.umm_json Global Ocean Ecosystem Dynamics (GLOBEC) optical measurements. proprietary GLOBEC_059_UK_009 A testbed for zooplankton models of the Irish Sea SCIOPS STAC Catalog 1970-01-01 -100, -60, 20, 67 https://cmr.earthdata.nasa.gov/search/concepts/C1214610556-SCIOPS.umm_json The aim is to establish a numerical model system providing a robust 3-dimensional physical environment within which ecosystem and zooplankton models of different structure and complexity will be compared and assessed. The principle aims are: to provide a hydrodynamic/ecological testbed for development and testing of models of zooplankton dynamics; to formally compare existing models of ecosystem dynamics in the testbed and evaluate performance against archived data; to identify important processes and scales of interaction for Irish Sea zooplankton populations and to determine the optimal complexity of marine hydrodynamic and ecosystem models necessary to describe zooplankton dynamics in the Irish Sea. proprietary +GLOBIO_barents Impact of human activities in the Barents region using the GLOBIO methodology CEOS_EXTRA STAC Catalog 1970-01-01 13.02604, 56.781113, 107.00503, 74.40539 https://cmr.earthdata.nasa.gov/search/concepts/C2232848150-CEOS_EXTRA.umm_json Abstract - Impact of human activities in the Barents region using the GLOBIO methodology. Distance impact from Infrastructure and defined in the GLOBIO report Purpose - To provide policy makers with a tool to help assess the likelihood of environmental impacts in the Barents Region Format - Windows NT Version 5.0 (Build 2195) Service Pack 2; ESRI ArcInfo 8.1.0.415 Raw data are the B1000 dataset from the Barents GIT and National Mapping agency over the Barents region Grid Cell - Row Count 5192 Cell Count 6436 Map Projection - Albers Conical Equal Area proprietary GLORTHO_001 G-LiHT Aerial Orthomosaic V001 LPCLOUD STAC Catalog 2017-02-21 -170, 10, -50, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2763264741-LPCLOUD.umm_json Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Aerial Orthomosaic data product (GLORTHO) is to provide orthorectified high-resolution aerial photography. This data is provided as a supplement to other G-LiHT data products. GLORTHO data are processed as a raster data product (GeoTIFF) at 1 inch spatial resolution over locally defined areas. A low resolution browse is also provided with a color map applied in PNG format. proprietary GLRADS_001 G-LiHT Hyperspectral Radiance V001 LPCLOUD STAC Catalog 2011-06-30 -170, 10, -50, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2763264744-LPCLOUD.umm_json Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the coterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Hyperspectral Radiance data product (GLRADS) is to provide high-resolution radiance data, ranging in wavelength from 418 to 920 nanometers across 114 spectral bands. Radiance data is computed as the ratio between observed upwelling radiance and downwelling hemispheric irradiance, then corrected for differences in cross-track illumination and Bidirectional Reflectance Distribution Function (BRDF) using an empirically derived multiplier. At a nominal flying height of 335 m above ground level (AGL), the at-sensor radiance is a close approximation of surface radiance. GLRADS data are processed as a zipped raster data product (GeoTIFF) with associated header file (.hdr) at 1 meter spatial resolution over locally defined areas. A low-resolution browse is also provided with a color map applied in PNG format. proprietary GLREFL_001 G-LiHT Hyperspectral Reflectance V001 LPCLOUD STAC Catalog 2011-06-30 -170, 10, -50, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2763264745-LPCLOUD.umm_json Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the coterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Hyperspectral Reflectance data product (GLREFL) is to provide high-resolution reflectance data, ranging in wavelength from 418 to 920 nanometers across 114 spectral ranges. Reflectance data is computed as the ratio between observed upwelling radiance and downwelling hemispheric irradiance and corrected for differences in cross-track illumination and Bidirectional Reflectance Distribution Function (BRDF) using an empirically derived multiplier. At a nominal flying height of 335 m above ground level (AGL), the at-sensor reflectance is a close approximation of surface reflectance. GLREFL data are processed as a zipped raster data product (GeoTIFF) with associated header file (.hdr) at 1-meter spatial resolution over locally defined areas. A low-resolution browse is also provided with a color map applied in PNG format. proprietary @@ -6388,12 +6558,14 @@ GRACE_GSM_L2_GRAV_GFZ_RL06_6.0 GRACE FIELD GEOPOTENTIAL COEFFICIENTS GFZ RELEASE GRACE_GSM_L2_GRAV_JPL_RL06_6.0 GRACE FIELD GEOPOTENTIAL COEFFICIENTS JPL RELEASE 6.0 POCLOUD STAC Catalog 2002-04-04 2017-06-30 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2491772133-POCLOUD.umm_json FOR EXPERT USE ONLY. This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model. proprietary GRACE_L1B_GRAV_JPL_RL02_2 GRACE LEVEL 1B JPL RELEASE 2.0 POCLOUD STAC Catalog 2002-04-04 2017-06-30 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2491772134-POCLOUD.umm_json FOR EXPERT USE ONLY. The GRACE Level 1B data provide all necessary inputs to derive monthly time variations in the Earth's gravity field. Level 1B data are also used for GRACE orbit and mean gravity field determination. It contains K-Band Ranging Data Product (KBR1B), Star Camera Data Product (SCA1B), Accelerometer Data Product (ACC1B), GPS Data Product (GPS1B), Vector Products (VGN1B, VGO1B, VGB1B, VCM1B, VKB1B, VSL1B), Quaternion Products (QSA1B, QSB1B), and Housekeeping Products (AHK1B, IHK1B, THR1B, TNK1B, MAG1B, MAS1B, TIM1B) proprietary GRACE_L1B_GRAV_JPL_RL03_3 GRACE LEVEL 1B JPL RELEASE 3.0 POCLOUD STAC Catalog 2002-04-01 2017-07-01 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2491772142-POCLOUD.umm_json FOR EXPERT USE ONLY. The GRACE Level 1B data provide all necessary inputs to derive monthly time variations in the Earth's gravity field. Level 1B data are also used for GRACE orbit and mean gravity field determination. It contains K-Band Ranging Data Product (KBR1B), Star Camera Data Product (SCA1B), Accelerometer Data Product (ACC1B), GPS Data Product (GPS1B), Vector Products (VGN1B, VGO1B, VGB1B, VCM1B, VKB1B, VSL1B), Quaternion Products (QSA1B, QSB1B), and Housekeeping Products (AHK1B, IHK1B, THR1B, TNK1B, MAG1B, MAS1B, TIM1B)The GRACE Level-1B RL03 data consists only of updated spacecraft attitude (SCA1B) and K-band inter-satellite ranging (KBR1B) data. All other Level-1B were not changed and it is recommended to use the RL02 products with the updated RL03 KBR1B and SCA1B products. The RL03 SCA1B data were corrected for a stellar aberration error in the onboard star tracker software and incorrect data weighting in the star tracker combination software. For the RL03 SCA1B data a new software module was developed that uses Kalman filtering, field of view error modeling, relative alignment adjustment and the inclusion of angular spacecraft body acceleration measurements from the ACC instrument. This new processing resulted in a significant reduction in high frequency noise and the elimination of jumps during transitions between dual and single star tracker operation. The KBR1B product is updated as well because the KBR antenna phase center range correction, range rate correction and range acceleration are computed using the spacecraft attitude information (SCA1B). Only these three correction values were updated in the KBR1B product. All other entries in the KBR1B remained the same. proprietary +GRAVCD-npra Gravity Data for the National Petroleum Reserve-Alaska CEOS_EXTRA STAC Catalog 1974-01-01 1980-01-01 -162, 60, -152, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2231548712-CEOS_EXTRA.umm_json This data set is part of a 2 CD-ROM set from NOAA's National Geophysical Data Center entitled Land and Marine Gravity Data - 1999 Edition. The gravity station data (53,520 records) were gathered by various governmental organizations (and academia) using a variety of methods. This data base was received in November 1980. Principal gravity parameters include Free-air Anomalies and Simple Bouguer Anomalies (no terrain correction applied). The observed gravity values are referenced to the International Gravity Standardization Net 1971 (IGSN 71). The gravity anomaly computation uses the Geodetic Reference System 1967 (GRS 67) theoretical gravity formula. The data are randomly distributed within the boundaries of the National Petroleum Reserve-Alaska (NPRA). proprietary GRAVITY_LD_WL_1967_1986_CSV_1 Gravity data collected from the Australian Antarctic Territory and subantarctic between 1967 and 1986 AU_AADC STAC Catalog 1967-01-01 1986-12-31 110, -67, 160, -38 https://cmr.earthdata.nasa.gov/search/concepts/C1214313476-AU_AADC.umm_json Gravity data collected from the Australian Antarctic Territory and subantarctic between 1967 and 1986. Data are mostly from the Casey region. The download file contains a large number of csv files, as well as a number of explanatory documents. proprietary GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06.3_RL06.3 JPL GRACE/GRACE-FO Gridded-AOD1B Water-Equivalent-Thickness Surface-Mass Anomaly RL06.3 dataset for Tellus Level-3 1.0-degree grid POCLOUD STAC Catalog 2002-04-04 -180, -89.5, 180, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C3215150173-POCLOUD.umm_json GRACE non-tidal high-frequency atmospheric and oceanic mass variation models are routinely generated at GFZ as so-called Atmosphere and Ocean De-aliasing Level-1B (AOD1B) products (in terms of corresponding spherical harmonic geopotential coefficients) to be added to the background static gravity model during GRACE monthly gravity field determination. AOD1B products are 3-hourly series of spherical harmonic coefficients up to degree and order 180 which are routinely provided to the GRACE Science Data System and the user community with only a few days time delay. These products reflect spatio-temporal mass variations in the atmosphere and oceans deduced from an operational atmospheric model and corresponding ocean dynamics provided by an ocean model. The variability is derived by subtraction of a long-term mean of vertical integrated atmospheric mass distributions and a corresponding mean of ocean bottom pressure as simulated with the ocean model. For further details, please refer to https://www.gfz-potsdam.de/en/aod1b/. The Gridded AOD1B data sets provided here contain the monthly mean AOD1B data in geolocated gridded form, smoothed or spatially aggregated to be consistent with the GRACE and GRACE-FO Tellus Level-3 data products of land and/or ocean mass anomalies. With these gridded AOD1B Level-3 products, users can remove or add the effects of the modeled mean monthly atmospheric and ocean bottom pressure change (e.g., to compare different models). proprietary GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06_RL06 JPL GRACE/GRACE-FO Gridded-AOD1B Water-Equivalent-Thickness Surface-Mass Anomaly RL06 dataset for Tellus Level-3 1.0-degree grid POCLOUD STAC Catalog 2002-04-04 -180, -89.5, 180, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C2036882154-POCLOUD.umm_json GRACE non-tidal high-frequency atmospheric and oceanic mass variation models are routinely generated at GFZ as so-called Atmosphere and Ocean De-aliasing Level-1B (AOD1B) products (in terms of corresponding spherical harmonic geopotential coefficients) to be added to the background static gravity model during GRACE monthly gravity field determination. AOD1B products are 3-hourly series of spherical harmonic coefficients up to degree and order 180 which are routinely provided to the GRACE Science Data System and the user community with only a few days time delay. These products reflect spatio-temporal mass variations in the atmosphere and oceans deduced from an operational atmospheric model and corresponding ocean dynamics provided by an ocean model. The variability is derived by subtraction of a long-term mean of vertical integrated atmospheric mass distributions and a corresponding mean of ocean bottom pressure as simulated with the ocean model. For further details, please refer to https://www.gfz-potsdam.de/en/aod1b/. The Gridded AOD1B data sets provided here contain the monthly mean AOD1B data in geolocated gridded form, smoothed or spatially aggregated to be consistent with the GRACE and GRACE-FO Tellus Level-3 data products of land and/or ocean mass anomalies. With these gridded AOD1B Level-3 products, users can remove or add the effects of the modeled mean monthly atmospheric and ocean bottom pressure change (e.g., to compare different models). proprietary GRC-GFO_GRIDDED_AOD1B_JPL_MASCON_RL06.3_RL06.3 JPL GRACE/GRACE-FO Gridded-AOD1B Water-Equivalent-Thickness Surface-Mass Anomaly RL06.3 dataset for Tellus Level-3 mascon 0.5-degree grid POCLOUD STAC Catalog 2002-04-04 -180, -89.5, 180, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C3215162709-POCLOUD.umm_json GRACE non-tidal high-frequency atmospheric and oceanic mass variation models are routinely generated at GFZ as so-called Atmosphere and Ocean De-aliasing Level-1B (AOD1B) products (in terms of corresponding spherical harmonic geopotential coefficients) to be added to the background static gravity model during GRACE monthly gravity field determination. AOD1B products are 3-hourly series of spherical harmonic coefficients up to degree and order 180 which are routinely provided to the GRACE Science Data System and the user community with only a few days time delay. These products reflect spatio-temporal mass variations in the atmosphere and oceans deduced from an operational atmospheric model and corresponding ocean dynamics provided by an ocean model. The variability is derived by subtraction of a long-term mean of vertical integrated atmospheric mass distributions and a corresponding mean of ocean bottom pressure as simulated with the ocean model. For further details, please refer to https://www.gfz-potsdam.de/en/aod1b/. The Gridded AOD1B data sets provided here contain the monthly mean AOD1B data in geolocated gridded form, smoothed or spatially aggregated to be consistent with the GRACE and GRACE-FO Tellus Level-3 data products of land and/or ocean mass anomalies. With these gridded AOD1B Level-3 products, users can remove or add the effects of the modeled mean monthly atmospheric and ocean bottom pressure change (e.g., to compare different models). proprietary GRC-GFO_GRIDDED_AOD1B_JPL_MASCON_RL06_RL06 JPL GRACE/GRACE-FO Gridded-AOD1B Water-Equivalent-Thickness Surface-Mass Anomaly RL06 dataset for Tellus Level-3 mascon 0.5-degree grid POCLOUD STAC Catalog 2002-04-04 -180, -89.5, 180, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C2036882163-POCLOUD.umm_json GRACE non-tidal high-frequency atmospheric and oceanic mass variation models are routinely generated at GFZ as so-called Atmosphere and Ocean De-aliasing Level-1B (AOD1B) products (in terms of corresponding spherical harmonic geopotential coefficients) to be added to the background static gravity model during GRACE monthly gravity field determination. AOD1B products are 3-hourly series of spherical harmonic coefficients up to degree and order 180 which are routinely provided to the GRACE Science Data System and the user community with only a few days time delay. These products reflect spatio-temporal mass variations in the atmosphere and oceans deduced from an operational atmospheric model and corresponding ocean dynamics provided by an ocean model. The variability is derived by subtraction of a long-term mean of vertical integrated atmospheric mass distributions and a corresponding mean of ocean bottom pressure as simulated with the ocean model. For further details, please refer to https://www.gfz-potsdam.de/en/aod1b/. The Gridded AOD1B data sets provided here contain the monthly mean AOD1B data in geolocated gridded form, smoothed or spatially aggregated to be consistent with the GRACE and GRACE-FO Tellus Level-3 data products of land and/or ocean mass anomalies. With these gridded AOD1B Level-3 products, users can remove or add the effects of the modeled mean monthly atmospheric and ocean bottom pressure change (e.g., to compare different models). proprietary GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04 Tellus Level-4 Greenland Mass Anomaly Time Series from JPL GRACE/GRACE-FO Mascon CRI Filtered Release 06.3 version 04 POCLOUD STAC Catalog 2002-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3206299308-POCLOUD.umm_json This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL06.3Mv04 dataset, which can be found at https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V3. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability is provided as an ASCII table. proprietary +GRID-INPE GRID-INPE; UNEP Global Resource Information Database - INPE Cooperating Center CEOS_EXTRA STAC Catalog 1993-04-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232849388-CEOS_EXTRA.umm_json This is a collection of data-sets held by GRID-INPE. Please contact the technical contact for further details and data-set breakdown. GRID-INPE is a cooperating center to UNEP's Global Resource Information Database. Grid is a system of cooperating centers within the United Nations Environmental Programme that is dedicated to making environmental information more readily accessible to environmental analysis as well as to international and national decision makers. Its mission is to provide timely and reliable geo-referenced environmental information. Besides acquiring and disseminating integrated, spatially-referenced environmental data, GRID provides decision-support services to environmental analysts and international and national decision makers, and fosters the use of geographic information systems (GIS) and satellite image processing (IP) as tools for environmental analysis. proprietary GSI_ABSOLUT_GRAVITY_ANT Absolute gravity measurement SCIOPS STAC Catalog 1992-01-01 39.5, -69, 39.5, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214590222-SCIOPS.umm_json The IAGBN aims to distribute gravity points worldwide and construct a network on which gravity observation is based. There are two kinds of points: A is a point set up in regions with stable crustal structure, and B is a point set up in regions where crustal activity is expected. Syowa Station in Antarctica was among the 36 A points. McMurdo Station of the U.S. is the only point in Antarctica other than Syowa Station that is classified as A. Introduced GSI in 1980, the upcast-type absolute gravity meter (GA60) generally called the Sakuma type, was used in this survey. The 36th JARE (1994) conducted observation using FG5 that the GSI introduced in 1992. Because FG5 measures gravity in a free-fall system, it is characterized by the ability to conduct automatic continuous measurement and allow for many measurements. proprietary GSI_JARE_TOPOMAPS 1:50,000 Topographic maps from Japan Antarctic Research Expedition (JARE) SCIOPS STAC Catalog 1989-04-01 23, -73, 28, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214610482-SCIOPS.umm_json The data set consists of 1:50,000 topographic maps which cover most areas of the Sor-Rondane Mountains, with 21 sheets. The contour interval is 20 m. All maps have been digitalized into raster data and are available in TIFF format. proprietary GSJ-DAM Aeromagnetic Reconnaissance Survey Data SCIOPS STAC Catalog 1964-01-01 123, 24, 145, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214608183-SCIOPS.umm_json The Geological Survey of Japan has carried out developments on the exploration and analysis techniques in aeromagnetic survey since 1964, when the research on aeromagnetic exploration was begun on full scale. And since 1969, explorations for various purposes as well as investigations for assessing the deposit of hydrocarbon resources in the continental shelf area surrounding Japan have been carried out. The results were already published as the Aerial Aeromagnetic Map series, and the data were stored in magnetic media in the form of file groups with unified formats. proprietary @@ -6634,6 +6806,7 @@ HRIRN3IM_001 HRIR/Nimbus-3 Images of Daytime and Nighttime Brightness Temperatur HRIRN3L1_001 HRIR/Nimbus-3 Level 1 Meteorological Radiation Data V001 (HRIRN3L1) at GES DISC GES_DISC STAC Catalog 1969-04-17 1970-03-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1273652190-GES_DISC.umm_json "HRIRN3L1 is the High Resolution Infrared Radiometer (HRIR) Nimbus-3 Level 1 Meteorological Radiance Data (NMRT) product and contains infrared radiances converted to equivalent black-body temperature or ""brightness"" temperature values. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. The HRIR flown on Nimbus-3 was modified to allow nighttime and daytime cloud cover mapping by use of dual band-pass filter which transmits 0.7 to 1.3 micron, and 3.4 to 4.2 micron radiation. The HRIR instrument was launched on the Nimbus-3 satellite and was operational from April 14, 1966 through July 22, 1969. Nighttime operation was made in the 3.4 to 4.2 micron near infrared region. Daytime operation was based on the predominance of reflected solar energy in the 0.7 to 1.3 micron region. Change-over from nighttime to daytime operation was accomplished automatically (or by ground station command), by actuating a relay in the early stages of the radiometer electronics. The system gain was reduced in the daytime mode to compensate for the higher energy levels. This product was previously available from the NSSDC with the identifier ESAD-00222 (old ID 69-037A-02C)." proprietary HRO USGS High Resolution Orthoimagery USGS_LTA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567548-USGS_LTA.umm_json High resolution orthorectified images combine the image characteristics of an aerial photograph with the geometric qualities of a map. An orthoimage is a uniform-scale image where corrections have been made for feature displacement such as building tilt and for scale variations caused by terrain relief, sensor geometry, and camera tilt. A mathematical equation based on ground control points, sensor calibration information, and a digital elevation model is applied to each pixel to rectify the image to obtain the geometric qualities of a map. A digital orthoimage may be created from several photographs mosaicked to form the final image. The source imagery may be black-and-white, natural color, or color infrared with a pixel resolution of 1-meter or finer. With orthoimagery, the resolution refers to the distance on the ground represented by each pixel. proprietary HRTS-II_ATLAS Active Region UV Atlas SCIOPS STAC Catalog 1978-02-13 1978-02-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214584432-SCIOPS.umm_json An ultraviolet spectral Atlas of a sunspot with high spectral and spatial resolution in the wavelength region 1190 - 1730 A is presented. The sunspot was observed with the High Resolution Telescope and Spectrograph (HRTS). The HRTS instrument was built at the U.S. Naval Research Laboratory (NRL), Washington, D.C. (Bartoe and Brueckner, 1975). The instrument combines high spatial, spectral, and time resolution with an extensive wavelength and angular coverage. This makes HRTS particularly well suited for studies of fine structure and mass flows in the upper solar atmosphere. HRTS has flown six times on rockets between 1975 and 1989 and as a part of Spacelab 2 in 1985. The spectrograms used for the Atlas are from the second HRTS rocket flight, known as HRTS II, flown on 13 February 1978 aboard a Black Brant VC rocket (NASA Flight 21.042) at White Sands, New Mexico. During the rocket flight the slit was oriented radially from the solar disc center through the active region McMath 15139, including a sunspot, and across the solar limb. The Solar Pointing Aerobee Rocket Control System (SPARCS) kept the spectrograph slit positioned on the solar surface during the observing time of 4.2 minutes. The spatial resolution on this flight was 2 arcsec with a time resolution from 0.2 - 20.2 sec. The HRTS spectra were recorded on Eastman Kodak 101-01 photographic film. Microphotometry of the spectrograms has been carried out at the Institute of Theoretical Astrophysics in Oslo. The data reduction includes correcting the spectral images for geometrical distortion, Fourier filtering to remove high frequency noise, transformation to absolute calibrated solar intensity and calibration of the wavelength scale. The absolute intensity calibration was obtained by comparing relative intensity scans of a quiet solar region with absolute intensities from the Skylab S082B calibration rocket, CALROC The resulting absolute intensities are accurate to within 30% (rms). The wavelength scale was established using solar lines from neutral and singly ionized atoms as reference lines. From this wavelength scale velocities accurate to 2 km/s can be measured over the entire wavelength range. The measured velocities are, however, relative to the average velocity in the chromosphere where the reference lines are formed. The Atlas contains spectra of three different areas in the sunspot and also of an active region and a quiet region. The selected areas are averaged over several arcsec, ranging from 3.5 arcsec in the sunspot to 18 arcsec in the quiet region. The transition region lines in the Atlas show the most extreme example known of downflowing gas above a sunspot, a phenomenon which seems to be commonly occurring in sunspots. One of the selected areas in the sunspot is a light bridge crossing the spot. This is the most interesting sunspot region where the continuum radiation is enhanced and measurable throughout the HRTS spectral range. A number of lines appear which do not occur in the regular sunspot spectrum. The Atlas is available in a machine readable form together with an IDL program to interactively measure linewidths, total intensities and solar wavelengths. See: http://zeus.nascom.nasa.gov/~pbrekke/HRTS/ proprietary +HUC250k Hydrologic Units Maps of the Conterminous United States for the EPA Clean Air Mapping and Analysis Program (C-MAP) CEOS_EXTRA STAC Catalog 1970-01-01 -127.91, 22.87, -65.32, 48.29 https://cmr.earthdata.nasa.gov/search/concepts/C2231552976-CEOS_EXTRA.umm_json The Geographic Information Retrieval and Analysis System (GIRAS) was developed in the mid 70s to put into digital form a number of data layers which were of interest to the USGS. One of these data layers was the Hydrologic Units. The map is based on the Hydrologic Unit Maps published by the U.S. Geological Survey Office of Water Data Coordination, together with the list descriptions and name of region, subregion, accounting units, and cataloging unit. The hydrologic units are encoded with an eight- digit number that indicates the hydrologic region (first two digits), hydrologic subregion (second two digits), accounting unit (third two digits), and cataloging unit (fourth two digits). The data produced by GIRAS was originally collected at a scale of 1: 250K. Some areas, notably major cities in the west, were recompiled at a scale of 1: 100K. In order to join the data together and use the data in a geographic information system (GIS) the data were processed in the ARC/INFO GUS software package. Within the GIS, the data were edge matched and the neatline boundaries between maps were removed to create a single data set for the conterminous United States. This data set was compiled originally to provide the National Water Quality Assessment (NAWQA) study units with an intermediate- scale river basin boundary for extracting other GIS data layers. The data can also be used for illustration purposes at intermediate or small scales (1:250,000 to 1:2 million). [Summary provided by EPA] proprietary HWSD_1247_1 Regridded Harmonized World Soil Database v1.2 ORNL_CLOUD STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216863856-ORNL_CLOUD.umm_json This data set describes select global soil parameters from the Harmonized World Soil Database (HWSD) v1.2, including additional calculated parameters such as area weighted soil organic carbon (kg C per m2), as high resolution NetCDF files. These data were regridded and upscaled from the Harmonized World Soil Database v1.2 The HWSD provides information for addressing emerging problems of land competition for food production, bio-energy demand and threats to biodiversity and can be used as input to model global carbon cycles. The data are presented as a series of 27 NetCDF v3/v4 (*.nc4) files at 0.05-degree spatial resolution, and one NetCDF file regridded to the Community Land Model (CLM) grid cell resolution (0.9 degree x 1.25 degree) for the nominal year of 2000. proprietary HYCODE_LEO-15_0 Hyperspectral Coastal Ocean Dynamics Experiment (HyCoDE) measurements at Long-term Ecosystem Observatory 15 (LEO-15) oceanographic and meteorological station OB_DAAC STAC Catalog 2000-07-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360372-OB_DAAC.umm_json Measurements from the Hyperspectral Coastal Ocean Dynamics Experiment (HyCoDE) LEO-15 station off the Atlantic Coast of New Jersey. proprietary Happy_Valley_Veg_Plots_1354_1 Arctic Vegetation Plots at Happy Valley, Alaska, 1994 ORNL_CLOUD STAC Catalog 1994-07-18 1994-07-31 -148.87, 69.12, -148.82, 69.17 https://cmr.earthdata.nasa.gov/search/concepts/C2170968750-ORNL_CLOUD.umm_json This dataset provides environmental, soil, and vegetation data collected in July 1994 from 56 study plots at the Happy Valley research site, located along the Sagavanirktok River in a glaciated valley of the northern Arctic Foothills of the Brooks Range. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in 17 plant communities that occur in 5 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools, soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geo-botanical factors in the Happy Valley region and across Alaska. proprietary @@ -6678,7 +6851,19 @@ ICE_RADAR_DATA_AMERY_1 Ice Radar Data collected 2002 Amery Ice Shelf AU_AADC STA ICE_RADAR_DATA_GILLOCK_1 Ice Radar Data collected 2002 Gillock Island AU_AADC STAC Catalog 2002-10-01 2002-11-01 68.18703, -70.7908, 77.95972, -68.57 https://cmr.earthdata.nasa.gov/search/concepts/C1214313564-AU_AADC.umm_json This data contains ASCII lat/long records extracted from the binary data. The binary data are ice radar soundings at 150 MHz from Aircraft flown at about 100 knots. This covers the area around Gillock Island to look at the grounding zone between the ice shelf and Gillock Island. The Radar unit was built by the Science and Technical Support group of the Australian Antarctic Division. This data are part of the Australian Antarctica and Southern Ocean Profiling Project (AASOPP) for continental mapping of the Australian continent (Geoscience Australia). See also the other metadata record for ice radar data. The files in this dataset are: ASCII lat/long records: Record Time (UTC) Latitude Longitude proprietary ICE_RADAR_DATA_PRIORITY_1 Ice Radar Data collected 2002 - Priority Flights AU_AADC STAC Catalog 2002-10-01 2002-11-01 68.18703, -70.7908, 77.95972, -68.57 https://cmr.earthdata.nasa.gov/search/concepts/C1214313565-AU_AADC.umm_json The data contains ASCII lat/long records extracted from the binary data. The binary data are ice radar soundings at 150 MHz from Aircraft flown at about 100 knots. This covers the priority flights in the Amery Ice Shelf area to look at the grounding zone between the ice shelf and Gillock Island. The Radar unit was built by the Science and Technical Support group of the Australian Antarctic Division. This data are part of the Australian Antarctica and Southern Ocean Profiling Project (AASOPP) for continental mapping of the Australian continent. See also the other metadata record for ice radar data. The files in this dataset are: ASCII lat/long records: Record Time (UTC) Latitude Longitude proprietary ICIMOD_KATHMANDU Administrative Boundaries and Demography of Kathmandu Valley, Nepal SCIOPS STAC Catalog 1991-01-01 1997-09-30 85, 27, 86, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214155319-SCIOPS.umm_json Digital data of Administrative Boundaries of Kathmandu Valley: - Districts and Village Development Committee from 1997 map. - Demographic data from 1991 census proprietary +ICId0001_202 Jhikku Khola Database CEOS_EXTRA STAC Catalog 1972-12-18 1998-12-15 85, 27, 85, 27 https://cmr.earthdata.nasa.gov/search/concepts/C2232847784-CEOS_EXTRA.umm_json Various GIS datasets on Jhikhu Khola Watershed (see members for details) Members informations: Attached Vector(s): MemberID: 1 Vector Name: Soil map of Arunachal Pradesh Source Map Name: Soil association map of Arunachal Pradesh Source Map Scale: 250000 Source Map Date: ? Projection: polyconic Projection_desc: orig: 87 / 0, sc.: 0.9999, Ell: Everest1830/Nepal Projection_meas: meters Feature_type: polygons Legend_file: lugen.avl Vector The soil resource inventory was carried out following a three tier approach viz. image interpretation, soil survey and chemical analysis and GIS application for thematic mapping and interpretation of database for developing a land use plan. The soil association maps on 1:250,000 scale were digitised toposheetwise (14 toposheets) using polyconic projection to bring out the state soil map. Various thematic maps were generated using 'reclassification' techniques and area calculation was carried out using 'map analysis' tools. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Soil map of Himachal Pradesh Projection_desc: orig: 87 / 0, sc.: 0.9999, Ell: Everest1830/Nepal Feature_type: polygon Members informations: Attached Vector(s): MemberID: 3 Vector Name: Soil map of Jammu&Kashmir Projection: transverse mercator Projection_desc: orig: 87 / 0, sc.: 0.9999, Ell: Everest1830/Nepal Projection_meas: meters Feature_type: polygon Legend_file: lugen.avl Members informations: Attached Vector(s): MemberID: 4 Vector Name: Soil map of Uttar Pradesh Feature_type: polygon Vector Attached Image(s): Member ID: 5 Image Name: Orthophoto mosaic Image Projection: Nepal zone87 Image Source name: camera Image Resolution: 1m Image Number of Rows: 12001 Image Number of Columns: 15201 Image Number of Bits: 8 Image Mosaic of digital orthophotos, 1m resolution, The orthophotos have been prepared from 1996 aerial photographs 1:20000, scanned at 600dpi, using GPS control points and the DEM Accuracy: 10-20m horizontal RMS; maximum errors ca. 50 (absolute) resp. 100m (relative vs the drainage) Members informations: Attached Vector(s): MemberID: 6 Vector Name: Land systems Source Map Name: Land systems map Source Map Scale: 20000 Source Map Date: 1905-06-12 Projection: Nepal 87 Feature_type: polygon Vector Land system classification, soil types Members informations: Attached Vector(s): MemberID: 7 Vector Name: Roads Source Map Name: GPS Source Map Scale: - Source Map Date: 1998/2000 Projection: Nepal 87 Feature_type: lines Vector Road network surveyed by differential GPS Attached Raster(s): Member_ID: 8 Raster Name: Land Capability Evaluation Raster Name: Land systems, DEM Raster Scale: 20000 Raster Date: 1905-06-12 Raster Projection: Nepal 87 Raster Resolution: 20 Number of Rows: 651 Number of Columns: 801 Number of Bits: 8 Raster Land capability evaluation according to refined LRMP-method Members informations: Attached Vector(s): MemberID: 9 Vector Name: Drainage Source Map Name: Jhikhu Khola Base Map Source Map Scale: 20000 Source Map Date: 1905-06-12 Projection: Nepal87 Feature_type: lines Vector Drainage Network; contains some substantial geometric distortions mainly in the upper parts Members informations: Attached Vector(s): MemberID: 10 Vector Name: Contours Source Map Name: Jhikhu Khola Base map Source Map Scale: 20000 Source Map Date: 1905-06-12 Projection: Nepal 87 Projection_meas: meters Feature_type: lines Vector Contours (25m interval), contains some substantial geometric distortions mainly in the upper parts Members informations: Attached Vector(s): MemberID: 11 Vector Name: VDC boundaries Source Map Name: Jhikhu Khola Base Map Source Map Scale: 20000 Source Map Date: 1905-06-12 Projection: Nepal 87 Feature_type: polygon Vector VDC (Village Development Committee) boundaries Members informations: Attached Vector(s): MemberID: 12 Vector Name: settlements Source Map Name: Jhikhu Khola Basemap Source Map Scale: 20000 Source Map Date: 1905-06-12 Projection: Nepal 87 Feature_type: point Vector Location and names of settlements proprietary +ICId0005_202 Kathmandu Valley GIS database CEOS_EXTRA STAC Catalog 1970-01-01 85, 27, 85, 27 https://cmr.earthdata.nasa.gov/search/concepts/C2232848526-CEOS_EXTRA.umm_json In the recent past, there has been continuing growth in using GIS and related technologies by many organizations engaged in planning and management of the Kathmandu Valley. As a result, the demand for accurate and homogenous spatial data of the Valley has been realized by government as well as research and development organizations. This study attempts to build a comprehensive GIS Database of the Kathmandu Valley with an aim to bridge the important data gaps in the Valley. The study employs a fresh approach in constructing a GIS database with the available maps and integrates many different kinds of satellite imageries. The maps presented in this publication visualize the different scenarios and raise the awareness of exiting digital database. The application presented in this publication shall increase awareness about the usefulness of digital database and demonstrate what can be achieved with the GIS and related technologies. The database thus developed shall improve the availability of information of the Kathmandu Valley and assist different stakeholders engaged in planning and management of the Valley. Furthermore, the study advocates a building block approach to development, management and revision of database in a complementary way and it hopes to avoid duplication of efforts in costly production of digital data. The study hopes to sensitise senior executives and decision-makers about the need for a sound policy on database sharing, development and standards. Such a policy, at the national level known as National Spatial Database Infrastructure (NSDI) should evolve in order to benefit from the prevailing GIS technology. In using GIS and related technologies, the study facilitated the establishment of Spatial Data Infrastructure of the Kathmandu Valley in a concrete manner. Members informations: Attached Vector(s): MemberID: 1 Vector Name: Contours Source Map Name: topo sheets Source Map Scale: 25000 Source Map Date: 1905-06-17 Projection: transverse mercator Projection_desc: origin 87E/ 0N, false easting=900000, scale=0.9999 Projection_meas: Meter Feature_type: lines Vector Contours digitized from topo sheets Members informations: Attached Vector(s): MemberID: 2 Vector Name: Roads Source Map Name: topo sheet Source Map Scale: 25000 Source Map Date: 1905-06-17 Projection: see member1 Feature_type: lines Vector Road Network Members informations: Attached Vector(s): MemberID: 3 Vector Name: Drainage Source Map Name: topo sheets Source Map Scale: 25000 Source Map Date: 1905-06-17 Projection: see member 1 Feature_type: lines Vector Drainage Network Members informations: Attached Vector(s): MemberID: 4 Vector Name: Land use 78 Source Map Name: LRMP Source Map Scale: 50000 Source Map Date: 1905-05-31 Feature_type: polygon Vector Land use Members informations: Attached Vector(s): MemberID: 5 Vector Name: Land use 1995 Source Map Name: topo sheet Source Map Scale: 25000 Source Map Date: 1905-06-17 Feature_type: polygon Vector Land cover Members informations: Attached Vector(s): MemberID: 6 Vector Name: Administrative boundaries Source Map Name: topo sheet Source Map Scale: 25000 Source Map Date: 1905-06-17 Feature_type: polygon Vector District and VDC boundaries and various socio-economic data Attached Report(s) Member ID: 7 Report Name: Kathmandu Valley GIS database Report Authors: B. Shrestha & S. Pradhan Report Publisher: ICIMOD Report Date: 2000-02-01 Report Report proprietary +ICId0012_202 Districts of Nepal - Indicators of Development CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232846594-CEOS_EXTRA.umm_json Atlas of district-based indicators on poverty and deprivation, socio-economic development, women's empowerment, and Natural resource endowment proprietary ICId0013_202 Climatic and Hydrological Atlas of Nepal CEOS_EXTRA STAC Catalog 1970-01-01 79.9, 26, 88.84, 30.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232847495-CEOS_EXTRA.umm_json Monthly averages of Temperature, Precipitation, Humidity, Sunshine etc. have been interpolated spatially from Meteo station data. Also contains some hydrographic charts and data. proprietary +ICId0015_202 Inventory of Glaciers and Glacial Lakes CEOS_EXTRA STAC Catalog 1970-01-01 79.9, 26, 92.37, 30.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232848528-CEOS_EXTRA.umm_json Inventory of glaciers and glacial lakes from aerial photographs, topo sheets of different years, and satellite images has been prepared. Potentially dangerous lakes (GLOF: Glacial Lake Outburst Floods) will be identified based on air phoitographs and field work. proprietary +ICId0016_202 IRS 1D LISS3 109-50 of 12 July 1998 CEOS_EXTRA STAC Catalog 1970-01-01 70.83, 15.06, 137.97, 56.58 https://cmr.earthdata.nasa.gov/search/concepts/C2232848991-CEOS_EXTRA.umm_json IRS 1D LISS3 109-50 of 12 July 1998 Satellite image proprietary +ICId0017_202 Duiling Deqing County GIS CEOS_EXTRA STAC Catalog 1970-01-01 70.83, 15.06, 137.97, 56.58 https://cmr.earthdata.nasa.gov/search/concepts/C2232849349-CEOS_EXTRA.umm_json Various datasets on land use and population proprietary +ICId0018_202 IRS WiFS data of HinduKush-Himalayan Region CEOS_EXTRA STAC Catalog 1996-09-30 1999-02-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232847560-CEOS_EXTRA.umm_json IRS WiFS coverage of most of the HKH region Attached Image(s): Member ID: 1 Image Name: 86-42 of 30 Sep 96 Image Resolution: 188 Image Number of Rows: 4489 Image Number of Columns: 4904 Image Number of Bits: 8 Image Satellite Image Attached Image(s): Member ID: 2 Image Name: 086-047_961024 Image Resolution: 188 Image Number of Rows: 4492 Image Number of Columns: 4918 Image Number of Bits: 8 Image sat. image Attached Image(s): Member ID: 3 Image Name: 086-052_961024 Image Resolution: 188 Image Number of Rows: 4494 Image Number of Columns: 4929 Image Number of Bits: 8 Image sat. image Attached Image(s): Member ID: 4 Image Name: 092-042_970122 Image Resolution: 188 Image Number of Rows: 4351 Image Number of Columns: 4892 Image Number of Bits: 8 Image sat. image Attached Image(s): Member ID: 5 Image Name: 092-047_961030 Image Resolution: 188 Image Number of Rows: 4492 Image Number of Columns: 4917 Image Number of Bits: 8 Image satellite image Attached Image(s): Member ID: 6 Image Name: 092-052_961205 Image Resolution: 188 Image Number of Rows: 4358 Image Number of Columns: 4899 Image Number of Bits: 8 Image sat. image Attached Image(s): Member ID: 7 Image Name: 098-042_961024 Image Resolution: 188 Image Number of Rows: 4349 Image Number of Columns: 4891 Image Number of Bits: 8 Image sat. img. Attached Image(s): Member ID: 8 Image Name: 098-047_980531 Image Resolution: 188 Image Number of Rows: 4350 Image Number of Columns: 4760 Image Number of Bits: 8 Image sat. img Attached Image(s): Member ID: 9 Image Name: 098-052_961012 Image Resolution: 188 Image Number of Rows: 4494 Image Number of Columns: 4929 Image Number of Bits: 8 Image sat.img. Attached Image(s): Member ID: 10 Image Name: 104-049_981104 Image Resolution: 188 Image Number of Rows: 4359 Image Number of Columns: 4726 Image Number of Bits: 8 Image sat. img. Attached Image(s): Member ID: 11 Image Name: 104-052_961018 Image Resolution: 188 Image Number of Rows: 4494 Image Number of Columns: 4929 Image Number of Bits: 8 Image sat.img. Attached Image(s): Member ID: 12 Image Name: 110-052_961129 Image Resolution: 188 Image Number of Rows: 4354 Image Number of Columns: 4923 Image Number of Bits: 8 Image sat.img. Attached Image(s): Member ID: 13 Image Name: 110-057_980209 Image Resolution: 188 Image Number of Rows: 4337 Image Number of Columns: 4862 Image Number of Bits: 8 Image sat.img. Attached Image(s): Member ID: 14 Image Name: 116-052_961205 Image Resolution: 188 Image Number of Rows: 4353 Image Number of Columns: 4910 Image Number of Bits: 8 Image sat.img. Attached Image(s): Member ID: 15 Image Name: 116-055_990226 Image Resolution: 188 Image Number of Rows: 4350 Image Number of Columns: 4791 Image Number of Bits: 8 Image sat.image Attached Image(s): Member ID: 16 Image Name: 116-062_970404 Image Resolution: 188 Image Number of Rows: 4365 Image Number of Columns: 4934 Image Number of Bits: 8 Image sat.image Attached Image(s): Member ID: 17 Image Name: Mosaic Image Projection: Albers Equal-Area Image Resolution: ? Image Number of Rows: ? Image Number of Columns: ? Image Number of Bits: 8 Image Geometrically controlled Mosaic of all 16 images, radiometrically not adjusted proprietary +ICId0019_202 Landsat TM images of Bhutan CEOS_EXTRA STAC Catalog 1970-01-01 88.8, 26.54, 92.37, 28.46 https://cmr.earthdata.nasa.gov/search/concepts/C2232848112-CEOS_EXTRA.umm_json Landsat TM scenes of Winter 98/99 Attached Image(s): Member ID: 1 Image Name: 137-041_990116 Image Resolution: 30 Image Number of Rows: 5728 Image Number of Columns: 6920 Image Number of Bits: 8 Image TM image Attached Image(s): Member ID: 2 Image Name: 138-041_981104 Image Resolution: 30 Image Number of Rows: 5728 Image Number of Columns: 6920 Image Number of Bits: 8 Image TM image Attached Image(s): Member ID: 3 Image Name: 139-041_981229 (Quadrant 4 only) Image Resolution: 30 Image Number of Rows: 2944 Image Number of Columns: 3500 Image Number of Bits: 7*8 Image Satellite image proprietary +ICId0020_202 Land cover Map of the Hindu Kush-Himalayan region CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232849129-CEOS_EXTRA.umm_json Supervised classification of IRS WiFS data; IGBP legend proprietary +ICId0021_202 IRS Pan 104-052 of 23 Nov 1996 CEOS_EXTRA STAC Catalog 1970-01-01 79.9, 26, 88.84, 30.88 https://cmr.earthdata.nasa.gov/search/concepts/C2232849217-CEOS_EXTRA.umm_json IRS Panchromatic image of Kathmandu Valley proprietary +ICId0023_202 Landsat TM 140-040 of 22 Sep 1992 CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848244-CEOS_EXTRA.umm_json Landsat TM 140-040 of 22 Sep 1992 Satellite image proprietary +ICId0028_202 Landsat TM 141-41 Q2 of 24 Jan 89 CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232849271-CEOS_EXTRA.umm_json Landsat TM 141-41 Q2 of 24 Jan 89 Satellite image proprietary ICO_Casey_1 In situ chemical oxidisation (ICO) of petroleum hydrocarbons at old Casey Station AU_AADC STAC Catalog 1999-12-01 2001-12-28 110.3, -66.5, 110.75, -66.2333 https://cmr.earthdata.nasa.gov/search/concepts/C1214313554-AU_AADC.umm_json In-situ chemical oxidation (ICO) is a remediation technology that involves the addition of chemicals to the substrate that degrade contaminants through oxidation processes. This series of field experiments conducted at the Old Casey Powerhouse/Workshop investigate the potential for the use of ICO technology in Antarctica on petroleum hydrocarbon contaminated sediments. Surface application was made using 12.5% sodium hyperchlorite, 6.25% sodium hydrechlorite, 30% hydrogen peroxide and Fentons Reagent (sodium hypchlorite with an iron catalyst) on five separate areas of petroleum hydrocarbon contaminated sediments. Sampling was conducted before and after chemical application from the top soil section (0 - 5 cm) and at depth (10 - 15 cm). The data are stored in an excel file. This work was completed as part of ASAC project 1163 (ASAC_1163). The spreadsheet is divided up as follows: The first 51 sheets are the raw GC-FID data for the 99/00 field season, labelled by sample name. These sheets use the same format as the radiometric GC-FID spreadsheet in the metadata record entitled 'Mineralisation results using 14C octadecane at a range of temperatures'. Sample name format consists of a location or experiment indicator (CW=Casey Workshop, BR= Small-scale field trial), the year the sample was collected (00=2000), the sample type (S=Soil) and a sequence number. SUMMARY and PRINTABLE VERSION are the same data in different formats, PRINTABLE VERSION is printer friendly. This summary data includes the hydrocarbon concentrations corrected for dry weight of soil and biodegradation and weathering indices. GRAPHS are graphs. FIELD MEASUREMENTS show the results of the measurements taken in the field and include PID (ppm), Soil temperature (C), Air temperature (C), Ph and MC (moisture content) (%). NOTES shows the chemicals added to each trial, and a short summary of the samples. The next 21 sheets show the raw GC-FID data for the 00/01 field season, labelled to previously explained method. PRINTABLE (0001) is a summary of the raw GC-FID data. The next 3 sheets show the raw GC-FID data for the 01/02 field season, labelled to previously explained method. PRINTABLE (0102) is a summary of the raw GC-FID data. MPN-NOTES shows lab book references and set up summary for the Most Probable Number (MPN) analysis. MPN-DETAILS shows the set up details, calculations and results for each MPN analysis. MPN-RESULTS shows the raw MPN data. MPN-Calculations show the results from the MPN Calculator. The fields in the dataset are: Retention Time Area % Area Height of peak Amount Int Type Units Peak Type Codes proprietary ICRAF_AfSIS_AfrHySRTM Africa Soil Information Service (AfSIS): Hydrologically Corrected/Adjusted SRTM DEM (AfrHySRTM) SCIOPS STAC Catalog 1970-01-01 -17.535833, -34.83917, 51.413334, 37.345833 https://cmr.earthdata.nasa.gov/search/concepts/C1214155420-SCIOPS.umm_json The Africa Soil Information Service: Hydrologically Corrected/Adjusted SRTM DEM (AfrHySRTM) is an adjusted elevation raster in which any depressions in the source Digital Elevation Model (DEM) have been eliminated (filled), but allowing for internal drainage since some landscapes contain natural depressions. These landscapes have their own internal drainage systems, which are not connected to adjacent watersheds. Null cells (drains) were placed in depressions exceeding a depth limit of 20 m and with no less than 1000 cells (pixels) during the DEM adjustment process. After filling depressions in the DEM, flowpaths can also be generated. AfrHySRTM uses the CGIAR-CSI SRTM 90m Version 4 as the source DEM The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. The purpose of the dataset is to serve a wide user community by providing a Digital Elevation Model for the continent of Africa that can be used to predict soil properties as well as for a range of other applications, including erosion and landslide risk. The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/. proprietary ICRAF_AfSIS_SCA Africa Soil Information Service (AfSIS): Specific Catchment Area (SCA) SCIOPS STAC Catalog 1970-01-01 -17.535833, -34.83917, 51.413334, 37.345833 https://cmr.earthdata.nasa.gov/search/concepts/C1214155401-SCIOPS.umm_json The Africa Soil Information Service (AfSIS): Specific Catchment Area (SCA) is a 90m raster dataset showing local flow accumulation and flow direction using the formula SCA = A/I, where A is unit contributing area of land upslope of a length of contour I. The specific catchment area contributing to flow at any given location can be used to determine relative saturation and water runoff and, together with other topographic factors, can be used to model erosion and landslides. The digital elevation model used to construct this dataset is AfHydSRTM, which is based on the CGIAR-SRTM 90m Version 4. The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. The specific catchment area is a useful parameter for modeling of runoff, soil erosion and sediment yield.The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/. proprietary @@ -6687,6 +6872,7 @@ IDBMG4_5 IceBridge BedMachine Greenland V005 NSIDC_ECS STAC Catalog 1993-01-01 2 IDCSI4_1 IceBridge L4 Sea Ice Freeboard, Snow Depth, and Thickness V001 NSIDC_ECS STAC Catalog 2009-03-19 2013-04-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000001460-NSIDC_ECS.umm_json This data set contains derived geophysical data products including sea ice freeboard, snow depth, and sea ice thickness measurements in Greenland and Antarctica retrieved from IceBridge Snow Radar, Digital Mapping System (DMS), Continuous Airborne Mapping By Optical Translator (CAMBOT), and Airborne Topographic Mapper (ATM) data sets. The data were collected as part of Operation IceBridge funded campaigns. proprietary IDHDT4_1 IceBridge ATM L4 Surface Elevation Rate of Change V001 NSIDC_ECS STAC Catalog 1993-06-23 2018-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000320-NSIDC_ECS.umm_json This data set contains surface elevation rate of change measurements derived from IceBridge and Pre-IceBridge Airborne Topographic Mapper (ATM) widescan elevation measurements data for Arctic and Antarctic missions flown under NASA's Operation IceBridge (OIB) and Arctic Ice Mapping (AIM) projects. proprietary IDS_LIS_0 Interdisciplinary Research in Earth Science, Long Island Sound OB_DAAC STAC Catalog 2017-09-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2358089978-OB_DAAC.umm_json Integration of new remote sensing tools for characterization of tidal marsh area extent, vegetation communities and inundation regimes, and advanced retrievals of estuarine biological and biogeochemical processes with multi-disciplinary ecological, paleoecological, and socioeconomic datasets, spatial econometric models of population growth, and a novel coupled hydrodynamic-photo-biogeochemical model specifically designed for the marsh-estuarine continuum in the heavily urbanized Long Island Sound. proprietary +IES Irrigation Equipment Supply Database CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232284371-CEOS_EXTRA.umm_json The Irrigation Equipment Supply Database is a joint initiative of the Water Resources, Development and Management Service of FAO and the International Programme for Technology and Research in Irrigation and Drainage (IPTRID). It has been developed as part of FAO's mandate to provide information on irrigation. Potential beneficiaries of IES are those who need to locate information on irrigation equipment at regional or country level. IES seeks to establish an up-to-date list of Suppliers/Manufacturers providing irrigation equipment worldwide. National Suppliers/Manufacturers can be displayed by clicking the dark blue countries on the map. Moreover, the website offers a database query facility for identifying Suppliers/Manufactures providing specific irrigation equipment as well as a description of irrigation equipment, a description of standards and links to other related sites. [Summary provided by the FAO.] proprietary IGBGM1B_1 IceBridge BGM-3 Gravimeter L1B Time-Tagged Accelerations V001 NSIDC_ECS STAC Catalog 2008-12-31 2011-12-23 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1000000360-NSIDC_ECS.umm_json This data set contains vertical acceleration values for Antarctica using the BGM-3 Gravimeter. The data were collected by scientists working on the the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge. proprietary IGBGM2_1 IceBridge BGM-3 Gravimeter L2 Geolocated Free Air Anomalies V001 NSIDC_ECS STAC Catalog 2009-01-08 2011-12-21 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1000000341-NSIDC_ECS.umm_json This data set contains free air anomaly measurements taken over Antarctica using the BGM-3 Gravimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge. proprietary IGBP-DIS_565_1 Global Soil Data Products CD-ROM Contents (IGBP-DIS) ORNL_CLOUD STAC Catalog 1995-01-01 1996-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216880820-ORNL_CLOUD.umm_json This dataset contains global data on soil properties, global maps of soil distributions, and the SoilData System developed by the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS). These data were originally distributed on CD-ROM, but are provided here as a single zip file. The SoilData System allows users to generate soil information and maps for geographic regions at soil depths and resolutions selected by the user. Derived surfaces of carbon density, nutrient status, water-holding capacity, and heat capacity are provided for modeling and inventory purposes. proprietary @@ -6735,10 +6921,28 @@ INC_NCMF A Nature Characterization Map of Flanders SCIOPS STAC Catalog 1970-01-0 INDOEX_0 India Ocean Experiment OB_DAAC STAC Catalog 1999-01-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360380-OB_DAAC.umm_json Measurements from the India Ocean Experiment (INDOEX) in 1999. proprietary INPE_AQUA1_MODIS Aqua 1 MODIS Imagery CEOS_EXTRA STAC Catalog 2005-06-12 -79, -36, -33, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456123-CEOS_EXTRA.umm_json Imagery from MODIS sensor, abord Aqua platform, held by INPE. proprietary INPE_CBERS2B_CCD CCD - High Resolution CCD Camera (CBERS 2B) CEOS_EXTRA STAC Catalog 2007-09-25 2010-03-10 -85, -60, -20, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456148-CEOS_EXTRA.umm_json The CCD camera provides images of a 113 km wide strip with 20m spatial resolution. Since this camera has a sideways pointing capability of ± 32 degrees, it is capable of taking stereoscopic images of a certain region. The CCD camera operates in 5 spectral bands that include a panchromatic one from 0.51 to 0.73 µm. A complete coverage cycle of the CCD camera takes 26 days. proprietary +INPE_CBERS2B_HRC HRC - High Resolution Camera (CBERS 2B) Imagery CEOS_EXTRA STAC Catalog 2007-09-25 2010-03-10 -79, -36, -33, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456153-CEOS_EXTRA.umm_json HRC camera operates in a single spectral band which covers visible and near-infrared bands. It is only present in CBERS-2B. It generates images of 27km width and resolution 2.7m, which will allow the observation of surface objects with large detail. Given its 27km swath, five 26 days cycles are necessary for the 113km standard CCD swath to be covered by HRC. proprietary INPE_CBERS2_CCD CCD - High Resolution CCD Camera (CBERS 2) Imagery CEOS_EXTRA STAC Catalog 2003-10-22 2009-01-01 -85, -60, -20, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456154-CEOS_EXTRA.umm_json CBERS-2 CCD - High Resolution CCD Camera. The CCD camera provides images of a 113 km wide strip with 20m spatial resolution. Since this camera has a sideways pointing capability of ± 32 degrees, it is capable of taking stereoscopic images of a certain region. The CCD camera operates in 5 spectral bands that include a panchromatic one from 0.51 to 0.73 µm. A complete coverage cycle of the CCD camera takes 26 days. proprietary +INPE_CBERS2_IRM IRMSS - Infrared Multispectral Scanner (CBERS 2) Imagery CEOS_EXTRA STAC Catalog 2003-10-22 2009-01-01 -85, -60, -20, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456081-CEOS_EXTRA.umm_json The CBERS-2 satellite is designed for global coverage and include cameras that make optical observations and a Data Collection System transponder to gather data on the environment. They are unique systems due to the use of on board cameras which combine features that are specially designed to resolve the broad range of space and time scales involved in our ecosystem. The IRMSS operates in 4 spectral bands, thus extending the CBERS spectral coverage up to the thermal infrared range. It images a 120 km swath with the resolution of 80m (160m in the thermal channel). In 26 days one obtains a complete Earth coverage that can be correlated with the images of the CCD camera. proprietary INPE_CBERS4_AWFI_1 AWFI - Wide Field Imaging Camera (CBERS 4) Imagery CEOS_EXTRA STAC Catalog 2015-01-01 -180, -45, 180, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456150-CEOS_EXTRA.umm_json The WFI - Wide Field Camera camera can make quick revisits to a certain area - usually in less than five days, aiming at support monitoring and surveillance activities. It complements other sensors with more revisit capability (e.g. AVHRR/NOAA or MODIS/Terra and Aqua), sensors with lower revisit capacity such as TM/Landsat, and the other CBERS-4 cameras. It has 4 4pectral bands: 0,45-0,52?m (B) 0,52-0,59?m (G) 0,63-0,69?m (R) 0,77-0,89?m (NIR). The swath width is 866 km, the spatial resolution is 64 m on nadir and the image data bit rate is 50 Mbit/s. proprietary INPE_CBERS4_IRS_1 CBERS-4 Infrared Medium Resolution Scanner Imagery CEOS_EXTRA STAC Catalog 2015-01-01 -180, -45, 180, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456152-CEOS_EXTRA.umm_json Infrared Medium Resolution Scanner. This camera is built under China responsibility and it is an upgrade of the Infrared Multispectral Scanner (IRMSS) of the CBERS-1 and 2 satellites. It has 4 spectral bands: B09: 0,50 - 0,90 ?m B10: 1,55 - 1,75 ?m B11: 2,08 - 2,35 ?m B12: 10,4 - 12,5 ?m Its spatial resolution is 40 meters in the panchromatic and SWIR (shortwave infrared) bands and 80 meters in the thermal band. A complete coverage cycle of the panchromatic camera takes 26 days. proprietary +INPE_CBERS4_MUX_1 MUX - MultiSpectral Camera (CBERS 4) Imagery CEOS_EXTRA STAC Catalog 2015-01-01 -180, -45, 180, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456083-CEOS_EXTRA.umm_json CBERS-4 MUX - Multispectral Camera. This camera is built under Brazilian responsibility. It is a multispectral camera with four spectral band covering the wavelength range from blue to near infrared (from 450 nm to 890 nm) with a ground resolution of 20 m and a ground swath width of 120 km A complete coverage cycle of the MUX camera takes 26 days. proprietary INPE_CBERS4_PAN10M_1 CBERS-4 Multispectral 10 Meters Camera Imagery CEOS_EXTRA STAC Catalog 2015-01-01 -180, -45, 180, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456075-CEOS_EXTRA.umm_json CBERS-4 Multispectral 10 Meters Camera. This camera is built under China responsibility. It is a panchromatic camera with 4 spectral bands: B02: 0,52 - 0,59 ?m B03: 0,63 - 0,69 ?m B04: 0,77 - 0,89 ?m The ground resolution is 10 m and the ground swath width is 60 km. A complete coverage cycle of the panchromatic camera takes 52 days. proprietary +INPE_CPTEC_CLIMATE_BRAZIL Monthly Climate Data Products for Brazil (INPE/CPTEC) CEOS_EXTRA STAC Catalog 1970-01-01 -74, -34, -34, -6 https://cmr.earthdata.nasa.gov/search/concepts/C2227456073-CEOS_EXTRA.umm_json This dataset contains monthly precipitation and temperature maps and their respective anomalies in relation to the 30-year climatology (1961-1990). Data was used from the Instituto Nacional de Meteorologia (INMET/BR). This dataset can be obtained via World Wide Web from the CPTEC Home Page. Link to: http://www.cptec.inpe.br/ proprietary +INPE_CPTEC_CLIMATE_GLOBAL Global Monthly Climate Data Products (INPE/CPTEC) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2227456060-CEOS_EXTRA.umm_json This dataset consists of Global monthly fields of several variables and their respective anomalies in relation to the 16 years climatology (1979-1995), using reanalysis data from National Center for Environmental Prediction (NCEP/USA). Variables include Geopotential Height, Streamlines(850hPa, 200hPa), upper level winds(850hPa, 200hPa) , sea level temperature, outgoing long wave radiation, and sea level pressure. This dataset can be obtained via World Wide Web from the CPTEC Home Page. Link to: http://www.cptec.inpe.br/ proprietary +INPE_CPTEC_GLOBAL_METEOGRAM Forecast Model Meteograms For 26 Locations in South America (INPE/CPTEC) CEOS_EXTRA STAC Catalog 1970-01-01 -75, -35, -34, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2227456145-CEOS_EXTRA.umm_json "Forecast model meteograms for 26 locations in South America are available from CPTEC (Centro de Previsao de Tempo e Estudos Climaticos) in Brazil. Forecast time steps range from the initial time out to six days. The user may view forecasts from the most recent forecast run back to the previous 36 hours at twelve hour steps. Parameters Forecasted: Relative Humidity (%) Precipitation (mm/h) Mean Sea Level Pressure (mb) Surface Wind (m/s) Surface Temperature (C) Forecasted meteograms may be obtained via World Wide Web from CPTEC's Home Page. Link to: ""http://www.cptec.inpe.br/""" proprietary +INPE_CPTEC_IR_SAT GOES-8 and Meteosat-5 Infrared Satellite Images of South America (INPE/CPTEC) CEOS_EXTRA STAC Catalog 1970-01-01 -76, -56, -34, -15 https://cmr.earthdata.nasa.gov/search/concepts/C2227456139-CEOS_EXTRA.umm_json GOES-8 and Meteosat-5 infrared images of South America are available from CPTEC (Centro de Previsao de Tempo e Estudos Climaticos) in Brazil. Only the most recent month's images are archived. A new image is provided every three hours. Please read carefully the Disclaimer and Copyright information. All satellite images and additional information may be obtained via the World Wide Web from the CPTEC Home Page. Link to: http://www.cptec.inpe.br/ proprietary +INPE_ER_SAR ERS SAR Data held by the National Institute for Space Research (INPE), Brazil CEOS_EXTRA STAC Catalog 1991-08-26 -80, -40, -33, 8 https://cmr.earthdata.nasa.gov/search/concepts/C2227456064-CEOS_EXTRA.umm_json INPE's only receiving station for ERS-1 and ERS-2 SAR is located in Cuiaba, MT (geographic coordinates approx. 15.5S/56.0W). The SAR tapes recorded at Cuiaba are air shipped to the processing and distribution center in Cachoeira Paulista, SP, where they are kept. A copy of all recorded tapes is sent to the ESA PAF at the DLR facilities in Germany. Early ERS-1 SAR data were acquired primarily for station checkout and Principal Investigator service. A small number of passes, 15 seconds to 2 minutes long, were acquired from August, 1991 to March, 1992, during the Commissioning and Ice phases where the repeat cycle was 3 days but the ground coverage was sparse. More extensive and regular acquisitions began in April, 1992, with the satellite already in the so-called Multidisciplinary phase (full ground coverage and 35-day repeat cycle). INPE is allowed to service user requests originated in Brazil only. Only digital products are available. Requests for products and for image search listings will be handled directly by the processing center (listed at the Data Center entry), through contacts by mail, phone or fax. No online remote access is available, although plans exist to implement it, including the International Directory protocols. No firm date is speculated for that, but hopes are that it can be made before 1998. Requests from countries other that Brazil must be routed to the ESA licensed regional distributors. Information can be obtained with the ESA ERS-1 Order Desk, c/o ESRIN, C.P. 64, I-00044 Frascati, Italy. The phone numbers are +39.6.941-80600 (voice) and +39.6.941-80510 (fax). proprietary +INPE_IRS_AWIFS IRS AWIFS Imagery CEOS_EXTRA STAC Catalog 2009-09-10 -79, -36, -33, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456088-CEOS_EXTRA.umm_json AWIFS, aboard IRS – P6 (RESOURCESAT-I), imagery held by INPE. proprietary +INPE_IRS_LISS3 IRS LISS3 Imagery CEOS_EXTRA STAC Catalog 2009-09-10 -79, -36, -33, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456136-CEOS_EXTRA.umm_json LISS 3, aboard IRS – P6 (RESOURCESAT-I), imagery held by INPE. proprietary +INPE_LANDSAT1_MSS LANDSAT-1 MSS Imagery CEOS_EXTRA STAC Catalog 1973-05-29 1976-10-19 -80, -40, -33, 8 https://cmr.earthdata.nasa.gov/search/concepts/C2227456156-CEOS_EXTRA.umm_json LANDSAT 1 MSS imagery held by the National Institute for Space Research (INPE), Brazil. proprietary +INPE_LANDSAT2_MSS LANDSAT-2 MSS Imagery CEOS_EXTRA STAC Catalog 1975-07-24 1982-02-06 -80, -40, -33, 8 https://cmr.earthdata.nasa.gov/search/concepts/C2227456137-CEOS_EXTRA.umm_json LANDSAT 2 MSS imagery held by the National Institute for Space Research (INPE), Brazil. proprietary +INPE_LANDSAT3_MSS LANDSAT-3 MSS Imagery CEOS_EXTRA STAC Catalog 1978-04-05 1982-07-31 -80, -40, -33, 8 https://cmr.earthdata.nasa.gov/search/concepts/C2227456100-CEOS_EXTRA.umm_json LANDSAT 3 MSS imagery held by the National Institute for Space Research (INPE), Brazil. proprietary +INPE_LANDSAT5_TM LANDSAT-5 TM Imagery CEOS_EXTRA STAC Catalog 1984-04-01 -80, -40, -33, 8 https://cmr.earthdata.nasa.gov/search/concepts/C2227456071-CEOS_EXTRA.umm_json LANDSAT 5 TM imagery held by the National Institute for Space Research (INPE), Brazil. proprietary +INPE_LANDSAT7_ETM LANDSAT-7 ETM+ Imagery CEOS_EXTRA STAC Catalog 1999-07-19 2003-06-01 -80, -40, -33, 8 https://cmr.earthdata.nasa.gov/search/concepts/C2227456065-CEOS_EXTRA.umm_json LANDSAT 7 ETM+ imagery held by the National Institute for Space Research (INPE), Brazil. proprietary +INPE_LS_MSS LANDSAT MSS Data held by the National Institute for Space Research (INPE), Brazil CEOS_EXTRA STAC Catalog 1973-05-14 1987-10-06 -80, -40, -33, 8 https://cmr.earthdata.nasa.gov/search/concepts/C2227456141-CEOS_EXTRA.umm_json INPE's only receiving station for Landsat MSS was located in Cuiaba, MT (geographic coordinates approx. 15.5S/56.0W). Data were acquired routinely over the entire range from 1973 until 1986, some time after TM data began being received. MSS data recordings were then reduced to Brazilian territory only. Also, many gaps exist, some of them several months long, related mostly to station downtimes caused by lightning. The MSS tapes recorded at Cuiaba were air shipped to the processing and distribution center in Cachoeira Paulista, SP, where they are kept. The holdings are estimated to be around 75,000 scenes (or 300,000 images, counting individually each of the four spectral bands), not all of them processed. The fifth band (thermal infrared) available on Landsat 3 is not counted for practical purposes. Very few were processed at INPE with disappointing results and it was soon dropped as a product. Demand for MSS products decreased sharply after TM products started being distributed. This determined the reduction and eventually the discontinuing of MSS recordings in 1987. The original processing system, based on 16-bit minicomputers, was dismantled in early 1991. An alternative ingestion system is being developed to allow limited processing of MSS data by the TM system, with a forecast to be ready in late 1998. Meanwhile, available products are limited to reproduction of existing photographic originals (about 150,000 black-and-white and color images). No digital products can be delivered, since no copies were kept from produced CCTs. Requests for products and for image search listings are handled directly by the processing center (listed at the Data Center entry), through contacts by mail, phone or fax. No online remote access is available to the moment, although plans exist to implement it, including the International Directory protocols. No firm date is speculated for that, but hopes are that it can be made before 1998. Information about costs, delivery time and available formats can be requested at the same address. proprietary +INPE_LS_RBV LANDSAT RBV Data held by the National Institute for Space Research (INPE), Brazil CEOS_EXTRA STAC Catalog 1978-11-03 1983-03-31 -80, -40, -33, 8 https://cmr.earthdata.nasa.gov/search/concepts/C2227456063-CEOS_EXTRA.umm_json INPE's only receiving station for Landsat RBV was located in Cuiaba, MT (geographic coordinates approx. 15.5S/56.0W). Data were acquired routinely over the entire range during the lifetime of Landsat 3. This means therefore the twin-camera, panchromatic version of the RBV. Some gaps exist, some of them several months long, related mostly to station downtimes caused by lightning. The RBV tapes recorded at Cuiaba were air shipped to the processing and distribution center in Cachoeira Paulista, SP, where they are kept. The holdings (not all of them processed to film) are estimated to be around 100,000 images. Demand for RBV products experienced a brief peak while they were a novelty with higher resolution than MSS (30m vs. 80m), but decreased quickly as the all-analog processing system allowed no digital products and the shading effect could not be effectively corrected, yielding poor quality images. Requests practically vanished after the Thematic Mapper came into scene, and RBV products were taken off the INPE products list in late 1984. The processing system was dismantled in early 1991. Some 50,000 photographic originals are still kept, but no intention of resuming distribution exists in principle. proprietary +INPE_LS_TM LANDSAT TM Data held by the National Institute for Space Research (INPE), Brazil CEOS_EXTRA STAC Catalog 1984-03-24 -80, -40, -33, 8 https://cmr.earthdata.nasa.gov/search/concepts/C2227456079-CEOS_EXTRA.umm_json INPE's only receiving station for Landsat TM is located in Cuiaba, MT (geographic coordinates approx. 15.5S/56.0W). Data were always acquired in a routine fashion, initially over Brazil only, with extension to the whole range in 1987. A few gaps exist, some of them several months long, related mostly to station downtimes caused by lightning. The TM tapes recorded at Cuiaba are air shipped to the processing and distribution center in Cachoeira Paulista, SP, where they are kept. The holdings are estimated to be around 70,000 scenes (or 500,000 images, counting individually each of the seven spectral bands). About 14,000 scenes have been processed to black-and-white or color photographic originals and can be reproduced as products quicker than unprocessed ones. No copies are kept from delivered digital products. Requests for products and for image search listings are handled directly by the processing center (listed at the Data Center entry), through contacts by mail, phone or fax. No online remote access is available to the moment, although plans exist to implement it, including the International Directory protocols. No firm date is speculated for that, but hopes are that it can be made before 1998. Information about costs, delivery time and available formats can be requested at the same address. proprietary INPE_TOPODATA Brazil national full-coverage geomorphometric database CEOS_EXTRA STAC Catalog 1970-01-01 -75.5, -34, -36, 6 https://cmr.earthdata.nasa.gov/search/concepts/C2227456089-CEOS_EXTRA.umm_json DTM and its local geomorphometric derivations from SRTM (Shuttle Radar Topography Mission) throughout the entire Brazilian territory. The processing, restricted to local geomorphometric derivations, targeted the production of information layers of the primary variables: elevation, slope, aspect, vertical curvature and horizontal curvature, in their full numerical expression and in interval class schemes. It also includes secondary (or combined) variables, layers respective to landforms, watershed delineation and solar illumination. proprietary INTEXA_AIRMAP_1 INTEX-A AIRMAP data LARC_ASDC STAC Catalog 2004-07-05 2004-07-05 -178, 27, -132, 54 https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-LARC_ASDC.umm_json Intercontinental Chemical Transport Experiment - North America Phase A (INTEX-A) is an integrated atmospheric field experiment performed over North America. The study seeks to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and their impact on air quality and climate. A particular focus in this study is to quantify and characterize the inflow and outflow of pollution over North America. The main constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. INTEX-NA is part of a larger international ITCT (Intercontinental Transport and Chemical Transformation) initiative. INTEX-NA goals are greatly facilitated and enhanced by a number of concurrent and coordinated national and international field campaigns and satellite observations. Synthesis of the ensemble of observation from surface, airborne, and space platforms, with the help of a hierarchy of models is an important goal of INTEX-NA. proprietary INTEXA_DC8_AIRCRAFT_1 INTEX-A Aircraft data LARC_ASDC STAC Catalog 2004-06-26 2004-08-14 -177.1, 27, -132, 54 https://cmr.earthdata.nasa.gov/search/concepts/C1000000480-LARC_ASDC.umm_json INTEXA_DC8_AIRCRAFT is the Intercontinental Chemical Transport Experiment - North America Phase A (INTEX-A) Aircraft data product. INTEX-A was an integrated atmospheric field experiment performed over North America. The study sought to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and their impact on air quality and climate. A particular focus in this study is to quantify and characterize the inflow and outflow of pollution over North America. The main constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases.INTEX-NA is part of a larger international ITCT (Intercontinental Transport and Chemical Transformation) initiative. INTEX-NA goals are greatly facilitated and enhanced by a number of concurrent and coordinated national and international field campaigns and satellite observations. Synthesis of the ensemble of observation from surface, airborne, and space platforms, with the help of a hierarchy of models is an important goal of INTEX-NA. proprietary @@ -6817,6 +7021,8 @@ IXBMI2ST_2 MISR L2 TOA/Cloud Stereo Product subset for the INTEX-B region V002 L IXBMIB2E_3 MISR L1B2 Ellipsoid Product subset for the INTEX-B region V003 LARC STAC Catalog 2006-02-28 2006-04-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000300-LARC.umm_json This file contains Ellipsoid-projected TOA Radiance,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22 for the INTEXB_2006 theme. proprietary IXBMIB2T_3 MISR L1B2 Terrain Product subset for the INTEX-B region V003 LARC STAC Catalog 2006-02-28 2006-04-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000281-LARC.umm_json This file contains Terrain-projected TOA Radiance,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22 for the INTEXB_2006 theme. proprietary IXBMIGEO_2 MISR Geometric Parameters subset for the INTEX-B region V002 LARC STAC Catalog 2006-02-28 2006-04-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000301-LARC.umm_json This file contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid for the INTEXB_2006 theme. proprietary +IZIKO_Fish iziko South African Museum - Fish Collection CEOS_EXTRA STAC Catalog 1829-08-01 2005-03-24 -155.518, -77.85, 179.14, 60.4667 https://cmr.earthdata.nasa.gov/search/concepts/C2232477688-CEOS_EXTRA.umm_json The iziko South African Museum has a comprehensive holdings comprising of identified bony and cartilaginous fish, mostly from Cape waters, but extending to Angola and Mozambique and the Southern, Indian and Atlantic Oceans. It currently contains 15048 records of 293 families. proprietary +IZIKO_Marine_Mammals iziko South African Museum - Marine Mammals Collection CEOS_EXTRA STAC Catalog 1880-10-14 1998-01-08 -170.47888, -70.16666, 174.5, 63.69417 https://cmr.earthdata.nasa.gov/search/concepts/C2232477689-CEOS_EXTRA.umm_json The iziko South African Museum has a comprehensive collection of cetacean and Cape fur seal skeletal material. Skeletal material from other marine mammals is also held. Part of this collection is on exhibition in the museum's Whale Well. It currently contains 14484 records of 46 families. proprietary IceMargin_79E-108E_1 Margin of the Antarctic ice cover derived from Synthetic Aperture Radar images for the sector 79E-108E AU_AADC STAC Catalog 1993-08-01 1997-10-31 79, -68, 108, -64 https://cmr.earthdata.nasa.gov/search/concepts/C1214313552-AU_AADC.umm_json Geographic location of the outer margin of the Antarctic ice cover for the sector between longitudes 79E and 108E, including margins of ice shelves, glaciers, and iceberg tongues. The data set does not in general include the grounding zone at the inland margin of the ice shelves or glaciers. The margin was defined by interpretation of an image mosaic generated from Synthetic Aperture Radar data. The image mosaic was built using navigation data accompanying the SAR images to transform the images to a map projection. The image navigation data were adjusted so that overlapping images were registered to one another, the indivual images merged into a mosaic, and the overall process adjusted so that the mosaic was tied to the few ground control points available in this large sector. Two separate mosaics were used to span the whole sector. The majority of the SAR data were acquired by the ERS-SAR instruments in August 1996, some ERS data were acquired in August 1993, and one Radarsat scene was acquired in September 1997. The data were pre-processed to produce a mosaic with a 100 m pixel size, and adjusted so that the majority of the coastline positions refer to the August 1996 epoch. The location data are internally consistent, and extracted at nominally 200 m intervals. The external position accuracy is generally better than 600 m. The coverage is complete over the whole sector. The coordinate set includes some island/ice rise features. Two very large grounded icebergs are included. Data are in an ascii arc/info export file format as geographic coordinates on the ITRF1996 system and contains attribute information. ERS-SAR data, copyright ESA, 1993, 1996 Radarsat data, copyright Canadian Space Agency, Agence spatiale canadienne, 1997. This work was completed as part of ASAC projects 454, 1125 and 2224 (ASAC_454, ASAC_1125 and ASAC_2224). proprietary Idaho_field_shrub_data_1503_1 Shrubland Species Cover, Biometric, Carbon and Nitrogen Data, Southern Idaho, 2014 ORNL_CLOUD STAC Catalog 2014-09-16 2014-10-17 -116.8, 42.3, -114.69, 43.21 https://cmr.earthdata.nasa.gov/search/concepts/C2767495334-ORNL_CLOUD.umm_json This dataset provides the results of the characterization of shrubland vegetation at two study areas in southern Idaho, USA: the Reynolds Creek Experimental Watershed (RCEW) and Hollister. Data were collected in September and October 2014. In each study area, several 10-m x 10-m plots were randomly established that are representative of the local dominant vegetation types. Measurements are reported for both plot and individual shrub attributes. Plot measurements include shrub density and biometric data, percent shrub cover derived from line intercept transects, percent plant species and bare ground cover derived from photo analysis, and average LAI. Measurements for selected individual shrubs include height, width, length, number of stems, and LAI. Leaf samples were collected for determining LAI, specific leaf area (SLA), carbon and nitrogen concentrations, and isotopic nitrogen and carbon. proprietary Image2006_NA Image 2006 European coverage ESA STAC Catalog 2005-02-03 2007-11-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336916-ESA.umm_json Image 2006 collection is a SPOT-4, SPOT-5 and ResourceSat-1 (also known as IRS-P6) cloud free coverage over 38 European countries in 2006 (from February 2005 to November 2007). The Level 1 data provided in this collection originate from the SPOT-4 HRVIR instrument (with 20m spatial resolution), from SPOT-5 HRG (with 10m spatial resolution resampled to 20m) and IRS-P6 LISS III (with 23m spatial resolution), each with four spectral bands. The swath is of about 60 km for the SPOT satellites and 140 km for the IRS-P6 satellite. In addition to the Level 1, the collection provides the same data geometrically corrected towards a European Map Projection with 25m resolution. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/Image2006/ available on the Third Party Missions Dissemination Service. proprietary @@ -6955,6 +7161,7 @@ KADAI-OUKA-SAKURAJIMA-1992 Air Pollution caused by Eruption of Volcano Mt.Sakura KAIMIMOANA_0 Measurements taken onboard R/V Kaimimoana between 1999 and 2002 OB_DAAC STAC Catalog 1999-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360390-OB_DAAC.umm_json Measurements from the NOAA ship, the Kaimimoana between 1999 and 2002. proprietary KFDBAM_ANU_1 Macquarie Island Baseline Invertebrate Survey 1994 AU_AADC STAC Catalog 1994-02-01 1994-03-15 158.76, -54.79, 158.965, -54.48 https://cmr.earthdata.nasa.gov/search/concepts/C1214313580-AU_AADC.umm_json Records from 69 sites, covering the whole island. Sites stratified by topography (2 classes: slopes or drainage lines), altitude (4 classes: 0-100 m, 100-200 m, 200-300 m, 300m+), vegetation type (5 types), aspect (2 classes: E,W), north-south position on island (3 classes: north, middle, south). Pitfall traps and yellow pan traps opened for 6 weeks (summer). Hand searches for worms, slugs and snails. Specimens identified to species level. We used the data to construct statistical models of the spatial distribution of species in relation to the above variables. Intended as a baseline survey to detect, monitor and predict effects of climate change and local human impacts (e.g. alien species introductions) on biota. This work was carried out as part of ASAC project 104 (ASAC_104). The fields in this dataset are: Site Topography Region Aspect Altitude Vegetation Type Method Notes Species The detergent column indicates whether a drop of detergent was added to the yellowpans or not. A 1 = yes. proprietary KILVOLC_FlowerKahn2021_1 MISR Derived Case Study Data for Kilauea Volcanic Eruptions Including Geometric Plume Height and Qualitative Radiometric Particle Property Information LARC_ASDC STAC Catalog 2000-10-25 2018-08-01 -161, 14, -150, 25 https://cmr.earthdata.nasa.gov/search/concepts/C2134682585-LARC_ASDC.umm_json The KILVOLC_FlowerKahn2021_1 dataset is the MISR Derived Case Study Data for Kilauea Volcanic Eruptions Including Geometric Plume Height and Qualitative Radiometric Particle Property Information version 1 dataset. It comprises MISR-derived output from a comprehensive analysis of Kilauea volcanic eruptions (2000-2018). Data collection for this dataset is complete. The data presented here are analyzed and discussed in the following paper: Flower, V.J.B., and R.A. Kahn, 2021. Twenty years of NASA-EOS multi-sensor satellite observations at Kīlauea volcano (2000-2019). J. Volc. Geo. Res. (in press). The data is subdivided by date and MISR orbit number. Within each case folder, there are up to 11 files relating to an individual MISR overpass. Files include plume height records (from both the red and blue spectral bands) derived from the MISR INteractive eXplorer (MINX) program, displayed in: map view, downwind profile plot (along with the associated wind vectors retrieved at plume elevation), a histogram of retrieved plume heights and a text file containing the digital plume height values. An additional JPG is included delineating the plume analysis region, start point for assessing downwind distance, and input wind direction used to initialize the MINX retrieval. A final two files are generated from the MISR Research Aerosol (RA) retrieval algorithm (Limbacher, J.A., and R.A. Kahn, 2014. MISR Research-Aerosol-Algorithm: Refinements For Dark Water Retrievals. Atm. Meas. Tech. 7, 1-19, doi:10.5194/amt-7-1-2014). These files include the RA model output in HDF5, and an associated JPG of key derived variables (e.g. Aerosol Optical Depth, Angstrom Exponent, Single Scattering Albedo, Fraction of Non-Spherical components, model uncertainty classifications and example camera views). File numbers per folder vary depending on the retrieval conditions of specific observations. RA plume retrievals are limited when cloud cover was widespread or the solar radiance was insufficient to run the RA. In these cases the RA files are not included in the individual folders. In cases where activity was observed from multiple volcanic zones in a single overpass, individual folders containing data relating to a single region, are included, and defined by a qualifier (e.g. '_1'). proprietary +KOMPSAT-2 KOMPSAT-2 Panchromatic and multispectral imagery CEOS_EXTRA STAC Catalog 2006-07-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2229454502-CEOS_EXTRA.umm_json The KOMPSAT-2 allows the generation of high resolution images with a GSD of better than 1 m for PAN data and 4 m for MS data with nadir viewing condition at the nominal altitude of 685 km. The MSC has a single PAN spectral band between 500 - 900 nm and 4 MS spectral bands between 450-900 nm. PAN imaging and MS imaging can be operated simultaneously during mission operations. The swath width is greater than or equal to 15 km at the mission altitude for PAN data and MS data. The system is equipped with a solid state recorder to record images not less than 1,000km long at the end of life. The satellite can be rolled up to ±30 degrees off-nadir to pre-position the MSC swath. The KOMPSAT-2 can provide across-track stereo images by multiple passes of the satellite using off-nadir pointing capability. The satellite is compatible with daily revisit operation by off-nadir pointing with degraded GSD. Also, the image products according to the requested products quality standard can be made within one (1) day after satellite passes over the KGS. proprietary KOMPSAT-2.ESA.archive_NA KOMPSAT-2 ESA archive ESA STAC Catalog 2007-04-18 2014-03-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336921-ESA.umm_json Kompsat-2 ESA archive collection is composed by bundle (Panchromatic and Multispectral separated images) products from the Multi-Spectral Camera (MSC) onboard KOMPSAT-2 acquired from 2007 to 2014: 1m resolution for PAN, 4m resolution for MS. Spectral Bands: • Pan: 500 - 900 nm (locate, identify and measure surface features and objects primarily by their physical appearance) • MS1 (blue): 450 - 520 nm (mapping shallow water, differentiating soil from vegetation) • MS2 (green): 520 - 600 nm (differentiating vegetation by health) • MS3 (red): 630 - 690 nm (differentiating vegetation by species) • MS4 (near-infrared): 760 - 900 nm (mapping vegetation, mapping vegetation vigor/health, Differentiating vegetation by species) proprietary KOPRI-KPDC-00000001_1 2007 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 2007-12-08 2007-12-11 -63.593556, -62.777306, -61.092444, -61.466739 https://cmr.earthdata.nasa.gov/search/concepts/C2244294500-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in northern sea area of the South Shetland Islands. The research period was from 07 Dec. to 14 Dec. (7 days) in 2007. Geophysical research including acquisition of multi-channel seismic data was preceded. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 7 researcher in the cruise." proprietary KOPRI-KPDC-00000002_1 2004 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 2004-11-29 2004-12-05 -51.372194, -61.652222, -47.042, -60.203167 https://cmr.earthdata.nasa.gov/search/concepts/C2244294814-AMD_KOPRI.umm_json "Korean Antarctic survey carried out the fifth year project as step 3 project in the last annual of ‘The Antarctic Undersea Geological Survey’ was conducted in the northern Fowell Basin of the Weddell Sea. The research period was from 25 Nov. to 9 Dec. (15 days) in 2004. Geophysical research including acquisition of multi-channel seismic data was preceded. According to the results of seismic investigation, the drilling investigation was conducted at the coring point. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 12 researcher in the cruise." proprietary @@ -8833,6 +9040,8 @@ KORUSAQ_Trajectory_Data_1 KORUS-AQ FLEXPART Backtrajectory Data Products LARC_AS KORUSAQ_jValue_AircraftInSitu_DC8_Data_1 KORUS-AQ DC-8 Aircraft In Situ J Value (Photolysis Rate) Data LARC_ASDC STAC Catalog 2016-04-17 2016-06-21 180, 25, -180, 67 https://cmr.earthdata.nasa.gov/search/concepts/C2211642139-LARC_ASDC.umm_json KORUSAQ_jValue_AircraftInSitu_DC8_Data are in-situ j-value (photolysis rate) measurements collected onboard the DC-8 aircraft during the KORUS-AQ field campaign. Photolysis rates were calculated from NCAR CAFS. Data collection for this product is complete. The KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea’s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments. Surface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok. The major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies. proprietary KORUS_0 Korea-United States Ocean Color expedition OB_DAAC STAC Catalog 2016-05-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360412-OB_DAAC.umm_json The KORUS-OC (Korea-United States Ocean Color) expedition was a venture among scientist from the Korean Institute of Ocean Science and Technology (KIOST), NASA, and other institutions to study the daily changes of the seas surrounding South Korea. proprietary KROCK_Ocean_1 Aurora Australis Southern Ocean oceanographic (CTD) data, cruise 1992/93 V7 (KROCK) AU_AADC STAC Catalog 1993-01-24 1993-02-26 60, -68.5, 80, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313530-AU_AADC.umm_json This dataset contains CTD (conductivity, temperature, depth) data obtained from the Krill and Rock (KROCK) 92/93 cruise of the Aurora Australis, during Jan - Mar 1993. 62 CTD casts were taken in the Prydz Bay region, as a supplement to the krill and geology research program. Casts were made about 200 m except for one off the shelf. This dataset is a subset of the whole cruise data. The fields in this dataset are: Pressure Temperature Sigma-T Salinity Geopotential Anomaly Specific volume Anomaly samples deviation conduction proprietary +KV1_MSS_0.1 MSS multispectral images from Kanopus-V CEOS_EXTRA STAC Catalog 2012-07-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231912416-CEOS_EXTRA.umm_json MSS multispectral images from Kanopus-V Multiband surveying sensor from ?Kanopus-V? satellite that has circular sun synchronous orbit. The sensor is designed for monitoring man-caused and natural-caused emergency situations. The sensor provides earth surface images in 4 spectral bands (blue ? 460-520 nm, green ? 510-600 nm, red ? 630-690 nm, near infrared 750-840 nm). Nadir spatial resolution is 12 m. Swath with of the system is 20 km. The system has the ability to point the sensor to 40� from nadir in either side, which enables swath view of 920 km. The revisit frequency depends on latitude and can vary from 3 to 16 days. Obtained data can be used for tackling various problems in agriculture, ecology, cartography, construction, forestry, natural resources inventory and emergency situations monitoring. proprietary +KYOTO_GREENHOUSEGASES Interactive Map of Greenhouse Gas Emissions and the Kyoto Protocol CEOS_EXTRA STAC Catalog 1990-01-01 2005-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848233-CEOS_EXTRA.umm_json "This set of interactive graphics was produced in preparation for the seventh Conference of the Parties (COP-7) to the United Nations Framework Convention on Climate Change (UNFCCC) held in The Netherlands in October-November 2001. They are based on several UNFCCC Secretariat documents compiling data from submissions by Annex I countries; these include First and Second National Communications, as well as annual national inventory data. Additional sources include updated reports from individual countries; exceptions are noted on the graphs. The graphs feature actual (1990-2000) and projected (2005, 2010) emissions of the six greenhouse gases: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF6). The emissions are aggregated and represented as CO2 equivalents in million tonnes (1012); please note that in UNFCCC documents, emissions are measured in gigagrams (10**9). For more information on the derivation of GHG emissions statistics, see: ""http://www.grida.no/db/maps/collection/climate6/about.htm""" proprietary Kennebec_0 Kennebec River Plume - Gulf of Maine ecosystem measurements OB_DAAC STAC Catalog 2005-03-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360391-OB_DAAC.umm_json Measurements made in the Gulf of Maine between 2005 and 2007. proprietary Kerg_Heard_Plateau_Under_Sea_Geomorphology_1 Kerguelen-Heard Island region - Mapping Under Sea Geomorphology AU_AADC STAC Catalog 2008-02-01 2010-11-30 68, -56, 80, -48 https://cmr.earthdata.nasa.gov/search/concepts/C1214311127-AU_AADC.umm_json The geomorphology was digitised using contours derived from the DEM created by Dr. R. Beaman from James Cook University for Geoscience Australia. The data, the metadata record and the report related to the creation of that DEM are available on the Geoscience Australia website: Name of data set: Kerguelen Plateau Bathymetric Grid 2010 Catalogue number: 71552 https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search?node=srv#/metadata/a05f7893-007f-7506-e044-00144fdd4fa6 Digitising: It must be stressed that neither seismic data, sea floor sediments, nor sea floor biota were used to determine the sea floor geomorphology. The description on how the geomorphology was derived is described in the attached report. The features described as slopes from the 900m to 1300m isobaths and from the 1300m to 2500m isobaths were identified for fisheries purposes and not geomorphology purposes. A geomorphologist may combine these slopes into a single feature. Some of the larger shallow features identified as banks may more properly be identified as plateaus. It would require a more in depth analysis of the DEM, slopes and sediments to accurately identify the feature as a bank or plateau. proprietary Kerguelen_emapex_1 Data from EM-APEX profiling floats across the northern Kerguelen Platueau in November 2008 AU_AADC STAC Catalog 2008-11-01 2009-09-30 65, -50, 90, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214313528-AU_AADC.umm_json "These data were collected by 8 EM-APEX profiling floats, which are a sophisticated version of the standard Argo float. They measure temperature, salinity and pressure, as for standard Argo. They also use electromagnetic techniques to measure horizontal velocity. The floats were deployed across the northern Kerguelen Platueau in November 2008, and drifted eastward with the Antarctic Circumpolar Current as they profiled between the surface and 1600 dbar. They transmitted data through the Iridium satellite system and continued to profile eastward until their batteries failed. The range of latitudes covered is approx. 40S-50S, and longitudes 65E-90E. Although most of the data is in the longitude band 65E-78E. The temporal range of the data is Nov 2008 to approx. Sep 2009. The file ""emapex_final.mat"" contains the quality-controlled and calibrated data from 8 EM-APEX profiling floats deployed across the northern Kerguelen Plateau during the Southern Ocean Finestructure (SOFine) experiment aboard the U.K. RRS James Cook, Cruise 29, 1st Nov-22nd Dec 2008, Cape Town to Cape Town. Funding for the EM-APEX component of the experiment was from the Australian Research Council Discovery Project DP0877098 (N. Bindoff, H. Phillips and S. Rintoul). The Australian Antarctic Division provided subantarctic clothing for Bindoff and Phillips under AAS project #3002 (H. Phillips and N. Bindoff). AAS project #3228 (N. Bindoff and H. Phillips) provided $27,000 for salary support for a research assistant to work on analysis of the data and publication of a manuscript. Significant in-kind support was provided by CSIRO Marine and Atmospheric Research for the EM-APEX component. Details of the shipboard operations and deployment of the EM-APEX floats can be found in the document ""emapex_deployment_report.pdf"". The complete voyage report is available from h.e.phillips@utas.edu.au. It may be cited as Naveira Garabato, A.; Bindoff, N.; Phillips, H.; Polzin, K.; Sloyan, B.; Stevens, D. and Waterman, S. RRS James Cook Cruise 29, 01 Nov - 22 Dec 2008. SOFine Cruise Report: Southern Ocean Finestructure National Oceanography Centre, Southampton, 2009 See the download file for more information, which contains a data report and a data description file as well as the data." proprietary @@ -8860,6 +9069,7 @@ L4_SIT_Open_NA SMOS-CryoSat L4 Sea Ice Thickness ESA STAC Catalog 2010-11-15 -1 L7PAN128112_141101_R_1 Georeferenced Landsat 7 image of the Prince Charles Mountains and Lambert Glacier AU_AADC STAC Catalog 2001-11-14 2001-11-14 66.7, -72, 67.7, -71 https://cmr.earthdata.nasa.gov/search/concepts/C1214311128-AU_AADC.umm_json Georeferenced Landsat 7 image of the Prince Charles Mountains and Lambert Glacier. The image was captured on the 14th of November, 2001. proprietary L7_ETM_SLC_OFF Landsat 7 ETM+ SLC-off (2003-present) USGS_LTA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567903-USGS_LTA.umm_json The USGS Earth Resources Observation and Science (EROS) Center archive holds data collected by the Landsat suite of satellites, beginning with Landsat 1 in 1972. All Landsat data held in the USGS EROS archive are available for download at no charge. proprietary LAB97_0 Labrador Sea 1997 optical measurements OB_DAAC STAC Catalog 1997-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360417-OB_DAAC.umm_json Bio-optical validation observations were made on the CCGS Hudson in spring from 9 May to 11 June 1997 in the Labrador Sea. Stations were occupied along several sections between Labrador and Greenland with some locations revisited more than once during a cruise. The most heavily sampled SW-NE section from Hamilton Bank on the Labrador Shelf to Cape Desolation on the Greenland Shelf is the AR7 line of the World Ocean Circulation Experiment. proprietary +LACHYSIS LACHYCIS (Sistema de informacion del Ciclo Hidrologico y las Actividades en recursos Hidricos de America latina) CEOS_EXTRA STAC Catalog 1970-01-01 -105, -60, -35, 30 https://cmr.earthdata.nasa.gov/search/concepts/C2232459312-CEOS_EXTRA.umm_json Information System on Hydrology and Water resources in Latin America and the Caribbean countries. proprietary LADSII_hydrographic_survey_1 Hydrographic survey LADSII by the RAN Australian Hydrographic Service at Macquarie Island, February to March 1999 AU_AADC STAC Catalog 1999-02-14 1999-03-15 158.864, -54.502, 159.02, -54.304 https://cmr.earthdata.nasa.gov/search/concepts/C1214311147-AU_AADC.umm_json The RAN Australian Hydrographic Service conducted an airborne hydrographic survey LADSII at Macquarie Island, February to March 1999. The areas surveyed included the northern coast between Handspike Point and Garden Bay and an area in the vicinity of Judge and Clerk Islets north of Macquarie Island. The survey dataset, which includes metadata, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available for download from a Related URL in this metadata record. The survey was lead by M.J.Sinclair. These data are not suitable for navigation. proprietary LAI_Africa_2325_1 MODIS-derived Aggregate, Woody and Herbaceous Leaf Area Index for Africa, 2002-2022 ORNL_CLOUD STAC Catalog 2002-07-05 2022-07-29 -21.28, -40.02, 63.86, 20.02 https://cmr.earthdata.nasa.gov/search/concepts/C2954717391-ORNL_CLOUD.umm_json This dataset provides leaf area index (LAI) estimates for Sub-Saharan Africa for woody, herbaceous, and aggregate vegetation types. The estimates were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 and the native MODIS LAI product (MCD15A2H Version 6.1), which provides LAI measurements every 8 days at 500-m pixel size. Data from the MCD15A2H product were processed further to generate three layers including: a smoothed and gap filled LAI layer referred to as aggregate leaf area index and two additional layers processed to separate woody LAI (tree and shrubs) and herbaceous LAI (grass and forbs). The data include 31 MODIS 10-degree tiles and cover 2002 to 2022. The data are provided in NetCDF format. proprietary LAI_Canada_816_1 Leaf Area Index Maps at 30-m Resolution, Selected Sites, Canada ORNL_CLOUD STAC Catalog 2000-01-01 2001-12-31 -135.05, 44.23, -65, 63.14 https://cmr.earthdata.nasa.gov/search/concepts/C2737900059-ORNL_CLOUD.umm_json This data set provides local LAI maps for the selected measured sites in Canada. These derived maps may also be useful for validating other LAI maps over these same sites given that the areas are protected from disturbance. The maps should be used for the given period of validity. The LAI data are suitable for use in modeling the carbon, water, energy, energy and trace gas exchange between the land surface and the atmosphere at regional scales. The data set may also be useful for monitoring changes in the land surface.The Leaf Area Index (LAI) maps are at 30-m resolution for the selected sites. LAI is defined here as half the total (all-sided) live foliage area per unit horizontal projected ground surface area. Overstory LAI corresponds to all tree foliage except for treeless areas where it corresponds to total foliage. The algorithms were developed from ground measurements and Landsat TM and ETM+ images (Fernandes et. al., 2003). A mask was developed using the Landsat ETM+/TM5 image and available land cover map to identify only those areas with land cover belonging to the sample land cover classes and with Landsat ETM+/TM5 spectral reflectance values that fell within the convex hull of the spectral reflectance values over the plots. LAI was mapped within the masked region using the Landsat ETM+/TM5 image and the developed transfer function. The final LAI map was scaled by a factor of 20 (offset 0). The LAI maps are in Tagged Image File Format (TIFF). proprietary @@ -8870,6 +9080,7 @@ LAMONT_ATL_0 Lamont-Doherty Earth Observatory measurements from the South Atlant LAMONT_GOM_0 Lamont-Doherty Earth Observatory measurements from the Gulf of Mexico (GOM) OB_DAAC STAC Catalog 2010-08-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360423-OB_DAAC.umm_json Measurements from the Gulf of Mexico (GOM) made by researchers at Columbia University's Lamont-Doherty Earth Observatory (LDEO). proprietary LAMONT_SAB_0 Lamont-Doherty Earth Observatory (LDEO) - South Atlantic Bight (SAB) OB_DAAC STAC Catalog 2005-05-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360424-OB_DAAC.umm_json Measurements from the South Atlantic Bight (SAB) made by researchers at Columbia University's Lamont-Doherty Earth Observatory (LDEO). proprietary LAMONT_SCS_0 Lamont-Doherty Earth Observatory measurements from the South China Sea (SCS) OB_DAAC STAC Catalog 2016-06-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360425-OB_DAAC.umm_json Measurements from the South China Sea (SCS) made by researchers at Columbia University's Lamont-Doherty Earth Observatory (LDEO). proprietary +LANDFIRE LANDFIRE National Products CEOS_EXTRA STAC Catalog 1970-01-01 -138, 13, -54, 68 https://cmr.earthdata.nasa.gov/search/concepts/C2231554672-CEOS_EXTRA.umm_json LANDFIRE National products comprise a set of 20+ digital maps of vegetation composition and structure; wildland fuel (crown and surface); and current departure from simulated historical vegetation conditions. LANDFIRE National procedures integrate relational databases, remote sensing, systems ecology, gradient modeling, and landscape simulation to create consistent and comprehensive products that are standardized across the entire United States. LANDFIRE will deliver national products on an incremental basis through FY 2009. LANDFIRE national data layers can be obtained through The National Map. proprietary LANDMET_1 Land Surface Atmospheric Boundary Interaction Product L3 V1(LANDMET) at GES DISC GES_DISC STAC Catalog 1998-01-01 2007-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1374187813-GES_DISC.umm_json This product is a multi-variate data compilation that reconciles the variation scales of these multiple measurements from varies resources, merges and maps them into a comprehensive description of the near-surface atmospheric properties together with the land surface property variations on diurnal-to-decadal time scales. Many of these data products, especially those based on surface measurements, are spatially and/or temporally sparse or incomplete in coverage, so procedures were developed to fill missing values. The data product is comprised of a sequence of daily global files, where quantities are mapped into 1.0-degree equivalent equal-area grid, with time sampling is reported at daily or 3-hourly intervals. The time period overlap among the products covers 10 years from 1998 to 2007. proprietary LANDMET_ANC_SM_1 LANDMET Ancillary Soil Moisture data L3 V1 (LANDMET_ANC_SM) at GES DISC GES_DISC STAC Catalog 1998-01-01 2007-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1374187876-GES_DISC.umm_json This ancillary climatology soil porosity and wetlands coverage information were derived from the daily GEWEX fusion of satellite active and passive microwave measurements. These quantities are re-mapped to a 1.0 degree equal-area grid from the original 0.25 degree equal-angle map grid. proprietary LANDMET_ANC_ST_1 LANDMET Ancillary ISCCP Surface Type Data L3 V1 (LANDMET_ANC_ST) at GES DISC GES_DISC STAC Catalog 1998-01-01 2007-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1374187873-GES_DISC.umm_json This is an ancillary product containing the land surface type information includes, for each map grid cell, the type and coverage fractions of the top three surface types present in the cell. The sum of these three fractions may not equal the land fraction if more types are present (usually a small difference). The product is a climatology data, no change in time, with spatial grid cell on equal-area mapping at 1.0-degree-equivalent. proprietary @@ -8999,8 +9210,10 @@ LGRIP30_L1_RAIN_002 Landsat-Derived Global Rainfed-Cropland Product L1 2020 30 m LGRIP30_L2_IRRI_002 Landsat-Derived Global Irrigated-Cropland Product L2 2020 30 m V002 LPCLOUD STAC Catalog 2019-01-01 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3263429968-LPCLOUD.umm_json The Landsat-Derived Global Irrigated-Cropland Product Level 2 2020 (LGRIP30_L2_IRRI) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP_L2_IRRI V2 maps agricultural lands by dividing them into irrigated single crop, double crop, and continuous croplands, and calculates applicable cropland areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. Each LGRIP L2 Irrigated 30 m resolution GeoTIFF file contains a layer that identifies areas of irrigated cropland (cropland that had at least one irrigation during the crop growing period) divided into single, double, and continuous crop classifications, non-irrigated land (rainfed cropland and areas not classified as cropland), and water bodies over a 10° by 10° area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. Currently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date. proprietary LGRIP30_L2_RAIN_002 Landsat-Derived Global Rainfed-Cropland Product L2 2020 30 m V002 LPCLOUD STAC Catalog 2019-01-01 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3263433662-LPCLOUD.umm_json The Landsat-Derived Global Rainfed-Cropland Product Level 2 2020 (LGRIP30_L2_RAIN) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP_L2_RAIN V2 maps agricultural lands by dividing them into rainfed single croplands and rainfed single croplands mixed with natural vegetation, and calculates applicable cropland areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. Each LGRIP L2 Rainfed 30 m resolution GeoTIFF file contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation without any artificial watering) divided into single crop and single crop that is mixed with natural vegetation, non-rainfed land (irrigated croplands and areas not classified as cropland), and water bodies over a 10° by 10° area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. Currently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date. proprietary LGRIP30_L3_002 Landsat-derived Global Rainfed and Irrigated-Cropland Product L3 2020 30 m V002 LPCLOUD STAC Catalog 2019-01-01 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3262817664-LPCLOUD.umm_json The Landsat-derived Global Rainfed and Irrigated-Cropland Product Level 3 2020 (LGRIP30_L3) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP L3 V2 maps agricultural croplands by dividing them into irrigated and rainfed croplands, and calculates irrigated and rainfed areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. Each LGRIP30 L3 V2 30 m resolution GeoTIFF file contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation without any artificial watering), irrigated cropland (cropland that had at least one irrigation during the crop growing period), non-cropland, and water bodies over a 10° by 10° area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. Currently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date. proprietary +LIDA Lidar Data from Brazil CEOS_EXTRA STAC Catalog 1972-02-23 -45, -23, -45, -23 https://cmr.earthdata.nasa.gov/search/concepts/C2227456105-CEOS_EXTRA.umm_json The FISAT home page on the WWW is http://www.laser.inpe.br/fisat/ . This set contains data obtained at the location of Sao Jose dos Campos (23 degrees S, 45 degrees W), only. >From 1972 to 1981 only night-time data of the Lidar backscatter return at 589.0 nm are available. The periodicity of the data is irregular. Generally short-duration measurements (less than 2 hours) are available at about one measurerent per week. Long-duration data covering most of the night are available in a few campaigns. Data are also given, in processed form, providing aerosol backscatter ratio from 15 to 30 km altitude and sodium density from 75 to 105 km altitude. >From 1981 to 1993, campaigns of sodium measurements taken during the day, including several diurnal cycles are also available. >From 1983 to the present day a new powerful laser at 593.0 nm provides the Rayleigh scatter profiles giving the atmospheric density and temperatures from 35 to nearly 70 km altitude. Data are currently obtained, approximately, on a weekly basis. proprietary LIDAR_0 Pigment measurements from 1989 and 1990 in the Gulf of St Lawrence OB_DAAC STAC Catalog 1989-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360430-OB_DAAC.umm_json Pigment measurements from 1989 and 1990 in the Gulf of St Lawrence. proprietary LIDAR_FOREST_CANOPY_HEIGHTS_1271_1 CMS: GLAS LiDAR-derived Global Estimates of Forest Canopy Height, 2004-2008 ORNL_CLOUD STAC Catalog 2004-10-03 2004-11-08 -161.41, -55.45, 179.89, 69.29 https://cmr.earthdata.nasa.gov/search/concepts/C2343105406-ORNL_CLOUD.umm_json This data set provides estimates of forest canopy height derived from the Geoscience Laser Altimeter System (GLAS) LiDAR instrument that was aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite. A global GLAS waveform data set (n=12,336,553) from collection periods between October 2004 and March 2008 was processed to obtain canopy height estimates.Estimates of GLAS maximum canopy height and crown-area-weighted Lorey's height are provided for 18,578 statistically-selected globally distributed forested sites in a point shapefile. Country is included as a site attribute.Also provided is the average canopy height for the forested area of each country, plus the number of GLAS data footprints (shots), number of selected sample sites, and estimates of the variance for each country. proprietary +LIFE_ECO_NBS_SIER_VEG_FUEL1 Fire Fuel Inventory, Yosemite National Park from the U.S. Geological Survey, Biological Resources Division CEOS_EXTRA STAC Catalog 1987-05-13 -120, 37, -119, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231550682-CEOS_EXTRA.umm_json The fuel inventory data involves 144 var. radius plots measured for vegetative cover and structure by species and fuel loading. Standing and downed fuel is estimated by size, class and type. Wood and leaf litter fall data are collected annually. This dataset was collected in Yosemite National Park road corridors between 1200 and 2400 meters. This dataset is part of the U.S. Geological Survey, Biological Resources Division, Global Change Program. proprietary LIMSN7L1PROFILER_001 LIMS/Nimbus-7 Level 1 Profiles of Radiance Data V001 (LIMSN7L1PROFILER) at GES DISC GES_DISC STAC Catalog 1978-10-25 1979-05-30 -180, -64, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1583647214-GES_DISC.umm_json LIMSN7L1PROFILER is the Nimbus-7 Limb Infrared Monitor of the Stratosphere (LIMS) Level-1 Profiles of Radiance Data product and contains selected daily vertical profiles across the earth’s atmospheric limb derived from the LIMS Level-1 Radiance Archival Tape (RAT) data product. Measurements are obtained, as a function of tangent height (or scan angle), once every 12 seconds in each of the six spectral bands (two 15-micrometer CO2 bands (narrow and wide), an 11.3-micrometer HNO3 band, a 9.6-micrometer O3 band, a 6.9-micrometer H2O band, and a 6.2-micrometer NO2 band) from the highest pressure level to the lowest in steps of 0.1 km Each file contains one days worth of data (~14 orbits per day). LIMS is a limb profiler and spatial coverage is near global between latitude -64 and +84 degrees. Vertical coverage is from about 10 to 50 km (O3 channel to 65 km), with vertical resolution of about 1.5 km. The data are available from 25 October 1978 through 30 May 1979. The principal investigators for the LIMS experiment were Dr. James M. Russell, III from NASA Langley and Dr. John Gille from NCAR. This product was previously available from the NASA National Space Science Data Center (NSSDC) under the name LIMS Radiance Archival Data with the identifier ESAC-00032 (old id 78-098A-01B). proprietary LIMSN7L1RAT_001 LIMS/Nimbus-7 Level 1 Radiance Data V001 (LIMSN7L1RAT) at GES DISC GES_DISC STAC Catalog 1978-10-25 1979-05-30 -180, -64, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C1582609108-GES_DISC.umm_json LIMSN7L1RAT is the Nimbus-7 Limb Infrared Monitor of the Stratosphere (LIMS) Level-1 Radiance Data product. It contains calibrated, earth-located radiances, as well as housekeeping information, instrument status, and data quality information. Radiances of the Earth limb were measured both day and night in six spectral bands (6.2, 6.3, 9.6, 11.3, and two at 15 micrometers). Though calibrated, the radiances are not corrected for instrument effects such as field-of-view, electronic delay, and spacecraft motion. Each file contains one orbit of data (~14 orbits per day). LIMS is a limb profiler and spatial coverage is near global between latitude -64 and +84 degrees. Vertical coverage is from about 10 to 50 km (O3 channel to 65 km), with vertical resolution of about 1.5 km. The data are available from 25 October 1978 through 30 May 1979. The principal investigators for the LIMS experiment were Dr. James M. Russell, III from NASA Langley and Dr. John Gille from NCAR. This product was previously available from the NASA National Space Science Data Center (NSSDC) under the name LIMS Radiance Archival Data with the identifier ESAC-00032 (old id 78-098A-01B). proprietary LIMSN7L2_006 LIMS/Nimbus-7 Level 2 Vertical Profiles of O3, NO2, H2O, HNO3, Geopotential Height, and Temperature V006 (LIMSN7L2) at GES DISC GES_DISC STAC Catalog 1978-10-25 1979-05-28 -180, -65, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C1273652156-GES_DISC.umm_json The Limb Infrared Monitor of the Stratosphere (LIMS) version 6 Level-2 data product consists of daily, geolocated, vertical profiles of temperature, geopotential height, and mixing ratios of ozone (O3), nitrogen dioxide (NO2), water vapor (H2O), and nitric acid (HNO3). Version 6 LIMS data have improved spatial resolution in both the vertical and along the orbital track, as well as improved accuracy and precision of measured geophysical parameters. The data files are in an ASCII text format and each data file is accompanied by three data screening files. The LIMS instrument was launched on the Nimbus-7 satellite and was operational from 25 October 1978 until May 28, 1979. These data supersede the previous version 5 product, known as the LIMS Inverted Profile Archival Tape (LAIPAT). proprietary @@ -9067,10 +9280,15 @@ LPRM_WINDSAT_DY_SOILM3_001 WindSat/Coriolis surface soil moisture (LPRM) L3 1 da LPRM_WINDSAT_NT_SOILM3_001 WindSat/Coriolis surface soil moisture (LPRM) L3 1 day 25 km x 25 km nighttime V001 (LPRM_WINDSAT_NT_SOILM3) at GES DISC GES_DISC STAC Catalog 2003-02-01 2012-08-01 -180, -64, 180, 83 https://cmr.earthdata.nasa.gov/search/concepts/C1488311688-GES_DISC.umm_json WindSat/Coriolis surface soil moisture (LPRM) L3 1 day 25 km x 25 km nighttime V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from polarimetric microwave radiometer data from WindSat, onboard the Naval Research Laboratory's Coriolis satellite, using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the nighttime product. The data set covers the period from February 2003 to July 2012. The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the WindSat's Ka-band (37.0 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the WindSat brightness temperatures (sdrLowRes) product, nighttime passes, as processed using LPRM (i.e., LPRM/WindSat/Coriolis L2 product, LPRM_WINDSAT_SOILM2_V001). proprietary LPRM_WINDSAT_SOILM2_001 WindSat/Coriolis surface soil moisture (LPRM) L2 V001 (LPRM_WINDSAT_SOILM2) at GES DISC GES_DISC STAC Catalog 2003-02-01 2012-08-01 -180, -64, 180, 83 https://cmr.earthdata.nasa.gov/search/concepts/C1488311935-GES_DISC.umm_json WindSat/Coriolis surface soil moisture (LPRM) L2 V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from polarimetric microwave radiometer data from WindSat, onboard the Naval Research Laboratory's Coriolis satellite, using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from February 2003 to July 2012. The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the WindSat's Ka-band (37.0 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the WindSat brightness temperatures (sdrLowRes) product, archived at the Goddard Earth Sciences Data and Information Services Center (GES DISC). proprietary LRIRN6L2IPAT_001 LRIR/Nimbus-6 Level 2 Inverted Profiles of Temperature and Ozone V001 (LRIRN6L2IPAT) at GES DISC GES_DISC STAC Catalog 1975-06-20 1976-01-06 -180, -64, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C1513197201-GES_DISC.umm_json LRIRN6L2IPAT is the Nimbus-6 Limb Radiance Inversion Radiometer (LRIR) Level 2 Inverted Profiles of Temperature and Ozone data product. The product contains daily profiles of temperature and ozone concentration profiles that were inverted from radiances measured in four spectral regions: two in the 15 micron carbon dioxide band; one in the 9.7 micron ozone band; and one located in the rotational water vapor band (23 to 27 microns). The calibrated radiances are also included in this product. There are a maximum of 13 orbits per day each with up to 115 profiles per orbit. LRIR is a limb profiler with spatial coverage from latitude -64 to +84 degrees. Vertical profiles are provided at 17 standard pressure levels (from 100 to 0.1 mbar, i.e., from 15 to 64 km) with about 1.5 km vertical resolution. The instrument operated successfully and data are available from 20 June 1975 to 6 January 1976. After this, the detector temperature began to rise rapidly, and the instrument was turned off. The principal investigator for the LRIR experiment was Dr. John Gille from NCAR. This product was previously available from the NSSDC with the identifier ESAD-00037 (old ID 75-052A-04A). proprietary +LSATUSERV LANDSAT User Service of the Brazilian National Institute on Space Research (INPE) CEOS_EXTRA STAC Catalog 1973-05-14 -77, -38, -35, 6 https://cmr.earthdata.nasa.gov/search/concepts/C2227456155-CEOS_EXTRA.umm_json The Brazilian receiving station for Landsat data is located in Cuiaba, state of Mato Grosso, Midwest region: 56 degrees 5 minutes 30 seconds West longitude, and 15 degrees 32 minutes 30 seconds South latitude. The Cuiaba station started to operate in May 1973. MSS data were acquired on a routine basis over Brazil and by special request over the other countries. RBV images were acquired during the Landsat 3 years. Thematic mapper data have been recorded over Brazil since February 1984. By October 1987, the Cuiaba station stopped recording MSS data and began to acquire Thematic Mapper images on routine fashion over the whole range of the antenna. MSS products are for while available just through the reproduction of existing photographic originals (about 150.000 B/W and color images). A new processing system was designed and began running digital frames in October 1995. Thematic Mapper images are available and can be ordered both in print and digital formats. proprietary LSC_Aeromonas_salmonicida Development of DNA primers to identify a Romet-resistance gene in Aeromonas salmonicida and its subsequent use for epidemiological studies CEOS_EXTRA STAC Catalog 1997-03-01 2000-02-28 -82.89, 36.96, -77.48, 40.88 https://cmr.earthdata.nasa.gov/search/concepts/C2231554667-CEOS_EXTRA.umm_json Furunculosis is a significant cause of disease and mortality to hatchery reared and wild populations of salmonid fishes, particularly the salmon species. The disease is caused by Aeromonas salmonicida, a Gram nagative bacterium that is highly virulent and is readily transmitted horozontally. An asymptomatic form of the disease occurs, including those individuals that survive epizootics, and these fish can serve as a source of infection in subsequent outbreaks. The disease is treated by antimicrobial therapy. Romet, a potentiated sulfonimide, was approved for use by the FDA in 1986 and is one of only three agents approved. Since approval of Romet, resistant strains of A. salmonicida have emerged and this removes treatment with Romet as an alternative. Recent work has described an R-plasmid mediated resistance in many of the resistant A. salmonicida strains. proprietary LSC_Aeromonasinsalmon Detection of covert Aeromonas salmonicida infection in Atlantic salmon and other salmonids CEOS_EXTRA STAC Catalog 1996-08-01 1999-09-30 -82.89, 36.96, -77.48, 40.88 https://cmr.earthdata.nasa.gov/search/concepts/C2231551151-CEOS_EXTRA.umm_json During the early 1900s, several researchers cultured Aeromonas salmonicida, cause of furunculosis disease, from the kidneys and intestines of apparently healthy trout. Home (1928) speculated that asymptomatic carriers become reservoirs of infection. Hence, a need was perceived for bacteriological examinations to be conducted even before asymptoimatic fish were stocked. During the devastating outbreaks of furunculosis that occurred in Great Britain in the 1920-30s, Mackie et al. (1933) noted that epizootics in natural waters correlated with the stocking of fish originating from infected farms. They also noted that the kidney was the usual site of harborage of the pathogen but culture often provided inadequate detection. More recent studies continue to emphasize the importance of asymptomatic, carrier fish as reservoirs of infection and spread of the disease within the natural environment (Jarp et al. 1993, Johnsen and Jensen 1994). proprietary LSC_AtlanticSalmon Assessment of Spatial and Temporal Distribution of Genetic Diversity in Atlantic Salmon (Salmo salar) CEOS_EXTRA STAC Catalog 1995-03-15 2000-09-30 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C2231550338-CEOS_EXTRA.umm_json The eastern coastal rivers of North America have historically supported anadromous populations of Atlantic salmon (Salmo salar). Numbers of these animals have declined due to overfishing and loss of habitat, and population numbers have been supplemented by stocking efforts that span at least the last hundred years. Often, these stockings used fish of diverse origins. This is exemplified by the fact that several Maine rivers were stocked with Canadian fish from at least two locations. Because of this stocking history, it is not known if significant remnants of native Atlantic salmon stocks exist in the coastal rivers of Maine. Atlantic salmon in five Maine rivers were designated as category 2 candidates for listing under the Endangered Species Act in 1991 in response to the precipitous decline in population numbers. In October 1993, all anadromous U.S. Atlantic salmon were included in a petition to the U.S. Fish and Wildlife Service (FWS) for a Rule to List the species under the Endangered Species Act. Information was obtained from http://www.lsc.usgs.gov proprietary +LSC_Flavobacteriumpsychrophilum Evaluation of the Genetic Diversity of Flavobacterium Psychrophilum from Various Origins CEOS_EXTRA STAC Catalog 1996-02-01 2002-09-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551122-CEOS_EXTRA.umm_json The US Fish and Wildlife Service along with the Washington Department of Fisheries and Wildlife work in cooperation on the Pacific salmon restoration effort. For a number of reasons, population numbers of some Pacific salmon species have declined dramatically over recent years and a large part of the restoration effort encompasses hatchery propagation of progeny from returning fish for subsequent planting into their natural waters. With hatchery rearing operations fish are maintained in high densities and this situation lends itself to disease problems. One such disease is bacterial cold water disease, caused by the Gram negative bacterium Flavobacterium psychrophilum. Source of the infection is the ubiquitous nature of the pathogen and because of the verticle transmissability. Only recently has the biochemical and taxonomic position of F. psychrophilum been elucidated more accurately. Information was obtained from http://www.lsc.usgs.gov proprietary +LSC_biomarkers Evaluation of Selected Histologic and Immunologic Biomarkers in Fish Collected for the BEST pilot program in the Mississippi CEOS_EXTRA STAC Catalog 1996-03-01 2000-10-01 -82.89, 36.96, -77.48, 40.88 https://cmr.earthdata.nasa.gov/search/concepts/C2231550647-CEOS_EXTRA.umm_json This study is part of a larger project entifled Biomonitoring of Environmental Status and Trends (BEST) Program: Testing and Implementation of Selected Aquatic Ecosystem Indicators in the Mississippi River System, 1995. This pilot project includes assessment of a variety of biomarkers of which we are responsible for the histologic and immunologic markers. During the period in which concentrations of persistent contaminants were declining, the use of and concerns about other chemicals, especially those that do not accumulate in biota, increased. At least part of this concern stemmed from increasingly frequent reports of fish kills and avi an wildlife mortality incidents related to the use of soft pesticides-highly toxic, but short-lived organophosphate and carbamate insecticides that do not accumulate (e.g.; Glaser 1995). Herbicides are also now widely distributed in surface and ground waters of agricultural areas. Information was obtained from http://www.lsc.usgs.gov/ proprietary +LSC_immunereprohistologic Immune, Reproductive and Histologic Biomarker Evaluation in Fish Collected for the Columbia and Rio Grande River Basin BEST Program, 1997 CEOS_EXTRA STAC Catalog 1997-08-01 2001-03-01 -115, 30, -105, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231553382-CEOS_EXTRA.umm_json "This study is part of a larger project entitled ""Contaminants and Biomarkers in Fish in the Columbia River and Rio Grande Basins, 1997"" ( Mid-Continent Ecological Science Center) This project is part of the Biomonitoring of Environmental Status and Trends (BEST) program. The BEST program incorporates both analytical chemistry arid a suite of biological responses to describe and track contaminant exposure and effects. Our part of this program is to measure and evaluate selected histologic, immunological and reproductive biomarkers. Our objectives are: to document the presence of selected histologic lesions which have been validated or widely accepted as indicators of contaminant exposure; to determine if there is evidence of immunosuppression using immune system biomarkers; evaluate gonad histology utilizing new potential biomarkers; determine if changes in gonad histology correlate with circulating vitellogenin levels; determine if these findings correlate with contaminant presence or concentration. Information was obtained from http://www.lsc.usgs.gov" proprietary LSM_807_1 Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies ORNL_CLOUD STAC Catalog 1996-01-15 1996-01-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2956539244-ORNL_CLOUD.umm_json The NCAR LSM 1.0 is a land surface model developed to examine biogeophysical and biogeochemical land-atmosphere interactions, especially the effects of land surfaces on climate and atmospheric chemistry. It can be run coupled to an atmospheric model or uncoupled, in a stand-alone mode, if an atmospheric forcing is provided. The model runs on a spatial grid that can range from one point to global. The model was designed for coupling to atmospheric numerical models. Consequently, there is a compromise between computational efficiency and the complexity with which the necessary atmospheric, ecological, and hydrologic processes are parameterized. The model is not meant to be a detailed micrometeorological model, but rather a simplified treatment of surface fluxes that reproduces at minimal computational cost the essential characteristics of land-atmosphere interactions important for climate simulations. The model is a complete executable code with its own time-stepping driver, initialization (subroutine lsmini), and main calling routine (subroutine lsmdrv). When coupled to an atmospheric model, the atmospheric model is the time-stepping driver. There is one call to subroutine lsmini during initialization to initialize all land points in the domain; there is one call per time step to subroutine lsmdrv to calculate surface fluxes and update the ecological, hydrological, and thermal state for all land points in the domain. The model writes its own restart and history files. These can be turned off if appropriate. Available for downloading from the ORNL DAAC are the LMS Model Documentation and User's Guide, the model source code, input data set, and scripts for running the model. Applications of the model are described in two additional companion files. proprietary +LS_TM_ARC Landsat TM Image Data Archived in China Remote Sensing Satellite Ground Station CEOS_EXTRA STAC Catalog 1986-06-01 90, 20, 140, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2226631645-CEOS_EXTRA.umm_json Landsat 5 was launched on March 1, 1984, carrying a seven-band TM sensor, and still operates properly at present. The satellite takes a sun-synchronized orbit with 705km altitude and 98.22 deg. inclination. A TM scene covers 185km by 170km earth surface approximately, with 30m ground resolution for band 1,2,3,4,5,7 and 120m for band6. For a particular place, the revisit cycle of the satellite is 16 days. Chaina Remote Sensing Satellite Ground Station(CRSGS) was inaugurated and become operational in Dec. 1986. Up to now it is the most important source of remote sensing satellite data in China for earth resouce exploration and environment monitoring. CRSGS has provided a large amount of satellite remote sensing products to more than 400 users, domestic and abroad. Applications of TM images have resulted in great economic and social benefits in a wide range of areas of national economy: resource survey and utilization, environment monitoring, geographic cartography, minerarl exploration, disaster detecting and assessing, etc. TM data received by CRSGS since 1986 have been archived. Through a Catalogue Archive and Browse System(CABS), users can retrieve useful information about data of interests. A image(or a group of images) could be searched according to date, location(latitude-longitude or path-row), and quality, etc. Text catalogue is available for all TM data in the archival. In addition to text contents, sub-sampled browse images are available for data acquired after Apr.,1994. The major products of CRSGS are TM data on CCTs, floppy disks and imagery on films or papaer prints. Products fall into two categories with respect to processing methods. 1. Standard processing includes systematic correction, precision correction, and geocoding, etc. 2.Special product(user dependent) includes multi-scene mosaicking, image classification, user defined annotation or administrative boundary adding, special juts enhancement, etc. proprietary LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001 Aerosol optical size distribution and soot core size distribution measured by a Single Particle Soot Photometer (SP2) for 30 days in summer 2018-2019 SCIOPS STAC Catalog 2018-01-12 2019-03-01 123, -75, 123, -75 https://cmr.earthdata.nasa.gov/search/concepts/C1605658799-SCIOPS.umm_json The data set comprise data measured with a Single Particle Soot Photometer (SP2) at the Italian/French Dome Concordia station in Antarctica. The station is located at 75°05′59″S 123°19′56″E at an elevation of 3233 m. The data was collected at the ATMOS clean air facility at the station between 1.12.2018 - 3.1.2019. The SP2 is a single particle instrument which recorded every particle detected for the duration of the measurements. Physical parameters derived from the recorded data include optical size of the particles and soot-core size of the soot containing particles. proprietary LTER_0 Long Term Ecological Research Network (LTER) OB_DAAC STAC Catalog 1981-09-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360464-OB_DAAC.umm_json Measurements from the Long Term Ecological Research Network (LTER) between 1981 and 1999. proprietary LUH2_GCB2019_1851_1 LUH2-GCB2019: Land-Use Harmonization 2 Update for the Global Carbon Budget, 850-2019 ORNL_CLOUD STAC Catalog 0850-01-01 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2756847743-ORNL_CLOUD.umm_json This dataset, referred to as LUH2-GCB2019, includes 0.25-degree gridded, global maps of fractional land-use states, transitions, and management practices for the period 0850-2019. The LUH2-GCB2019 dataset is an update to the previous Land-Use Harmonization Version 2 (LUH2-GCB) datasets prepared as required input to land models in the annual Global Carbon Budget (GCB) assessments, including land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, afforestation, and crop rotations. Compared with previous LUH2-GCB datasets, the LUH2-GCB2019 takes advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil, as far back as 1950. LUH2-GCB datasets are used by bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB. proprietary @@ -10355,6 +10573,7 @@ Marine_Virus_Southern_Ocean_Evans_IPY71_NL_1 Abundances of algae, bacteria, viru Marlon_Lewis_92_0 Marlon Lewis drifting buoys 1992 OB_DAAC STAC Catalog 1992-08-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360473-OB_DAAC.umm_json Data from 3 drifting buoys deployed in fall, 1992. Two of the buoys were air launched near 140W, -999 degrees in the Pacific Ocean, and one was deployed in Monterey Bay attached to a fixed mooring. The fixed mooring was recovered and subjected to post-calibration. proprietary Marn10k_1 Marine Plain 1:10000 Topographic GIS Dataset AU_AADC STAC Catalog 1958-01-06 1979-01-26 78.0007, -68.666, 78.216, -68.597 https://cmr.earthdata.nasa.gov/search/concepts/C1214313613-AU_AADC.umm_json This dataset details features of Marine Plain in the Vestfold Hills, Antarctica. The dataset includes coastline, 5 metre interval contours and lake shores. These data were captured from aerial photography and are the basis of the Marine Plain Orthophoto Map published for the Australian Antarctic Division in 1993. This map is available from a URL provided in this metadata record. proprietary Maryland_Temperature_Humidity_1319_1 In-situ Air Temperature and Relative Humidity in Greenbelt, MD, 2013-2015 ORNL_CLOUD STAC Catalog 2013-09-05 2015-12-28 -76.86, 38.99, -76.84, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2736724792-ORNL_CLOUD.umm_json This data set describes the temperature and relative humidity at 12 locations around Goddard Space Flight Center in Greenbelt MD at 15 minute intervals between November 2013 and November 2015. These data were collected to study the impact of surface type on heating in a campus setting and to improve the understanding of urban heating and potential mitigation strategies on the campus scale. Sensors were mounted on posts at 2 m above surface and placed on 7 different surface types around the centre: asphalt parking lot, bright surface roof, grass field, forest, and stormwater mitigation features (bio-retention pond and rain garden). Data were also recorded in an office setting and a garage, both pre- and post-deployment, for calibration purposes. This dataset could be used to validate satellite-based study or could be used as a stand-alone study of the impact of surface type on heating in a campus setting. proprietary +MassBay_LongTerm Long-Term Oceanographic Observations in Massachusetts Bay, 1989-2006 CEOS_EXTRA STAC Catalog 1989-01-01 2006-12-31 -71, 42, -70.5, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552981-CEOS_EXTRA.umm_json This data report presents long-term oceanographic observations made in western Massachusetts Bay at long-term site LT-A (42° 22.6' N., 70° 47.0' W.; nominal water depth 32 meters) from December 1989 through February 2006 and long-term site B LT-B (42° 9.8' N., 70° 38.4' W.; nominal water depth 22 meters) from October 1997 through February 2004. The observations were collected as part of a U.S. Geological Survey (USGS) study designed to understand the transport and long-term fate of sediments and associated contaminants in Massachusetts Bay. The observations include time-series measurements of current, temperature, salinity, light transmission, pressure, oxygen, fluorescence, and sediment-trapping rate. About 160 separate mooring or tripod deployments were made on about 90 research cruises to collect these long-term observations. This report presents a description of the 16-year field program and the instrumentation used to make the measurements, an overview of the data set, more than 2,500 pages of statistics and plots that summarize the data, and the digital data in Network Common Data Form (NetCDF) format. This research was conducted by the USGS in cooperation with the Massachusetts Water Resources Authority and the U.S. Coast Guard. proprietary MassGIS_GISDATA.COQHMOSAICSCDS_POLY 2001 MrSID Mosaics CD-ROM Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592880-SCIOPS.umm_json CD-ROM index scheme for the 2001 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm 2001 MrSID Mosaics DVD Index SCIOPS STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592858-SCIOPS.umm_json DVD index scheme for the 2001 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQHMOSAICS_POLY 2001 MrSID Mosaics Index SCIOPS STAC Catalog 2002-08-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592815-SCIOPS.umm_json This data layer is used to index the half-meter MrSID mosaics for the 2001/03 1:5,000 Color Ortho Imagery. proprietary @@ -10543,6 +10762,8 @@ NASA_ARC_ASHOE_MAESA_DATA Airborne Southern Hemisphere Ozone Experiment Measurem NASA_Airborne_Lidar_Flights_1 Data from NASA Langley Airborne Lidar flights. LARC_ASDC STAC Catalog 1982-07-01 1992-05-26 180, -50, -180, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1536056467-LARC_ASDC.umm_json Data from the 1982 NASA Langley Airborne Lidar flights following the eruption of El Chichon beginning in July 1982 and continuing to January 1984. Data in ASCII format. proprietary NASA_OMI_NA Aura OMI complete NASA dataset ESA STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336929-ESA.umm_json "The OMI observations provide the following capabilities and features: • A mapping of ozone columns at 13 km x 24 km and profiles at 13 km x 48 km • A measurement of key air quality components: NO2, SO2, BrO, HCHO, and aerosol • The ability to distinguish between aerosol types, such as smoke, dust and sulfates • The ability to measure aerosol absorption capacity in terms of aerosol absorption optical depth or single scattering albedo • A measurement of cloud pressure and coverage • A mapping of the global distribution and trends in UV-B radiation The OMI data are available in the following four levels: Level 0, Level 1B, Level 2, and Level 3. • Level 0 products are raw sensor counts. Level 0 data are packaged into two-hour ""chunks"" of observations in the life of the spacecraft (and the OMI aboard it) irrespective of orbital boundaries. They contain orbital swath data. • Level 1B processing takes Level 0 data and calibrates, geo-locates and packages the data into orbits. They contain orbital swath data. • Level 2 products contain orbital swath data. • Level 3 products contain global data that are composited over time (daily or monthly) or over space for small equal angle (latitude longitude) grids covering the whole globe." proprietary NASMo_TiAM_250m_2326_1 NASMo-TiAM 250m 16-day North America Surface Soil Moisture Dataset ORNL_CLOUD STAC Catalog 2002-06-26 2020-12-31 -180, 14.53, -40, 82.72 https://cmr.earthdata.nasa.gov/search/concepts/C2905457765-ORNL_CLOUD.umm_json This NASMo-TiAM (North America Soil Moisture Dataset Derived from Time-Specific Adaptable Machine Learning Models) dataset holds gridded estimates of surface soil moisture (0-5 cm depth) at a spatial resolution of 250 meters over 16-day intervals from mid-2002 to December 2020 for North America. The model employed Random Forests to downscale coarse-resolution soil moisture estimates (0.25 deg) from the European Space Agency Climate Change Initiative (ESA CCI) based on their correlation with a set of static (terrain parameters, bulk density) and dynamic covariates (Normalized Difference Vegetation Index, land surface temperature). NASMo-TiAM 250m predictions were evaluated through cross-validation with ESA CCI reference data and independent ground-truth validation using North American Soil Moisture Database (NASMD) records. The data are provided in cloud optimized GeoTIFF format. proprietary +NAWQA GIS Coverage of the National Water-Quality Assessment Study-Unit Investigations in the conterminous United States (NAWQA) CEOS_EXTRA STAC Catalog 1970-01-01 -127.88, 22.87, -65.35, 48.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231548614-CEOS_EXTRA.umm_json "This is a coverage of the boundaries and codes used for the U.S. Geological Survey National Water-Quality Assessment (NAWQA) Program Study-Unit investigations for the conterminous United States, excluding the High Plains Regional Ground-Water Study. The National Water-Quality Assessment Program is designed to describe the status and trends in the quality of the Nation's ground- and surface-water resources and to provide a sound understanding of the natural and human factors that affect the quality of these resources (Leahy and others, 1990). A ""Study Unit"" is a major hydrologic system in which NAWQA studies are focused. Study Units are geographically defined by a combination of ground- and surface-water features (Gilliom and others, 1995). As part of the NAWQA program, Study-Unit investigations were planned for 60 areas throughout the Nation to provide a framework for national and regional water-quality assessments (Leahy and others, 1990). The 60 planned Study-Units were divided into three groups of 20. Each group would be intensively studied on a rotational basis with 20 studies beginning in fiscal year 1991 (FY 1991 runs from October 1990-September 1991), 20 more studies beginning in fiscal year 1994 (October 1993-September 1994), and the final 20 studies beginning in fiscal year 1997 (October 1996-September 1997). Each study cycle would span 10 years. In 1996, the number of Study-Units was scaled back to 59 when two of the original 60 Study Units combined. Also, because of budgetary restraints, some of the original planned Study Units have been scheduled to start later than originally planned and others have not even been scheduled to start yet. This coverage contains the boundaries for the 57 Study Units within the conterminous United States, excluding the High Plains Regional Ground Water-Study, which was conceived in late 1997. The coverage also includes the name, starting date, and NAWQA standard abbreviation of each Study Unit plus various codes to help display the data. This data set is used primarily to display the location of NAWQA Study Units and for analysis of data at the national scale. It is not recommended for either local or regional analysis due to the small scale of most of the features. This coverage can be used in conjunction with other NAWQA datasets including the point coverage of NAWQA Trace Element Sampling Sites (NAWQA_TE) and the point coverage of NAWQA Nutrients Sampling Sites (NAWQA_NU). Detailed information on these two coverages can be found in their respective metadata. Originally, Study-Unit boundaries in this coverage were composed of 1: 2,000,000-scale hydrologic unit boundaries (Allord, 1992) and state boundaries (Negri, 1994). As the NAWQA project has progressed and Study-Unit Investigations have gotten underway, many Study-Unit boundaries have been modified. In addition, Study Units have enhanced their boundary coverages with features at higher resolutions. As these modifications are made, Study Units submit their new boundary coverages to National Synthesis teams, who are responsible for summarizing the results from all of the Study Units, and the changes are incorporated into this coverage. As a result, this coverage is composed of linear features at various scales (for example, 1: 100,000, 1: 250,000), but the majority remain at the 1: 2,000,000 scale. The original version of this coverage was generated by the the USGS Cartographic and Publishing Program (CAPP) in Madison, Wisconsin, in the fall of 1991. The procedures used to create this coverage are described below. Each NAWQA Study Unit was asked for a description of their boundary definition. Once this information was gathered, CAPP created the coverage by extracting digital features from the 1: 2,000,000 Hydrologic Unit boundaries coverage and the 1: 2,000,000 state boundaries coverage. Since the majority of Study-Unit boundaries are defined from hydrologic unit boundaries, most of the features were directly copied from the Hydrologic Units coverage. An exception to this was the boundary defining the Georgia-Florida Coastal Plain Study Unit where the northern boundary was defined by the northern edge of the Florida Aquifer. To incorporate this boundary into the coverage, the aquifer boundary was digitized from the U.S. Geological Survey's ""Ground-Water Atlas of the United States"", HA-730 (G) (Miller, 1990). In November 1991, responsibility for maintaining the coverage was transferred to NAWQA's National Synthesis staff. Major milestones in the development of the coverage and various revisions to the coverage are listed under the Lineage section. The NAWQA Program has used the coverage for various analyses and displays and for various published reports, for example, Leahy and Thompson (1994) and Gilliom and others (1995). The coverage is reviewed by one of the NAWQA National Synthesis GIS staff members prior to release. Related_Spatial_and_Tabular_Data_Sets: Alaska (Cook Inlet) and Hawaii (Oahu) NAWQA Study-Unit boundaries are maintained in separate data sets. The High Plains Regional Ground-Water Study boundary is in a separate data set. Cook, Oahu, and High Plains study boundaries should be used with this data set to give the full picture of NAWQA Study Units nationwide. [Summary provided by EPA]" proprietary +NAWQAHIS GIS Coverage for the National Water-Quality Assessment (NAWQA) Program Retrospective Database for Nutrients in Surface Water: Monitoring Locations CEOS_EXTRA STAC Catalog 1970-01-01 -127.88, 22.87, -65.35, 48.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231553303-CEOS_EXTRA.umm_json The retrospective database is a compilation of historical water-quality and ancillary data collected before NAWQA Study Units initiated sampling in 1993. This coverage contains the point locations of monitoring locations where historical water-quality data was collected. Water-quality data were obtained by study-unit personnel from the U.S. Geological Survey (USGS) National Water Information System (NWIS), from records of State water-resource agencies, and from STORET, the U.S. Environmental Protection Agency national database. Ancillary data describing characteristics of sampled sites were compiled by NAWQA Study Units or obtained from national-scale digital maps. Mueller and others (1995) used this data to determine preexisting water-quality conditions in the first 20 NAWQA Study Units that began in 1991. Also, Nolan and Ruddy (1996) used the data to describe areas of the United States at risk of nitrate contamination of ground water. Supplemental_Information: The retrospective database includes over 22,000 surface-water samples. The surface-water data are for samples collected during 1980-90 at sites that had a minimum of 25 monthly samples. Year of sampling is included in the retrospective database because it was reported most often by the various Study Units. Year of sampling also is convenient because some Study Units reported median constituent concentrations. If sampling date ranges for median values fell within a single year, then year of sampling was retained in the national data set for that sample. Because sampling, preservation, and analytical techniques associated with these historical data changed during the period of record and are different for different agencies, reported nutrient concentrations were aggregated into the following groups: (1) ammonia as N, (2) nitrate as N, (3) total nitrogen, (4) orthophosphate as P, and (5) total phosphorus. For example, ammonia includes both ammonium ions and un-ionized ammonia. More information on methods used to aggregate constituent data is available in the report by Mueller and others (1995). Much of the ancillary data, such as well and aquifer descriptions and land-use classification for surface-water drainage basins, were provided by NAWQA Study Units. Data evaluated at the national scale include land use, soil hydrologic group, nitrogen input to the land surface, and the ratios of pasture or woodland to cropland. Land-use classification of surface-water sites is based on Anderson Level I categories (Anderson and others, 1976). Land use at surface-water sites was classified by NAWQA Study Unit personnel based on the Anderson Level I categories. Many surface-water sites were affected by mixed land uses, such as Forest and Agricultural, or Agricultural and Urban. Surface-water sites with very large drainage areas (greater than 10,000 square miles) were considered to be affected by multiple land uses, and were designated as Integrated land use. More detailed descriptions of the land-use categories in the retrospective database are given by Mueller and others (1995). Soil hydrologic group was determined from digital maps compiled by the U.S. Soil Conservation Service (1993). The categorical values (A, B, C, and D) from the digital maps were converted to numbers to permit aggregation (Mueller and others, 1995). Surface-water sites were assigned the area-weighted mean for soil mapping units in the upstream drainage basin. Many surface-water sites did not have digitized basin boundaries available, so hydrologic group could not be evaluated. Fertilizer and manure applications were estimated from national databases of fertilizer sales (U.S. Environmental Protection Agency, 1990) and animal populations (U.S. Bureau of the Census, 1989). Nitrogen input by atmospheric deposition was derived from data provided by the National Atmospheric Deposition Program/National Trends Network (1992). Population data were obtained from the U.S. Bureau of the Census (1991). Total population in the upstream drainage was compiled for the surface-water data set. Within the database, concentrations less than detection are reported as negative values of the detection limit. Missing values are indicated by a decimal point. (During processing of the tabular data, these decimal points were replaced will NULL values; See Data_Quality_Information section. Historical data can be of limited use in national assessments because of inconsistencies between and within agencies in database structure and format and in sample collection, preservation, and analytical procedures. For example, changes in sample collection and analytical procedures can cause shifts in constituent concentrations that are unrelated to possible changes in environmental factors. See Mueller and others (1995) for assumptions and limitations associated with the retrospective database. [Summary provided by the EPA.] proprietary NA_MODIS_Surface_Biophysics_1210_1 MODIS-derived Biophysical Parameters for 5-km Land Cover, North America, 2000-2012 ORNL_CLOUD STAC Catalog 2000-01-01 2012-12-31 -160, 20, -40, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2784871888-ORNL_CLOUD.umm_json This data set provides MODIS-derived surface biophysical climatologies of bidirectional distribution function (BRDF), BDRF/albedo, land surface temperature (LST), leaf area index (LAI), and evapotranspiration (ET) as separate files for each of the MODIS land cover types, and four radiative forcing data files for four scenarios of potential vegetation shifts in North America. Each biophysical variable has temporal periods that represent the average of all 8-day periods from the years 2000-2012. The data have a spatial resolution of 0.05 degree (~5 km) and a temporal resolution of eight days. Additionally, a file containing diffuse fraction of surface downward solar radiation (DiffuseFraction) at a monthly scale, and a file containing snow water equivalent (SWE) are provided. The extent of the data covers the land area of North America, from 20 to 60 degrees N. The land-cover map used was synthesized from nine yearly 500-m MODIS land-cover layers (MCD12 Q1 Collection 5) for 2001-2008. These high-resolution land data were originally developed for quantifying biophysical forcing from land-use changes associated with forestry activities, such as radiative forcing from altered surface albedo. proprietary NA_TreeAge_1096_1 NACP Forest Age Maps at 1-km Resolution for Canada (2004) and the U.S.A. (2006) ORNL_CLOUD STAC Catalog 1950-01-01 2006-12-31 179.25, 7.71, -39.87, 67.01 https://cmr.earthdata.nasa.gov/search/concepts/C2556019064-ORNL_CLOUD.umm_json This data set provides forest age map products at 1-km resolution for Canada and the United States (U.S.A.). These continental forest age maps were compiled from forest inventory data, historical fire data, optical satellite data, and the images from the NASA Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) project. These input data products have various sources and creation dates as described in the source paper by Pan et al. (2011). Canadian maps were produced with data available through 2004 and U.S.A. maps with data available through 2006. A supplementary map of the standard deviations for age estimates was developed for quantifying uncertainty.Note that the Pan et al. (2011) paper is included as a companion file with this data set and was the source of descriptions in the guide.Forest age, implicitly reflecting the past disturbance legacy, is a simple and direct surrogate for the time since disturbance and may be used in various forest carbon analyses that concern the impact of disturbances. By combining geographic information about forest age with estimated carbon dynamics by forest type, it is possible to conduct a simple but powerful analysis of the net CO2 uptake by forests, and the potential for increasing (or decreasing) this rate as a result of direct human intervention in the disturbance/age status. proprietary NBCD2000_V2_1161_2 NACP Aboveground Biomass and Carbon Baseline Data, V.2 (NBCD 2000), U.S.A., 2000 ORNL_CLOUD STAC Catalog 1999-01-01 2002-12-31 -126.46, 26.52, -67.96, 49.79 https://cmr.earthdata.nasa.gov/search/concepts/C2539954386-ORNL_CLOUD.umm_json The NBCD 2000 (National Biomass and Carbon Dataset for the Year 2000) data set provides a high-resolution (30 m) map of year-2000 baseline estimates of basal area-weighted canopy height, aboveground live dry biomass, and standing carbon stock for the conterminous United States. This data set distributes, for each of 66 map zones, a set of six raster files in GeoTIFF format. There is a detailed README companion file for each map zone. There is also an ArcGIS shapefile (mapping_zone_shapefile.shp) with the boundaries of all the map zones. A mosaic image of biomass at 240 m resolution for the whole conterminous U.S. is also included.Please read this important note regarding the differences of Version 2 from Version 1 of the NBCD 2000 data. With Version 1, in some mapping zones, certain land cover types (in particular Shrubs, NLCD Type 52) were missing from and unaccounted for in modeled estimates because of a lack of reference data. In Version 1, when landcover types were missing in the models, the model for the deciduous tree cover type was applied. While more woody vegetation was mapped, the authors think this had little effect on model performance as in most cases NLCD version 1 cover type was not a strong predictor of modeled estimates (See companion Mapping Zone Readme files). In Version 2, after renewed modeling efforts and user feedback, these previously unaccounted for cover types are now included in modeled estimates.All 66 mapping zones were updated with the previously unmapped land cover types now mapped. The authors recommend use of the new version for all analyses and will only support the updated version.Development of the data set used an empirical modeling approach that combined USDA Forest Service Forest Inventory and Analysis (FIA) data with high-resolution InSAR data acquired from the 2000 Shuttle Radar Topography Mission (SRTM) and optical remote sensing data acquired from the Landsat ETM+ sensor. Three-season Landsat ETM+ data were systematically compiled by the Multi-Resolution Land Characteristics Consortium (MRLC) between 1999 and 2002 for the entire U.S. and were the foundation for development of both the USGS National Land Cover Dataset 2001 (NLCD 2001) and the Landscape Fire and Resource Management Planning Tools Project (LANDFIRE). Products from both the NLCD 2001 (landcover and canopy density) and LANDFIRE (existing vegetation type) projects as well as topographic information from the USGS National Elevation Dataset (NED) were used within the NBCD 2000 project as spatial predictor layers for canopy height and biomass estimation. Forest survey data provided by the USDA Forest Service FIA program were made available to the project under a national Memorandum of Understanding. The response variables (canopy height and biomass) used in model development and validation were derived from the FIA database (FIADB). Production of the NLCD 2001 and LANDFIRE projects was based on a mapping zone approach in which the conterminous U.S. was split into 66 ecoregionally distinct mapping zones. This mapping zone approach was also adopted by the NBCD 2000 project. proprietary @@ -10552,19 +10773,37 @@ NBId0007_101 Africa Administrative Units (GIS Coverage of Administrative Boundar NBId0012_101 Cattle and Buffalo distribution (Africa) CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848567-CEOS_EXTRA.umm_json The Cattle and Buffalo distribution dataset shows cattle and buffalo distribution for sub-Saharan, East and Central Africa. It is part of the East Coast Fever (ECF) dataset. The ECF study determined both areas at risk and potential migration of the disease by cattle and a potential pool of infection for transmitting the disease to domestic cattle by Buffalo. Buffalo is the main wildlife host of the ECF. The study was carried out in Nairobi in collaboration with United Nations Environment Program, Global Resource Information Database (UNEP/GRID) and the International Laboratory for Research on Animal Diseases (ILRAD), now called International Livestock Research Institute (ILRI). proprietary NBId0016_101 Africa FAO Agro-Ecological Zones (GIS Coverage) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848041-CEOS_EXTRA.umm_json New-ID: NBI16 Agro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10. The Africa Agro-ecological Zones Dataset documentation Files: AEZBLL08.E00 Code: 100025-011 AEZBLL09.E00 100025-012 AEZBLL10.E00 100025-013 Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. The Africa agro-ecological zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. The daset was developed by United Nations Environment Program (UNEP), Kenya. The base maps that were used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Global Navigation and Planning Charts (various 1976-1982) and the National Geographic Atlas of the World (1975). basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. This edit step required appending the country boundaries from Administrative Unit map and then producing the computer plot. Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA 92373, USA The AEZBLL08 data covers North-West of African continent The AEZBLL09 data covers North-East of African continent The AEZBLL10 data covers South of African continent References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates:1976-1982). Scale 1:5000000. Washington DC. G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society, Washington DC. FAO. Statistical Data on Existing Animal Units by Agro-ecological Zones for Africa (1983). Prepared by Todor Boyadgiev of the Soil Resources, Management and Conservation Services Division. FAO. Statistical Data on Existing and Potential Populations by Agro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of the Soil Resources, Management and Conservation Services Division. FAO. Report on the Agro-ecological Zones Project. Vol.I (1978), Methodology & Result for Africa. World Soil Resources No.48. Source : UNESCO/FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Miller Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets, Landuse (100013/05, New-ID: 05 FAO Irrigable Soils Datasets and Water balance (100050/53) proprietary NBId0018_101 Africa FAO Major Infrastructure and Human Settlements (GIS Coverage) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849221-CEOS_EXTRA.umm_json New-ID: NBI18 The Africa Major Infrastructure and Human Settlements Dataset Files: TOWNS2.E00 Code: 100022-002 ROADS2.E00 100021-002 Vector Members: The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename The Africa major infrastructure and human settlements dataset form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA. 92373, USA The ROADS2 file shows major roads of the African continent The TOWNS2 file shows human settlements and airports for the African continent References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source: FAO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Points Format: Arc/Info export non-compressed Related Datasets: All UNEP/FAO/ESRI Datasets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments: There is no outline of Africa proprietary +NBId0019_101 FAO Major Elevation Zones of Africa (GIS Coverage) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849111-CEOS_EXTRA.umm_json New-ID: NBI19 The Africa Major Elevation Zones Dataset documentation File: ELEVLL Code: 100070-003 Vector Member The above file is in Arc/Info Export format and should be imported using the Arc/Info command Import cover In-Filename Out-Filename The Africa elevation major zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The manuscript derived from the topographic film separates of the UNESCO/FAO Soil Map of the World (1977) in Miller Oblated Stereographic projection was used to provide a generalized coverage of elevation values providing information as both line-related and polygonal form. The map was prepared by overlaying the topography film separate with a matte drafting film and then delineating the selected elevation contours. Some of the line crenulation was removed during the delineation process, because this map was designed to define general elevation zones rather than constitute a true topographic base. Code values were recorded directly on the map and were key-entered during the digitizing process with a spatial resolution of 0.002 inches, as part of the polygon or line sequence indentification number. The map was then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy. ESRI, 380 New York Street, Redlands, CA 92373, USA The ELEVLL2 data shows Major Elevation zones of Africa, in lat/lon References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO/FAO Soil Map of the World(1977). Scale 1:5000000. UNESCO, Paris DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society Washington DC. Source: FAO Soil Map of the World, scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Polygon and line Format: Arc/Info export non compressed Related Datasets: All UNEP/FAO/ESRI Datasets AFELBA elevation and Bathymetry (100048) proprietary NBId0020_101 Countries, Coasts and Islands of Africa (Global Change Data Base - Digital Boundaries and Coastlines) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848088-CEOS_EXTRA.umm_json New-ID: NBI20 Countries, Coasts and Islands Dataset documentation (Micro World Data Bank II) Files: COASTS.E00 Code: 100051-001 COUNTRY.E00 100052-001 ISLANDS.E00 100054-001 Vector Members Original files were in IDRISI VEC format coverted to Arc/Info. The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. Micro World Data Bank II (MWDB-II) comprising Coastlines, Country boundries and Islands data sets is part of NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II and is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact: NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The COASTS file shows African Coastlines The COUNTRY file shows African Country Boundaries without coast, no names - only lines The ISLANDS file shows African Islands References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, Vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map: digitized from available sources Publication Date: Jun 1992 Projection: Lat/Lon Type: Polygon and line Format: Arc/Info Export non-compressed proprietary NBId0022_101 Africa Olson World Ecosystems CEOS_EXTRA STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232846860-CEOS_EXTRA.umm_json "New-ID: NBI22 OLSON WORLD ECOSYSTEMS DATASET DOCUMENTATION File: AFWE20.IMG Code: 100032-001 Raster Member This IMG file is in IDRISI format Olson World Ecosystems data base is part of Global Change Data Base produced by The World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC) and for cooperative project called Global Ecosystems Database Project between NDAA(National Oceanic & Atmospheric Administration, USA)/NGDC and the U.S. Environmental Protection Agency. The software (known as IDRISI) was developed and adopted for this project at Clark University. The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California, has joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/Latitude) projection. Each data set is accompanied by an ASCII documentation file. Which contains information necessary for use of the data set in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWE20 file shows Olson ecosystem classes version 1.4 References: Olson, J.S. Earth""'""s Vegetation and Atmospheric Carbon Dioxide, in Carbon Dioxide Review: 1982. Ed. by W.C. Clark (1983), Exford Univ. Press, New York, pp.388-398. Olson, J.S., J.A. Watts, and L.J. Allison. Carbon in Live Vegetation of Major World Ecosystems (1983). Report ORNL-5862, Oark Ridge Laboratory, Oak Ridge, Tennessee. Olson, J.S. and J.A. Watts. Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation (1982). Oak Ridge National Laboratory, Oak Ridge, Tennesse (map). Source map : from available maps and observations. Publication Date : 1989 Projection : lat/lon. Type : Raster Format : IDRISI" proprietary NBId0023_101 Africa Holdridge Life Zone Classification (Vegetation and Climate) CEOS_EXTRA STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847334-CEOS_EXTRA.umm_json New-ID: NBI23 Holdridge Life Zone is a coverage showing zone classification, vegetation relation to climate and vice versa. proprietary NBId0024_101 Africa Soil Classification by Wilson and Henderson-Sellers CEOS_EXTRA STAC Catalog 1970-01-01 12.88, 6.67, 24.97, 24.19 https://cmr.earthdata.nasa.gov/search/concepts/C2232848824-CEOS_EXTRA.umm_json New-ID: NBI24 Wilson and Henderson-Sellers soil classes and soil class reliability. The Wilson and Henderson-Sellers Soil Classes Dataset Files: AFWSOILS.IMG Code: 100043-001 AFWSOILR.IMG 100043-002 Raster Members The IMG files are in IDRISI format. The Wilson and Henderson-Sellers soils data set is part of Wilson Henderson-Sellers land cover and soils for global circulation modeling project was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II. This data Bank is provided on a Database on diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : Roy Jenne, NCAR, P.O. Box 3000, Boulder, CO 80307-3000 The AFWSOILS file shows Wilson/Henderson-Sellers Soil Classes The ASWSOILR file shows Wilson/Henderson-Sellers Soil Class Reliability References: Wilson, M.F/ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general ciruclation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World. Oxford Regional Economic Atlas of USSR and Eastern Europe Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary NBId0025_101 Africa Soil Classification by Zobler CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848306-CEOS_EXTRA.umm_json New-ID: NBI25 Africa ZOBLER Soil Type, Soil Texture, Surface Slope Classes Dataset Documentation Files: AFZSOILS.IMG Code: 100090-001 AFZTEX.IMG 100090-002 AFZSUBSD.IMG 100090-003 AFZSP3.IMG 100090-004 AFZPHS.IMG 100090-005 AFZSLOPE.IMG 100092-001 Raster Members The IMG files are in IDRISI format The Zobler soil type, soil texture and surface slope dataset was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of a larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFZSOILS file shows Zobler soil types The AFZTEX file shows Zobler soil texture The AFZSUBSD file shows subsidiary soil units The AFZSP3 file shows Zobler special codes The AFZPHS file shows Zobler phase codes The AFZSLOPE file shows Zobler surface slope References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. -----. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map : various Publication Date : 1987 Projection : Lat/lon Type : Raster Format : IDRISI proprietary NBId0036_101 Africa Lakes and Rivers (World Data Bank II) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849206-CEOS_EXTRA.umm_json New-ID: NBI36 Africa Lakes and Rivers. Lakes and Rivers Dataset documentation (Micro World Data Bank II) Files: LAKES.VEC Code: 100055-001 RIVERS.VEC 100061-001 AFRIVER.IMG 100002-001 Raster Members The VEC and IMG files are in IDRISI format Africa lakes and rivers datasets are part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The LAKES file shows African lakes The RIVERS file shows African rivers The AFRIVER file shows African rivers References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. vol. 2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : digitized from available sources Publication Date : 1988 Projection : Lat/lon Type : Raster Format : IDRISI proprietary +NBId0041_101 FNOC Elevation (meters), Terrain and Surface Characteristics for Africa CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847281-CEOS_EXTRA.umm_json New-ID: NBI41 Africa FNOC Elevation (meters), Terrain and Surface characteristics. Africa Elevation (meters), Terrain, and Surface Characteristics Dataset Documentation Files: AFMAX.IMG Code: 100082-001 AFMIN.IMG 100082-002 AFMOD.IMG 100082-003 Raster Members The IMG files are in IDRISI format Africa elevation dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFMAX file shows maximum elevation (meters) The AFMIN file shows minimum elevation (meters) The AFMOD shows modal elevation (meters) Reference: Cuming, Michael J. and Barbara A. Hawkins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary +NBId0042_101 NOAA Monthly 10-Minute Normalized Vegetation Index (April 1985-December 1988) for Africa CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848152-CEOS_EXTRA.umm_json "New-ID: NBI42 NOAA monthly Normalized Vegetation Index (NDVI) for Africa. NOAA Monthly 10-Min Normalized Vegetation Index Dataset (APRIL 1985 - DECEMBER 1988) Files: AFAPR85.IMG-AFDEC85.IMG Code: 100041-001 AFJAN86.IMG-AFDEC86.IMG 100041-001 AFJAN87.IMG-AFDEC87.IMG 100041-001 AFJAN88.IMG-AFDEC88.IMG 100041-001 Raster Members The IMG files are in IDRISI format Africa monthly 10-min normalized difference vegetation index dataset is part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA AFAPR85-AFDEC88 (45 months) show monthly Normalized Vegetation Index (NDVI) References: Kidwell, Katherin B. (ed.). Global Vegetion Index User""'""s Guide (1990). NOAA/NHESDIS/SDSD. for additional references see Appendix A-26-A32 of the Global Change Data Base documentation Source map : digitized from available maps and reprocessed Publication Date : Jun 1992 Projection : Lat/lon Type : Raster Format : IDRISI" proprietary NBId0043_101 Africa Integrated Elevation and Bathymetry CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847599-CEOS_EXTRA.umm_json "New-ID: NBI43 Africa Integrated Elevation and Bathymetry (feet). Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1992 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary NBId0044_101 Africa Ocean Mask CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849137-CEOS_EXTRA.umm_json "New-ID: NBI44 Ocean mask for Africa. Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1985 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary NBId0053_101 Africa Revised FNOC Percent Water Cover CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847596-CEOS_EXTRA.umm_json New-ID: NBI53 Africa Revised FNOC Percent Water Cover Dataset Documentation File: AFWATER.IMG Code: 100082-005 Raster Member The IMG file is in IDRISI format The percent water cover dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWATER file shows the revised FNOC percent water cover for Africa. Reference: Cuming, Michael J. and Barbara A. Hwakins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lon/lat Type : Raster Format : IDRISI proprietary +NBId0079_101 Lake Chad Datasets, Africa CEOS_EXTRA STAC Catalog 1970-01-01 13, 7, 24, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2232848788-CEOS_EXTRA.umm_json The Lake Chad Dataset which is a detailed case study of the UNEP/FAO/ESRI Family was developed by UNEP/GRID, on behalf of the UNEP/Fresh Water Unit for the Lake Chad Commission on Sustainable Development. Lake Chad Dataset covers parts of 7 countries: Cameroon, Chad, Nigeria and Niger, Sudan, Central African Republic and Libya and is a clip (regional version) of Africa Outline Dataset (NBI01). The base maps used for the continental version were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit and the Rand-McNally New International Atlas (1982) to clarify unit boundaries. Files: ADMIN.E00 Code: 115001-001 BASE.E00 115002-001 COUNTRIES.E00 115003-001 Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. The ADMIN polygon dataset showing administrative areas for 7 countries around Lake Chad. The BASE is a polygon Dataset showing the countries with inland water bodies. The COUNTRIES is a polygon Dataset showing only the country boundaries. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. FAO/UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris FAO. Maps and Statistical Data by Administrative Unit (unpublished) Rand-McNally. New International Atlas (1982). Rand-McNally & Company. Chicago Source: FAO/UNESCO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1988 Projection: Miller Type: Polygon and line Format: Arc/Info Export, non-compressed Related Datasets: All the Lake Chad Datasets of the UNEP/FAO/ESRI family. proprietary +NBId0083_101 Kenya Administrative Units, Drainage, Agro-Climatic Zones, Rainfall, Infrastructure CEOS_EXTRA STAC Catalog 1970-01-01 33, -5, 43, 6 https://cmr.earthdata.nasa.gov/search/concepts/C2232847488-CEOS_EXTRA.umm_json Description: These datasets (Administrative Units, Drainage, Agro-Climatic Zones, Rainfall, Infrastructure) were scanned by the Canadian Land data Systems Division, Land Directorate, Dept of Environment, Ottawa, Canada. This was in response to the request to GRID by the Kenya Ministry of Agriculture to assist in creating the datasets. The source information and scales are varied; Rivers, Agroecological Zones, Soils, Boundaries, Towns, Lakes, Transport, and the Districts, Provinces (administrative boundary), Elevation were based on the scale of 1: 1 000 000 and of which the source information was derived from Ministry of Agriculture and Survey of Kenya maps. The Landuse dataset was based on the Kenya Rangeland Ecological Monitoring Unit (KREMU now DRSRS) map at the scale of 1: 1 000 000.The Mean Annual Rainfall dataset was based on an East Africa map(1966) at the scale of 1: 2 000 000 Rainfall data was originally provided by Kenya Meteorological Department. These were collected from a total of 79 Stations for the period between 1982-1988. More records were added by GRID which extended the period to 1991 The data consists of the rainfall,Potential Evapotranspiration (PET) and Temperature information. Sample Files: RAINFALL.E00 FILL8291.PLU, PETALL.DBF/.NDX, ADD82,83,84,85,86.DAT (Others available on request) Vector Members: - Files are in an ArcInfo Export format proprietary +NBId0089_101 Kenya Soils (GIS Coverage from UNEP/GRID Nairobi) CEOS_EXTRA STAC Catalog 1979-12-30 1982-12-30 33, -5, 43, 6 https://cmr.earthdata.nasa.gov/search/concepts/C2232848109-CEOS_EXTRA.umm_json "New-ID: NBI89 SOIL MAP OF KENYA. Produced by the Republic of Kenya, Kenya Soil Survey in the Ministry of Agriculture Nairobi. Agro-climatic classification and map preparation was done by H. M. H. Braun and other staff of the Kenya soil survey. Cartography and lithography was done by the Soil Survey Insitute Wageningen, The Netherlands. There are three items in the info table which are of importance namely TYPE1, TYPE2 and SOIL. TYPE1 and TYPE2 are an alpha-numeric code which represent the soil type in the item SOIL. This code was given in order to facilitate manipulation and calculations of the info tables, which is more easily done using integers rather than using character strings. TYPE1 is the first part of the character string in the item SOIL and TYPE2 is the second part of the character string in the item SOIL, as seen in the info table below in SOIL# 19. For details on the actual soil types and associated information see the documentation ""Exploratory Soil Map and Agro-climatic Zone Map of Kenya, 1980. MAP TITLE Exploratory Soil Map and Agro-climatic Zone Map of Kenya, 1980. Arc/info table AREA PERIMETER SOIL# SOIL-ID TYPE1 TYPE2 SOIL -47.552 39.567 1 0 0 0 '' 0.068 3.258 2 9009 479 0 ' H9' 0.013 0.634 3 9010 645 0 ' Y5' 0.000 0.053 4 9011 60937 0 ' Ux7' 0.001 0.132 5 9012 403 0 ' A3' 0.002 0.284 6 9013 645 0 ' Y5' 0.009 0.524 7 9014 60937 0 ' Ux7' 0.001 0.150 8 9015 479 0 ' H9' 0.009 0.602 9 9016 516 0 ' L6' 0.052 1.562 10 9017 645 0 ' Y5' 0.022 0.975 11 9018 558821 0 ' Ps21' 0.127 2.573 12 9019 558821 0 ' Ps21' 0.000 0.085 13 9020 479 0 ' H9' 0.073 4.595 14 9021 403 0 ' A3' 0.238 5.943 15 9022 60937 0 ' Ux7' 0.002 0.231 16 9023 458 0 ' F8' 0.142 3.913 17 9024 408 0 ' A8' 0.004 0.263 18 9025 479 0 ' H9' 0.004 0.249 19 9026 431 55813 ' D1 + Pl3' 0.018 0.855 20 9027 408 0 ' A8' 0.044 1.360 21 9028 479 0 ' H9'" proprietary +NBId0093_101 Kenya Coastal Zone - International/Administrative Boundaries and Schools CEOS_EXTRA STAC Catalog 1970-01-01 34, -5, 42, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2232847490-CEOS_EXTRA.umm_json The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. proprietary +NBId0098_101 Kenya Coastal Zone - Coastal Features, Landuse, Marine/Terrestrial Parks, Elevation, Bathymetry CEOS_EXTRA STAC Catalog 1970-01-01 39, -5, 41, -1 https://cmr.earthdata.nasa.gov/search/concepts/C2232847562-CEOS_EXTRA.umm_json The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. proprietary +NBId0106_101 Kenya Coastal Zone - Scientific Cruise CEOS_EXTRA STAC Catalog 1970-01-01 39, -4, 42, -1 https://cmr.earthdata.nasa.gov/search/concepts/C2232847399-CEOS_EXTRA.umm_json The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. proprietary +NBId0110_101 Kenya Coastal Zone - Lakes, Rivers, Watersheds, Boreholes CEOS_EXTRA STAC Catalog 1970-01-01 38, -5, 42, -1 https://cmr.earthdata.nasa.gov/search/concepts/C2232848268-CEOS_EXTRA.umm_json The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. proprietary +NBId0115_101 Kenya Coastal Zone - Marine Life, Fishing, Sport, Aquaculture, Vessels, Lighthouses CEOS_EXTRA STAC Catalog 1970-01-01 38, -5, 42, -1 https://cmr.earthdata.nasa.gov/search/concepts/C2232848234-CEOS_EXTRA.umm_json The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. proprietary +NBId0127_101 Kenya Coastal Zone - Dumping Grounds, Pollution, Erosion CEOS_EXTRA STAC Catalog 1970-01-01 39, -4, 40, -3 https://cmr.earthdata.nasa.gov/search/concepts/C2232848654-CEOS_EXTRA.umm_json The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. proprietary +NBId0128_101 Kenya Coastal Zone - Wildlife: Bird Species and Mammals CEOS_EXTRA STAC Catalog 1970-01-01 39, -4, 41, -1 https://cmr.earthdata.nasa.gov/search/concepts/C2232846793-CEOS_EXTRA.umm_json The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. proprietary +NBId0129_101 Kenya Coastal Zone - Infrastructure and Hotel, Health, Recreation Facilities CEOS_EXTRA STAC Catalog 1970-01-01 38, -5, 41, -1 https://cmr.earthdata.nasa.gov/search/concepts/C2232847291-CEOS_EXTRA.umm_json The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. proprietary +NBId0131_101 Kenya Archeological Sites the Coastal Zone CEOS_EXTRA STAC Catalog 1970-01-01 39, -4, 41, -1 https://cmr.earthdata.nasa.gov/search/concepts/C2232846610-CEOS_EXTRA.umm_json The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. proprietary +NBId0136_101 Kenya Coastal Zone - Mining, Towns, Industry, Utilities CEOS_EXTRA STAC Catalog 1970-01-01 39, -4, 41, -1 https://cmr.earthdata.nasa.gov/search/concepts/C2232848495-CEOS_EXTRA.umm_json The Eastern African Coastal and Marine Environment Resource Database is a comprehensive 1:250,000-scale vector database of the Kenya Coastal Zone. It consists of geographic, attribute, and textual data which can be accessed, queried, displayed, and modified. The database was developed under the Eastern African Action Plan, with the collaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary sources of data are the Survey of Kenya 1:250,000 series, National Museums of Kenya, Kenya Ports Authority, Coastal Development Authority, Kenya Wildlife Service, and Kenya Government Departments: Fisheries, Agriculture, Meteorology, Mines and Geology. Data Sources The primary sources of data are the Survey of Kenya 1:250,000 series, Landsat Thematic Mapper images, and socio-economic data from various Government ministries, departments, and institutes. Naming Conventions A naming convention was established to allow users to identify the contents of layers based on their name. Coverage names begin with a two letter abbreviation representing the country code (for example, KE:-Kenya) followed by a descriptive term representing the theme. The country code being two characters in length plus the descriptive term, which is usually six characters long, conforms to the Direct Operating System (DOS) naming convention. An example of this would be as follows:- the elevation coverage would be called KEELEVAT, the KE representing the abbreviation for Kenya and the ELEVAT representing elevation. A full list of country codes for the East African region is as follows:- List by country name Country Name Data Code Comoros CN Kenya KE Madagascar MA Mauritius MP Mozambique MZ Reunion RE Seychelles SE Somalia SO Tanzania TZ Edgematching Edgematching was done manually working from the top most map sheet (Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way any errors would be distributed in a systematic way. The greatest errors are in the order of 750 meters on the ground which is 3 millimeters on the map. These errors occurred between the following map sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) / Lushoto (SB-37-2). Annotation All feature names which include points, lines or polygons have an entry in the attribute table describing the feature. Additional attributes may also exist for the particular feature, however this varies from feature to feature. General production process Coverages developed for the database were derived from three sources: 1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat Thematic Mapper Data, and socio-economic data from the Kenya Marine Fisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other Government Ministries and NGO's. 1:250,000 paper maps Twelve TIC's (control points) were selected from the map sheet, based on the latitude/longitude grid on the sheet. The reason for this is that one of the other sheets only has a latitude/longitude grid where as the others have both a latitude/longitude grid as well as a UTM grid. In this way, consistency is being maintained between all the map sheets covering the Kenya coastal zone. Arc/info conversion MACRO PROGRAMME was used to convert a standard ASCII text file of Latitude/Longitude coordinates into Universal Transverse Mercator coordinates. SML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37 INPUT PROJECTION GEOGRAPHIC SET INPUT UNITS AS DECIMAL DEGREES UNITS DD SPHEROID CLARKE1880 PARAMETERS OUTPUT PROJECTION UTM UNITS METERS YSHIFT 10000000 PARAMETERS 39 00 00 00 00 00 END [ARC] Createcov ***tic {ARC} Tables > Select ***tic.tic > Add > TICID = ? > XTIC = ? > YTIC = ? This is done for all 12 UTM Tic Ids and coordinates. >List This is done to check that the Tic Ids and coordinates are correct. > Q stop A tile boundary was then added to the coverage to help in the digitization of the map sheet. This coverage <***tic> was then used in the creation of all the other coverage layers that were digitized from the map sheet. 1:250,000 paper maps were used due to lack of stable mylar or acetate copies. All features:- points, lines, and polygons were digitized using PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90 PC and a CalComp 9100 digitizing board. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale using a Hewlett Packard Design Jet 650 C inkjet plotter and then compared to the source for positional accuracy, completeness, and topological correctness. All of the data layers were checked using this method and all edits were verified. Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assign after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Landsat Thematic Mapper Data Two full scenes and one quad were used in the land cover classification, more details concerning the methodology used in the classification can be found in Annex 3. Line and Point attribute codes were assigned interactively at the time of initial data cature. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. Socio-economic Data These data came in various forms (from digital data to reports), which were then converted into Line and Point attribute codes were assigned interactively at the time of initial data capture. Polygon attributes were assigned after topology had been reached. Editing was carried out to eliminate obvious errors, after which the data was plotted at scale and then compared to the source correctness. All of the data layers were checked using this method and all edits were verified. All the data was finally plotted at scale in a single composite and attribute code value validity, attribute code consistency, topology errors, attribute field definition correctness, and internal data structure correctness were checked. In addition Arc/View 2.1 was used to carry out a quick visual check on the data. proprietary NBId0153_101 Benito River dataset of Equatorial Guinea CEOS_EXTRA STAC Catalog 1970-01-01 -15.19, 6.77, -6.87, 13.02 https://cmr.earthdata.nasa.gov/search/concepts/C2232846569-CEOS_EXTRA.umm_json New-ID: NBI153 The Equatorial Guinea Benito River dataset documentation File: EGRIVER.E00 Code: 121001-001 Vector Member The E00 file is in Arc/info format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. The Equatorial Guinea Benito River dataset was generated for the Elephant Database that was completed in 1991, as a result of cooperation between European Communities (EEC), Elsa Wild Animal Appeal (EWAA) and UNEP/GRID. Source map used was from the Institute Geographic National, Paris of unknown scale. The dataset was then projected into Miller Oblated Stereographic at 1:1000000. Contact : UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service 00100, Rome, Italy. ESRI, 380 New York Street, Redlands, CA 92373, USA File EGRIVER represents Benito River of Equatorial Guinea REMARK: GET ROAD-CODE = 5 from EGRDS.E00 for complete set of rivers of Equatorial Guinea. Reference: UNEP/EEC/EWAA Technical Report on the African Elephant Database (1991) Source map : from Institut Geographic National, scale 1:1000000 Publication Date : September 1991 Projection : Miller Type : Line Format : Arc/Info Export non-compressed Related dataset : EGRDS.E00 Vector: The EGRIVER data per area is: Benito River Description of SINGLE precision coverage egriver ARCS POLYGONS Arcs = 12 Polygons = 1 Segments = 118 Polygon Topology is present. 28 bytes of Arc Attribute Data 16 bytes of Polygon Attribute Data NODES POINTS Nodes = 13 Label Points = 0 0 bytes of Node Attribute Data TOLERANCES SECONDARY FEATURES Fuzzy = 0.002 N Tics = 4 Dangle = 0.000 N Links = 0 COVERAGE BOUNDARY Xmin = 10.558 Ymin = 17.816 Xmax = 14.347 Ymax = 18.817 STATUS The coverage has not been Edited since the last BUILD or CLEAN. NO COORDINATE SYSTEM DEFINED Lines COLUMN ITEM NAME WIDTH OUTPUT TYPE N.DEC ALTERNATE NAME INDEXED? 1 FNODE# 4 5 B - - 5 TNODE# 4 5 B - - 9 LPOLY# 4 5 B - - 13 RPOLY# 4 5 B - - 17 LENGTH 4 12 F 3 - 21 EGRIVER# 4 5 B - - 25 ROAD-CODE 4 5 B - - colunm 25 ROAD-CODE = 5 = BENITO River proprietary NBId0161_101 Climate Dataset of Senegal CEOS_EXTRA STAC Catalog 1970-01-01 -17.53, 12.02, -10.89, 17.14 https://cmr.earthdata.nasa.gov/search/concepts/C2232849116-CEOS_EXTRA.umm_json New-ID: NBI161 The Climate Dataset of Senegal documentation Files: SENEGAL4.IMG Code: 146005-001 SENEGAL5.IMG 146006-001 SENEGAL6.IMG 146007-001 Raster Members IMG files are in IDRISI format The Senegal Climate Dataset was originally digitized for the UNEP/FAO/ESRI Database for Africa from hand-drawn maps provided by FAO for the Desertification Hazard Mapping project. GRID-Geneva rasterized the coverages for UNEP/GRID/WHO/CISFAM Senegal Database with a cell size of 30 seconds and two minutes lat/lon (approximately one- and four kilometeter-square pixels, respectively). Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy The SENEGAL4 file shows mean annual wind velocity meters per second (8 classes). The SENEGAL5 file shows number of wet days per year (6 classes). The SENEGAL6 file shows mean annual rainfall in millimeters (10 classes). REMARK: file may have limited applicability at national scale as was extracted from continental. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP. CISFAM. Consolidated Information System for Famine Management in Africa, Phase I Report (Apr. 1987), Univ. of Louvain, Brussels, Belgium. Source and scale : unknown Report Publication Date : Dec 1988 Projection : lat/lon Type : Raster Format : IDRISI Related Datasets : All UNEP/FAO/ESRI climate Datasets proprietary NBId0169_101 Baringo (Kenya) Pilot Study for Desertification Assessment and Mapping CEOS_EXTRA STAC Catalog 1984-01-01 1992-12-30 35, -1, 36, 0 https://cmr.earthdata.nasa.gov/search/concepts/C2232849286-CEOS_EXTRA.umm_json The purpose of the Kenya Pilot Study was to evaluate the FAO/UNEP Provisional Methodology for Assessment and Mapping of Desertification, and to recommend an effective, simple methodology for desertification assessment within Kenya. The FAO/UNEP Provisional Methodology (1984) proposes seven processes for consideration in desertification assessment: degradation of vegetation, water erosion, wind erosion, salinization, reduction of organic content, soil crusting and compaction. In late 1985, a pilot project for the assessment of the FAO/UNEP Methodology within Kenya was proposed, and in 1987 a memorandum of understanding between the Government of Kenya and UNEP for the implementation of that study was signed. The study areas were: 1) Models can be useful to assist in desertification assessment. Models can be developed from FAO/UNEP Methodology. 2) Any modeling output requires verification. 3) Ground survey and remote sensing can be important sources of data. 4) An evaluation of data and methodologies necessary to allow verification of desertification assessment modeling is required. 5) A human use component should be incorporated into desertification assessment that considers management implications and social, as well as, economic context. 6) Computer implementation of desertificaiton assessment can be effective, however, procedures should be well defined. This study within the Baringo Study Area was designed to address these concerns. The Baringo Study Area identified in this study would be typical of such a training area. The models developed during this study could be applied to the general region. The study area lies between 0 15'-1 N and 35 30' -36 30' E. It is located between the Laikipia escarpment to the East and the Tugen Hills to the West. Topographic elevations vary from 900m on the Njemps flats to 2000m in the Puka, Tangulbei and Pokot highlands. The size of the study area is approximately 15ookm2. 4.0 DATA COLLECTION A wide variety of data was collected. Detailed data was required to provide a basis for evaluating more general cost effective data gathering techniques and to provide a basis for model verification, particularly the socio/economic data. Physical Environment Topographic Data Topographic contours were digitized directly from 1:250,000 Survey of Kenya topographic maps. The contour interval was 200 feet. A digital elevation model was constructed using triangular irregular networks (TIN). Soil Data Soil types were mapped at 1:100,000 scale using existing soil maps, manual interpretation of SPOT imagery, and field investigations (Figure 3). During field trips, soil samples were taken from each soil unit and analyzed by the Kenya National Agricultural Center. 4.2 Climate Data 4.2.1 Rainfall Data Rainfall data from the Kenya Meteorological Department was analyzed for 33 stations within and surrounding the study area. A rainfall erosivity index was calculated based on the Fourier Index (R). 12 RE (p /P) 12 where P = annual rainfall p = monthly rainfall A relationship between this erosivity index and the annual rainfall for each station was calculated using linear regression (Bake, 1988). A map of rainfall erosivity was generated for the study area by relating annual rainfall isoheyts to the following: y = 0.108x - 0.68 This data was coded and digitized. Wind Erosion Potential The following required conditions were determined to create high wind erosion potential (Kinuthia, 1989): 1) Annual rainfall less than 300mm. 2) P/E greater than zero and less than 1, where: P=mean monthly rainfall (cm). E=mean monthly PET (cm). 3) Wind velocity greater than 4 m/s at 10m height. Vegetation Data A vegetation map for the study area was produced at a scale of 1:100,000 through manual interpretation of a SPOT image and field investigations (Figure 6). A structural classification system as adopted by DRSRS was used for naming vegetation types (Grunb). Systematic Reconnaissance Flight Data Since 1977, DRSRS has been conducting aerial surveys of Kenyan rangelands. In addition to data on the number of wildlife and livestock, observations of land use and environmental condition are also made. Socio/economic Data Social Factors A wide variety of data was collected through literature review and a field administered questionnaire. Nutritional status was estimated by measurement of childrens' mid upper arm. Such data is useful for a Level 1 type assessment. Permanent Structures Data For the Level 2 assessment, data on permanent structures was extracted from DRSRS SRF data. This data was used to indicate presence and concentration of sedentary populations. Example Files: VDS.E00 (Vegetation degradation) DES.E00 (Plant Species) Others available on request. proprietary +NBId0177_101 Laikipia (Kenya) Research Programme GIS Datasets CEOS_EXTRA STAC Catalog 1990-01-01 1994-12-30 36, 0, 37, 1 https://cmr.earthdata.nasa.gov/search/concepts/C2232848187-CEOS_EXTRA.umm_json Laikipia Research Programme GIS Datasets are divided into two main different study area scales: the Regional level [Laikipia district, the Ewaso Ng'iro Basin] and the Local level [Land parcels-farm(s), catchments of a few kilometer square]. Coordinate Reference System Coverage data is organized thematically as a series of layers. The coordinate reference systems used in LRP dataset are:- (a) global coordinate system - Universal Transverse Mercator (UTM), (b) Local coordinate system. Digitizing Scale and Fuzzy Tolerance The initial digitizing scale for the LRP GIS Dataset is dependent on the scale of the study areas. There are two major research levels carried by LRP namely Regional and Local. The scales used for regional level are 1:250,000 and 1:50,000. FUZZY TOLERANCE is the minimum distance between coordinates in a coverage. The resolution of a coverage is defined by the minimum distance separating the coordinates used to store coverage features. Resolution is limited by the map scale in initial digitizing. The fuzzy tolerance can be calculated as follows for digitizing table: Initial Scale for Coverage of Fuzzy Tolerance Digitizing Units Value 1;250,000 Meters 6.35 1:50,000 Meters 1.25 1:10,000 Meters 0.25 1:5,000 Meters 0.125 1:2,500 Meters 0.0625 Files: Roads.E00 (Roads) Settle.E00 (Settlement Pattern) Centres.E00 (Urban Centres) (other files exist also) proprietary NBId0203_101 Africa Water Balance high/lowland crops, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847252-CEOS_EXTRA.umm_json "The Africa Water Balance data set which is prepared by watershed and by country, belongs to the group of ""Irrigation and Water Resources Potential"" study. It covers 55 countries and 25 major basins which contain 335 watersheds. The digitized data base for Africa and the World was originally prepared for an FAO/UNEP project on Desertification in 1982-1984. UNEP financed preparation and analysis of the digitized map data and FAO prepared the data and methodology. The main input maps (all in Miller Oblated Stereographic projection) are the 1975 UNESCO Geological Map of Africa (originally at a scale of 1:10 million); the FAO/UNESCO Soil Map of Africa; Mean Annual Rainfall Map from hand drawn FAO/AGS climate maps; Template; Watersheds; and Administrative Units map - all at a scale 1:5 m. The methodology was based on water balance approach. This determines the suitability of the soil for irrigation and estimates the amount of water the soil requires. Estimates of the surface and groundwater are then compared to the potential irrigation use. If use exceeds available water resources, the irrigable area is correspondingly reduced; in the event of water surplus, some of the water is routed to the downstream basin. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except lowland crops). For further details refer to FAO contact for the 1987 FAO Irrigation and Water Resources Potential for Africa AGL/MISC/11/87. FAO, Land and Water Development Division via Delle Terme di Caracalla, 00100, Rome, Italy Vector Member The file is in Arc/Info Export format. Reference: FAO. Irrigation and Water Resources Potential for Africa. (1987) FAO. Final Report UNEP/FAO world and Africa GIS data base (1984), unpublished publication of ESRI, FAO and UNEP. UNESCO. Geological Map of Africa (1975). Scale 1:5 000 000." proprietary +NBId0207_101 IGADD Member Countries Crop types and distribution by administrative units, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 22, -12, 51, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2232849119-CEOS_EXTRA.umm_json "The IGADD (Inter-Governmental Authority on Drought and Development) crop zones dataset is part of the Africa UNEP/FAO/ESRI Crops Data. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. The data was provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service, Land and Water Development Division, Italy. The datasets were then developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Administrative Units map and the World Atlas of Agriculture (1969). All sources were re-registered to the base map by comparing known features on the base map and the source maps. In the original Database (Africa), a considerable study was made of crop water requirements for a range of crops in the various African climates during the time of the year when irrigation would be required. It was found that a relatively simple relationship exists between annual rainfall and the crop irrigation water requirements for the African food grain crops. It was also observed that water requirements for food grains vary between fruit and vegetable crops on the one side and fiber crops and fodder on the other. No attempt was made to produce complex crop patterns. There is a maximum of 13 crop types in one country. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO/UNESCO Soil Map of the Africa (1977). Scale 1:5000000. UNESCO, Paris. FAO. Administration units map. Scale 1:5 000 000. Rome. FAO. Irrigation and Water Resources Potential for Africa. (1987) Source :UNESCO/FAO Soil Map of the World. Scale 1:5000000 Publication Date :Nov 1987 Projection :Miller Type :Polygon Format :Arc/Info Export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets FAO Irrigable Data sets 100050: "" IRRIGLB lowland crops, best soils "" IRRIGLT lowland crops, best plus suitable soils "" IRRIGUB upland crops, best soils "" IRRIGUT upland crops, best plus suitable soils FAO Soil water balance 100053: "" WATBALLB lowland crops, best soils "" WATBALLT lowland crops, best plus suitable soils "" WATBALUB upland crops, best soils "" WATBALUT upland crops, best plus suitable soils FAO Agro-ecological zones AEZBLL08 North-west of continent AEZBLL09 North-east of continent AEZBLL10 South of continent" proprietary NBId0208_101 Africa Major Human Settlements and Landuse, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848068-CEOS_EXTRA.umm_json The Africa Human Settlements and Landuse data sets form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the base map those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Miller Type :Points Format :Arc/Info export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments : no outline of Africa proprietary NBId0211_101 Africa Irrigation Potential, Best soils, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848204-CEOS_EXTRA.umm_json The Africa Irrigation Potential data set, which represents the best soils suitable for upland, is part of the FAO Irrigation and Water Resources Potential Database. The main input maps were the 1977 FAO/UNESCO Soil Map of the Africa, UNESCO Geological World Atlas (scale 1:10 m), Mean Annual Rainfall map from hand drawn FAO/AGS climate maps, Template with water related features, Administrative Units map, and Watersheds map. All maps, apart from where specified were at a scale of 1:5 million, and all in Miller Oblated Stereographic projection. The soil suitability for irrigation was determined by evaluating the properties of all soil components: dominant soil, associations and inclusions, phases, slope, drainage, and texture. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops). The soils source includes a list of attributes for each soil unit including: slope, drainage, texture and phase (re: UNEP/FAO/ESRI ITU 100004). Then for both cases (lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops)), two maps were generated. One with all soils which are suitable, and one where slope, texture, drainage and phase were considered. Each different soil type is classed according to suitability, S1 irrigation with no constraints, S2 irrigation with some constraints, N1 not suitable without major improvements, N2 permanently not suitable. Because one soil unit can consist of more soil components (unit Af26-a can mean 30%Bf and 70% Af) the suitability is expressed in percentage of the unit that is suitable (1 >50% suitable, 2 = 25-50% etc.). Then the soil characteristics are used to refine the ranking. This refining is done were the original soil rank is increased decreased or changed from their original suitability to a new suitability (so or soil gets new class S1, N1 etc. or ranking changes like, -1 lower soil rank by one, +1 raise soil rank with one). The Ranking of Soils is as follows The soils considered not suitable are: Lithosols, Arenosols, Rendzinas, Yermosols, Podzols, Thionic Fluvisols, Miscellaneous land units such as rock debris, desert debris, Gypsum units, Soils with stonic, lythic or petrogypsic phase. proprietary NBId0216_101 Africa Number of Wet Days per Year and Wind Velocity, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849224-CEOS_EXTRA.umm_json "The Africa Number of Wet Days per year and Wind Velocity data sets are part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa in this case. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). ""Internal Publication from ESRI, FAO and UNEP ""FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris ""FAO. Map of Mean Annual Rainfall and general Climate zones for P/Pet for Africa. (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resources, Management and Conservation Service. FAO, Rome ""FAO. Maps of Mean annual Wind Velocity for Africa (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome"" ""FAO. Maps of Number of Wet Days per Year (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Type :Polygon and line Format :Arc/Info Export non-compressed Related Datasets :All UNEP/FAO/ESRI Data sets" proprietary @@ -10690,6 +10929,7 @@ NFRDI_0 National Fisheries Research and Development Institute (NFRDI) OB_DAAC ST NGLI_Lake_Bourne_0 Northern Gulf Littoral Initiative (NGLI) measurements in Lake Bourne, Louisiana OB_DAAC STAC Catalog 2001-04-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360520-OB_DAAC.umm_json Measurements made under the Northern Gulf Littoral Initiative (NGLI) in the Gulf of Mexico near the Mississippi River outflow region in 2001. proprietary NHAP National High Altitude Photography USGS_LTA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220566467-USGS_LTA.umm_json The National High Altitude Photography (NHAP) program, which was operated from 1980 - 1989, was coordinated by the U.S. Geological Survey as an interagency project to eliminate duplicate photography in various Government programs. The aim of the program was to cover the 48 conterminous states of the USA over a 5-year span. In the NHAP program, black-and-white and color-infrared aerial photographs were obtained on 9-inch film from an altitude of 40,000 feet above mean terrain elevation and are centered over USGS 7.5-minute quadrangles. The color-infrared photographs are at a scale of 1:58,000 (1 inch equals about .9 miles) and the black-and-white photographs are at a scale of 1:80,000 (1 inch equals about 1.26 miles). proprietary NHICEM_001 Northern Hemisphere Ice Cover Monthly Statistics at 1 Degree Resolution V001 (NHICEM) at GES DISC GES_DISC STAC Catalog 2000-01-01 2014-11-30 -180, 0, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239898024-GES_DISC.umm_json This product is monthly Ice Cover Statistics. The dataset was prepared by Dr. Peter Romanov at Cooperative Institute for Climate Studies(CICS) of the University of Maryland for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program. The product includes the monthly ice statistics (frequency of occurrence) for Northern Hemisphere at 1x1 degree spatial resolution. The dataset covers the time period starting January 2000 to November 2014. The data was derived from daily ice cover charts produced at NOAA/NESDIS within Interactive Multisensor Ice Mapping System (IMS). proprietary +NHS National Hydrological Services (NHS) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848605-CEOS_EXTRA.umm_json "The National Hydrological and Hydrometeorological Services of the World Meteorological Organization (WMO) provides data on hydrology and water resources assessment activities. The data are available regionally: Region I - Africa Benin - Service de l'Hydrologie Botswana - Department of Water Affairs Burkina Faso - Direction G?n?rale de l'Hydraulique Cameroun - Centre de Recherches Hydrologiques Congo - Direction G?n?rale de la Recherche Scientifique et Technique Egypt - Ministry of Public Work and Water Resources Guinee - Direction nationale de la gestion des ressources en eau Mali - Direction Nationale de l'Hydraulique et de l'Energie Morocco - Direction G?n?rale de l'Hydraulique Mozambique - Direc??o nacional de ?guas Niger - Direction des ressources en eau Republique Centrafricaine - Direction de la M?t?orologie Nationale, Service de l'Hydrologie South Africa - Department of Water Affairs and Forestry Tanzania - Ministry of Water Tchad - Direction des Ressources en Eau et de la M?t?orologie Uganda - Directorate of Water Development Region II - Asia Bangladesh Water Development Board - Flood Forecasting and Warning Centre China - Ministry of Water Resources India - Central Water Commission Islamic Republic of Iran - Water Resources Management Organization Japan River Bureau Mongolia Institute of Meteorology and Hydrology Nepal Department of Hydrology and Meteorology Pakistan Flood Forecasting Bureau Republic of Korea Water Resources Bureau Saudi Arabia Ministry of Agriculture and Water Region III - South America Argentina - Instituto nacional del agua Bolivia Servicio nacional de meteorolog?a e hidrolog?a Brazil ANA - National Water Agency (in Portuguese) Chile - Direcci?n General de Aguas Colombia IDEAM - Institute of Hydrology, Meteorology and Environment Studies Ecuador INAMHI - National Institute of Meteorology and Hydrology Guyana Hydrometeorological Service Peru SENAMHI - National Meteorological and Hydrological Service Venezuela Ministry of Environment and Natural Resources Region IV - North and Central America Bahamas - Water and Sewerage Corporation British Caribbean Territories - Caribbean Institute for Meteorology and Hydrology Canada Environment Canada Dominica - Caribbean Institute for Meteorology and Hydrology El Salvador Servicio Nacional De Estudios Territoriales Jamaica Water Resources Authority Mexico Comisi?n nacional del agua Panama Departamento de Hidrometeorolog?a Republica Dominicana INDRHI - Instituto Nacional de Recursos Hidraulicos USA United States Geological Survey Region V - South-West Pacific Australia Hydrometeorological Advisory Service (HAS) - Bureau of Meteorology Malaysia Department of Irrigation and Drainage New Zealand National Institute of Water and Atmospheric Research Philippines National Water Resources Board Region VI - Europe (including Middle East) Armenia Department of Hydrometeorology ARMHYDROMET Austria BMLF Hydrological Service Azerbaijan State Hydrometeorological Committee of the Azerbaijan Republic Bosnia and Herzegovina - Federal Meteorological Institute Bulgaria National Institute of Meteorology and Hydrology Croatia Meteorological and Hydrological Service Cyprus Water Development Sector Czech Republic Czech Hydrometeorological Institute Denmark Geological Survey of Denmark and Greenland Estonia Estonian Meteorological and Hydrological Institute Finland Finnish Environment Institute - Hydrology and Water Management Division France R?seau National des Donn?es sur l'Eau Germany BfG - Federal Institute for Hydrology Hungary VITUKI RT (mainly in Hungarian) Iceland Hydrological Service Ireland Office of Public Works Italy National Hydrographic and Oceanographic Service (in Italian) Latvia Latvian Hydrometeorological Agency Lithuania Lithuanian Hydrometeorological Service The former Yugoslav Republic of Macedonia Hydrometeorological Institute Malta Water Services Corporation Netherlands Institute for Inland Watermanagement and Wastewater Treatment (RIZA) Norway NVE - Norwegian Water Resources and Energy Administration Poland IMGW - Institute of Meteorology and Water Management Portugal Instituto da ?gua - Water Institute Romania National Institute of Meteorology and Hydrology Russian Federation State Hydrological Institute Slovakia Slovak Hydrometeorological Institute Slovenia Hydrometeorological Service Spain Ministerio de Medio Ambiente Sweden Swedish Meteorological and Hydrological Institute Switzerland Swiss Federal Office for Water and Geology Turkey DSI General Directorate of State Hydraulic Works United Kingdom Centre for Ecology and Hydrology Yugoslavia Federal Hydrometeorological Institute Information taken from ""http://www.wmo.ch/web/homs/links/linksnhs.html"" Data link: ""http://www.wmo.ch/web/homs/links/linksnhs.html""" proprietary NHSNOWM_001 Northern Hemisphere Snow Cover Monthly Statistics at 1 Degree Resolution V001 (NHSNOWM) at GES DISC GES_DISC STAC Catalog 2000-01-01 2014-11-30 -180, 0, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239898025-GES_DISC.umm_json This product is Snow Cover Statistics. The dataset was prepared by Dr. Peter Romanov at Cooperative Institute for Climate Studies(CICS) of the University of Maryland for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program. The product includes the monthly snow statistics (frequency of occurrence) for Northern Hemisphere at 1x1 degree spatial resolution. The dataset covers the time period from January 2000 to November 2014. Monthly data were derived from daily snow cover charts produced at NOAA/NESDIS within Interactive Multisensor Ice Mapping System (IMS). proprietary NIH-NSF_Lake_Erie_0 Lake Erie optical measurements OB_DAAC STAC Catalog 2013-08-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360521-OB_DAAC.umm_json Measurements made in Lake Erie funded by National Institutes of Health and the National Science Foundation. proprietary NIMBUS7_ERB_Ch10C_TSI_NAT_1 Nimbus-7 Total Solar Irradiance Data in Native Format LARC_ASDC STAC Catalog 1978-11-16 1993-12-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1373953856-LARC_ASDC.umm_json The NIMBUS7_ERB_Ch10C_TSI_NAT data set is the Nimbus-7 Channel 10C (Ch10C) Total Solar Irradiance (TSI) aboard the Earth Radiation Budget (ERB) satellite Data in Native (NAT) format.The Nimbus 7 research-and-development satellite served as a stabilized, earth-oriented platform for the testing of advanced systems for sensing and collecting data in the pollution, oceanographic and meteorological disciplines. The polar-orbiting spacecraft consisted of three major structures: (1) a hollow torus-shaped sensor mount, (2) solar paddles, and (3) a control housing unit that was connected to the sensor mount by a tripod truss structure. proprietary @@ -10738,6 +10978,7 @@ NMSO2-PCA-L2-NRT_2 OMPS/NPP PCA SO2 Total Column 1-Orbit L2 Swath 50x50km NRT OM NMTO3NRT_2 OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital NRT OMINRT STAC Catalog 2011-10-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1439272084-OMINRT.umm_json The OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital product provides total ozone measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite.The total column ozone amount is derived from normalized radiances using 2 wavelength pairs 317.5 and 331.2 nm under most conditions, and 331.2 and 360 nm for high ozone and high solar zenith angle conditions. Additionally, this data product contains measurements of UV aerosol index and reflectivity at 331 nm.Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir. The L2 NM Ozone data are written using the Hierarchical Data Format Version 5 or HDF5. proprietary NOAA_0 National Oceanic and Atmospheric Administration (NOAA) OB_DAAC STAC Catalog 1996-02-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360523-OB_DAAC.umm_json NOAA measurements from 1996 to 1999 along the Eastern US coastal region. proprietary NOAA_AVHRR_L1B_LAC_NA AVHRR Level-1B Local Area Coverage Imagery ESA STAC Catalog 1981-01-01 2020-12-31 -30, 35, 70, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689675-ESA.umm_json "This collection is composed of AVHRR L1B products (1.1 km) reprocessed from the NOAA POES and Metop AVHRR sensors data acquired at the University of Dundee and University of Bern ground stations and from the ESA and University of Bern data historical archive. The product format is the NOAA AVHRR Level 1B that combines the AVHRR data from the HRPT stream with ancillary information like Earth location and calibration data which can be applied by the user. Other appended parameters are time codes, quality indicators, solar and satellite angles and telemetry. Two data collections cover the Europe and the neighbouring regions in the period of 1 January 1981 to 31 December 2020 and the acquired data in the context of the 1-KM project in the ‘90s. During the early 1990’s various groups, including the International Geosphere-Biosphere Programme (IGBP), the Commission of the European Communities (CEC), the Moderate Resolution Imaging Spectrometer (MODIS) Science Team and ESA concluded that a global land 1-KM AVHRR data set would have been crucial to study and develop algorithms for several land products for the Earth Observing System. USGS, NOAA, ESA and other non-U.S. AVHRR receiving stations endorsed the initiative to collect a global land 1-km multi-temporal AVHRR data set over all land surfaces using NOAA's TIROS ""afternoon"" polar-orbiting satellite. On the 1st of April 1992, the project officially began up to the end of 1999 with the utilisation of 23 stations worldwide plus the NOAA local area coverage (LAC) on-board recorders. The global land 1-km AVHRR dataset is composed of 5 channels, raw AVHRR dataset at 1.1km resolution from the NOAA-11 and NOAA-14 satellites covering land surfaces, inland water and coastal areas. Spatial coverage: Check the spatial coverage of the collection on a map available on the Third Party Missions Dissemination Service: _$$AVHRR L1B 1.1 KM$$ https://tpm-ds.eo.esa.int/socat/AVHRR_L1B_1_1KM _$$AVHRR L1B LAC Out-of-Europe$$ https://tpm-ds.eo.esa.int/socat/NOAA_AVHRR_L1B_LAC_out-of-Europe" proprietary +NOAA_CDR_NDVI NOAA Climate Data Record Normalized Difference Vegetation Index CEOS_EXTRA STAC Catalog 1981-01-01 2013-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552492-CEOS_EXTRA.umm_json National Oceanic and Atmospheric Administration (NOAA) Climate Data Records (CDR) provide historical climate information using data from weather satellites. This dataset contains daily Normalized Difference Vegetation Index (NDVI) derived from surface reflectance data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor. This long-term record spans from 1981 to 2013 and utilizes AVHRR data from seven NOAA polar orbiting satellites: NOAA 7, 9, 11, 14, 16, and 18. This NDVI collection provides the global change and resource management communities with vegetation data for historical trend analysis and vegetation monitoring studies for land surfaces around the globe. proprietary NOAA_ToF_CIMS_Instrument_Data_1921_2 ATom: L2 Measurements from NOAA ToF Chemical Ionization Mass Spectrometer, Version 2 ORNL_CLOUD STAC Catalog 2017-09-28 2018-05-21 -180, -86.18, 180, 82.94 https://cmr.earthdata.nasa.gov/search/concepts/C2677134970-ORNL_CLOUD.umm_json This dataset provides the mixing ratios of reactive nitrogen and halogen species measured by the NOAA Iodide Ion Time-of-Flight Chemical Ionization Mass Spectrometer (NOAA CIMS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission for ATom-3 and ATom-4 campaigns. The NOAA CIMS uses chemical ionization mass spectrometric detection of gas phase organic and inorganic analytes via I- adduct formation. Measurements for ATom include N2O5 (dinitrogen pentoxide), ClNO2 (chloro nitrite), Cl2 (Chlorine), HCOOH (formic acid), C2H4O3S (hydroperoxymethyl thioformate), BrCl (bromine monochloride), BrCN (cyanogen bromide), and BrO (bromine monoxide). ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2-13 km altitude. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate. proprietary NOBM_DAY_R2017 NASA Ocean Biogeochemical Model assimilating satellite chlorophyll data global daily VR2017 (NOBM_DAY) at GES DISC GES_DISC STAC Catalog 1998-01-01 2015-12-31 -180, -84, 180, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1404080675-GES_DISC.umm_json This is the assimilated daily data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007). It spans the domain from -84 to 72 degree latitude in increments of 1.25 degree longitude by 2/3 degree latitude, including only open ocean areas where bottom depth > 200m. NOBM contains 4 phytoplankton groups, 4 nutrient groups, a single herbivore group, and 3 detrital pools, and the major ocean carbon components, dissolved organic and inorganic carbon (DOC and DIC). proprietary NOBM_MON_R2017 NASA Ocean Biogeochemical Model assimilating satellite chlorophyll data global monthly VR2017 (NOBM_MON) at GES DISC GES_DISC STAC Catalog 1998-01-01 2015-12-31 -180, -84, 180, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1404080681-GES_DISC.umm_json This is the assimilated monthly data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007). It spans the domain from -84 to 72 degree latitude in increments of 1.25 degree longitude by 2/3 degree latitude, including only open ocean areas where bottom depth >200m. NOBM contains 4 phytoplankton groups, 4 nutrient groups, a single herbivore group, and 3 detrital pools, and the major ocean carbon components, dissolved organic and inorganic carbon (DOC and DIC). proprietary @@ -10829,6 +11070,7 @@ NPP_XLN_156_2 NPP Grassland: Xilingol, China, 1980-1989, R1 ORNL_CLOUD STAC Cata NPP_surfaces_750_1 BigFoot NPP Surfaces for North and South American Sites, 2000-2004 ORNL_CLOUD STAC Catalog 2000-01-01 2004-12-31 -156.61, -2.86, -54.96, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2751481549-ORNL_CLOUD.umm_json The BigFoot project gathered Net Primary Production (NPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html. proprietary NPS_YNP_30M_DEM 30 Meter DEM of Yellowstone National Park SCIOPS STAC Catalog 1970-01-01 -112, 44, -109, 46 https://cmr.earthdata.nasa.gov/search/concepts/C1214607703-SCIOPS.umm_json Digital Elevation Models are useful for deriving elevations; modeling 3D surfaces; creating derived products such as slope, aspect, and relief layers; creating watersheds and conducting watershed analyses; and conducting other types of terrain analyses. Digital Elevation Model (DEM) is the terminology adopted by the USGS to describe terrain elevation data sets in a digital raster form. The 7.5-minute DEM (30- by 30-m cell size, in a Universal Transverse Mercator (UTM) projection) provides coverage in 7.5- by 7.5-minute blocks. Each product provides the same coverage as a standard USGS 7.5-minute quadrangle without over edge. The DEM data are stored as a series of profiles in which the spacing of the elevations along and between each profile is in regular whole number intervals.The Yellowstone National Park 30 m DEM was compiled from a combination of of Level I and II USGS 30 m DEMs, elevation values range from 1528 to 4186, describing 1528 - 4186 meters. The parkwide DEM was compiled by Lisa Landenburger, Geographic Information and Analysis Center (GIAC), Montana State University, Bozeman, MT. This data set is unpublished. material. This summary was abstracted from the FGDC metadata file. proprietary NPWRC_alienplantsrankingsystem_version 5.1, Version 30 Sep 2002 Alien Plants Ranking System (APRS) Implementation Team CEOS_EXTRA STAC Catalog 1970-01-01 -115, 30, -85, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2231551762-CEOS_EXTRA.umm_json The Alien Plants Ranking System (APRS) is a computer-implemented system to help land managers make difficult decisions concerning invasive nonnative plants. The management of invasive plants is difficult, expensive, and requires a long-term commitment. Therefore, land managers must focus their limited resources, targeting the species that cause major impacts or threats to resources within their management, or the species that impede attainment of management goals. APRS provides an analytical tool to separate the innocuous species from the invasive ones (typically around 10% of the nonnative species). APRS not only helps identify those species that currently impact a site, but also those that have a high potential do so in the future. Finally, the system addresses the feasibility of control of each species, enabling the manager to weigh the costs of control against the level of impact. This system has been developed and tested primarily in grassland and prairie parks in the central United States. proprietary +NPWRC_effectsoffireonbirdpops Effects of Fire on Bird Populations in Mixed-grass Prairie CEOS_EXTRA STAC Catalog 1997-01-01 1997-12-31 -101, 46.5, -97, 48.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549248-CEOS_EXTRA.umm_json The mixed-grass prairie is one of the largest ecosystems in North America, originally covering about 69 million hectares (Bragg and Steuter 1995). Although much of the natural vegetation has been replaced by cropland and other uses (Samson and Knopf 1994, Bragg and Steuter 1995), significant areas have been preserved in national wildlife refuges, waterfowl production areas, state game management areas, and nature preserves. Mixed-grass prairie evolved with fire (Bragg 1995), and fire is frequently used as a management tool for prairie (Berkey et al. 1993). Much of the mixed-grass prairie that has been protected is managed to enhance the reproductive success of waterfowl and other gamebirds, but nongame birds now are receiving increasing emphasis. Despite the importance of the area to numerous species of birds and the aggressive management applied to many sites, relatively little is known about the effects of fire on the suitability of mixed-grass prairie for breeding birds. Several studies have examined effects of fire on breeding birds in the tallgrass prairie (e.g., Tester and Marshall 1961, Eddleman 1974, Halvorsen and Anderson 1983, Westenmeier and Buhnerkempe 1983, Zimmerman 1992, Herkert 1994), in western sagebrush grasslands (Peterson and Best 1987), and in shrubsteppe (Bock and Bock 1987). Studies of fire effects in the mixed-grass prairie are limited. Huber and Steuter (1984) examined the effects on birds during the breeding season following an early-May prescribed burn on a 122-ha site in South Dakota. They contrasted the bird populations on that site to those on a nearby 462-ha unburned site that had been lightly grazed by bison (Bison bison). Pylypec (1991) monitored breeding bird populations occurring in fescue prairies of Canada on a single 12.9-ha burned area and on an adjacent 5.6-ha unburned fescue prairie for three years after a prescribed burn. proprietary NRMSC_carnivorerecolonisation Carnivore Re-Colonisation: Reality, Possibility and a Non-Equilibrium Century for Grizzly Bears in the Southern Yellowstone Ecosystem CEOS_EXTRA STAC Catalog 1900-01-01 2000-12-31 -111, 44, -110, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2231549197-CEOS_EXTRA.umm_json Most large native carnivores have experienced range contractions due to conflicts with humans, although neither rates of spatial collapse nor expansion have been well characterized. In North America, the grizzly bear (Ursus arctos) once ranged from Mexico northward to Alaska, however its range in the continental United States has been reduced by 95-98%. Under the U.S. Endangered Species Act, the Yellowstone grizzly bear population has re-colonized habitats outside Yellowstone National Park. We analyzed historical and current records, including data on radio-collared bears, (i) to evaluate changes in grizzly bear distribution in the southern Greater Yellowstone Ecosystem over a 100-year period, (ii) to utilise historical rates of recolonization to project future expansion trends and (iii) to evaluate the reality of future expansion based on human limitations and land use. Analysis of distribution in 20-year increments reflects range reduction from south to north (1900-1940) and expansion to the south (1940-2000). Expansion was exponential and the area occupied by grizzly bears doubled approximately every 20 years. A complementary analysis of bear occurrence in Grand Teton National Park also suggests an unprecedented period of rapid expansion during the last 20-30 years. The grizzly bear population currently has re-occupied about 50% of the southern GYE. Based on assumptions of continued protection and ecological stasis, our model suggests total occupancy in 25 years. Alternatively, extrapolation of linear expansion rates from the period prior to protection suggests total occupancy could take > 100 years. Analyses of historical trends can be useful as a restoration tool because they enable a framework and timeline to be constructed to pre-emptively address the social challenges affecting future carnivore recovery. One of the purposes of the dataset is to predict when grizzly bear occupation of Southern Yellowstone Ecosystem will be total. We focused on a 24,000 square kilometer mosaic of mostly public land that is managed by various federal and state agencies. Our analysis of changes in grizzly bear distribution during 1900-2000 was divided into 20-year periods. For each, we used various data sources for grizzly bear occurrence to create digital maps of bear distribution using ArcView GIS 32. (ESRI, Redlands, CA) We digitized reports, interviews, conflicts, mortalities and observations as points. We created a polygon for the 1920 source data, a hand-drawn distribution map by Merriam (1922). More methodology given in Pyare, 2004 paper. proprietary NRSCC_GLASS_ FAPAR_MODIS_0.05D_11 NRSCC_GLASS_ FAPAR_MODIS_0.05D NRSCC STAC Catalog 2010-02-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351149-NRSCC.umm_json This Global LAnd Surface Satellite (GLASS) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product was generated using MODIS products. proprietary NRSCC_GLASS_ FAPAR_MODIS_1KM_11 NRSCC_GLASS_ FAPAR_MODIS_1KM NRSCC STAC Catalog 2000-02-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351155-NRSCC.umm_json This Global LAnd Surface Satellite (GLASS) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product was developed using MODIS datasets. proprietary @@ -11102,6 +11344,7 @@ NSIDC-0793_1 MEaSUREs ITS_LIVE Greenland Monthly 120 m Ice Sheet Extent Masks, 1 NSIDC-0794_1 MEaSUREs ITS_LIVE Antarctic Annual 240 m Ice Sheet Extent Masks, 1997-2021 V001 NSIDC_ECS STAC Catalog 1997-10-01 2021-03-14 -180, -90, 180, -57.6 https://cmr.earthdata.nasa.gov/search/concepts/C3179071550-NSIDC_ECS.umm_json This ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of 240 m Antarctic Ice Sheet extent masks at roughly annual resolution from 1997 through 2021. The ice masks were generated by combining data acquired by multiple satellite-borne optical, thermal, and radar sensors. The ice thickness and velocity data used to determine the presence of ice are also provided. proprietary NSIDC-0796_1 Glacial and Fast Ice Distributions in Southeast Greenland Fjords V001 NSIDC_ECS STAC Catalog 2015-01-01 2019-12-31 -44.199176, 60.286876, -22.410516, 70.009956 https://cmr.earthdata.nasa.gov/search/concepts/C3226155848-NSIDC_ECS.umm_json This data set provides spatial distributions of fast ice and glacial ice in eight fjords spanning the Southeast Greenland coast: Nansen, Kangerlusruaq, Ikertivaq, Skjoldungen, Tingmiarmiut, Napasorsvaq, Anoritup, and Kangerlluluk. Temporal coverage is discontinuous, depending on the availability and quality of images. Fjord data were sourced from USGS EarthExplorer, Copernicus Open Access Hub, and the NSIDC. Landsat-8 and MODIS imagery for ice identification were collected from NASA Worldview and USGS EarthExplorer. Fjord, fast ice, and glacial ice boundaries were manually delineated using ArcGIS. Glacial ice was further categorized as dense glacial melange (Type 3), substantial glacial ice with large icebergs (Type 2), low-density glacial ice with large icebergs (Type 1), consistent small ice surface without large icebergs (Type 0), or glacier surface (Type 99). proprietary NSIDC-0797_1 SMAP/CYGNSS EASE-Grid Soil Moisture V001 NSIDC_ECS STAC Catalog 2017-04-01 2023-12-31 -180, -40, 180, 40 https://cmr.earthdata.nasa.gov/search/concepts/C3286281558-NSIDC_ECS.umm_json "This data set is derived by downscaling Soil Moisture Active Passive (SMAP) enhanced Level-3 9 km brightness temperatures (TB) using Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data and employing a slightly modified version of the baseline SMAP active-passive TB algorithm, the Single Channel Algorithm – Vertical polarization (SCA-V). The main parameter of this data set is surface soil moisture presented on the Global EASE-Grid 2.0 projection, with each data point representing the top 5 cm of the soil column. For SMAP-derived data, see SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture, Version 5; for CYGNSS-derived data, see CYGNSS Level 1, Version 2.1." proprietary +NURE_SEDIMENT_CHEM National Uranium Resource Evaluation Program: Sediment Chemistry of the Conterminous United States CEOS_EXTRA STAC Catalog 1964-01-01 1996-01-01 -179, 19, -68, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2231552690-CEOS_EXTRA.umm_json From NURE Sediment Chemistry FAQ: These maps are derived from a subset of the National Uranium Resource Evaluation (NURE) Hydrogeochemical and Stream Sediment Reconnaissance (HSSR) data. Approximately 260,000 samples were analyzed in the continental U.S. and consisted of solid samples, including stream, lake, pond, spring, and playa sediments, and soils. Data for eleven elements: Na, Ti, Fe, Cu, Zn, As, Ce, Hf, Pb, Th, and U were analyzed and included on the National Geochemical Atlas CD and the digital release NURE Sediment Chemistry. These publications are intended to allow the rapid visualization of the geochemical landscape of the conterminous U.S. using NURE HSSR data. The raw data used in the production of these publications are available on the following CD-ROM: Hoffman, J.D., Kim P. Buttleman, Russell A. Ambroziak, and Christine A. Cook, 1996, National Uranium Resource Evaluation (NURE) Hydrogeochemical and Stream Sediment Reconnaissance (HSSR) data. available proprietary NVAP_CLIMATE_Layered-Precipitable-Water_1 NASA Water Vapor Project MEaSUREs (NVAP-M) CLIMATE Layered Precipitable Water LARC_ASDC STAC Catalog 1988-01-01 2009-12-01 180, -90, -179.9, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1595664379-LARC_ASDC.umm_json NVAP_CLIMATE_Layered-Precipitable-Water data set is designed to provide the most stable water vapor data set over time for use in climate applications. NASA Water Vapor Project MEaSUREs (NVAP-M) Climate only includes data from stable instruments that have undergone intercalibration efforts to ensure consistency between data from the same instrument flying on multiple satellite platforms. The new NVAP data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets. proprietary NVAP_CLIMATE_Total-Precipitable-Water_1 NASA Water Vapor Project MEaSUREs (NVAP-M) CLIMATE Total Precipitable Water LARC_ASDC STAC Catalog 1988-01-01 2009-12-01 180, -90, -179.9, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1600001034-LARC_ASDC.umm_json NVAP_CLIMATE_Total-Precipitable-Water data set is designed to provide the most stable water vapor dataset over time for use in climate applications. NASA Water Vapor Project MEaSUREs (NVAP-M) Climate only includes data from stable instruments that have undergone intercalibration efforts to ensure consistency between data from the same instrument flying on multiple satellite platforms. The new NVAP data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets. proprietary NVAP_OCEAN_Total-Precipitable-Water_1 NASA Water Vapor Project MEaSUREs (NVAP-M) OCEAN Total Precipitable Water LARC_ASDC STAC Catalog 1988-01-01 2009-12-01 180, -90, -179.9, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1599730870-LARC_ASDC.umm_json NVAP_OCEAN_Total-Precipitable-Water data set includes only data from the Special Sensor Microwave/Imager (SSM/I) and intends to mirror other available SSM/I-only water vapor data sets. The data set is used for studies of climate change, interannual variability, and independent comparison to other ocean-only data sets. The new NASA Water Vapor Project (NVAP) data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets. proprietary @@ -11110,6 +11353,7 @@ NVAP_WEATHER_Total-Precipitable-Water_1 NASA Water Vapor Project MEaSUREs (NVAP- NWS0007 Compilation/Evaluation of Historical Tsunamis in the Pacific Using the USGS/NEIC Earthquake Data, NOAA/NGDC Tsunami Data, and Imamura-Iida Scale CEOS_EXTRA STAC Catalog 1690-01-01 95, -60, -65, 65 https://cmr.earthdata.nasa.gov/search/concepts/C2231550342-CEOS_EXTRA.umm_json These data sets are based on an area-by-area study of the Pacific Basin to document historical tsunamis and quantify historical coastal damage both near the source and at far-field locations. An operational modification of the Imamura-Iida Scale is used for this purpose. proprietary NWT_Burn_Severity_Maps_1694_1 ABoVE: Burn Severity of Soil Organic Matter, Northwest Territories, Canada, 2014-2015 ORNL_CLOUD STAC Catalog 2014-05-01 2015-10-01 -124.03, 58.29, -108.83, 65.55 https://cmr.earthdata.nasa.gov/search/concepts/C2143402644-ORNL_CLOUD.umm_json This dataset provides maps at 30-m resolution of landscape surface burn severity (surface litter and soil organic layers) from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. The maps were derived from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery and two separate multiple linear regression models trained with field data; one for the Plains and a second for the Shield ecoregion. Field observations were used to estimate area burned in each of five severity classes (unburned, singed, light, moderate, severely burned) in six stratified randomly selected plots of 10 x 10-m in size across a 1-ha site. Using this five class scale a burn severity index (BSI) for each 1-ha site was calculated using multiple weighted and averaged field parameters. Pre- and post-fire phenologically paired Landsat 8 images were used to model the five discrete severity classes using midpoints as breaks. proprietary NW_microcosm_results_1 Mineralisation results using 14C octadecane at a range of water, nutrient levels and freeze thaw cycles AU_AADC STAC Catalog 2001-06-01 2001-10-29 110.45953, -66.31249, 110.59637, -66.261 https://cmr.earthdata.nasa.gov/search/concepts/C1214313663-AU_AADC.umm_json Geochemical, microbial and 14C data on remediation of petroleum hydrocarbons in Antarctica. This record is part of ASAC project 1163 (ASAC_1163). Microcosm study using Old Casey petroleum hydrocarbon contaminated sediment investgating the effect of water, nutrients and freze/thaw cycles on biodegradation. Temperature range -4 to 28 degrees. Microcosms with three different levels of nutrients and three different levels of water were investigated. The experiment was run over 95 days. Degradation was traced by radiometric methods and total aliphatic hydrocarbons were measured by gas chromatography. Radiometric data in file radiometric_01.xls, Gas Chromatography data in file gc_01.xls. This work was completed as part of ASAC project 1163 (ASAC_1163). The radiometric spreadsheet is divided up as follows: CODES is a summary of what went into each microcosm. CALCULATIONS is how much nutrients, water, radioactivity was added to the sediment. SUMMARY is what went into each microcosm flask. CT1, CT2 etc is the raw data, what was measured and calculations of radioactivity and recovery of isotope. Note that the Evaporation flasks (i.e., E10a) the number refers to the temperature that the flasks were incubated at, 'a' and 'b' refer to duplicates. AVERAGE is the average recoveries and first order rates of the triplicate microcosm for each treatment. GRAPHS is the graphs. The fields in this dataset are: Days Hours Initial flask weight NaOH removed NaOH added Weight of NaOH (g) Count (dpm) Discarded dpm's Volume NaOH (ml) dpm in trap Absolute dpm's %dpm recovered millimole octadecane mineralised proprietary +NatalMuseum Natal Museum - Mollusc Collection (Bivalvia and Gastropoda) CEOS_EXTRA STAC Catalog 1894-01-01 2005-07-09 11.38667, -43.19167, 55.13334, -11 https://cmr.earthdata.nasa.gov/search/concepts/C2232477685-CEOS_EXTRA.umm_json The Natal Museum's Department of Mollusca had its origins in the shell collection and library of Henry Burnup, a dedicated amateur who was honorary curator of molluscs until his death in 1928. Subsequently, the collection has been expanded many times over through field work, donation, exchange and purchase. Its historical value was greatly increased by absorption of important shell collections housed the Transvaal Museum (1978) and Albany Museum (1980), as well as the Rodney Wood collection from the Seychelles received from the Mutare Museum in Zimbabwe and the Kurt Grosch collection, built up over 25 years of residence in northern Mozambique. The mollusc collection now ranks among the 15 largest in the world and is certainly the largest both in Africa and on the Indian Ocean rim. It currently contains 7233 Bivalvia records, and 20112 Gastropoda records (total 27345 records of 282 families). The collection will be updated in the near future. proprietary Nested_DGGE_1 Molecular comparison of bacterial diversity in uncontaminated and hydrocarbon contaminated marine sediment AU_AADC STAC Catalog 1997-11-01 1998-11-30 110.32471, -66.51764, 110.67627, -66.2226 https://cmr.earthdata.nasa.gov/search/concepts/C1214313662-AU_AADC.umm_json Sediment samples which were originally collected as part of ASAC 868 (ASAC_868) are now being investigated using molecular microbial techniques as part of ASAC 1228 (ASAC_1228). Samples were collected in a nested survey design in two hydrocarbon impacted areas and two unimpacted areas. Denaturing gradient gel electrophoresis (DGGE) of a region of the 16S RNA gene was used to investigate the microbial community structure. Banding patterns obtained from the DGGE were transformed into a presence / absence matrix and analysed with a multivariate statistical approach. The download file contains an excel spreadsheet, a csv version of the data, plus a readme file. proprietary NetCDF.GOMOS_UFP_Gridded_NA Envisat GOMOS Level 2 - Atmospheric constituents profiles - Gridded User Friendly Product [GOMOS_UFP_gridded] ESA STAC Catalog 2002-04-15 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336932-ESA.umm_json This data product describes atmospheric constituents profiles: In particular the vertical and line density profiles of ozone, NO2, NO3, O2, H2O, air, aerosols, temperature, turbulence. Coverage is as follows: Elevation range: +62 deg to +68 deg Azimuth range: +90 deg to +190 deg (with respect to the flight direction) The GOMOS data are now also available as user friendly products in the NetCDF4-format. These files are Level 2 constituent profiles and are altitude gridded. These Level 2 files include quality flags and are based and collected on a yearly basis. proprietary NetCDF.GOMOS_UFP_NA Envisat GOMOS Level 2 - Atmospheric constituents profiles - User Friendly Product [GOMOS_UFP] ESA STAC Catalog 2002-04-15 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336931-ESA.umm_json This data product describes atmospheric constituents profiles: In particular the vertical and line density profiles of ozone, NO2, NO3, O2, H2O, air, aerosols, temperature, turbulence. Coverage is as follows: Elevation range: +62 deg to +68 deg Azimuth range: +90 deg to +190 deg (with respect to the flight direction) The GOMOS data are now also available as user friendly products in the NetCDF4-format. These files are occultation based (dark and bright) and include all GOMOS Level 2 constituent profiles and HRTP profiles with all the essential parameters. For further information, please see the news published on 1 March 2017 and 1 August 2017. proprietary @@ -11299,6 +11543,7 @@ ODIN.SMR_NA ODIN SMR data products ESA STAC Catalog 2001-02-01 -180, -90, 180, ODU_CBM_0 Old Dominion University (ODU) - Chesapeake Bay Mouth (CBM) measurements OB_DAAC STAC Catalog 2004-05-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360566-OB_DAAC.umm_json Measurements made of the Chesapeake Bay Mouth (CBM) by Old Dominion University (ODU) between 2004 and 2006. proprietary OFR_94-212 A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212 CEOS_EXTRA STAC Catalog 1980-05-01 1988-09-06 -122, 46, -122, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2232411623-CEOS_EXTRA.umm_json Airborne monitoring of Mount St. Helens by the USGS began in May 1980 for sulfur dioxide emissions and in July 1980 for carbon dioxide emissions. A correlation spectrometer, or COSPEC, was used to measure sulfur dioxide in Mount St. Helens' plume. The upward-looking COSPEC was mounted in a fixed-wing aircraft and flown below and at right angles to the plume. Typically, three to six traverses were made underneath the plume to determine the SO2 burden (concentration x pathlength) within a cross-section of the plume. Knowing the burden along with the plume width and plume velocity (assumed to be the same as ambient wind speed), we could then calculate the emission rate of SO2. The use of correlation spectroscopy for determining the sulfur dioxide output of volcanoes is well established and the technique has been discussed in detail by a number of investigators (Malinconico, 1979; Casadevall and others, 1981; Stoiber and others, 1983). Carbon dioxide in the Mount St. Helens plume was measured by an infrared spectrometer tuned to the 4.26 um CO2 absorption band. An external sample tube was attached to the fuselage of a twin-engine aircraft to deliver outside air to the gas cell of the spectrometer. The aircraft was then flown at several different elevations through the plume at right angles to plume trajectory to define plume area and carbon dioxide concentration in a vertical cross-section of the plume. These two parameters along with the density of CO2 for the altitude of the plume and the plume velocity (assumed as above to be equal to ambient wind speed) were then used to calculate the CO2 emission rate (Harris and others, 1981). proprietary OFR_95-55 A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994 CEOS_EXTRA STAC Catalog 1990-03-20 1994-07-07 -154, 56, -152, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2232411611-CEOS_EXTRA.umm_json This report contains all of the available daily sulfur dioxide and carbon dioxide emission rates from Cook Inlet volcanoes as determined by the U.S. Geological Survey (USGS) from March 1990 through July 1994. Airborne sulfur dioxide gas sampling of the Cook Inlet volcanoes (Redoubt, Spurr, Iliamna, and Augustine) began in 1986 when several measurements were carried out at Augustine volcano during the eruption of 1986. Systematic monitoring for sulfur dioxide and carbon dioxide began in March 1990 at Redoubt volcano and continues to the present. Intermittent measurements at Augustine and Iliamna volcanoes began in 1990 and continues to the present. Intermittent measurements began at Spurr volcano in 1991, and were continued at more regular intervals from June, 1992 through the 1992 eruption at the Crater Peak vent to the present. proprietary +OFR_95-78_1 Geometeorological data collected by the USGS Desert Winds Project at Gold Spring, Great Basin Desert, northeastern Arizona, 1979-1992 CEOS_EXTRA STAC Catalog 1979-01-27 1992-12-31 -111, 35, -111, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231550505-CEOS_EXTRA.umm_json This data set contains meteorological data files pertaining to the Gold Spring Geomet research site. Documentation files and data-accessing display software are also included. The meteorological data are wind speed, peak gust, wind direction, precipitation, air temperature, soil temperature, barometric pressure, and humidity. Data from the monitoring station are voluminous; 14 observations from each station are made as often as ten times per hour, totaling more than a million observations per station per year. proprietary OISSS_L4_multimission_7day_v1_1.0 Multi-Mission Optimally Interpolated Sea Surface Salinity Global Dataset V1 POCLOUD STAC Catalog 2011-08-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2095055342-POCLOUD.umm_json This is a level 4 product on a 0.25-degree spatial and 4-day temporal grid. The product is derived from the level 2 swath data of three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. The product offers a continuous record from August 28, 2011 to present by concatenating the measurements from Aquarius (September 2011 - June 2015) and SMAP (April 2015 present). ESAs SMOS data was used to fill the gap in SMAP data between June and July 2019, when the SMAP satellite was in a safe mode. The two-month overlap (April - June 2015) between Aquarius and SMAP was used to ensure consistency and continuity in data record. The product covers the global ocean, including the Arctic and Antarctic in the areas free of sea ice, but does not cover internal seas such as Mediterranean and Baltic Sea. In-situ salinity from Argo floats and moored buoys are used to derive a large-scale bias correction and to ensure consistency and accuracy of the OISSS dataset. This dataset is produced by the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide. proprietary OISSS_L4_multimission_7day_v2_2.0 Multi-Mission Optimally Interpolated Sea Surface Salinity Global Dataset V2 POCLOUD STAC Catalog 2011-08-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2589160971-POCLOUD.umm_json This is a level 4 product on a 0.25-degree spatial and 4-day temporal grid. The product is derived from the level 2 swath data of three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. The product offers a continuous record from August 28, 2011 to present by concatenating the measurements from Aquarius (September 2011 - June 2015) and SMAP (April 2015 present). ESAs SMOS data was used to fill the gap in SMAP data between June and July 2019, when the SMAP satellite was in a safe mode. The two-month overlap (April - June 2015) between Aquarius and SMAP was used to ensure consistency and continuity in data record. The product covers the global ocean, including the Arctic and Antarctic in the areas free of sea ice, but does not cover internal seas such as Mediterranean and Baltic Sea. In-situ salinity from Argo floats and moored buoys are used to derive a large-scale bias correction and to ensure consistency and accuracy of the OISSS dataset. This dataset is produced by the Earth and Space Research (ESR), Seattle, WA and the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide. proprietary OISSS_L4_multimission_monthly_v1_1.0 Multi-Mission Optimally Interpolated Sea Surface Salinity Global Monthly Dataset V1 POCLOUD STAC Catalog 2011-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2179010138-POCLOUD.umm_json This is a level 4 product on a 0.25-degree spatial and monthly temporal grid. The product is the monthly mean of the level 4 OISSS dataset using three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. This dataset is produced by the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide and Addendum I to the product Technical Notes. proprietary @@ -11721,6 +11966,7 @@ PATEX_0 PATagonia EXperiment (PATEX) Project OB_DAAC STAC Catalog 2004-11-03 -1 PAZ.ESA.archive_NA PAZ ESA archive ESA STAC Catalog 2018-09-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2547579176-ESA.umm_json "The PAZ ESA archive collection consists of PAZ Level 1 data previously requested by ESA supported projects over their areas of interest around the world and, as a consequence, the products are scattered and dispersed worldwide and in different time windows. The dataset regularly grows as ESA collects new products over the years. Available modes are: • StripMap mode (SM): SSD less than 3m for a scene 30km x 50km in single polarization or 15km x 50km in dual polarisation • ScanSAR mode (SC): the scene is 100 x 150 km2, SSD less than 18m in signle pol only • Wide ScanSAR mode (WS): single polarisation only, with SS less than 40m and scene size of 270 x 200 km2 • Spotlight modes (SL): SSD less than 2m for a scene 10km x 10km, both single and dual polarization are available • High Resolution Spotlight mode (HS): in both single and dual polarisation, the scene is 10x5 km2, SSD less than 1m • Staring Spotlight mode (ST): SSD is 25cm, the scene size is 4 x 4 km2, in single polarisation only. The available geometric projections are: • Single Look Slant Range Complex (SSC): single look product, no geocoding, no radiometric artifact included, the pixel spacing is equidistant in azimuth and in ground range • Multi Look Ground Range Detected (MGD): detected multi look product, simple polynomial slant-to-ground projection is performed in range, no image rotation to a map coordinate system is performed • Geocoded Ellipsoid Corrected (GEC): multi look detected product, projected and re-sampled to the WGS84 reference ellipsoid with no terrain corrections • Enhanced Ellipsoid Corrected (EEC): multi look detected product, projected and re-sampled to the WGS84 reference ellipsoid, the image distortions caused by varying terrain height are corrected using a DEM The following table summarises the offered product types EO-SIP product type Operation Mode Geometric Projection PSP_SM_SSC Stripmap (SM) Single Look Slant Range Complex (SSC) PSP_SM_MGD Stripmap (SM) Multi Look Ground Range Detected (MGD) PSP_SM_GEC Stripmap (SM) Geocoded Ellipsoid Corrected (GEC) PSP_SM_EEC Stripmap (SM) Enhanced Ellipsoid Corrected (EEC) PSP_SC_MGD ScanSAR (SC) Single Look Slant Range Complex (SSC) PSP_SC_GEC ScanSAR (SC) Multi Look Ground Range Detected (MGD) PSP_SC_EEC ScanSAR (SC) Geocoded Ellipsoid Corrected (GEC) PSP_SC_SSC ScanSAR (SC) Enhanced Ellipsoid Corrected (EEC) PSP_SL_SSC Spotlight (SL) Single Look Slant Range Complex (SSC) PSP_SL_MGD Spotlight (SL) Multi Look Ground Range Detected (MGD) PSP_SL_GEC Spotlight (SL) Geocoded Ellipsoid Corrected (GEC) PSP_SL_EEC Spotlight (SL) Enhanced Ellipsoid Corrected (EEC) PSP_HS_SSC High Resolution Spotlight (HS) Single Look Slant Range Complex (SSC) PSP_HS_MGD High Resolution Spotlight (HS) Multi Look Ground Range Detected (MGD) PSP_HS_GEC High Resolution Spotlight (HS) Geocoded Ellipsoid Corrected (GEC) PSP_HS_EEC High Resolution Spotlight (HS) Enhanced Ellipsoid Corrected (EEC) PSP_ST_SSC Staring Spotlight (ST) Single Look Slant Range Complex (SSC) PSP_ST_MGD Staring Spotlight (ST) Multi Look Ground Range Detected (MGD) PSP_ST_GEC Staring Spotlight (ST) Geocoded Ellipsoid Corrected (GEC) PSP_ST_EEC Staring Spotlight (ST) Enhanced Ellipsoid Corrected (EEC) PSP_WS_SSC Wide ScanSAR (WS) Single Look Slant Range Complex (SSC) PSP_WS_MGD Wide ScanSAR (WS) Multi Look Ground Range Detected (MGD) PSP_WS_GEC Wide ScanSAR (WS) Geocoded Ellipsoid Corrected (GEC) PSP_WS_EEC Wide ScanSAR (WS) Enhanced Ellipsoid Corrected (EEC)" proprietary PAZ.Full.Archive.and.New.Tasking_NA PAZ Full Archive and New Tasking ESA STAC Catalog 2018-09-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689657-ESA.umm_json PAZ Image Products can be acquired in 8 image modes with flexible resolutions (from 1 m to 40 m) and scene sizes. Thanks to different polarimetric combinations and processing levels the delivered imagery can be tailored specifically to meet the requirements of the application. Available modes are: • StripMap mode (SM) in single and dual polarisation: The ground swath is illuminated with a continuous train of pulses while the antenna beam is pointed to a fixed angle, both in elevation and in azimuth. • ScanSAR mode (SC) in single polarisation: the swath width is increased respecting to the StripMap mode, it is composed of four different sub-swaths, which are obtained by antenna steering in elevation direction. • Wide ScanSAR mode (WS), in single polarisation: the usage of six sub-swaths allows to obtain a higher swath coverage product. • Spotlight modes: in single and dual polarisation: Spotlight modes take advantage of the beam steering capability in the azimuth plane to illuminate for a longer time the area of interest: a sensible improvement of the azimuth resolution is achieved at the expense of a shorter scene size. Spotlight mode (SL) is designed to maximise the azimuth scene extension at the expense of the spatial resolution, and High Resolution Spotlight mode (HS) is designed to maximize the spatial resolutions at the expense of the scene extension. • Staring Spotlight mode (ST), in single polarisation: The virtual rotation point coincides with the center of the beam: the image length in the flight direction is constrained by the projection on- ground of the azimuth beamwidth and it leads to a target azimuth illumination time increment and to achieve the best azimuth resolution. There are two main classes of products: • Spatially Enhanced products (SE): designed with the target of maximize the spatial resolution in pixels with squared size, so the larger resolution value of azimuth or ground range determines the square pixel size, and the smaller resolution value is adjusted to this size and the corresponding reduction of the bandwidth is used for speckle reduction. • Radiometrically Enhanced products (RE): designed with the target of maximize the radiometry, so the range and azimuth resolutions are intentionally decreased to significantly reduce speckle by averaging several looks. The following geometric projections are offered: • Single Look Slant Range Complex (SSC): single look product of the focused radar signal: the pixels are spaced equidistant in azimuth and in slant range. No geocoding is available, no radiometric artifacts included. Product delivered in the DLR-defined binary COSAR format. The SSC product is intended for applications that require the full bandwidth and phase information, e.g. for SAR interferometry and polarimetry. • Multi Look Ground Range Detected (MGD): detected multi look product in GeoTiff format with reduced speckle and approximately square resolution cells on ground. The image coordinates are oriented along flight direction and along ground range; the pixel spacing is equidistant in azimuth and in ground range. A simple polynomial slant to ground projection is performed in range using a WGS84 ellipsoid and an average, constant terrain height parameter. No image rotation to a map coordinate system is performed and interpolation artifacts are thus avoided. • Geocoded Ellipsoid Corrected (GEC): multi look detected product in GeoTiff format. It is projected and re-sampled to the WGS84 reference ellipsoid assuming one average terrain height. No terrain correction performed. UTM is the standard projection, for polar regions UPS is applied. • Enhanced Ellipsoid Corrected (EEC): multi look detected product in GeoTiff format. It is projected and re-sampled to the WGS84 reference ellipsoid. The image distortions caused by varying terrain height are corrected using an external DEM; therefore the pixel localization in these products is highly accurate. UTM is the standard projection, for polar regions UPS is applied. StripMap Single Mode ID: SM-S Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: 30 x 50 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 2.99 - 3.52 at (45° - 20°) - MGD, GEC, EEC (RE)[Ground range] 6.53 - 7.65 at (45° - 20°) - SSC[Slant range] 1.1 (150 MHz bandwidth) 1.7 (100 MHz bandwidth) Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 3.05 - MGD, GEC, EEC (RE) 6.53 - 7.60 at (45° - 20°) - SSC 3.01 StripMap Dual Mode ID: SM-D Polarizations: HH/VV, HH/HV, VV/VH Scene size (Range x Azimuth) [km]: 15 x 50 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 6 - MGD, GEC, EEC (RE)[Ground range] 7.51 - 10.43 at (45° - 20°) - SSC[Slant range] 1.18 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 6.11 - MGD, GEC, EEC (RE) 7.52 - 10.4 at (45° - 20°) - SSC ScanSAR Mode ID: SC Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: 100 x 150 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] N/A - MGD, GEC, EEC (RE)[Ground range] 16.79 - 18.19 at (45° - 20°) - SSC[Slant range] 1.17 - 3.4 (depending on range bandwidth) Azimuth Resolution [m]: - MGD, GEC, EEC (SE) N/A - MGD, GEC, EEC (RE) 17.66 - 18.18 at (45° - 20°) - SSC 18.5 Wide ScanSAR Mode ID: WS Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: [273-196] x 208 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] N/A - MGD, GEC, EEC (RE)[Ground range] 35 - SSC[Slant range] 1.75 - 3.18 (depending on range bandwidth) Azimuth Resolution [m]: - MGD, GEC, EEC (SE) N/A - MGD, GEC, EEC (RE) 39 - SSC 38.27 Spotlight Single Mode ID: SL-S Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: 10 x 10 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 1.55 - 3.43 at (55° - 20°) - MGD, GEC, EEC (RE)[Ground range] 3.51 - 5.43 at (55° - 20°) - SSC[Slant range] 1.18 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 1.56 - 2.9 at (55° - 20°) - MGD, GEC, EEC (RE) 3.51 - 5.4 at (55° - 20°) - SSC 1.46 Spotlight Dual Mode ID: SL-D Polarizations: HH/VV, HH/HV, VV/VH Scene size (Range x Azimuth) [km]: 10 x 10 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 3.09 - 3.5 at (55° - 20°) - MGD, GEC, EEC (RE)[Ground range] 4.98 - 7.63 at (55° - 20°) - SSC[Slant range] 1.17 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 3.53 - MGD, GEC, EEC (RE) 4.99 - 7.64 at (55° - 20°) - SSC 3.1 HR Spotlight Single Mode ID: HS-S Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: 10-6 x 5 (depending on incident angle) Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 1 - 1.76 at (55° - 20°) - MGD, GEC, EEC (RE)[Ground range] 2.83 - 3.11 at (55° - 20°) - SSC[Slant range] 0.6 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 1 - 1.49 at (55 °- 20°) - MGD, GEC, EEC (RE) 2.83 - 3.13 at (55° - 20°) - SSC 1.05 HR Spotlight Dual Mode ID: HS-D Polarizations: HH/VV, HH/HV, VV/VH Scene size (Range x Azimuth) [km]: 10 x 5 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 2 - 3.5 at (55° - 20°) - MGD, GEC, EEC (RE)[Ground range] 4 - 6.2 at (55° - 20°) - SSC[Slant range] 1.17 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 2.38 - 2.93 at (55° - 20°) - MGD, GEC, EEC (RE) 4 - 6.25 at (55° - 20°) - SSC 2.16 Staring Spotlight Mode ID: ST Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: [9-4.6] x [2.7-3.6] Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 0.96 - 1.78 at (45°- 20°) - MGD, GEC, EEC (RE)[Ground range] 0.97 - 1.78 at (45°-20°) - SSC[Slant range] 0.59 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 0.38 - 0.7 at (45°-20°) - MGD, GEC, EEC (RE) 0.97 - 1.42 at (45°-20°) - SSC 0.22 All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. For archive data, the user is invited to search PAZ products by using the USP (User Service Provider) web portal (http://www.geos.hisdesat.es/) (self registration required) in order to verify the availability over the Area of Interest in the Time of Interest. proprietary PC06_ECMWF_LBA_1141_1 LBA-HMET PC-06 ECMWF Modeled Precipitation and Surface Flux, Rondonia, Brazil: 1999 ORNL_CLOUD STAC Catalog 1999-01-01 1999-03-31 -62.37, -10.85, -61.87, -10.75 https://cmr.earthdata.nasa.gov/search/concepts/C2768943309-ORNL_CLOUD.umm_json This data set provides the mean diurnal cycle of precipitation, near-surface thermodynamics, and surface fluxes generated from short-term forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) model.The model outputs were 12- to 36-hour short-range forecasts, run at a triangular truncation of T319 and a vertical resolution of 60 levels, from each daily 1200 (UTC) analysis. The version of the forecast model used to prepare this data product was the operational ECMWF model in fall 2000, which included the tiled land-surface scheme (TESSEL) (Van den Hurk et al., 2000) and recent revisions to the convection, radiation, and cloud schemes described by Gregory et al., (2000). The ECMWF model was run for two Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) campaigns conducted in Rondonia, Brazil, during January and February of 1999: the Wet Season Atmospheric Mesoscale Campaign (WETAMC) and the Tropical Rainfall Measuring Mission (TRMM). See Silva Dias et al.,(2002) for additional information regarding the WETAMAC and TRMM campaigns. There are two comma-delimited data files with this data set: the ECMWF model output data and a file containing the mean hourly precipitation observations used to check the model output for biases. proprietary +PCD_INPE_web Meteorological Data Collection Platform Network from Brazilian Institute for Space Research CEOS_EXTRA STAC Catalog 1996-01-01 -75.64, -35.81, -32.74, 7.12 https://cmr.earthdata.nasa.gov/search/concepts/C2227456061-CEOS_EXTRA.umm_json Web access to data of a network of Meteorological Automatic Stations covering the Brazilian area proprietary PEACETIME_0 ProcEss studies at the Air-sEa Interface after dust deposition in the MEditerranean sea project (PEACETIME) OB_DAAC STAC Catalog 2017-05-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360590-OB_DAAC.umm_json Measurements from the PEACETIME (ProcEss studies at the Air-sEa Interface after dust deposition in the MEditerranean sea) project in the Mediterranean Sea to characterize biogeochemical processes in the atmosphere, at the air-sea boundary layer, and in the water. proprietary PERU_0 Optical measurements along the northern coast of Peru in 2003 OB_DAAC STAC Catalog 2003-05-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360594-OB_DAAC.umm_json Measurements made along the northern coast of Peru in 2003. proprietary PET_PU_3H025_001 RM-OBS/PU Potential Evapotranspiration and Supporting Forcing L4 3-hourly 0.25x0.25 degree V001 (PET_PU_3H025) at GES DISC GES_DISC STAC Catalog 1984-01-01 2006-12-31 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1575363595-GES_DISC.umm_json The Princeton University MEaSUREs Potential Evapotranspiration (PET) dataset provides a set of estimates of PET based on near surface meteorology and surface radiation data derived from a combination of reanalysis, satellite and gridded gauge data. The rationale of the project is to reduce the error from the input meteorological forcing and provide a variety of widely-used PET methods for different research and application purposes. PET is estimated using three methods: Penman open-water method (Penman), Priestley-Taylor method (PT), Reference crop evapotranspiration using the UN Food and Agricultural Organization approach (FAO). The Penman equation assumes PET occurs from an open water surface and calculates PET based on observations of surface net radiation, near-surface air temperature, wind speed, and specific humidity (Shuttleworth, 1993). The PT equation calculates PET based on surface net radiation and near-surface air temperature and does not account for the aerodynamic component (Priestley and Taylor, 1972). The FAO equation is a specific application of the Penman-Monteith equation for crop and short-grass reference surfaces and is based on surface net radiation, near-surface air temperature, wind speed, and specific humidity (Allen, 1998). This first version of dataset is estimated at a 3 hourly temporal resolution and 0.25x0.25 degrees spatial resolution globally, spanning the 23-year period 1984-2006. Datasets are stored as a 3-dimensional array with dimension 720 x 1440 x 8 for each day, in NetCDF-4 format. proprietary @@ -11880,6 +12126,7 @@ RBLE_917_1 Pre-LBA Rondonia Boundary Layer Experiment (RBLE) Data ORNL_CLOUD STA RDBTS4_2 Likely Basal Thermal State of the Greenland Ice Sheet V002 NSIDC_ECS STAC Catalog 1993-06-23 2017-05-20 -88.33, 58.91, 6.62, 83.56 https://cmr.earthdata.nasa.gov/search/concepts/C2429890326-NSIDC_ECS.umm_json The Likely Basal Thermal State of the Greenland Ice Sheet (GrIS) product contains key data sets that show how the likely basal thermal state was inferred from existing airborne and satellite data sets and recent methods, and provides a synthesis mask of the likely basal thermal state over the Greenland Ice Sheet. proprietary RDEFT4_1 CryoSat-2 Level-4 Sea Ice Elevation, Freeboard, and Thickness V001 NSIDC_ECS STAC Catalog 2010-09-20 -180, 55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1431413941-NSIDC_ECS.umm_json This data set contains estimates of Arctic sea ice thickness and concentration, ice freeboard and surface roughness, as well as snow density and depth, derived from the ESA CryoSat-2 Synthetic Aperture Interferometric Radar Altimeter (SIRAL). The data are provided daily on a 25 km grid as 30-day averages for the months between September and May. proprietary RDGBV4_1 Level-4 9ka Greenland Ice Sheet Balance Velocity V001 NSIDC_ECS STAC Catalog 1993-06-23 2013-04-26 -88.33, 58.91, 6.62, 81.51 https://cmr.earthdata.nasa.gov/search/concepts/C1430937702-NSIDC_ECS.umm_json This data set contains calculated balance velocity of the Greenland Ice Sheet during the last three quarters of the Holocene epoch (9ka). proprietary +RDK1_GTNL1_0.1 Geoton-L1 multispectral images from Resurs-DK CEOS_EXTRA STAC Catalog 2006-06-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231912423-CEOS_EXTRA.umm_json "Geoton-L1 multispectral images from Resurs-DK Multispectral highly detailed-resolution optoelectronic sensor from ""Resurs-DK"" satellite that has circular sun synchronous orbit. Archival satellite images are presented by panchromatic data (580-800 nm) with 2,8 m spatial resolution and multispectral data (green - 500-600 nm, red - 600-700 nm, near infrared - 700-800 nm) with 3-5 m spatial resolution. Swath with of the system is 8-16 km. Data can be used for solving disparate issues in agriculture, ecology, cartography, construction, forestry, natural resources inventory and emergency situations monitoring." proprietary RDSISC4_1 IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Images V001 NSIDC_ECS STAC Catalog 2010-03-23 2018-04-16 -180, 60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1655873481-NSIDC_ECS.umm_json "This data set contains reprocessed images depicting labels that indicate the sea ice surface category, created by processing IceBridge DMS L0 Raw Imagery with the Open Source Sea-ice Processing Algorithm. The images are provided as TIFF files (.tif). Additional metadata are provided as CSV text files (.csv), which are available as a single zip file named RDSISC4_metadata.zip. An orthorectified version of this data set is available as IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Orthorectified Images." proprietary RDSISCO4_1 IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Orthorectified Images V001 NSIDC_ECS STAC Catalog 2010-03-23 2017-07-25 -180, 60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1655873880-NSIDC_ECS.umm_json "This data set contains reprocessed, orthorectified images depicting labels that indicate the sea ice surface category, created by processing IceBridge DMS L0 Raw Imagery with the Open Source Sea-ice Processing Algorithm. Orthorectification was done using digital elevation models from the IceBridge DMS L3 Ames Stereo Pipeline Photogrammetric DEM collection. The standard (non-orthorectified) images are available as IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Images." proprietary RDWES1B_1 CryoSat-2 Level-1B Waveforms, Sea Ice Elevation, and Surface Roughness V001 NSIDC_ECS STAC Catalog 2010-09-15 -180, 55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1454786108-NSIDC_ECS.umm_json This data set contains surface elevations from retracked CryoSat-2 waveforms, as well as model fitting parameters used to retrack the waveform. The primary data set used in the production of these data come from the ESA CryoSat-2 satellite. proprietary @@ -11899,6 +12146,10 @@ RIO-SFE_0 Remote and In Situ Observations - San Francisco Bay and Delta Ecosyste ROAVERRS_0 Research on Ocean-Atmosphere Variability and Ecosystem Response in the Ross Sea (ROAVERRS) Project OB_DAAC STAC Catalog 1996-12-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360632-OB_DAAC.umm_json Measurements taken off the Antarctic coast in the Ross Sea between 1996 and 1998 under the Research on Ocean-Atmosphere Variability and Ecosystem Response in the Ross Sea (ROAVERRS). proprietary RONGOWAI_L1_SDR_V1.0_1.0 Rongowai-CYGNSS Airborne Level 1 Science Data Record Version 1.0 POCLOUD STAC Catalog 2022-10-20 165, -47, 179, -34 https://cmr.earthdata.nasa.gov/search/concepts/C2784494745-POCLOUD.umm_json The Rongowai Level 1 Science Data Record Version 1.0 dataset is generated by the University of Auckland (UoA) Rongowai Science Payloads Operations Centre in New Zealand. This initiative is supported by NASA and the New Zealand Space Agency. The data collection process is conducted using the Next-generation receiver (NgRx) mounted on the Air New Zealand domestic aircraft Q300.

This Level 1 (L1) dataset contains the Version 1.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument onboard an Air New Zealand domestic Q300 (tail number ZK-NFA). 20 DDMs are contained within a typical L1 netcdf corresponding to 10 Left-Hand-Circularly Polarized (LHCP) and 10 Right-Hand-Circularly Polarized (RHCP) channels. Other useful scientific and engineering measurement parameters include the co- and cross-polarized Normalized Bistatic Radar Cross Section (NBRCS) of the specular point, the Leading Edge Slope (LES) of the integrated delay waveform and the normalized waveforms. The L1 dataset contains a number of other engineering and science measurement parameters, including coherence detection and a coherence state metric, sets of quality flags/indicators, error estimates, Fresnel-zone geometry estimates (and thereby the estimated per-sample spatial resolution) as well as a variety of timekeeping, and geolocation parameters. Each netCDF data files corresponds to a single flight between airports within New Zealand (flight durations typically range between 45 min and 1hr 30min with a median of 7 flights/day) and measurements are reported at 1 second increments. Latency is approximately 1 days (or better) from the last recorded measurement time. proprietary RON_BROWN_0 Measurements made by the NOAA R/V Ron H. Brown OB_DAAC STAC Catalog 2000-10-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360633-OB_DAAC.umm_json Measurements made by the NOAA research vessel, the Ron H. Brown between 2000 and 2002. proprietary +RP1_GSA_0.1 Hyperspectral imaging from Resurs-P N1 CEOS_EXTRA STAC Catalog 2013-06-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231912409-CEOS_EXTRA.umm_json Hyperspectral imaging from Resurs-P N1 Hyperspectral sensor from ?Resurs-P N1? satellite that has circular sun synchronous orbit. The sensor surveys earth surface in the 96 narrow spectral bands from 400 to 1100 nm with 30 m spatial resolution and 3 days revisit frequency. Swath with of the sensor is 25 km. The data can be used for solving wide variety of problems in agriculture, ecology, cartography, construction, forestry, natural resources inventory, emergency situations monitoring. Collected data allows identifying vegetation composition, pollution films composition, mineral composition of soils, subsoils, rocks and other parameters of natural and anthropogenic objects. proprietary +RP1_GTNL1_0.1 Geoton-L1 multispectral images from Resurs-P N1 CEOS_EXTRA STAC Catalog 2013-06-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231912397-CEOS_EXTRA.umm_json Geoton-L1 multispectral images from Resurs-P N1 Multispectral high-resolution optoelectronic sensor from ?Resurs-P N1? satellite that has circular sun synchronous orbit. The sensor can survey earth surface in 7 spectral bands (450-520 nm, 520-600 nm, 610-680 nm, 670-700 nm, 700-730nm, 720-800 nm, 800-900 nm) with 3 m spatial resolution and panchromatic band (580-800 nm) with 1 m spatial resolution. Swath with of the sensor is 38 km. The revisit frequency is less than 3 days. Data can be used for infrastructure monitoring, cadastral and topographical surveying, engineering surveying, natural resources inventory and emergency situation monitoring. proprietary +RP2_GSA_0.1 Hyperspectral imaging from Resurs-P N2 CEOS_EXTRA STAC Catalog 2015-12-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231912393-CEOS_EXTRA.umm_json Hyperspectral imaging from Resurs-P N2 Hyperspectral sensor from ?Resurs-P N2? satellite that has circular sun synchronous orbit. The sensor allows surveying earth surface in the 96-255 narrow spectral bands from 400 to 1100 nm with 30 m spatial resolution and 3 days revisit frequency. Swath with of the sensor is 25 km. Received data can be used to address different problems in agriculture, ecology, cartography, construction, forestry, natural resources inventory, emergency situations monitoring. This data allows identification of vegetation composition, pollution, mineral composition of soils, subsoils, rock. Many other parameters of natural and anthropogenic objects can also be determined. proprietary +RP2_GTNL1_0.1 Geoton-L1 multispectral images from Resurs-P N2 CEOS_EXTRA STAC Catalog 2014-12-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231912426-CEOS_EXTRA.umm_json Geoton-L1 multispectral images from Resurs-P N2 Multispectral high resolution optoelectronic sensor from ?Resurs-P N2? satellite that has circular sun synchronous orbit. The sensor allows surveying earth surface in 7 spectral bands (450-520 nm, 520-600 nm, 610-680 nm, 670-700 nm, 700-730nm, 720-800 nm, 800-900 nm) with 3 m spatial resolution and panchromatic band (580-800 nm) with 1 m spatial resolution. Swath with of the sensor is 38 km. The revisit frequency is less than 3 days. Data from the sensor can be used for infrastructure monitoring, cadastral and topographical surveying, engineering surveying, natural resources inventory, emergency situation monitoring. proprietary RRRAG4_1 Radiostratigraphy and Age Structure of the Greenland Ice Sheet V001 NSIDC_ECS STAC Catalog 1993-06-23 2013-04-26 -88.33, 58.91, 6.62, 81.51 https://cmr.earthdata.nasa.gov/search/concepts/C1000001640-NSIDC_ECS.umm_json This data set contains the traced deep radiostratigraphy of the Greenland Ice Sheet from airborne deep ice-penetrating radar data collected by The University of Kansas Improved Coherent Radar Depth Sounder (ICORDS), Advanced Coherent Radar Depth Sounder (ACORDS), Multi-Channel Radar Depth Sounder (MCRDS), and Multichannel Coherent Radar Depth Sounder (MCoRDS) instruments between 1993 and 2013. This is an IceBridge-related data set. proprietary RS2_AWIF_STUC00GTD_1.0 Resourcesat 2 AWIFS Standard Products ISRO STAC Catalog 2011-05-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1235641681-ISRO.umm_json The coarse resolution multi-spectral sensor, AWIFS operates in four spectral bands - B2, B3, B4, B5 in visible near infrared (VNIR) and B5 in Short Wave Infrared (SWIR) providing data with 56m resolution. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products. proprietary RS2_LIS3_STUC00GTD_1.0 Resourcesat 2 LIS3 Standard Products ISRO STAC Catalog 2011-05-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1235641695-ISRO.umm_json The medium resolution multi-spectral sensor, LISS-3 operates in four spectral bands - B2, B3, B4 in visible near infrared (VNIR) and B5 in Short Wave Infrared (SWIR) providing data with 23.5m resolution. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products. proprietary @@ -12797,6 +13048,7 @@ SRTMIMGR_003 NASA Shuttle Radar Topography Mission Swath Image Data V003 LPCLOUD SRTMSWBD_003 NASA Shuttle Radar Topography Mission Water Body Data Shapefiles & Raster Files V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763268445-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs)(https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the Water Body Data Shapefiles and Raster Files (~30 m) product. Version 3.0 contains the vectorized coastline masks used by National Geospatial-Intelligence Agency (NGA) in the editing, called the SRTM Waterbody Data (SWBD), in shapefile and rasterized formats. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the NGA (previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. proprietary SSBUVIRR_008 Shuttle SBUV (SSBUV) Solar Spectral Irradiance V008 (SSBUVIRR) at GES DISC GES_DISC STAC Catalog 1989-10-19 1996-01-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1273652226-GES_DISC.umm_json The Shuttle Solar Backscatter Ultraviolet (SSBUV) level-2 irradiance data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flown on the NOAA polar orbiting satellites. Data are available in an ASCII text format. UV irradiance data are available for the following days from the eight missions: Flight #1: 1989 October 19, 20, 21 Flight #2: 1990 October 7, 8, 9 Flight #3: 1991 August 3, 4, 5, 6 Flight #4: 1992 March 29, 30 Flight #5: 1993 April 9, 11, 13, 15, 16 Flight #6: 1994 March 14, 15, 17 Flight #7: 1994 November 5, 7, 10, 13 Flight #8: 1996 January 12, 16, 18 The Shuttle SBUV (SSBUV) instrument measured solar spectral UV irradiance during the maximum and declining phase of solar cycle 22. The SSBUV data accurately represent the absolute solar UV irradiance between 200-405 nm, and also show the long-term variations during eight flights between October 1989 and January 1996. These data have been used to correct long-term sensitivity changes in the NOAA-11 SBUV/2 data, which provide a near-daily record of solar UV variations over the 170-400 nm region between December 1988 and October 1994. These data demonstrate the evolution of short-term solar UV activity during solar cycle 22. proprietary SSBUVO3_008 Shuttle SBUV (SSBUV) Level 2 Ozone Profile and Total Column, Aerosol Index, and UV-Reflectivity V008 (SSBUVO3) at GES DISC GES_DISC STAC Catalog 1989-10-19 1996-01-18 -180, -57, 180, 58 https://cmr.earthdata.nasa.gov/search/concepts/C1273652228-GES_DISC.umm_json The Shuttle Solar Backscatter Ultraviolet (SSBUV) Level-2 Ozone data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flying on the NOAA satellites. Data are available in the ASCII AMES text format. Ozone profiles of the upper atmosphere and total column ozone values are available for the following time periods: Flight #1: 1989 October 19, 20, 21. Flight #2: 1990 October 7, 8, 9. Flight #3: 1991 August 3, 4, 5, 6. Flight #4: 1992 March 29, 31. Flight #5: 1993 April 9, 11, 13, 15, 16. Flight #6: 1994 March 14, 15, 17. Flight #7: 1994 November 5, 7, 10, 13. Flight #8: 1996 January 12, 16, 18. SSBUV measures spectral ultraviolet radiances backscattered by the earth's atmosphere. For the ozone measurements the instrument steps over wavelengths between 252.2 and 339.99 nm while viewing the earth in the nadir position (50 km x 50 km footprint at nadir) at 19 pressure levels between 0.3 mb and 100 mb. proprietary +SSDP_HAZARD_EARTHQUAKE Earthquakes and Planning for Ground Rupture Hazards CEOS_EXTRA STAC Catalog 1970-01-01 -116, 33, -115.5, 33.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231553786-CEOS_EXTRA.umm_json Detailed maps bring a greater resolution to the number and locations of active faults. Preparing maps at a higher resolution requires extensive field study, and with a GIS, information, such as tract and parcel data, utility corridors, and flood hazard zones, can be incorporated to help decision makers in locating remediation facilities. After the Sylmar earthquake in 1972, building codes were strengthened, and the Alquist-Priolo Special Studies Zone Act was passed. Its purpose is to mitigate the hazard of fault rupture by prohibiting the location of most human occupancy structures across the traces of active faults. Earthquake fault zones are regulatory zones that encompass surface traces of active faults with a potential for future surface fault rupture. The zones are generally established about 500 feet on either side of the surface trace of active faults. Active faults and strips of state-mandated zoning along faults (Alquist-Priolo zones) riddle the Salton Sea Basin. The primary fault, the San Andreas, steps from the northeast side of the Salton Sea across the southern end, along a series of poorly understood faults, to the Brawley and Imperial fault systems. This stepover region has not had a historic ground-rupturing earthquake. Alquist-Priolo zones could not be defined because the faults are not well-located. Faults parallel to, and splaying from, the San Andreas are also capable of major earthquakes. Initial plans for remediation facilities take into account the generalized information (at 1:750,000 scale) on active faults, and the fault maps do not provide information on strong ground shaking. The shaking can damage facilities that lie far from an earthquake epicenter and far from active faults. Information on near-surface materials is required to estimate the ground-shaking hazards. proprietary SSEC-AMRC-AIRCRAFT Aircraft meteorological reports over Antarctica SCIOPS STAC Catalog 2004-04-04 2015-08-31 -180, -90, 180, 0 https://cmr.earthdata.nasa.gov/search/concepts/C1214605495-SCIOPS.umm_json The AMRC has been archiving the Aircraft data since the 2000's in the ftp archive. Products used to be made in real-time, but data collection has ended starting 31 August, 2015. proprietary SSFR_irradiance_841_1 SAFARI 2000 Solar Spectral Flux Radiometer Data, Southern Africa, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-08-17 2000-09-16 5, -35, 60, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2788411266-ORNL_CLOUD.umm_json The Solar Spectral Flux Radiometer (SSFR) was deployed on the University of Washington CV-580 during the dry season component of the Southern African Regional Science Initiative, August 1 - September 20, 2000. The SSFR made simultaneous measurements of both downwelling and upwelling net solar spectral irradiance at varying flight levels. Data have been provided for twenty flights in netcdf format for the period August 17 - September 16, 2000.For a complete detailed guide to the extensive measurements obtained aboard the UW Convair-580 aircraft in support of SAFARI 2000, see the UW Technical Report for the SAFARI 2000 Project. proprietary STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data_1 STAQS JSC GV GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator Data LARC_CLOUD STAC Catalog 2023-06-26 2023-08-17 -120.3, 33.36, -72, 44.56 https://cmr.earthdata.nasa.gov/search/concepts/C2862468660-LARC_CLOUD.umm_json STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data is the remotely sensed trace gas data for the JSC Gulfstream V aircraft taken by the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science and enhance societal benefit. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, assessing the benefit of assimilating TEMPO data into chemical transport models, and linking air quality patterns to socio-demographic data. proprietary @@ -13081,6 +13333,7 @@ South Africa Crop Type Competition_1 South Africa Crop Type Competition MLHUB ST Southern_Boreal_Plot_Attribute_1740_1 ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016 ORNL_CLOUD STAC Catalog 2016-05-30 2016-06-16 -109.17, 54.09, -104.69, 57.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143402623-ORNL_CLOUD.umm_json This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques. proprietary Southern_Ocean_Drifter_0 Southern Pacific Ocean drifter measurements in 1996 OB_DAAC STAC Catalog 1996-09-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360666-OB_DAAC.umm_json Measurements taken by a drifter in the Southern Pacific Ocean in 1996. proprietary Spire.live.and.historical.data_NA Spire live and historical data ESA STAC Catalog 2016-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689697-ESA.umm_json "The data collected by Spire from it's 110 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height. The following products can be requested: ADS-B Data Stream Global ADS-B satellite data observed by Spire satellites and processed through the ground stations network. Historical ADS-B data older than 6 months can be delivered as data cuts containing CSV file(s) accessible through a Web Service or Cloud storage solutions. Live ADS-B data is available through a streaming API, and recent historical data can be accessed through a REST API. Data is distributed as a monthly subscription: historical data can be requested starting from 3 December 2008, the time period for live data starts from a user-defined date and continues for 30 days. AIS AIS messages include satellite AIS (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third party sensor stations (up to 40 million messages per day). Historical AIS data are delivered as a cvs file with availability back to June 2016 or via Historical API from December 2018; live AIS data are pushed to end users via TCP or through Messages API. Data is distributed as a monthly subscription, from a user-defined date and continues for a 30 day period. GNSS-Radio Occultation GNSS Radio Occultation (GNSS-RO) measurements are collected globally on a continuous basis, generating profiles of the Earth’s atmosphere. Derived Level 1 and Level 2 products include both atmospheric and ionospheric products. Historical data for most of the GNSS-RO products are available from December 2018 to the present. Near real-time (within 90 minutes or less latency from collection to delivery) GNSS-RO profiles are also available upon request. GNSS Reflectometry GNSS Reflectometry (GNSS-R) is a technique to measure Earth’s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: conventional, near-nadir incidence LHCP reflections collected by the Spire GNSS-R satellites (e.g., Spire GNSS-R “Batch-1” satellites) and grazing angle (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. Historical grazing angle GNSS-R data are available from May 2019 to the present, while conventional GNSS-R data are available from December 2020 to the present. Name: Automatic Identification System (AIS) Description: The automatic identification system (AIS) is an automatic tracking system that uses transponders on ships and is used by vessel traffic services. Spire data includes satellite AIS (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third party sensor stations. Data format and content: .parquet.gz files The AIS files contain time-series data on received AIS messages, both the raw NMEA message and added post-processing data for each message. Application: Supply chain analysis, commodity trading, identification of illegal fishing or dark targets, ship route and fuel use optimization, analysis of global trade patterns, anti-piracy, autonomous vessel software, ocean currents. Name: Automatic Dependent Surveillance-Broadcast (ADS-B) Description: Spire AirSafe ADS-B products give access to satellite and terrestrial ADS-B data from captured aircrafts. Data format and content: .csv.gz files The decompressed csv file contains a list of hexadecimal representations of ADS-B messages associated with the timestamp they were received on the satellite. Application: Fleet management, ICAO regulatory compliance, route optimization, predictive maintenance, global airspace, domain awareness. Name: Global Navigation Satellite System Radio Occultation (GNSS-RO) Description: GNSS atmospheric radio occultation (GNSS-RO) relies on the detection of a change in a radio signal as it passes through a planet's atmosphere, i.e. as it is refracted by the atmosphere. This data set contains precise orbit determination (POD) solutions, satellite attitude information, high-rate occultation observations, excess phase, and derived atmospheric dry temperature profiles. Data format and content: podObs*.rnx This file contains raw pseudorange, carrier phase, Doppler frequency, and signal-to-noise measurements for each observed GPS signal from a single Spire satellite which allow to estimate the positions and velocities of each Spire satellite and also used to derive ionospheric total electron content data. leoOrb*.sp3 This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file leoAtt*.log It contains 1 Hz rate quaternion information measured from a single Spire satellite describing the satellite orientation. opnGns*ro.bin, opnGns*rst.bin these files contain raw measurements from the occulting GNSS satellite (one for each signal frequency) and raw phase data from one or more reference GNSS satellites. atmPhs* The file contains occultation excess phase delay. Also contains SNR values, ransmitter and receiver positions and open loop model information. atmPrf*.nc The file contains profiles of atmospheric dry pressure, dry temperature and neutral refractivity as a function of altitude produced from full processing of one occultation event. bfrPrf*.bufr The file contains derived profiles of dry pressure, dry temperature, refractivity and bending angle for each occultation. Application: Atmospheric profiles of pressure, dry temperature, bending angle, and refractivity used in numerical weather prediction data assimilation and climate change studies. Name: Raw IF samples from GNSS-RO satellites Description: Raw intermediate frequency (IF) sampled data (I/Q) from the GNSS receiver front-end of GNSS-RO satellites. Data format and content: rocRIF*.zip Binary raw IF data and associated ancillary data (e.g., POD data) in a zip archive per collection event. Application: GNSS-RO studies, GNSS RFI and jamming monitoring, research. Name: Raw IF samples from GNSS-R satellites Description: Raw intermediate frequency (IF) sampled data (I/Q) from the GNSS receiver front-end of conventional GNSS-R satellites. Data format and content: gbrRIF*.zip Binary raw IF data and associated ancillary data (e.g., POD data) in a zip archive per collection event. Application: GNSS-R studies, GNSS RFI and jamming monitoring, research, etc. Name: Grazing angle GNSS-R observations Description: During grazing angle GNSS-R events, signal reflection at two frequencies is observed through the limb-facing antenna and is trackedusing an open-loop tracking technique thatrelies on a model topredict the propagationdelay and Doppler of thereflected signal. Simultaneous open-looptracking of the signaldirectly along theline-of-sight from thetransmitter to thereceiver is alsoperformed to provideadditional data that maybenecessary for signalcalibration. The mainoutput of the open-looptracking are in-phase (I)and quadrature (Q)accumulation samples(nominally at 50 Hz),which represent the residual Doppler (phase) from the model. Data format and content: grzObs*.nc L1A filecontains rawopen loopcarrier phasemeasurementsat 50 Hzsampling forgrazingangleGNSS-Rreflectionscaptured in the GNSS-RO RHC Pantennas, (bothdirect andreflectedsignals) on GNSS-RO satellites. Application: Sea surface and sea ice height extent, and classification. Name: Georeferenced grazing angle GNSS-R observations Description: The low-levelobservations of the high-rate grazing angle GNSS-R observationsbut withthegeoreferenced bistatic radar parameters of the satellite receiver,specular reflection, and GNSS transmitter included. Data format and content: grzRfl*.nc L1B file contains the georeferenced grazing angle GNSS-R data collected by Spire GNSS-RO satellites, including the low-level observables and bistatic radar geometries (e.g., receiver, specular reflection, and the transmitter locations). Application: Sea surface and sea ice height extent, and classification Name: GNSS-R calibrated bistatic radar reflectivities Description: Higher level product used to derive land-surface reflectivity. Data format and content: gbrRfl*.nc L1A along-track calibrated relative power between reflected and direct signals (e.g., bistatic radar reflectivities) measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies, soil moisture, ocean wind, and sea ice applications Name: GNSS-R calibrated bistatic radar cross-sections Description: Higher level product used to derive ocean surface roughness products. Data format and content: gbrRCS*.nc L1B along-track calibrated and normalized bistatic radar cross-sections measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies, ocean wind and sea ice applications Name: Combined Surface Soil Moisture Description: Combined CYGNSS and SMAP soil moisture data are provided as a combined surface soil moisture (COMB-SSM) product in two data level formats: L2U1 and L3U1. 6 x 6 km grid cell. L-band measurements of surface soil moisture benefit from better vegetation penetration in comparison to traditional C-band measurements. Data format and content: COMB-SSM.nc This file contains the combined data product containing measurements from both CYGNSS and SMAP reported on a 6 km global Equi7Grid grid. Application: Agriculture, crop insurance, farming solutions, climatology, terrain awareness, peatlands and wetlands monitoring etc. Name: Ionosphere total electron content Description: Spire routinely collects and processes a large volume of total electron content (TEC) data, representing the line-of-sight integration of electron density between a Spire satellite and a GNSS satellite. Each file contains line-of-sight ionospheric total electron content (TEC) estimates derived for a ‘single viewing arc’ contained in the POD observation file. Viewing arcs are at least 10 minutes in duration. Data format and content: podTec*.nc This file contains the line-of-sight total electron content with associated orbital information. Application: Space weather research, tsunamigenic earthquakes, weather applications, space situational awareness (SSA), autonomous vehicles etc Name: Ionosphere scintillation Description: The scintillation index for each GNSS frequency is computed onboard the spacecraft. This index provides a measure of the fluctuations of the GNSS signal over the course of 10 seconds caused by propagation of the radio signals through electron density irregularities in the ionosphere. After the raw indices are downlinked to the ground, they are packaged along with associated metadata such as orbit position to create the final scintillation data product. Data format and content: scnLv1*.nc This file contains on-board computed scintillation data (S4 only) with associated orbital information Application: Space weather research, solar events, TIDs, weather applications positioning and navigation, communications etc. Name: Electron density profile Description: Electron density profiles are retrieved as a function of altitude. Electron density profiles are processed from podTec netcdf files, which span a sufficient elevation angle range. A standard Abel inversion algorithm is applied to retrieve the profiles. Data format and content: ionPrf*.nc This file contains electron density profile retrieved from podTec files spanning appropriate elevation angle range Application: Space weather research, solar events, TIDs, weather applications positioning and navigation, communications. The products are available as part of the Spire provision with worldwide coverage. All details about the data provision, data access conditions and quota assignment procedure are described in the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639)." proprietary +Stream_GIS_USGS Digital Line Graphs of U.S. Streams for the EPA Clean Air Mapping and Analysis Program (C-MAP) CEOS_EXTRA STAC Catalog 1970-01-01 -127.77, 23.25, -65.71, 48.15 https://cmr.earthdata.nasa.gov/search/concepts/C2231553171-CEOS_EXTRA.umm_json This is a 1:2,000,000 coverage of streams for the conterminous United States. This coverage was intended for use as a background display for the National Water Summary program. The stream layer was extracted from the 1:2,000,000 Digital Line Graph files. Originally, each state was stored as a separate coverage. In this version, the individual state coverages all have been appended. [Summary provided by EPA] proprietary Surface_Oligo_Med_Sea_0 Surface oligotrophic measurements in the West-central Mediterranean Sea OB_DAAC STAC Catalog 2008-07-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360667-OB_DAAC.umm_json Measurements taken in the west-central Mediterranean Sea of surface oligotrophic water in 2008. proprietary Survey_1980_81_Ingrid_Christenson_1 Gravity and Miscellaneous Fieldwork Report - Ingrid Christenson Coast 1980-81 AU_AADC STAC Catalog 1980-10-01 1981-02-28 75, -69.5, 79, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313835-AU_AADC.umm_json Report on field season on Ingrid Christenson coast summer 1980-81. Program aims: Helicopter Geophysical (gravity) Glaciological Survey; Palaeomagnetism, Vertical Air Photography. See the report for more details. proprietary Survey_1988_89_Mawson_npcms_1 1988/89 Summer season, surveying and mapping program, Mawson - North Prince Charles Mountains - Davis AU_AADC STAC Catalog 1988-10-01 1989-02-28 62, -70, 79, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313847-AU_AADC.umm_json Field season report of these programs: 1988/89 Summer Season surveying and mapping North Prince Charles Mountains; ...mapping program Northern PCM's - Mawson Doppler Translocation Support; ....mapping program Voyage 6 stopover Davis. Includes maps and mapsheet layouts. See the report for full details on the program. Contents are: Introduction Preparation Voytage to Antarctica 1988/89 Summer Season Surveying and Mapping Program, Northern Prince Charles Mountains 1988/89 Summer Season Surveying and Mapping Program, Voyage 6 Stopover, Davis Performance of Equipment Station Marking Field Camping Climatic Conditions Conclusion Appendices proprietary @@ -13824,6 +14077,8 @@ UM0809_33_nano Abundance and composition of nano, picoplankton, microzooplankton UMD_GEOL388_0 Measurements from the Atlantic Ocean made by the University of Maryland (UMD) OB_DAAC STAC Catalog 2003-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360691-OB_DAAC.umm_json Measurements from the Atlantic Ocean made by the University of Maryland between New England, Bermuda, and Brazil in 2003. proprietary UNEP_GRID_SF_AFRICA_third version Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1960-01-01 1990-12-31 -18, -35, 52, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2232848311-CEOS_EXTRA.umm_json The African administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change This documentation describes the third version of a database of administrative units with associated population figures for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP 1992, Deichmann and Eklundh 1991), while the second version represented an update and expansion of this first product (Deichmann 1994, WRI 1995). The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. proprietary UNEP_GRID_SF_ASIA Asia Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 26, -12, 155, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2232847540-CEOS_EXTRA.umm_json The Asian administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. This project (which has been carried out as a cooperative activity between NCGIA, CGIAR and UNEP/GRID between Oct. 1995 and present) has pooled available data sets, many of which had been assembled for the global demography project. All data were checked, international boundaries and coastlines were replaced with a standard template, the attribute database was redesigned, and new, more reliable population estimates for subnational units were produced for all countries. From the resulting data sets, raster surfaces representing population distribution and population density were created in collaboration between NCGIA and GRID-Geneva. proprietary +UNEP_GRID_SF_GLOBAL Global Population Distribution Database from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232849256-CEOS_EXTRA.umm_json Population databases are forming the backbone of many important studies modelling the complex interactions between population growth and environmental degradation, predicting the effects of global climate change on humans, and assessing the risks of various hazards such as floods, air pollution and radiation. Detailed information on population size, growth and distribution (along with many other environmental parameters) is of fundamental importance to such efforts. This database includes rural population distributions, population distrbution for cities and gridded global population distributions. This project has provided a population database depicting the worldwide distribution of population in a 1X1 latitude/longitude grid system. The database is unique, firstly, in that it makes use of the most recent data available (1990). Secondly, it offers true apportionment for each grid cell that is, if a cell contains populations from two different countries, each is assigned a percentage of the grid cell area, rather than artificially assigning the whole cell to one or the other country (this is especially important for European countries). Thirdly, the database gives the percentage of a country's total population accounted for in each cell. So if a country's total in a given year around 1990 (1989 or 1991, for example) is known, then population in each cell can be calculated by using the percentage given in the database with the assumption that the growth rate in each cell of the country is the same. And lastly, this dataset is easy to be updated for each country as new national population figures become available. proprietary +UNEP_GRID_SF_LATINAMERICA_1.0 Latin America and Caribbean Population Distribution Database from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1960-01-01 1990-12-31 -120, -60, -31, 36 https://cmr.earthdata.nasa.gov/search/concepts/C2232848778-CEOS_EXTRA.umm_json The Latin America population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. This documentation describes the Latin American Population Database, a collaborative effort between the International Center for Tropical Agriculture (CIAT), the United Nations Environment Program (UNEP-GRID, Sioux Falls) and the World Resources Institute (WRI). This work is intended to provide a population database that compliments previous work carried out for Asia and Africa. This data set is more detailed than the Africa and Asia data sets. Population estimates for 1960, 1970, 1980, 1990 and 2000 are also provided. The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). proprietary UNEP_SDG14_2022_0 United Nations Environment Programme - Sustainable Development Goal 14(2022): Index of coastal eutrophication in Latin America OB_DAAC STAC Catalog 2022-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776559368-OB_DAAC.umm_json Validation campaign in support of the United Nations Environment - Sustainable Development Goal 14.1.1a of 2022: Index of coastal eutrophication in Latin America. This dataset contains validation data for ocean color satellite data products and collects nutrient data on eutrophication. The data will be used to evaluate the effectiveness of the satellite-derived indicators and to develop more specific, level 2 satellite data indicators for the member countries in the future. proprietary USAP-0231006_1 Antarctic Notothenioid Fish Freeze Avoidance and Genome-wide Evolution for Life in the Cold AMD_USAPDC STAC Catalog 1999-12-23 2012-12-11 163, -77.8, 168, -76.5 https://cmr.earthdata.nasa.gov/search/concepts/C2534799884-AMD_USAPDC.umm_json Antarctic notothenioid fishes exhibit two adaptive traits to survive in frigid temperatures. The first of these is the production of anti-freeze proteins in their blood and tissues. The second is a system-wide ability to perform cellular and physiological functions at extremely cold temperatures.The proposal goals are to show how Antarctic fishes use these characteristics to avoid freezing, and which additional genes are turned on, or suppressed in order for these fishes to maintain normal physiological function in extreme cold temperatures. Progressively colder habitats are encountered in the high latitude McMurdo Sound and Ross Shelf region, along with somewhat milder near?shore water environments in the Western Antarctic Peninsula (WAP). By quantifying the extent of ice crystals invading and lodging in the spleen, the percentage of McMurdo Sound fish during austral summer (Oct-Feb) will be compared to the WAP intertidal fish during austral winter (Jul-Sep) to demonstrate their capability and extent of freeze avoidance. Resistance to ice entry in surface epithelia (e.g. skin, gill and intestinal lining) is another expression of the adaptation of these fish to otherwise lethally freezing conditions. The adaptive nature of a uniquely characteristic polar genome will be explored by the study of the transcriptome (the set of expressed RNA transcripts that constitutes the precursor to set of proteins expressed by an entire genome). Three notothenioid species (E.maclovinus, D. Mawsoni and C. aceratus) will be analysed to document evolutionary genetic changes (both gain and loss) shaped by life under extreme chronic cold. A differential gene expression (DGE) study will be carried out on these different species to evaluate evolutionary modification of tissue-wide response to heat challenges. The transcriptomes and other sequencing libraries will contribute to de novo ice-fish genome sequencing efforts. proprietary USAP-0424589 Center for Remote Sensing of Ice Sheets (CReSIS) AMD_USAPDC STAC Catalog 2005-06-01 2017-05-31 143, -87.2, -88, -74.2 https://cmr.earthdata.nasa.gov/search/concepts/C2532071823-AMD_USAPDC.umm_json This award is for the continuation of the Center for Remote Sensing of Ice Sheets (CReSIS), an NSF Science and Technology Center (STC) established in June 2005 to study present and probable future contributions of the Greenland and Antarctic ice sheets to sea-level rise. The Center's vision is to understand and predict the role of polar ice sheets in sea level change. In particular, the Center's mission is to develop technologies, to conduct field investigations, to compile data to understand why many outlet glaciers and ice streams are changing rapidly, and to develop models that explain and predict ice sheet response to climate change. The Center's mission is also to educate and train a diverse population of graduate and undergraduate students in Center-related disciplines and to encourage K-12 students to pursue careers in science, technology, engineering and mathematics (STEM-fields). The long-term goals are to perform a four-dimensional characterization (space and time) of rapidly changing ice-sheet regions, develop diagnostic and predictive ice-sheet models, and contribute to future assessments of sea level change in a warming climate. In the first five years, significant progress was made in developing, testing and optimizing innovative sensors and platforms and completing a major aircraft campaign, which included sounding the channel under Jakobshavn Isbr. In the second five years, research will focus on the interpretation of integrated data from a suite of sensors to understand the physical processes causing changes and the subsequent development and validation of models. Information about CReSIS can be found at http://www.cresis.ku.edu. The intellectual merits of the STC are the multidisciplinary research it enables its faculty, staff and students to pursue, as well as the broad education and training opportunities it provides to students at all levels. During the first phase, the Center provided scientists and engineers with a collaborative research environment and the opportunity to interact, enabling the development of high-sensitivity radars integrated with several airborne platforms and innovative seismic instruments. Also, the Center successfully collected data on ice thickness and bed conditions, key variables in the study of ice dynamics and the development of models, for three major fast-flowing glaciers in Greenland. During the second phase, the Center will collect additional data over targeted sites in areas undergoing rapid changes; process, analyze and interpret collected data; and develop advanced process-oriented and ice sheet models to predict future behavior. The Center will continue to provide a rich environment for multidisciplinary education and mentoring for undergraduate students, graduate students, and postdoctoral fellows, as well as for conducting K-12 education and public outreach. The broader impacts of the Center stem from addressing a global environmental problem with critical societal implications, providing a forum for citizens and policymakers to become informed about climate change issues, training the next generation of scientists and engineers to serve the nation, encouraging underrepresented students to pursue careers in STEM-related fields, and transferring new technologies to industry. Students involved in the Center find an intellectually stimulating atmosphere where collaboration between disciplines is the norm and exposure to a wide variety of methodologies and scientific issues enriches their educational experience. The next generation of researchers should reflect the diversity of our society; the Center will therefore continue its work with ECSU to conduct outreach and educational programs that attract minority students to careers in science and technology. The Center has also established a new partnership with ADMI that supports faculty and student exchanges at the national level and provides expanded opportunities for students and faculty to be involved in Center-related research and education activities. These, and other collaborations, will provide broader opportunities to encourage underrepresented students to pursue STEM careers. As lead institution, The University of Kansas (KU) provides overall direction and management, as well as expertise in radar and remote sensing, Uninhabited Aerial Vehicles (UAVs), and modeling and interpretation of data. Five partner institutions and a DOE laboratory play critical roles in the STC. The Pennsylvania State University (PSU) continues to participate in technology development for seismic measurements, field activities, and modeling. The Center of Excellence in Remote Sensing, Education and Research (CERSER) at Elizabeth City State University (ECSU) contributes its expertise to analyzing satellite data and generating high-level data products. ECSU also brings to the Center their extensive experience in mentoring and educating traditionally under-represented students. ADMI, the Association of Computer and Information Science/Engineering Departments at Minority Institutions, expands the program's reach to underrepresented groups at the national level. Indiana University (IU) provides world-class expertise in CI and high-performance computing to address challenges in data management, processing, distribution and archival, as well as high-performance modeling requirements. The University of Washington (UW) provides expertise in satellite observations of ice sheets and process-oriented interpretation and model development. Los Alamos National Laboratory (LANL) contributes in the area of ice sheet modeling. All partner institutions are actively involved in the analysis and interpretation of observational and numerical data sets. proprietary @@ -13900,23 +14155,48 @@ USAP-9615281_1 Air-Ground Study of Tectonics at the Boundary Between the Eastern USAP-9725024_1 Circumpolar Deep Water and the West Antarctic Ice Sheet AMD_USAPDC STAC Catalog 1988-03-01 2002-02-28 140, -68, 150, -65 https://cmr.earthdata.nasa.gov/search/concepts/C2532072042-AMD_USAPDC.umm_json This project will study the dynamics of Circumpolar Deep Water intruding on the continental shelf of the West Antarctic coast, and the effect of this intrusion on the production of cold, dense bottom water, and melting at the base of floating glaciers and ice tongues. It will concentrate on the Amundsen Sea shelf, specifically in the region of the Pine Island Glacier, the Thwaites Glacier, and the Getz Ice Shelf. Circumpolar Deep Water (CDW) is a relatively warm water mass (warmer than +1.0 deg Celsius) which is normally confined to the outer edge of the continental shelf by an oceanic front separating this water mass from colder and saltier shelf waters. In the Amundsen Sea however, the deeper parts of the continental shelf are filled with nearly undiluted CDW, which is mixed upward, delivering significant amounts of heat to the base of the floating glacier tongues and the ice shelf. The melt rate beneath the Pine Island Glacier averages ten meters of ice per year with local annual rates reaching twenty meters. By comparison, melt rates beneath the Ross Ice Shelf are typically twenty to forty centimeters of ice per year. In addition, both the Pine Island and the Thwaites Glacier are extremely fast-moving, and have a significant effect on the regional ice mass balance of West Antarctica. This project therefore has an important connection to antarctic glaciology, particularly in assessing the combined effect of global change on the antarctic environment. The particular objectives of the project are (1) to delineate the frontal structure on the continental shelf sufficiently to define quantitatively the major routes of CDW inflow, meltwater outflow, and the westward evolution of CDW influence; (2) to use the obtained data set to validate a three-dimensional model of sub-ice ocean circulation that is currently under construction, and (3) to refine the estiamtes of in situ melting on the mass balance of the antarctic ice sheet. The observational program will be carried out from the research vessel Nathaniel B. Palmer in February and March, 1999. proprietary USARC_AERIAL_PHOTOS Aerial Photography of Antarctica CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231551700-CEOS_EXTRA.umm_json "The USARC maintains all US Aerial Antarctic Mapping photography and USGS flight indexes of the Antarctic. There are over 500,000 photographs in the collection. Most photographs are 9"" x 9"" black and white images taken with three Fairchild cameras each with a metrogon lense resulting in trimetrogon photography (left oblique, vertical and right oblique photographs). Special-purpose photographs showing sites of specific scientific interest ""vertical and handheld oblique as well as photographs taken from helicopters"" are also on file. Some color photographs are also available. Line indexes to identify coverage are available for most aerial photographic missions. Contact prints in either matte or glossy finish are available for inspection or stereoscopic viewing. Special feature options, such as ice and rock enhancements, may be special ordered." proprietary USArray_Ground_Temperature_1680_1.1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021 ORNL_CLOUD STAC Catalog 2016-05-13 2021-07-08 -165.35, 59.25, -141.59, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2143403529-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location. proprietary +USDA0113 Groundwater Quality in Beaver Creek Watershed, Tennessee CEOS_EXTRA STAC Catalog 1992-07-01 1992-08-31 -90.74, 34.56, -81.22, 37.12 https://cmr.earthdata.nasa.gov/search/concepts/C2232411621-CEOS_EXTRA.umm_json Analysis for 400 domestic wells for selected constituents. Reconnaissance of Ground Water Quality in Beaver Creek Watershed, Shelby, Tipton, Fayette, and Haywood counties, Tennessee. Collection Organization: USDA-CSREES/USGS - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by UTAES staff, trained volunteers, and USGS Personnel - USGS conducted field and laboratory analysis. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 400 wells; 20 parameters per sample. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Dissemination Media: USGS Data Base Access Instructions: Contact the data center. proprietary +USDA0114 Groundwater Quality in Bedford and Coffee Counties, Tennessee CEOS_EXTRA STAC Catalog 1991-06-01 1991-07-31 -90.74, 34.56, -81.22, 37.12 https://cmr.earthdata.nasa.gov/search/concepts/C2232411616-CEOS_EXTRA.umm_json Analysis for 200 domestic wells and springs for selected constituents. Reconnaissance of Ground Water Quality in Bedford and Coffee Counties, TN. Collection Organization: USDA-CSREES/USGS - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by UTAES staff, trained volunteers, and USGS Personnel - USGS conducted field and laboratory analysis. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 200 wells/springs; 7 parameters per sample. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Media: USGS Data Base Access Instructions: Contact the data center. proprietary +USDA0115 Groundwater Quality in Tennessee CEOS_EXTRA STAC Catalog 1984-01-01 1990-12-31 -90.74, 34.56, -81.22, 37.12 https://cmr.earthdata.nasa.gov/search/concepts/C2232411608-CEOS_EXTRA.umm_json Analysis of 150 wells for selected constituents, reconnaissance of Ground Water Quality in Tennessee. Collection Organization: USDA-CSREES - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by USGS staff. USGS conducted field and laboratory analysis at their national lab. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 150 wells on farmsteads across Tennessee; 7 parameters per well. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Media: USGS Data Base. Access Instructions: Contact the data center. proprietary +USGS-DDS-058_1.0 Geologic and Geophysical Characterization Studies of Yucca Mountain, Nevada, A Potential High-Level Radioactive-Waste Repository CEOS_EXTRA STAC Catalog 1970-01-01 -120.35, 34.65, -113.69, 42.34 https://cmr.earthdata.nasa.gov/search/concepts/C2231553976-CEOS_EXTRA.umm_json The safe disposal of high-level radioactive wastes is one of the most pressing environmental issues of modern times. At present, most of these materials are being stored under temporary conditions at many of the individual nuclear power plants where they were produced. In recognition of the need for permanent waste storage, Yucca Mountain in southwestern Nevada has been investigated by Federal agencies since the 1970's as one of the Nation's potential geologic disposal sites. In 1987, Congress selected Yucca Mountain for an expanded and more detailed site characterization effort, and a broad multidisciplinary program of studies was developed by the U.S. Department of Energy to further evaluate the suitability of the mountain as a safe and permanent underground disposal facility. The scope and objectives of the many kinds of investigations to be pursued were guided in large measure by regulations governing the siting of geologic repositories for high-level radioactive wastes that were issued by the U.S. Nuclear Regulatory Commission (Code of Federal Regulations 10CFR60) and supplmented by further requirements set forth by the U.S. Department of Energy (Code of Federal Regulations 10CFR960). As an integral part of the planned site-characterization program, the U.S. Geological Survey began a series of detailed geologic, geophysical, and related investigations designed to characterize the tectonic setting, fault behavior, and seismicity of the Yucca Mountain area. A broad goal was to provide essential data for assessing the possible risks posed by future seismic and fault activity in the area that may affect the design and long-term performance, and the safe operation, of the potential surface and subsurface repository facilities. The results of 13 of the many studies undertaken to increase understanding of the tectonic environment of Yucca Mountain and the adjacent area are presented in this report. [Summary provided by the USGS.] proprietary USGS-DDS-066_1.0 Assessment of the Alluvial Sediments in the Big Thompson River Valley, Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -109.41, 36.64, -101.49, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2231554040-CEOS_EXTRA.umm_json To obtain subsurface geologic information about the alluvium in the Big Thompson River valley, S -wave refraction data were collected along three roads that cross the valley. The traveltimes were processed to estimate velocities and thicknesses for a layered-earth model; from these models, three cross sections of the river valley were constructed. The river valleys are covered by a layer of soil, which is 0.2 to 1.5 m thick. Beneath the soil, there is one layer of alluvium at some locations and two layers at other locations. For the two westernmost cross sections, the total thickness of the alluvium ranges from about 6 to 10 m near the center of the valley and from about 2 to 6 m near the sides of the valley. The easternmost cross section is somewhat more complex than the other two, because it is near the confluence of the Big Thompson and the Little Thompson Rivers. In this cross section, the thickness of the alluvium ranges from about 8 to 10 m in the southern half of the valley and from about 3 to 13 m in the northern half. In all three cross sections, the alluvium overlies bedrock, which is the upper transition member of the Pierre Shale. [Summary provided by the USGS.] proprietary +USGS-DDS-067 Geologic Studies of Deep Natural Gas Resources CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231554437-CEOS_EXTRA.umm_json In 1997, the U. S. Geological Survey published USGS Bulletin 2146, comprising 12 chapters dealing with geologic, geochemical, and assessment issues related to deep gas resources (Dyman and others, 1997). A primary goal of that report was to provide geology-based information that might aid in future improvements to technology for deep gas exploration and development. Chapters of this report represent a continuation of that work. The current work is funded by the U. S. Department of Energy, National Energy Technology Laboratory, Morgantown, W. Va. (contract No. DE-AT26-98FT40032), Gas Technology Institute (GTI), Chicago, Ill. (contract No. 5094-210-3366 through a Cooperative Research and Development Agreement with Advanced Resources International, Arlington, Va.), and the U. S. Geological Survey, Denver, Colo. [Summary provided by the USGS.] proprietary +USGS-DDS-11 Geology of the Conterminous United States at 1:2,500,000 Scale -- A Digital Representation of the 1974 P.B. King and H.M. Beikman Map CEOS_EXTRA STAC Catalog 1970-01-01 -162, 24, -66, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2231549169-CEOS_EXTRA.umm_json Conversion of the geologic map of the U.S. to a digital format was undertaken to facilitate the presentation and analysis of earth-science data. Digital maps can be displayed at any scale or projection, whereas a paper map has a fixed scale and projection. However, the geology on this disc is not intended to be used at any scale finer than 1:2,500,000. This CD-ROM contains a digital version of the Geologic Map of the United States, originally published at a scale of 1:2,500,000 (King and Beikman, 1974b). It excludes Alaska and Hawaii. In addition to the graphical formats, the map key is included in ASCII text. A geographic information system (GIS) allows combining and overlaying of layers for analysis of spatial relations not readily apparent in the standard paper publication. This disc contains only geology. However, digital data on geology, geophysics, and geochemistry can be combined to create useful derivative products-- for example, see Phillips and others (1993). This CD-ROM contains a copy of the text and figures from Professional Paper 901 by King and Beikman (1974a). This text describes the historical background of the map, details of the compilation process, and limitations to interpretation. The digital version of the text can be searched for keywords or phrases. For DOS users, the CD-ROM contains menu-driven analytical software, in which the user selects from an array of topics. The CD-ROM also contains MAPPER display software, a user-friendly package that displays the interactive vector map. The raster image of the geologic map can be displayed with VIEWLBL. For other types of computer users, the map must be converted from one of the following formats included on the CD-ROM: ARC/INFO 6.1.1 Export Digital Line Graph (DLG) Optional Drawing Exchange File (DXF) Map Overlay Statistical System (MOSS) proprietary +USGS-DDS-18-A_1.0 National Geochemical Database: National Uranium Resource Evaluation Data for the Conterminous United States CEOS_EXTRA STAC Catalog 1970-01-01 -162, 24, -66, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2231552333-CEOS_EXTRA.umm_json This is an online version of a CD-ROM publication. It is intended for use only on DOS-based computer systems. The files must be downloaded onto your computer before they can be used. The files are presented here in two forms: as the original folders that were published on the CD-ROM and as a large zip file that you can use to download the entire product in one step. This publication contains National Uranium Resource Evaluation (NURE) data for the conterminous United States. The data has been compressed and requires GSSEARCH software for access. GSSEARCH, supplied below, runs only under DOS. [Summary provided by the USGS.] proprietary +USGS-DDS-19 Geology and Resource Assessment of Costa Rica at 1:500,000 Scale CEOS_EXTRA STAC Catalog 1970-01-01 -86, 8, -82, 11 https://cmr.earthdata.nasa.gov/search/concepts/C2231554233-CEOS_EXTRA.umm_json PROJECT OVERVIEW Conversion of the information from the original folio to a computerized digital format was undertaken to facilitate the presentation and analysis of earth-science data. Digital maps can be displayed at any scale or projection, whereas a paper map has a fixed scale and projection. However, most of the maps on this disc are not intended to be used at any scale more detailed than 1:500,000. A geographic information system (GIS) allows combining and overlaying of layers for analysis of spatial relations not readily apparent in the standard paper publication. Digital information on geology, geophysics, and geochemistry can be combined to create useful derivative products. HISTORY OF THE MAPS In 1986 and 1987, the U.S. Geological Survey (USGS), the Dirección General de Geología, Minas e Hidrocarburos, and the Universidad de Costa Rica conducted a mineral-resource assessment of the Republic of Costa Rica. The results were published as a large 80- by 50-cm color folio (U.S. Geological Survey and others, 1987). The 75-page document consists of maps as well as descriptive and interpretive text in English and Spanish covering physiographic, geologic, geochemical, geophysical, and mineral site themes as well as a mineral-resource assessment. The following maps are present in the original folio: 1) Physiographic base map at a scale of 1:500,000 with hypsography, place names, and drainage. 2) Geologic map at a scale of 1:500,000. 3) Regional geophysical maps, including gravity, aeromagnetic, and seismicity maps at various scales. 4) Mineral sites map at a scale of 1:500,000 showing mines, prospects, and occurrences. 5) Volcanological framework of the Tilarán region important for epithermal gold deposits at a scale of 1:100,000. 6) Rock sample locations, mining areas, and vein locations for several parts of the country. 7) Permissive areas delineated for selected mineral deposit types. 8) Digital elevation model. This CD-ROM contains most of the above maps; it lacks items 1 and 8 and the seismicity and aeromagnetic maps of item 3. The linework was digitized from preliminary and printed maps with GSMAP (Selner and Taylor, 1987), a USGS-authored program for map editing and publishing. Conversion from GSMAP to ARC/INFO was accomplished through the use of the GSMARC program (Green and Selner, 1988). The arcs and polygons were tagged using Alacarte (Wentworth and Fitzgibbon, 1991). [Summary provided by the USGS.] proprietary +USGS-DDS-27_1 Monthly average polar sea-ice concentration - USGS-DDS-27 CEOS_EXTRA STAC Catalog 1978-10-25 1991-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231553834-CEOS_EXTRA.umm_json The purpose of this data set is to provide paleoclimate researchers with a tool for estimating the average seasonal variation in sea-ice concentration in the modern polar oceans and for estimating the modern monthly sea-ice concentration at any given polar oceanic location. It is expected that these data will be compared with paleoclimate data derived from geological proxy measures such as faunal census analyses and stable-isotope analyses. The results can then be used to constrain general circulation models of climate change. This data set represents the results of calculations carried out on sea-ice-concentration data from the SMMR and SSM/I instruments. The original data were obtained from the National Snow and Ice Data Center (NSIDC). The data set also contains the source code of the programs that made the calculations. The objective was to derive monthly averages for the whole 13.25-year series (1978-1991) and to derive a composite series of monthly averages representing the variation of an average year. The resulting file set contains monthly images for each of the polar regions for each year, yielding 160 files for each pole, and composite monthly averages in which the years are combined, yielding 12 more files. Averaging the images in this way tends to reduce the number of grid cells that lack valid data; the composite averages are designed to suppress interannual variability. Also included in the data set are programs that can retrieve seasonal ice-concentration profiles at user-specified locations. These nongraphical data retrieval programs are provided in versions for UNIX, extended DOS, and Macintosh computers. Graphical browse utilities are included for the same computing platforms but require more sophisticated display systems. The data contained in this data set are derived from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/ Imager (SSM/I) data produced by the National Snow and Ice Data Center (NSIDC) at the University of Colorado in cooperation with the U.S. National Aeronautics and Space Administration (NASA) and the U.S. National Oceanic and Atmospheric Administration (NOAA). The basic data come from satellites of the U.S. Air Force Defense Meteorological Satellite Program. NSIDC distributes three collections of sea- ice-concentration grids on CD-ROM: data from the Nimbus-7 SMMR (October 25, 1978 through August 20, 1987) are provided on volume 7 of the SMMR Polar Data series (NASA, 1992); data from the SSM/I are provided on two separate volumes, covering the periods from July 9 of 1987 to December 31 of 1989, and from January 1 of 1990 through December 31 of 1991, respectively. The NASATEAM data from revision 2 of the SSM/I CD-ROM's were used to create the present data set. SMMR images were collected every 2 to 3 days, whereas SSM/I data are provided in daily ice-concentration grids. Apart from a number of small gaps (5 or fewer days) in the record, the only long period for which no data are available is December 3 of 1987 through January 12 of 1988, inclusive. As ancillary data, the ETOPO5 global gridded elevation and bathymetry data (Edwards, 1989) were interpolated to the resolution of the NSIDC data; the interpolated topographic data are included. The images are provided in three formats: Hierarchical Data Format (HDF), a flexible scientific data format developed at the National Center for Supercomputing Applications; Graphics Interchange Format (GIF); and Macintosh PICT format. The ice- concentration grids are distributed by NSIDC in HDF format. proprietary USGS-DDS-3 A Geologic Map of the Sea Floor in Western Massachusetts Bay, Constructed from Digital Sidescan-Sonar Images, Photography, and Sediment Samples CEOS_EXTRA STAC Catalog 1970-01-01 -71.5, 42, -70, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2231550375-CEOS_EXTRA.umm_json This data set describes sea floor characteristics for the Western Massachusetts Bay. This data set was created using sidescan-sonar imagery, photography, and sediment samples. proprietary USGS-DDS-33_1.0 3-D Reservoir Characterization of the House Creek Oil Field, Powder River Basin, Wyoming, V1.00 CEOS_EXTRA STAC Catalog 1970-01-01 -111.4, 40.65, -103.7, 45.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553827-CEOS_EXTRA.umm_json "The Upper Cretaceous Sussex ""B"" sandstone was deposited as a probable transgressive-marine sand-ridge complex in a mid-shelf position. The ""B"" sandstone is bounded by upper and basal disconformities and encased in mudstones and low-porosity and low-permeability sandstones of the Cody Shale. Reservoir characteristics are controlled primarily by depositional and diagenetic heterogeneity at megascopic (field), macroscopic (well), and microscopic (rock sample) levels. To simplify, this means production of oil is controlled by stacking and interbedding of sandstone and mudstone beds and by geochemical changes through time that affect flow of fluids through the rock. More than 24.8 million barrels of oil (MMBO) have been produced from the Sussex ""B"" sandstone in the House Creek field, Powder River Basin, Wyoming. Greatest oil production, porosity, and permeability, the thickest reservoir sandstone intervals, and best lateral continuity of the primary reservoir facies are all located parallel and proximal to field axes. Decrease in reservoir quality west of the axes is due to greater heterogeneity from interbedding of low- and moderate-depositional-energy facies, with associated drop in porosity and permeability. Decrease in production east of the axes results primarily from a combination of seaward thinning of the primary reservoir facies and non-deposition of sand ridges. The House Creek field has two axis orientations; these are related to depositional patterns of the four sand ridges. Deposition of the ""B"" sandstone began in the southeastern corner of the field with sand ridge 1; axis orientation is about north 20 degrees west. Later-deposited sand ridges 2 through 4 are located west and north of sand ridge 1; their axis orientations are approximately north 32 degrees west. Progressive northward deposition of later sand ridges is probably concurrent with uplift of the northeast-trending Belle Fourche arch. Movement along the arch and of lineaments may have caused topographic highs that localized Sussex and Shannon deposition proximal to the arch. [Summary provided by the USGS.]" proprietary +USGS-DDS-74_2.0 Long-term Oceanographic Observations in Western Massachusetts Bay Offshore of Boston, Massachusetts: Data Report for 1989-2002 CEOS_EXTRA STAC Catalog 1989-12-01 2002-12-01 -71, 42, -70.5, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231551840-CEOS_EXTRA.umm_json Long-term oceanographic observations have been made at two locations in western Massachusetts Bay: (1) Site A (42ý 22.6' N, 70ý 47.0' W, 33 m water depth) from from 1989 to 2002, and (2) Site B (42ý 9.8' N, 70ý 38.4' W, 21 m deter depth) from 1997 to 2002. Site A is approximately 1 km south of the new ocean outfall that began discharging treated sewage effluent from the Boston metropolitan area into Massachusetts Bay in September 2000. These long-term oceanographic observations have been collected by the U.S. Geological Survey (USGS) in partnership with the Massachusetts Water Resources Authority (MWRA) and with logistical support from the U. S. Coast Guard (USCG). This report presents time series data collected through December 2002, updating a similar report that presented data through December 2000 (Butman and others, 2002). The long-term observations at these two stations are part of a USGS study designed to understand the transport and long-term fate of sediments and associated contaminants in the Massachusetts Bays (see //woodshole.er.usgs.gov/project-pages/bostonharbor / and Butman and Bothner, 1997). The long-term observations document seasonal and inter-annual changes in currents, hydrography, and suspended-matter concentration in western Massachusetts Bay, and the importance of infrequent catastrophic events, such as major storms or hurricanes, in sediment resuspension and transport. They also provide observations for testing numerical models of circulation. This data report presents a description of the field program and instrumentation, an overview of the data through summary plots and statistics, and the data in NetCDF and ASCII format for the period December 1989 through December 2002. The objective of this report is to make the data available in digital form, and to provide summary plots and statistics to facilitate browsing of the long-term data set . [Summary provided by the USGS.] proprietary USGS-DDS-79 Coastal Erosion and Wetland Change in Louisiana: Selected USGS Products CEOS_EXTRA STAC Catalog 1970-01-01 -94.3, 28.67, -88.54, 33.29 https://cmr.earthdata.nasa.gov/search/concepts/C2231552152-CEOS_EXTRA.umm_json Louisiana contains 25 percent of the vegetated wetlands and 40 percent of the tidal wetlands in the 48 conterminous States. These critical natural systems are being lost. Louisiana leads the Nation in coastal erosion and wetland loss as a result of a complex combination of natural processes (e.g. storms, sea-level rise, subsidence) and manmade alterations to the Mississippi River and the wetlands over the past 200 years. Erosion of several of the barrier islands, which lie offshore of the estuaries and wetlands and buffer and protect these important ecosystems from the open marine environment, exceeds 20 meters/year. The average rate of shoreline erosion is over 10 meters/year. Within the past 100 years, Louisiana's barrier islands have decreased in area by more than 40 percent, and some islands have lost more than 75 percent of their land area. If these loss rates continue, several of the barriers are expected to erode completely within the next three decades. Their disappearance will contribute to further loss and deterioration of wetlands and back-barrier estuaries and increase the risk to infrastructure. Coastal wetland environments, which include associated bays and estuaries, support a rich harvest of renewable natural resources with an estimated annual value of over $1 billion. More than 30 percent of the Nation's fisheries come from these wetlands, as well as 25 percent of oil and gas coming through the wetlands. Louisiana also has the highest rate of wetland loss: 80 percent of the Nation's total loss of wetlands has occurred in this State. The rate of wetland loss in the Mississippi River delta plain is estimated to be about 70 square kilometers/year -- the equivalent of a football field every 20 minutes. If these rates continue, an estimated 4,000 square kilometers of wetlands will be lost in the next 50 years. Losses of this magnitude have direct implications on the Nation's energy supplies, economic security, and environmental integrity. Over the past two decades, the USGS, working in partnership with other scientists in universities and State agencies, has led the research effort to document barrier erosion and wetland loss and understand the natural and manmade causes responsible. Some products resulting from this research, included in this DVD, are providing the baseline data and information being used for Federal-State wetlands restoration programs underway and being planned. [Summary provided by the USGS.] proprietary USGS-DDS_30_P-10_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231552106-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 10 (San Joaquin Basin) are listed here by play number, type, and name: Number Type Name 1001 conventional Pliocene Non-associated Gas 1002 conventional Southeast Stable Shelf 1003 conventional Lower Bakersfield Arch 1004 conventional West Side Fold Belt Sourced by Post-Lower Miocene Rocks. 1005 conventional West Side Fold Belt Sourced by Pre-Middle Miocene Rocks 1006 conventional Northeast Shelf of Neogene Basin 1007 conventional Northern Area Non-associated Gas 1008 conventional Tejon Platform 1009 conventional South End Thrust Salient 1010 conventional East Central Basin and Slope North of Bakersfield Arch 1011 conventional Deep Overpressured Fractured Rocks of West Side Fold and Overthrust Belt proprietary USGS-DDS_30_P10_conventional 1995 National Oil and Gas Assessment Conventional Plays within the San Joaquin Basin Province CEOS_EXTRA STAC Catalog 1970-01-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231550316-CEOS_EXTRA.umm_json The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. proprietary USGS-DS-91_1.1 Depth to the Juan De Fuca Slab Beneath the Cascadia Subduction Margin: A 3-D Model for Sorting Earthquakes CEOS_EXTRA STAC Catalog 1970-01-01 -130, 40, -120, 51 https://cmr.earthdata.nasa.gov/search/concepts/C2231552778-CEOS_EXTRA.umm_json The USGS presents an updated model of the Juan de Fuca slab beneath southern British Columbia, Washington, Oregon, and northern California, and use this model to separate earthquakes occurring above and below the slab surface. The model is based on depth contours previously published by Flück and others (1997). Our model attempts to rectify a number of shortcomings in the original model and to update it with new work. The most significant improvements include (1) a gridded slab surface in geo-referenced (ArcGIS) format, (2) continuation of the slab surface to its full northern and southern edges, (3) extension of the slab surface from 50-km depth down to 110-km beneath the Cascade arc volcanoes, and (4) revision of the slab shape based on new seismic-reflection and seismic-refraction studies. We have used this surface to sort earthquakes and present some general observations and interpretations of seismicity patterns revealed by our analysis. In addition, we provide files of earthquakes above and below the slab surface and a 3-D animation or fly-through showing a shaded-relief map with plate boundaries, the slab surface, and hypocenters for use as a visualization tool. [Summary provided by the USGS.] proprietary +USGS-OFR-92-299_1.0 Molecular and Isotopic Analyses of the Hydrocarbon Gases within Gas Hydrate-Bearing Rock Units of the Prudhoe Bay-Kuparuk River Area in Northern Alaska CEOS_EXTRA STAC Catalog 1979-05-01 1990-09-01 -150, 70, -148, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2231550014-CEOS_EXTRA.umm_json "Information about and data from the USGS Open-File Report 92-299 (Molecular and isotopic analyses of the hydrocarbon gases within gas hydrate-bearing rock units of the Prudhoe Bay-Kuparuk River area in northern Alaska) are available On-line via the World Wide Web: ""http://pubs.usgs.gov/of/of92-299//"" or ""http://pubs.usgs.gov/of/1992/of92-299/"" The following information about the data set was provided by the data center contact: The objective of this study was to document the molecular and isotopic composition of the gas trapped within the gas hydrate-bearing stratigraphic intervals overlying the Prudhoe Bay and Kuparuk River oil fields. To reach this objective, we have analyzed cuttings gas and free gas samples collected from 10 drilling-production wells in the Prudhoe Bay and Kuparuk River fields. The dataset includes a report documenting the materials, the procedures used to analyze them, and the results. Results are given in tabular form as spreadsheets showing headspace, headspace/free gas, and blended headspace analyses. Gas characteristics analyzed include nitrogen, carbon dioxide, methane, ethane, ethene, propane, propene, isobutane, n-butane, isopentane, n-pentane, stable carbon isotope composition of the methane, ethane, and carbon dioxide fractions, and deuterium isotope composition of the methane fraction. Methane is the most abundant hydrocarbon gas within the gas hydrate- bearing rock units of the Prudhoe Bay-Kuparuk River area in the North Slope of Alaska. Isotopic analysis indicates that both microbial and thermogenic processes have contributed to the formation of this methane. The thermogenic component probably migrated into the rock units from greater depths, since vitrinite reflectance measurements show that the units never endured temperatures within the thermogenic range. Approximately 50 to 70 percent of the methane within the gas hydrate units is thermogenic in origin. This is U.S. Geological Survey Open-File Report 92-299 This report is preliminary and has not been reviewed for conformity with U.S. Geological Survey editorial standards or with the North American Stratigraphic Code. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government." proprietary +USGS-PRISM-PACIFIC-OSTRACODES Modern and fossil ostracode census data from the Western Pacific Ocean and seas around Japan CEOS_EXTRA STAC Catalog 1990-01-01 1993-12-31 122, 25, 165, 63 https://cmr.earthdata.nasa.gov/search/concepts/C2231551101-CEOS_EXTRA.umm_json "This data set is part of the Pliocene Research, Interpretation, and Synoptic Mapping (PRISM) Project. This data set describes marine ostracode species and related sample and stratigraphic information produced as part of the USGS PRISM Project (Pliocene Research, Interpretation, and Synoptic Mapping). The general goals of PRISM are to reconstruct global climate during a period of extreme warmth about 3 million years ago and to determine the causes of the warmth and the subsequent climatic change towards colder climates about 2.5 million years ago. To do this, PRISM has been studying Pliocene deposits and their microfaunas and, by comparison with modern assemblages, estimating past boundary conditions such as ocean temperatures. To obtain more reliable estimates of past environments in paleoclimate studies, the use of ecologically sensitive species requires extensive modern datasets on living species with limited environmental tolerances. Thus, much of the data generated by PRISM consists of species counts from modern samples that form a ""coretop"" dataset applicable not only to PRISM Pliocene assemblages but also to Quaternary assemblages as well. This situation was especially true for ostracodes, a group of Crustacea that includes many species that have limited range of water temperatures required for survival, reproduction, or both. Fossil assemblages of ostracodes can therefore yield information on past bottom water conditions on continental shelves in the mixed ocean layer above the thermocline and they are especially useful where planktic foraminifers are rare or absent. However comprehensive datasets with quantitative ostracode data were not available for application to regional paleoceanographic studies. Further, because of the endemic nature of ostracodes living on continental shelves, separate modern datasets needed to be developed for regions of the Pacific, Atlantic and Arctic Oceans. The data contained in the files in this folder come from the western North Pacific Ocean, mainly the seas around Japan. These regions encompass subtropical to cold temperate and subfrigid marine climate zones and include faunas from the major Western North Pacific water masses such as the Oyashio and Kuroshio current systems. The ostracode data sets were developed in collaboration with Prof. Noriyuki Ikeya, Institute of Geosciences, Shizuoka University, Shizuoka, Japan, Prof. Ikeya's students, and other Japanese colleagues, with support from the USGS Global Change and Climate History Program and grants from the National Science Foundation (NSF grant INT: LTV-9013402) and the Japanese Society for the Promotion of Science (JSPS grant EPAR- 093). Most of the faunal slides are housed at Shizuoka University. Separate PRISM ostracode data sets contain modern and Pliocene species data from continental shelves of the Arctic and Atlantic Oceans and from deep sea environments. Among the various types of quantitative analyses used to evaluate the ostracode data, the Squared Chord Distance (SCD) coefficient of dissimilarity was found to be useful in identifying modern analog assemblages for fossil assemblages on the basis of the proportions of shared species between two samples. The ostracode data and analyses of them are discussed in detail in the following published scientific papers: Ikeya, Noriyuki and Cronin, Thomas. M., 1993, Quantitative analysis of Ostracoda and water masses around Japan: Application to Pliocene and Pleistocene paleoceanography: Micropaleontology, v. 39, p. 263-281. Cronin, T.M., Kitamura, A., Ikeya, N., Watanabe, M., and Kamiya, T., in press. Late Pliocene climate change 3.4-2.3 Ma: Paleoceanographic record from the Yabuta Formation, Sea of Japan: Palaeogeography, Palaeoclimatology, Palaeoecology." proprietary USGSPHOTOS U.S. Geological Survey Aerial Photography USGS_LTA STAC Catalog 1937-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220566204-USGS_LTA.umm_json The U.S. Geological Survey (USGS) Aerial Photography data set includes over 2.5 million film transparencies. Beginning in 1937, photographs were acquired for mapping purposes at different altitudes using various focal lengths and film types. The resultant black-and-white photographs contain less than 5 percent cloud cover and were acquired under rigid quality control and project specifications (e.g., stereo coverage, continuous area coverage of map or administrative units). Prior to the initiation of the National High Altitude Photography (NHAP) program in 1980, the USGS photography collection was one of the major sources of aerial photographs used for mapping the United States. Since 1980, the USGS has acquired photographs over project areas that require photographs at a larger scale than the photographs in the NHAP and National Aerial Photography Program collections. proprietary USGS_ALASKA_RADIOCARBON Alaska Radiocarbon Data Base; USGS, Menlo Park CEOS_EXTRA STAC Catalog 1951-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231549723-CEOS_EXTRA.umm_json This data base contains published radiocarbon dates with entries consisting of laboratory and reference numbers. The data set is subdivided into two segments including RCFILE which contains the radiocarbon dates and author citation; and RCBIB which is a complete bibliography of all published dates. The RCFILE can be sorted by date, author citation, latitude and longitude, geographic region, and quadrangle. The RCFILE is run using the software program 'Nutshell.' The combined size of the two files is 1,092,908 bytes. There are 3,609 radiocarbon age determinations (published ages with a reference). The following is a breakdown of the number of age determinations by geographic region: Northern 997, East-Central 417, West-Central 332, Southern 769, Southwestern 603, Southeastern 448, Offshore 35, and General 8. proprietary USGS_ARSENIC_H2O Arsenic in ground water of the United States CEOS_EXTRA STAC Catalog 1973-01-01 1997-01-01 -125, 25, -67, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2232411686-CEOS_EXTRA.umm_json "[From Arsenic in ground water of the United States, ""http://water.usgs.gov/nawqa/trace/arsenic/"" Arsenic is a naturally occurring element in the environment. Arsenic in ground water is largely the result of minerals dissolving naturally from weathered rocks and soils. Several types of cancer have been linked to arsenic in water. The US Environmental Protection Agency is currently reviewing the maximum contaminant level of arsenic permitted in drinking water, and will likely lower it, as recommended last year by the National Research Council. The USGS has developed a map that shows where and to what extent arsenic occurs in ground water across the country. Highest concentrations were found throughout the West and in parts of the Midwest and Northeast." proprietary +USGS_ASC_MarineEcoregionsLayer_1.0 Marine_Ecoregions_AK CEOS_EXTRA STAC Catalog 2007-01-01 -180, 42.42584, 180, 74.238594 https://cmr.earthdata.nasa.gov/search/concepts/C2231549548-CEOS_EXTRA.umm_json "ABSTRACT: To better understand of how and why marine ecosystems vary, we developed a map of ""Large Marine Ecosystems"" (LME) for the area surrounding Alaska. These LMEs were constructed using the best information available on bathymetry, currents, temperature, and primary productivity." proprietary +USGS_ASTER_HydrothermalAlterationMaps Hydrothermal Alteration Maps of the Central and Southern Basin and Range Province of the United States Compiled From Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Data CEOS_EXTRA STAC Catalog 2013-01-01 -120.40977, 30.652391, -107.4039, 42.39188 https://cmr.earthdata.nasa.gov/search/concepts/C2231554154-CEOS_EXTRA.umm_json ABSTRACT: Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and Interactive Data Language (IDL) logical operator algorithms were used to map hydrothermally altered rocks in the central and southern parts of the Basin and Range province of the United States. The hydrothermally altered rocks mapped in this study include (1) hydrothermal silica-rich rocks (hydrous quartz, chalcedony, opal, and amorphous silica), (2) propylitic rocks (calcite-dolomite and epidote-chlorite mapped as separate mineral groups), (3) argillic rocks (alunite-pyrophyllite-kaolinite), and (4) phyllic rocks (sericite-muscovite). A series of hydrothermal alteration maps, which identify the potential locations of hydrothermal silica-rich, propylitic, argillic, and phyllic rocks on Landsat Thematic Mapper (TM) band 7 orthorectified images, and shape files of hydrothermal alteration units are provided. proprietary +USGS_BIO_KATRINA Hurricane Katrina - Biological Resources CEOS_EXTRA STAC Catalog 1970-01-01 -97.87498, 26.042156, -82.72492, 32.819813 https://cmr.earthdata.nasa.gov/search/concepts/C2231549509-CEOS_EXTRA.umm_json This website provides information regarding the emergency response and rescue efforts provided by USGS personnel from the National Wetlands Research Center and USGS Louisiana Water Science Center to the population and area impacted by Hurricane Katrina. This website also chronicles the activities by the USGS to provide geospatial technology to aid in locating stranded hurricane victims. Impacts to the biological resources affected by Hurricane Katrina are also being assessed. Information on these resources can be accessed from this website. proprietary USGS_BISON Biodiversity Information Serving Our Nation (BISON) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -17, -63, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2231552510-CEOS_EXTRA.umm_json The USGS Biodiversity Information Serving Our Nation (BISON) project is an online mapping information system consisting of a large collection of species occurrence datasets (e.g., plants and animals) found in the United States, with relevant geospatial layers. Species occurrences are records of organisms at a particular time and location that are often collected as part of biological field studies and taxonomic collections. These data serve as a foundation for biodiversity and conservation research. proprietary +USGS_BRD_SageSTEP Joint Fire Science SageSTEP (Sagebrush Steppe Treatment Evaluation Project) CEOS_EXTRA STAC Catalog 2006-01-01 2011-12-31 -120, 35, -110, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231554186-CEOS_EXTRA.umm_json To study the effects of land management options, two experiments will be conducted across a regional network of sites in sagebrush communities. Using this regional network of sites will allow us to understand the thresholds between healthy and unhealthy sagebrush communities over a broad range of conditions across the Great Basin. Management treatment effects on plants, potential for wildfire, soils and nutrients, water runoff/erosion, and birds and insects will be documented. Additionally, an economic analysis will be conducted to assist managers in selecting optimal management strategies, and citizens’ and managers’ views about the treatments will be explored. The first experiment is focused on cheatgrass invasion (Cheatgrass Network), and the second experiment is focused on woodland encroachment (Woodland Network). Cheatgrass Network: For this experiment, sites will be located in sagebrush communities threatened by cheatgrass invasion, and we will study the effects of four land management options: control (no management action), prescribed fire, mechanical thinning of sagebrush by mowing, and herbicide application (to thin old, unproductive sagebrush plants and encourage growth of young sagebrush and native understory grasses). An additional herbicide application to control cheatgrass will be applied within portions of treated areas. The objective is to address the question of what amount of native perennial bunchgrasses needs to be present in the understory of a sagebrush community in order for managers to improve land health without having to conduct expensive restoration, such as reseeding of native grasses. Woodland Network: For this experiment, sites will be located in sagebrush communities threatened by woodland encroachment, and we will study the effects of no management action (control), prescribed fire, and mechanical removal of trees (chainsaw cutting). The objective is to address the question of what amount of the native sagebrush/bunchgrass community there needs to be in order for managers to improve land health without having to conduct expensive restoration. proprietary USGS_BioData BioData - Aquatic Bioassessment Data for the Nation CEOS_EXTRA STAC Catalog 1991-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231550157-CEOS_EXTRA.umm_json The U.S. Geological Survey (USGS) BioData Retrieval system provides access to aquatic bioassessment data (biological community and physical habitat data) collected by USGS scientists from stream ecosystems across the nation. USGS scientists collect fish-, aquatic macroinvertebrate-, and algae-community samples and conduct stream physical habitat surveys as part of its fundamental mission to describe and understand the Earth. The publicly available BioData Retrieval system disseminates data from over 15,000 fish, aquatic macroinvertebrate, and algae community samples. Additionally, the system serves data from over 5000 physical data sets (samples), such as reach habitat and light availability, that were collected to support the community sample analyses. The system contains sample data that were collected and processed since 1991 using the protocols of the National Water-Quality Assessment (NAWQA). As of 2010, the system has added data collected by USGS scientists using the procedures and protocols of the U.S. Environmental Protection Agency National Rivers and Streams Assessment program (NRSA). proprietary +USGS_Bulletin_2064-A_1.0 Map Showing Geologic Terranes of the Hailey 1 deg. x 2 deg. Quadrangle and the western part of the Idaho Falls 1 deg. x 2 deg. Quadrangle, south-central Idaho CEOS_EXTRA STAC Catalog 1970-01-01 -116, 43, -113.25, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2231549382-CEOS_EXTRA.umm_json This dataset was developed to provide a geologic GIS database of the terranes of the Hailey 1x2 quadrangle and the western part of the Idaho Falls 1x2 quadrangle in south-central Idaho for use in spatial analysis. The paper version of Map Showing Geologic Terranes of the Hailey 1x2 Quadrangle and the western part of the Idaho Falls 1x2 Quadrangle, south-central Idaho was compiled by Ron Worl and Kate Johnson in 1995. The plate was compiled on a 1:250,000 scale topographic base map. TechniGraphic System, Inc. of Fort Collins Colorado digitized this map under contract for N.Shock. G.Green edited and prepared the digital version for publication as a geographic information system database. The digital geologic map database can be queried in many ways to produce a variety of geologic maps. proprietary +USGS_Bulletin_2064-C_1.0 Geologic map of outcrop areas of sedimentary units in eastern Hailey 1 deg. x 2 deg. Quadrangle and southern Challis 1 deg. x 2 deg. Quadrangle, Idaho CEOS_EXTRA STAC Catalog 1970-01-01 -115, 43.25, -114, 44.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231548472-CEOS_EXTRA.umm_json This dataset was developed to provide a geologic GIS database of the Geologic map of outcrop areas of sedimentary units in the eastern part of the Hailey 1 deg. x 2 deg. Quadrangle and part of the southern part of the Challis 1 deg. x 2 deg. Quadrangle, south-central Idaho for use in spatial analysis. The paper version of the Geologic map of outcrop areas of sedimentary units in the eastern part of the Hailey 1 deg. x 2 deg. Quadrangle and part of the southern part of the Challis 1 deg. x 2 deg. Quadrangle, south-central Idaho was compiled by Paul Link and others in 1995. The plate was compiled on a 1:100,000 scale topographic base map. TechniGraphic System, Inc. of Fort Collins Colorado digitized this map under contract for N.Shock. G.Green edited and prepared the digital version for publication as a GIS database. The digital geologic map database can be queried in many ways to produce a variety of geologic maps. proprietary USGS_CLUES Climate, Land Use, and Environmental Sensitivity (CLUES) CEOS_EXTRA STAC Catalog 1970-01-01 -168, 9, -52, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2231554626-CEOS_EXTRA.umm_json Vegetation changes caused by climatic variations and/or land use may have large impacts on forests, agriculture, rangelands, natural ecosystems, and endangered species. Climate modeling studies indicate that vegetation cover, in turn, has a strong influence on regional climates, and this must be better understood before models can estimate future environmental conditions. To address these issues, this project investigates vegetational response to climatic change, and vegetation-land surface impacts on climate change. The project involves calibration of the modern relations between the range limits of plant species and climatic variables, relations that are then used: 1) to estimate past climatic fluctuations from paleobotanical data for a number of time periods within the late Quaternary; 2) to 'validate' climate model simulations of past climates; 3) to explore the potential influences of land cover changes on climate change; and 4) to estimate the potential future ranges of plant species under a number of future climate scenarios. Methodologies and data developed by this project are being used as part of the national global change assessment of potential impacts of future climate changes. [Summary provided by the USGS.] proprietary USGS_CORE_RESEARCH_CENTER Core Research Center Information System; USGS, Denver CEOS_EXTRA STAC Catalog 1973-01-01 -126, 28, -96, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231555272-CEOS_EXTRA.umm_json Descriptive data of core samples housed within the Core Research Center. The database contains information about drill hole locations, intervals of core availability, formation names, and geologic ages. CORE information sets also indicate availability of non-automated information including analyses, photographs, cuttings, and thin sections. proprietary +USGS_CT_NATTEN Nitrogen Transport and Attenuation in the Connecticut River Basin, New Hampshire, Vermont, and Massachusetts CEOS_EXTRA STAC Catalog 2005-01-01 2005-12-31 -130, 33, -46, 57 https://cmr.earthdata.nasa.gov/search/concepts/C2231550196-CEOS_EXTRA.umm_json The objective of this project is to estimate the rate of nitrogen loss in selected reaches of the Connecticut River. In-stream loss of nitrogen may influence the total nitrogen loads being input to Long Island Sound (LIS); therefore, an improved understanding of nitrogen attenuation is needed to plan effective strategies for meeting the goals of the LIS Total Maximum Daily Load (TMDL) allocation plan approved by the U.S. Environmental Protection Agency (USEPA) in 2001. The TMDL plan was instituted to reduce the problem of chronic seasonal hypoxia (low dissolved oxygen) that results from excessive nitrogen loading in Long Island Sound. Two study methods were used to measure nitrogen loss in selected study reaches of the Connecticut River during 2005: a mass-balance study to observe in-stream changes in total nitrogen, and a dissolved nitrogen gas study to measure denitrification. For the mass-balance study, samples were collected from all major tributaries and at the upstream and downstream ends of two 30- to 40-mile study reaches, and were analyzed for total nitrogen (including ammonia, nitrite, nitrate, and organic nitrogen). Streamflow data (from USGS gaging stations or manual measurements) were also taken at the time of sampling so that the mass flux of nitrogen could be computed at each site. To assess the effects of different hydrologic conditions and water temperatures on nitrogen attenuation in the Connecticut River, the study reaches were sampled two times in the spring and summer. The calculations of nitrogen mass flux entering and exiting each study reach will indicate when and where nitrogen removal processes are significant. The study of dissolved nitrogen gas was performed on a 6-mile sub-reach of the Connecticut River during a period of late summer when warm temperatures and low-flow conditions are most conducive to observing measurable rates of denitrification. Denitrification is estimated by measuring the downstream change in dissolved nitrogen after compensating for gas exchange with the atmosphere and dilution from inflows. Gas exchange is computed from the downstream concentration changes of SF6 gas and Bromide, which are injected at the head of the study reach. The data from this study will be useful for verifying predictions of nitrogen inputs, transport, and loss from water-quality models such as the New England SPARROW model and the RivR-N model. The results will assist state resource managers in the development of nitrogen reduction strategies for the Connecticut River Watershed, including the selection of sources in which to target these strategies. Results of the study will be presented in a journal paper in 2007. [Summary provided by the USGS.] proprietary +USGS_CascadeRange_HydrothermalMonitoring Hydrothermal monitoring data from the Cascade Range, northwestern United States CEOS_EXTRA STAC Catalog 2009-06-01 -124, 40, -120, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231552978-CEOS_EXTRA.umm_json Traditionally, most measurement and sampling of hydrothermal fluids has been on a highly intermittent basis. Such intermittent data, with sampling frequencies typically >1 year, are not well-suited for comparison with continuous seismic and geodetic monitoring data. Further, when volcanic unrest becomes evident from other geophysical observations, baseline hydrothermal observations are sometimes non-existent, and are often limited to the season when weather conditions are most amenable to field work. The preponderance of field-season, daytime data means that there is limited information on seasonal or diurnal variability. Beginning in the summer of 2009, motivated by the dramatic hydrothermal anomalies associated with volcanic unrest at South Sister volcano (Wicks and others, 2002; Evans and others, 2004), the USGS made a concerted effort to develop hourly hydrothermal records in the Cascade Range. The 25 selected monitoring sites show evidence of magmatic influence in the form of high 3He/4He ratios and (or) large fluxes of magmatic CO2 or heat. The monitoring sites can be grouped into three broad categories (Fig. 1): (1) sites with continuous pressure-temperature-conductivity monitoring and intermittent liquid sampling and discharge measurements; (2) sites with continuous temperature monitoring and intermittent gas sampling; and (3) sites that lack hourly data, but where the USGS has carried out intermittent flux measurements over a period of several decades. For most sites, correlations have been developed to convert pressure-temperature-conductivity data into a flux of heat or (more often) to the flux of a solute species of interest. We relate (1) specific electrical conductance to lab-measured concentrations of dissolved constituents and (2) pressure (depth of water) to field-measured discharge. The metadata includes descriptions of the sites and methods and plots of the calculated fluxes. The workbook files contain all of the data and correlations upon which those fluxes are based. Part of the database compilation is a list of relevant references for each area. These lists include all references cited in the metadata. proprietary +USGS_DDS-27_1 Monthly average polar sea-ice concentration CEOS_EXTRA STAC Catalog 1978-10-25 1991-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231553986-CEOS_EXTRA.umm_json The purpose of this data set is to provide paleoclimate researchers with a tool for estimating the average seasonal variation in sea-ice concentration in the modern polar oceans and for estimating the modern monthly sea-ice concentration at any given polar oceanic location. It is expected that these data will be compared with paleoclimate data derived from geological proxy measures such as faunal census analyses and stable-isotope analyses. The results can then be used to constrain general circulation models of climate change. This data set represents the results of calculations carried out on sea-ice-concentration data from the SMMR and SSM/I instruments. The original data were obtained from the National Snow and Ice Data Center (NSIDC). The data set also contains the source code of the programs that made the calculations. The objective was to derive monthly averages for the whole 13.25-year series (1978-1991) and to derive a composite series of monthly averages representing the variation of an average year. The resulting file set contains monthly images for each of the polar regions for each year, yielding 160 files for each pole, and composite monthly averages in which the years are combined, yielding 12 more files. Averaging the images in this way tends to reduce the number of grid cells that lack valid data; the composite averages are designed to suppress interannual variability. Also included in the data set are programs that can retrieve seasonal ice-concentration profiles at user-specified locations. These nongraphical data retrieval programs are provided in versions for UNIX, extended DOS, and Macintosh computers. Graphical browse utilities are included for the same computing platforms but require more sophisticated display systems. The data contained in this data set are derived from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/ Imager (SSM/I) data produced by the National Snow and Ice Data Center (NSIDC) at the University of Colorado in cooperation with the U.S. National Aeronautics and Space Administration (NASA) and the U.S. National Oceanic and Atmospheric Administration (NOAA). The basic data come from satellites of the U.S. Air Force Defense Meteorological Satellite Program. NSIDC distributes three collections of sea- ice-concentration grids on CD-ROM: data from the Nimbus-7 SMMR (October 25, 1978 through August 20, 1987) are provided on volume 7 of the SMMR Polar Data series (NASA, 1992); data from the SSM/I are provided on two separate volumes, covering the periods from July 9 of 1987 to December 31 of 1989, and from January 1 of 1990 through December 31 of 1991, respectively. The NASATEAM data from revision 2 of the SSM/I CD-ROM's were used to create the present data set. SMMR images were collected every 2 to 3 days, whereas SSM/I data are provided in daily ice-concentration grids. Apart from a number of small gaps (5 or fewer days) in the record, the only long period for which no data are available is December 3 of 1987 through January 12 of 1988, inclusive. As ancillary data, the ETOPO5 global gridded elevation and bathymetry data (Edwards, 1989) were interpolated to the resolution of the NSIDC data; the interpolated topographic data are included. The images are provided in three formats: Hierarchical Data Format (HDF), a flexible scientific data format developed at the National Center for Supercomputing Applications; Graphics Interchange Format (GIF); and Macintosh PICT format. The ice- concentration grids are distributed by NSIDC in HDF format. proprietary +USGS_DDS-46 Geology and resource assessment of the Venezuelan Guayana Shield at 1:500,000 scale CEOS_EXTRA STAC Catalog 1970-01-01 -75, 0, -57, 13 https://cmr.earthdata.nasa.gov/search/concepts/C2231552370-CEOS_EXTRA.umm_json Conversion of the Venezuela maps to a computerized digital format was undertaken for the following reasons: 1) The digital format facilitates the presentation and analysis of earth-science data. Digital maps can be displayed at any scale or projection, whereas a paper map has a fixed scale and projection. However, the maps on this disc are not intended to be used at any scale more detailed than 1:500,000. A geographic information system (GIS) allows combining and overlaying of layers for analysis of spatial relations not readily apparent in the standard paper publication. Digital data on geology, geophysics, and geochemistry can be combined to create useful derivative products. 2) The digital format was used to facilitate publication in both paper and electronic form. For the Rio Caura paper map publication (Brooks and others, 1995), digital images were sent to the Gerber plotter, a vector-to-film processor. The other 1:500,000-scale MF maps were reproduced photographically from electrostatic plotter output on clear mylar. The published digital formats include this CD-ROM and ARC/INFO Export files to be located on the World Wide Web on the Internet. The data in this CD-ROM are based on a mineral resource assessment of the Venezuelan Guayana Shield, conducted between 1987 and 1991 by the U.S. Geological Survey and Corporacion Venezolana de Guayana, Tecnica Minera, (USGS, 1993). The Venezuelan Shield occupies about 415,000 sq km in the south and east part of Venezuela. The study area is bounded on the north by the Rio Orinoco. It includes all of the Territorio Federal Amazonas, Estado Bolivar, and part of Estado Delta Amacuro. The original resource assessment publication USGS Bulletin 2062 consists of 121 pages of text and figures as well as eight full-color maps: Geographic Geologic and tectonic Bouguer gravity Two mineral-occurrence maps Side-looking airborne radar image Two permissive domain maps The side-looking airborne radar image and the Bouguer gravity map are not included in this CD-ROM. The geology layer from the 1993 Bulletin was revised and published as a series of MF and I maps. proprietary +USGS_DDS-55_EF_1.0 Gulf of Mexico Marine Geology and Geophysics from Field Activity: A-1-97-GM: East Flower Garden Bank bathymetry and backscatter data CEOS_EXTRA STAC Catalog 1997-01-01 1997-12-31 -132, 30, -114, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231551486-CEOS_EXTRA.umm_json Accurate base maps are a prerequisite for any geological study, regardless of the objectives. Land-based studies commonly utilize aerial photographs, USGS 7.5-minute quadrangle maps, and satellite images as base maps. Until now, studies that involve the ocean floor have been at a disadvantage due to an almost complete lack of accurate marine base maps. Many base maps of the sea floor have been constructed over the past century but with a wide range in navigational and depth accuracies. Only in the past few years has marine surveying technology advanced far enough to produce navigational accuracy of 1 meter and depth resolutions of 50 centimeters. The Pacific Seafloor Mapping Project, U.S. Geological Survey, Western Coastal and Marine Geology Program, Menlo Park, California, U.S.A. in cooperation with the Ocean Mapping Group, University of New Brunswick, Canada is using this new technology to systematically map the ocean floor and lakes. This type of marine surveying, called Multibeam surveying, collects high-resolution bathymetry and backscatter data that can be used for a variety of basemaps, GIS coverages, and scientific visualization methods. [Summary provided by the USGS.] proprietary +USGS_DDS-55_WF Gulf of Mexico Marine Geology and Geophysics from Field Activity: A-1-97-GM: West Flower Garden Bank bathymetry and backscatter data CEOS_EXTRA STAC Catalog 1997-01-01 1997-12-31 -132, 30, -114, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231548694-CEOS_EXTRA.umm_json "These data and information are intended for science researchers, students from elementary through college, policy makers, and general public. Pacific Seafloor Mapping Project Test cruise. Bathymetry and seafloor backscatter data for the Flower Gardens National Marine Sanctuary are provided in TIFF image format. This data set contains data, metadata, and formal metadata associated with a marine data collection activity referred to by the USGS/CMG Activity ID: A-1-97-GM Similar information is available for over 1500 other USGS/CMG-related Activities. If known, available are Activity-specific navigation, gravity, magnetic, bathymetry, seismic, and sampling data; track maps; and equipment information; as well as summary overviews, crew lists, and information about analog materials. Primary access to the USGS/CMG Information Bank's digital data, analog data, and metadata is provided through ""http://walrus.wr.usgs.gov/infobank/programs/html/main/activities.html"" This page accommodates a variety of search approaches (e.g., by ship, by region, by scientist, by equipment type, etc.). Please recognize the U.S. Geological Survey (USGS) as the source of this information. Physical materials are under controlled on-site access. Some USGS information accessed through this means may be preliminary in nature and presented without the approval of the Director of the USGS. This information is provided with the understanding that it is not guaranteed to be correct or complete and conclusions drawn from such information are the responsibility of the user. This information is not intended for navigational purposes. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government." proprietary USGS_DDS-66_1.0 Assessment of the Alluvial Sediments in the Big Thompson River Valley, Colorado - USGS_DDS-66 CEOS_EXTRA STAC Catalog 1970-01-01 -109.41, 36.64, -101.69, 41.36 https://cmr.earthdata.nasa.gov/search/concepts/C2231551049-CEOS_EXTRA.umm_json To obtain subsurface geologic information about the alluvium in the Big Thompson River Valley, S-wave refraction data were collected along three roads that cross the valley. The refraction data were used to estimate velocities and thickness for a layered-earth model from these models, three cross sections of the river valley were constructed. These cross sections show the thickness and gross stratigraphy of the alluvium. [Summary provided by the USGS.] proprietary USGS_DDS-68 Coastal Vulnerability to Sea-Level Rise: A Preliminary Database for the U.S. Atlantic, Pacific, and Gulf of Mexico Coasts CEOS_EXTRA STAC Catalog 1970-01-01 -124.7608, 24.5485, -66.9578, 48.388 https://cmr.earthdata.nasa.gov/search/concepts/C2231553183-CEOS_EXTRA.umm_json "Coastal Changes Due to Sea-Level Rise: One of the most important applied problems in coastal geology today is determining the physical response of the coastline to sea-level rise. Predicting shoreline retreat, beach loss, cliff retreat, and land loss rates is critical to planning coastal zone management strategies and assessing biological impacts due to habitat change or destruction. Presently, long-term (>50 years) coastal planning and decision-making has been done piecemeal, if at all, for the nation's shoreline (National Research Council, 1990; 1995). Consequently, facilities are being located and entire communities are being developed without adequate consideration of the potential costs of protecting or relocating them from sea-level rise related erosion, flooding and storm damage. Recent estimates of future sea-level rise based on climate modeling (Wigley and Raper, 1992) suggest an increase in global eustatic sea-level of between 15 and 95 cm by 2100, with a ""best estimate"" of 50 cm (IPCC, 1995). This is more than double the rate of eustatic rise for the past century (Douglas, 1997; Peltier and Jiang, 1997). The prediction of coastal evolution is not straightforward. There is no standard methodology, and even the kinds of data required to make such predictions are the subject of much scientific debate. A number of predictive approaches have been used (National Research Council, 1990), including: 1. extrapolation of historical data (for example, coastal erosion rates); 2. static inundation modeling; 3. application of a simple geometric model (for example, the Bruun Rule); 4. application of a sediment dynamics/budget model; or 5. Monte Carlo (probabilistic) simulation based on parameterized physical forcing variables. Each of these approaches, however, has its shortcomings or can be shown to be invalid for certain applications (National Research Council, 1990). Similarly, the types of input data required vary widely, and for a given approach (for example, sediment budget), existing data may be indeterminate or may simply not exist (Klein and Nicholls, 1999). Furthermore, human manipulation of the coast in the form of beach nourishment, construction of seawalls, groins, and jetties, as well as coastal development itself, may dictate Federal, State and local priorities for coastal management without proper regard for geologic processes. Thus, the long-term decision to renourish or otherwise engineer a coastline may be the primary determining factor in how that coastal segment evolves. Variables Affecting Coastal Vulnerability: We use here a fairly simple classification of the relative vulnerability of different U.S. coastal environments to future rises in sea-level. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, and yields a relative measure of the system's natural vulnerability to the effects of sea-level rise (Klein and Nicholls, 1999). The vulnerability classification is based upon the relative contributions and interactions of six variables: 1. Tidal range, which contributes to inundation hazards. 2. Wave height, which is linked to inundation hazards. 3. Coastal slope (steepness or flatness of the coastal region), which is linked to the susceptibility of a coast to inundation by flooding and to the rapidity of shoreline retreat. 4. Shoreline erosion rates, which indicate how the given section of shoreline has been eroding. 5. Geomorphology, which indicates the relative erodibility of a given section of shoreline. 6. Historical rates of relative sea-level rise, which correspond to how the global (eustatic) sea-level rise and local tectonic processes (land motion such as uplift or subsidence) have affected a section of shoreline. The input data for this database of coastal vulnerability have been assembled using the original, and sometimes variable, horizontal resolution, which then was resampled to a 3-minute grid cell. A data set for each risk variable is then linked to each grid point. For mapping purposes, data stored in the 3-minute grid is transferred to a 1:2,000,000 vector shoreline with each segment of shoreline lying within a single grid cell. [Summary provided by the USGS.]" proprietary USGS_DDS-72 Bathymetry and Acoustic Backscatter of Crater Lake, Oregon from Field Activity: S-1-00-OR CEOS_EXTRA STAC Catalog 2000-07-28 2000-08-03 -122.16555, 42.904907, -122.049835, 42.978516 https://cmr.earthdata.nasa.gov/search/concepts/C2231551066-CEOS_EXTRA.umm_json "These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) or other software to display bathymetry and backscatter data of Crater Lake, Oregon. These data include high-resolution bathymetry and calibrated acoustic backscatter in XYZ ASCII and ArcInfo GRID format generated from the 2000 multibeam sonar survey of Crater Lake, Oregon. Information for USGS Coastal and Marine Geology related activities are online at ""http://walrus.wr.usgs.gov/infobank/s/s100or/html/s-1-00-or.meta.html"" These data not intended for navigational purposes. Please recognize the U.S. Geological Survey (USGS) as the source of this information. USGS-authored or produced data and information are in the public domain. Although these data have been used by the U.S. Geological Survey, U.S. Department of the Interior, these data and information are provided with the understanding that they are not guaranteed to be usable, timely, accurate, or complete. Users are cautioned to consider carefully the provisional nature of these data and information before using them for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences. Conclusions drawn from, or actions undertaken on the basis of, such data and information are the sole responsibility of the user. Neither the U.S. Government nor any agency thereof, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any data, software, information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights. Trade, firm, or product names and other references to non-USGS products and services are provided for information only and do not constitute endorsement or warranty, express or implied, by the USGS, USDOI, or U.S. Government, as to their suitability, content, usefulness, functioning, completeness, or accuracy." proprietary +USGS_DDS_10_1 Modern Average Global Sea-Surface Temperature CEOS_EXTRA STAC Catalog 1981-10-01 1989-12-31 -180, -66, 180, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231552931-CEOS_EXTRA.umm_json The purpose of this data set is to provide paleoclimate researchers with a tool for estimating the average seasonal variation in sea-surface temperature (SST) throughout the modern world ocean and for estimating the modern monthly and weekly sea-surface temperature at any given oceanic location. It is expected that these data will be compared with temperature estimates derived from geological proxy measures such as faunal census analyses and stable isotopic analyses. The results can then be used to constrain general circulation models of climate change. The data contained in this data set are derived from the NOAA Advanced Very High Resolution Radiometer Multichannel Sea Surface Temperature data (AVHRR MCSST), which are obtainable from the Distributed Active Archive Center at the Jet Propulsion Laboratory (JPL) in Pasadena, Calif. The JPL tapes contain weekly images of SST from October 1981 through December 1990 in nine regions of the world ocean: North Atlantic, Eastern North Atlantic, South Atlantic, Agulhas, Indian, Southeast Pacific, Southwest Pacific, Northeast Pacific, and Northwest Pacific. This data set represents the results of calculations carried out on the NOAA data and also contains the source code of the programs that made the calculations. The objective was to derive the average sea-surface temperature of each month and week throughout the whole 10-year series, meaning, for example, that data from January of each year would be averaged together. The result is 12 monthly and 52 weekly images for each of the oceanic regions. Averaging the images in this way tends to reduce the number of grid cells that lack valid data and to suppress interannual variability. As ancillary data, the ETOPO5 global gridded elevation and bathymetry data (Edwards, 1989) were interpolated to the resolution of the SST data; the interpolated topographic data are included. The images are provided in three formats: a modified form of run-length encoding (MRLE), Graphics Interchange Format (GIF), and Macintosh PICT format. Also included in the data set are programs that can retrieve seasonal temperature profiles at user-specified locations and that can decompress the data files. These nongraphical SST retrieval programs are provided in versions for UNIX, MS-DOS, and Macintosh computers. Graphical browse utilities are included for users of UNIX with the X Window System, 80386- based PC's, and Macintosh computers. proprietary USGS_DDS_P12_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Santa Maria Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -121.977486, 34.488464, -119.44189, 36.40565 https://cmr.earthdata.nasa.gov/search/concepts/C2231553039-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 12 (Santa Maria Basin) are listed here by play number, type, and name: Number Type Name 1201 conventional Anticlinal Trends - Onshore 1202 conventional Basin Margin 1204 conventional Diagenetic 1211 conventional Anticlinal Trends - Offshore State Waters proprietary USGS_DDS_P12_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Santa Maria Basin Province - USGS_DDS_P12_conventional CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -121.977486, 34.488464, -119.44189, 36.40565 https://cmr.earthdata.nasa.gov/search/concepts/C2231551861-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 12 (Santa Maria Basin) are listed here by play number and name: Number Name 1201 Anticlinal Trends - Onshore 1202 Basin Margin 1204 Diagenetic 1211 Anticlinal Trends - Offshore State Waters proprietary USGS_DDS_P13_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Ventura Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231554781-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 13 (Ventura Basin) are listed here by play number, type, and name: Number Type Name 1301 conventional Paleogene - Onshore 1302 conventional Neogene - Onshore 1304 conventional Cretaceous 1311 conventional Paleogene - Offshore State Waters 1312 conventional Neogene - Offshore State Waters proprietary @@ -13938,20 +14218,30 @@ USGS_DDS_P2_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the USGS_DDS_P2_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Alaska Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231551071-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 2 (Central Alaska) are listed here by play number and name: Number Name 201 Central Alaska Cenozoic Gas 202 Central Alaska Mesozoic Gas 203 Central Alaska Paleozoic Oil 204 Kandik Pre-Mid-Cretaceous Strata 205 Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary USGS_DOQ USGS Digital Orthophoto Quadrangles USGS_LTA STAC Catalog 1970-01-01 -126, 24, -66, 49 https://cmr.earthdata.nasa.gov/search/concepts/C1220566203-USGS_LTA.umm_json A Digital Orthophoto Quadrangle (DOQ) is a computer-generated image of an aerial photograph in which the image displacement caused by terrain relief and camera tilt has been removed. The DOQ combines the image characteristics of the original photograph with the georeferenced qualities of a map. DOQs are black and white (B/W), natural color, or color-infrared (CIR) images with 1-meter ground resolution. The USGS produces three types of DOQs: 1. 3.75-minute (quarter-quad) DOQs cover an area measuring 3.75-minutes longitude by 3.75-minutes latitude. Most of the U.S. is currently available, and the remaining locations should be complete by 2004. Quarter-quad DOQs are available in both Native and GeoTIFF formats. Native format consists of an ASCII keyword header followed by a series of 8-bit binary image lines for B/W and 24-bit band-interleaved-by-pixel (BIP) for color. DOQs in native format are cast to the Universal Transverse Mercator (UTM) projection and referenced to either the North American Datum (NAD) of 1927 (NAD27) or the NAD of 1983 (NAD83). GeoTIFF format consists of a georeferenced Tagged Image File Format (TIFF), with all geographic referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W quarter quad is 40-45 megabytes, and a color file is generally 140-150 megabytes. Quarter-quad DOQs are distributed via File Transfer Protocol (FTP) as uncompressed files. 2. 7.5-minute (full-quad) DOQs cover an area measuring 7.5-minutes longitude by 7.5-minutes latitude. Full-quad DOQs are mostly available for Oregon, Washington, and Alaska. Limited coverage may also be available for other states. Full-quad DOQs are available in both Native and GeoTIFF formats. Native is formatted with an ASCII keyword header followed by a series of 8-bit binary image lines for B/W. DOQs in native format are cast to the UTM projection and referenced to either NAD27 or NAD83. GeoTIFF is a georeferenced Tagged Image File Format with referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W full quad is 140-150 megabytes. Full-quad DOQs are distributed via FTP as uncompressed files. 3. Seamless DOQs are available for free download from the Seamless site. DOQs on this site are the most current version and are available for the conterminous U.S. [Summary provided by the USGS.] proprietary USGS_DS-845_PierScoutDatabase_1.0 A pier-scour database: 2,427 field and laboratory measurements of pier scour CEOS_EXTRA STAC Catalog 1970-01-01 19.6, 16.916668, -52.62, 83.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231553801-CEOS_EXTRA.umm_json The U.S. Geological Survey conducted a literature review to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet called the 2014 USGS Pier-Scour Database (PSDb-2014) consisting of 569 laboratory and 1,858 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 23 States within the United States and from 6 other countries. The digital spreadsheet is available on the Internet and offers a valuable resource to engineers and researchers seeking to understand pier-scour relations in the laboratory and field. proprietary +USGS_DS_2006_171 JAMSTEC multibeam surveys and submersible dives around the Hawaiian Islands: A collaborative Japan-USA exploration of Hawaii's deep seafloor CEOS_EXTRA STAC Catalog 1998-01-01 2002-12-31 -161, 16.75, -152.99988, 25.25005 https://cmr.earthdata.nasa.gov/search/concepts/C2231554487-CEOS_EXTRA.umm_json This database release, USGS Data Series 171, contains data collected during four Japan-USA collaborative cruises that characterize the seafloor around the Hawaiian Islands. The Japan Agency for Marine-Earth Science and Technology (JAMSTEC) sponsored cruises in 1998, 1999, 2001, and 2002, to build a greater understanding of the deep marine geology around the Hawaiian Islands. During these cruises, scientists surveyed over 600,000 square kilometers of the seafloor with a hull-mounted multibeam seafloor-mapping sonar system (SEA BEAM® 2112), observed the seafloor and collected samples using robotic and manned submersible dives, collected dredge and piston-core samples, and performed single-channel seismic surveys. To date, 32 research papers have been published describing results from these cruises. For a list of these articles see the bibliography. This digital database was compiled with ESRI ArcInfo version 7.2.2 and ArcGIS 9.0. The GIS files contain multibeam bathymetry, and acoustic backscatter data in ESRI grid format, and dive, seafloor sampling, and siesmic location data in ESRI shapefile format; ArcInfo-compatible GIS software is therefore required to use the files of this database. Metadata for the GIS files are available as text files. The GIS files were also symbolized and used to create Portable Document Format (PDF) files that are ready to be printed. Adobe Reader or other software that can translate PDFs is necessary to print these files. [Summary provided by the USGS.] proprietary USGS_DS_2006_177 Digital database of recently active traces of the Hayward Fault, California CEOS_EXTRA STAC Catalog 1970-01-01 -128, 35, -120, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231553624-CEOS_EXTRA.umm_json The purpose of this map is to show the location of and evidence for recent movement on active fault traces within the Hayward Fault Zone, California. The mapped traces represent the integration of the following three different types of data: (1) geomorphic expression, (2) creep (aseismic fault slip),and (3) trench exposures. This publication is a major revision of an earlier map (Lienkaemper, 1992), which both brings up to date the evidence for faulting and makes it available formatted both as a digital database for use within a geographic information system (GIS) and for broader public access interactively using widely available viewing software. The pamphlet describes in detail the types of scientific observations used to make the map, gives references pertaining to the fault and the evidence of faulting, and provides guidance for use of and limitations of the map. [Summary provided by the USGS.] proprietary USGS_DS_2006_180_1.0 Capitol Lake, Washington, 2004 Data Summary CEOS_EXTRA STAC Catalog 2004-09-21 2005-02-28 -122.9142, 47.0219, -122.9034, 47.0447 https://cmr.earthdata.nasa.gov/search/concepts/C2231548768-CEOS_EXTRA.umm_json At the request of the Washington Department of Ecology (WDOE), the US Geological Survey (USGS) collected bathymetry data in Capital Lake, Olympia, Wash., on September 21, 2004. The data are to be used to calculate sediment infilling rates within the lake as well as for developing the bottom boundary conditions for numerical models of water quality, sediment transport, and morphological change. In addition, the USGS collected sediment samples in Capitol Lake in February, 2005, to help characterize bottom sediment for numerical model calculations and substrate assessment. [Summary provided by the USGS.] proprietary USGS_DS_2006_184_1.0 Database for the Geologic Map of the Chelan 30-Minute by 60-Minute Quadrangle, Washington CEOS_EXTRA STAC Catalog 1970-01-01 -121.01906, 47.478214, -119.971146, 48.021763 https://cmr.earthdata.nasa.gov/search/concepts/C2231549879-CEOS_EXTRA.umm_json This digital map database has been prepared by R.W. Tabor from the published Geologic map of the Chelan 30-Minute Quadrangle, Washington. Together with the accompanying text files as PDF, it provides information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The authors mapped most of the bedrock geology at 1:100,000 scale, but compiled Quaternary units at 1:24,000 scale. The Quaternary contacts and structural data have been much simplified for the 1:100,000-scale map and database. The spatial resolution (scale) of the database is 1:100,000 or smaller. This database depicts the distribution of geologic materials and structures at a regional (1:100,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. [Summary provided by the USGS.] proprietary USGS_DS_2006_190 Database of Ground-Water Levels in the Vicinity of Rainier Mesa, Nevada Test Site, Nye County, Nevada, 1957-2005 CEOS_EXTRA STAC Catalog 1957-01-01 2005-12-31 -116.17, 37.1, -116, 37.9 https://cmr.earthdata.nasa.gov/search/concepts/C2231549884-CEOS_EXTRA.umm_json More than 1,200 water-level measurements from 1957 to 2005 in the Rainier Mesa area of the Nevada Test Site were quality assured and analyzed. Water levels were measured from 50 discrete intervals within 18 boreholes and from 4 tunnel sites. An interpretive database was constructed that describes water-level conditions for each water level measured in the Rainier Mesa area. Multiple attributes were assigned to each water-level measurement in the database to describe the hydrologic conditions at the time of measurement. General quality, temporal variability, regional significance, and hydrologic conditions are attributed for each water-level measurement. The database also includes hydrograph narratives that describe the water-level history of each well. [Summary provided by the USGS.] proprietary +USGS_DS_2006_199_1.0 Digital Geologic Map and GIS Database of Venezuela CEOS_EXTRA STAC Catalog 1970-01-01 -74, 0, -60, 12 https://cmr.earthdata.nasa.gov/search/concepts/C2231554824-CEOS_EXTRA.umm_json "The digital geologic map and GIS database of Venezuela captures GIS compatible geologic and hydrologic data from the ""Geologic Shaded Relief Map of Venezuela,"" which was released online as U.S. Geological Survey Open-File Report 2005-1038. Digital datasets and corresponding metadata files are stored in ESRI geodatabase format; accessible via ArcGIS 9.X. Feature classes in the geodatabase include geologic unit polygons, open water polygons, coincident geologic unit linework (contacts, faults, etc.) and non-coincident geologic unit linework (folds, drainage networks, etc.). Geologic unit polygon data were attributed for age, name, and lithologic type following the Léxico Estratigráfico de Venezuela. All digital datasets were captured from source data at 1:750,000. Although users may view and analyze data at varying scales, the authors make no guarantee as to the accuracy of the data at scales larger than 1:750,000. [Summary provided by the USGS.]" proprietary USGS_DS_2006_203 Archive of Digital Boomer Seismic Reflection Data Collected During USGS Cruise 97CCT01 Offshore of Central South Carolina, June 1997 CEOS_EXTRA STAC Catalog 1997-06-01 1997-06-04 -79.81418, 32.73532, -79.55894, 32.889748 https://cmr.earthdata.nasa.gov/search/concepts/C2231554319-CEOS_EXTRA.umm_json In June of 1997, the U.S. Geological Survey, in cooperation with Coastal Carolina University, conducted a geophysical survey of the shallow geologic framework of the continental shelf offshore of central South Carolina from the Isle of Palms to Bull Island. Data were collected as part of the USGS Coastal Change and Transport (CCT) Project. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, observers' logbooks, Field Activity Collection System (FACS) logs, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. [Summary provided by the USGS.] proprietary USGS_DS_2006_216 Base-Flow Yields of Watersheds in the Berkeley County Area, West Virginia CEOS_EXTRA STAC Catalog 2005-07-25 2006-05-04 -78.1, 39.15, -77.5, 39.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231554130-CEOS_EXTRA.umm_json Base-flow yields at approximately 50 percent of the annual mean ground-water recharge rate were estimated for watersheds in the Berkeley County area, W.Va. These base-flow yields were determined from two sets of discharge measurements made July 25-28, 2005, and May 4, 2006. Two sections of channel along Opequon Creek had net flow losses that are expressed as negative base-flow watershed yields; these and other base-flow watershed yields in the eastern half of the study area ranged from -940 to 2,280 gallons per day per acre ((gal/d)/acre) and averaged 395 (gal/d)/acre. The base-flow yields for watersheds in the western half of the study area ranged from 275 to 482 (gal/d)/acre and averaged 376 (gal/d)/acre. [Summary provided by the USGS.] proprietary +USGS_DS_2006_220 Hurricane Rita Surge Data, Southwestern Louisiana and Southeastern Texas, September to November 2005 CEOS_EXTRA STAC Catalog 1970-01-01 -98, 29, -90, 33 https://cmr.earthdata.nasa.gov/search/concepts/C2231548576-CEOS_EXTRA.umm_json Pressure transducers and high-water marks were used to document the inland water levels related to storm surge generated by Hurricane Rita in southwestern Louisiana and southeastern Texas. On September 22-23, 2005, an experimental monitoring network consisting of 47 pressure transducers (sensors) was deployed at 33 sites over an area of about 4,000 square miles to record the timing, extent, and magnitude of inland hurricane storm surge and coastal flooding. Sensors were programmed to record date and time, temperature, and barometric or water pressure. Water pressure was corrected for changes in barometric pressure and salinity. Elevation surveys using global-positioning systems and differential levels were used to relate all storm-surge water-level data, reference marks, benchmarks, sensor measuring points, and high-water marks to the North American Vertical Datum of 1988 (NAVD 88). The resulting data indicated that storm-surge water levels over 14 feet above NAVD 88 occurred at three locations and rates of water-level rise greater than 5 feet per hour occurred at three locations near the Louisiana coast. Quality-assurance measures were used to assess the variability and accuracy of the water-level data recorded by the sensors. Water-level data from sensors were similar to data from co-located sensors, permanent U.S. Geological Survey streamgages, and water-surface elevations performed by field staff. Water-level data from sensors at selected locations were compared to corresponding high-water mark elevations. In general, the water-level data from sensors were similar to elevations of high quality high-water marks, while reporting consistently higher than elevations of lesser quality high-water marks. [Summary provided by the USGS.] proprietary +USGS_DS_2006_221 Land-Cover and Imperviousness Data for Regional Areas near Denver, Colorado; Dallas-Fort Worth, Texas; and Milwaukee-Green Bay, Wisconsin - 2001 CEOS_EXTRA STAC Catalog 1999-01-01 2002-12-31 -106, 31, -86, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2231548697-CEOS_EXTRA.umm_json This report describes the processing and results of land-cover and impervious surface derivation for parts of three metropolitan areas being studied as part of the U.S. Geological Survey's (USGS) National Water-Quality Assessment (NAWQA) Program Effects of Urbanization on Stream Ecosystems (EUSE). The data were derived primarily from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) satellite imagery from the period 1999-2002, and are provided as 30-meter resolution raster datasets. Data were produced to a standard consistent with data being produced as part of the USGS National Land Cover Database 2001 (NLCD01) Program, and were derived in cooperation with, and assistance from, NLCD01 personnel. The data were intended as surrogates for NLCD01 data because of the EUSE Program's time-critical need for updated land-cover for parts of the United States that would not be available in time from the NLCD01 Program. Six datasets are described in this report: separate land-cover (15-class categorical data) and imperviousness (0-100 percent continuous data) raster datasets for parts of the general Denver, Colorado area (South Platte River Basin), Dallas-Fort Worth, Texas area (Trinity River Basin), and Milwaukee-Green Bay, Wisconsin area (Western Lake Michigan Drainages). [Summary provided by the USGS.] proprietary USGS_DS_2006_224 Aeromagnetic Survey of Taylor Mountains Area in Southwest Alaska, A Website for the Distribution of Data CEOS_EXTRA STAC Catalog 2004-04-17 2004-05-31 -160, 60, -156, 61 https://cmr.earthdata.nasa.gov/search/concepts/C2231550160-CEOS_EXTRA.umm_json An airborne high-resolution magnetic and coincidental horizontal magnetic graviometer survey was completed over the Taylor Mountains area in southwest Alaska. The flying was undertaken by McPhar Geosurveys Ltd. on behalf of the United States Geological Survey (USGS). First tests and calibration flights were completed by April 7, 2004, and data acquisition was initiated on April 17, 2004. The final data acquisition and final test/calibrations flight was completed on May 31, 2004. Data acquired during the survey totaled 8,971.15 line-miles. [Summary provided by the USGS.] proprietary +USGS_DS_2006_234_1.0 Nevada Magnetic and Gravity Maps and Data: A Website for the Distribution of Data CEOS_EXTRA STAC Catalog 1970-01-01 -120, 35, -114, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231548572-CEOS_EXTRA.umm_json Magnetic anomalies are due to variations in the Earth's magnetic field caused by the uneven distribution of magnetic minerals (primarily magnetite) in the rocks that make up the upper part of the Earth's crust. The features and patterns of the magnetic anomalies can be used to delineate details of subsurface geology, including the locations of buried faults and magnetite-bearing rocks and the depth to the base of sedimentary basins. This information is valuable for mineral exploration, geologic mapping, and environmental studies. The Nevada magnetic map is constructed from grids that combine information (see data processing details) collected in 82 separate magnetic surveys conducted between 1947 and 2004. The data from these surveys are of varying quality. The design and specifications (terrain clearance, sampling rates, line spacing, and reduction procedures) varied from survey to survey depending on the purpose of the project and the technology of that time. [Summary provided by the USGS.] proprietary USGS_DS_2007_119 Archive of Digital Boomer Seismic Reflection Data Collected During USGS Field Activity 04SGI01 in the Withlacoochee River of West-Central Florida, March 2004 CEOS_EXTRA STAC Catalog 2004-03-01 2004-03-05 -82.4575, 28.519396, -82.168434, 29.043365 https://cmr.earthdata.nasa.gov/search/concepts/C2231550488-CEOS_EXTRA.umm_json In March of 2004, the U.S. Geological Survey conducted a geophysical survey in the Withlacoochee River of west-central Florida. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. [Summary provided by the USGS.] proprietary USGS_DS_2007_242 Archive of Digital Chirp Seismic Reflection Data Collected During USGS Cruise 05SCC01 Offshore of Port Fourchon and Timbalier Bay, Louisiana, August 2005 CEOS_EXTRA STAC Catalog 2005-08-08 2005-08-11 -90.417816, 29.022211, -89.955574, 29.114426 https://cmr.earthdata.nasa.gov/search/concepts/C2231551890-CEOS_EXTRA.umm_json In August of 2005, the U.S. Geological Survey conducted geophysical surveys offshore of Port Fourchon and Timbalier Bay, Louisiana, and in nearby waterbodies. This report serves as an archive of unprocessed digital chirp seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. [Summary provided by the USGS.] proprietary +USGS_DS_2007_244 Geochemical Database for Intrusive Rocks of North-Central and Northeast Nevada CEOS_EXTRA STAC Catalog 2001-01-01 2007-12-31 -119, 38, -114, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231550906-CEOS_EXTRA.umm_json North-central and northeast Nevada contains numerous large plutons and smaller stocks but also contains many small, shallowly emplaced intrusive bodies, including dikes, sills, and intrusive lava dome complexes. Decades of geologic investigations in the study area demonstrate that many ore deposits, representing diverse ore deposit types, are spatially, and probably temporally and genetically, associated with these igneous intrusions. However, despite the number and importance of igneous intrusions in the study area, no synthesis of geochemical data available for these rocks has been completed. This report presents a synthesis of geochemical data for these rocks. The product represents the first phases of an effort to evaluate the time-space-compositional evolution of Mesozoic and Cenozoic magmatism in the study area and identify genetic associations between magmatism and mineralizing processes in this region. [Summary provided by the USGS.] proprietary +USGS_DS_2007_246_1.0 Flow Velocity and Sediment Data Collected During 1990 and 1991 at National Canyon, Colorado River, Arizona CEOS_EXTRA STAC Catalog 1990-01-01 1991-12-31 -114, 35, -111, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2231550397-CEOS_EXTRA.umm_json During 1990 and 1991, a series of research flows were released from Glen Canyon Dam. Data collected at the streamflow-gaging station on the Colorado River above National Canyon near Supai from that period have been compiled and entered into the U.S. Geological Survey database. The data consist of measurements of suspended-sediment concentration and sand sizes in suspension, sand sizes of streambed sediment, and velocity of the Colorado River above National Canyon near Supai streamflow-gaging site. Velocity and sediment data are available upon request from the Arizona Water Science Center and from the U.S. Geological Survey water-quality database (http://waterdata.usgs.gov/az/nwis/qw). [Summary provided by the USGS.] proprietary +USGS_DS_2007_250 Modal Composition and Age of Intrusions in North-central and Northeast Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -119, 38, -114, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231550917-CEOS_EXTRA.umm_json North-central and northeast Nevada contains numerous large plutons and smaller stocks but also contains many small, shallowly emplaced intrusive bodies, including dikes, sills, and intrusive lava dome complexes. Decades of geologic investigations in the study area demonstrate that many ore deposits, representing diverse ore deposit types, are spatially, and probably temporally and genetically, associated with these igneous intrusions. However, despite the number and importance of igneous instrusions in the study area, no synthesis of geochemical data available for these rocks has been completed. This report presents a synthesis of composition and age data for these rocks. The product represents the first phases of an effort to evaluate the time-space-compositional evolution of Mesozoic and Cenozoic magmatism in the study area and identify genetic associations between magmatism and mineralizing processes in this region. [Summary provided by the USGS.] proprietary USGS_DS_2007_254 Archive of Digital CHIRP Seismic Reflection Data Collected During USGS Cruise 06FSH01 Offshore of Siesta Key, Florida, May 2006 CEOS_EXTRA STAC Catalog 2006-05-10 2006-05-15 -82.62488, 27.18107, -82.531654, 27.265392 https://cmr.earthdata.nasa.gov/search/concepts/C2231551460-CEOS_EXTRA.umm_json In May of 2006, the U.S. Geological Survey conducted geophysical surveys offshore of Siesta Key, Florida. This report serves as an archive of unprocessed digital chirp seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. proprietary USGS_EDC_EO1_ALI EO-1 (Earth Observing-1) Advanced Land Imager (ALI) Instrument Level 1R, Level 1Gs, Level 1Gst Data USGS_LTA STAC Catalog 2001-03-16 -180, -83.85, 180, 83.1 https://cmr.earthdata.nasa.gov/search/concepts/C1220566654-USGS_LTA.umm_json "Advanced Land Imager (ALI) provides image data from ten spectral bands (band designations). The instrument operates in a pushbroom fashion, with a spatial resolution of 30 meters for the multispectral bands and 10 meters for the panchromatic band. The standard scene width is 37 kilometers. Standard scene length is 42 kilometers, with an optional increased scene length of 185 kilometers (additional information). For Advanced Land Imager (ALI) data, the following levels of correction are available: Level 1R radiometrically corrected with no geometric correction applied. The image data are provided in 16-bit radiance values. The data are available in Hierarchical Data Format (HDF) and are distributed on CD-ROM, DVD, and File Transfer Protocol (FTP). Level 1Gs is geometrically corrected and will be provided as a single ""stitched"" file. The image data are provided in 16-bit radiance values. The data are available in Hierarchical Data Format (HDF) or Geographic Tagged Image-File Format (GeoTIFF) and are distributed on DVD and File Transfer Protocol (FTP). Level 1Gst is terrain corrected and will be provided as a single ""stitched"" file. The image data are provided in 16-bit radiance values. The data are available in Hierarchical Data Format (HDF) or Geographic Tagged Image-File Format (GeoTIFF) and are distributed on DVD and File Transfer Protocol (FTP). [Source: USGS/EDC Homepage]" proprietary USGS_EDC_EO1_Hyperion EO-1 Hyperion USGS_LTA STAC Catalog 2001-05-01 -180, -83.57, 180, 83.15 https://cmr.earthdata.nasa.gov/search/concepts/C1220567951-USGS_LTA.umm_json The Earth-Observing One (EO-1) satellite was decommissioned March 2017. The EO-1 satellite was launched on November 21, 2000 with the NASA's New Millennium Program (NMP). The NMP was an advanced-technology development program created a new generation of technologies and mission concepts into future Earth and space science missions. Information of the EO-1 mission can be found on the EOPortal. All EO-1 ALI and Hyperion historical data will continue to be available through EarthExplorer for the foreseeable future.  EO-1 Product Description The Earth Observing-1 (EO-1) satellite was launched November 21, 2000 as a one-year technology demonstration/validation mission. After the initial technology mission was completed, NASA and the USGS agreed to the continuation of the EO-1 program as an Extended Mission. The EO-1 Extended Mission is chartered to collect and distribute Hyperion hyperspectral and Advanced Land Imager (ALI) multispectral products according to customer tasking requests. Hyperion Instrument on board the EO-1 spacecraft Hyperion collects 220 unique spectral channels ranging from 0.357 to 2.576 micrometers with a 10-nm bandwidth. The instrument operates in a pushbroom fashion, with a spatial resolution of 30 meters for all bands. The standard scene width is 7.7 kilometers. Standard scene length is 42 kilometers, with an optional increased scene length of 185 kilometers (additional information). All Hyperion and Advanced Land Imager (ALI) data in the archive will be attempted to be processed to the Level 1Gst level of correction. If the scene fails the Level 1Gst processing level, it will be removed from the archive and will become unavailable. As of June 15th, 2009, not all of the EO-1 data has been processed; please continue to check back if the scene of your interest is not available. We will be making attempts to process the failed scene as time and workload permits; however there are no guarantees that all of the EO-1 scenes will be able to be processed. proprietary USGS_EDC_IFSAR Interferometric Synthetic Aperture Radar (IFSAR) Alaska USGS_LTA STAC Catalog 1970-01-01 130, 54.666668, 173, 71.833336 https://cmr.earthdata.nasa.gov/search/concepts/C1220567953-USGS_LTA.umm_json The U.S. Geological Survey (USGS) National Geospatial Program (NGP) developed the Alaska Mapping Initiative (AMI) to collaborate with the State and other Federal partners to acquire 3-dimensional elevation data to improve statewide topographic maps for Alaska. AMI coordinates Federal activities through the Alaska Mapping Executive Committee (AMEC) and State efforts through Alaska's Statewide Digital Mapping Initiative (SDMI) to ensure a unified approach for consistent data acquisition and enhancement of elevation data products. AMI attained interferometric synthetic aperture radar (IFSAR) to generate digital elevation model (DEM) data. This radar mapping technology is an effective tool for collecting data in challenging circumstances such as cloud cover, extreme weather conditions, rugged terrain, and remote locations. Airborne IFSAR data were flown over South Central Alaska in the summer of 2010 and over Northwest Alaska in 2012. proprietary USGS_EDC_NRCS Natural Resources Conservation Service SD State Farm Service Agency Color Slide Scans USGS_LTA STAC Catalog 1970-01-01 -96, 43, -104, 46 https://cmr.earthdata.nasa.gov/search/concepts/C1220567935-USGS_LTA.umm_json The unique landscape of South Dakota, known for its diverse wetlands and large areas of native prairie, provides critical habitat for many of the nation’s migratory birds, including grassland birds. proprietary +USGS_FORT_Mesa_Verda_NP_veg Mesa Verde National Park Vegetation Mapping Project - Spatial Vegetation Data CEOS_EXTRA STAC Catalog 2003-01-01 2006-12-31 -108.57052, 37.14272, -108.32347, 37.36232 https://cmr.earthdata.nasa.gov/search/concepts/C2231554965-CEOS_EXTRA.umm_json The Mesa Verde National Park Vegetation Map Database was developed as a primary product in the Mesa Verde National Park Vegetation Classification, Distribution, and Mapping project. The map database maps vegetation at three levels of thematic organization at the park: the base, group, and management map classes. Most of the base map classes represent plant communities identified to National Vegetation Classification associations. The associated report, Vegetation Classification and Distribution Mapping Report: Mesa Verde National Park, describes in detail the methods used to develop the map database and map classes. The project was sponsored by the USA-National Vegetation Mapping Program and the National Park Service (NPS) Southern Colorado Plateau Network and the work was executed by a multi-agency and organizational team. The vegetation map database covers the park and an approximately 1 kilometer buffer around the park boundary. proprietary +USGS_FORT_WY_WindTurbines2012 Locations and Attributes of Wind Turbines in Wyoming, 2012 CEOS_EXTRA STAC Catalog 1970-01-01 -110.83864, 41.119976, -104.89066, 43.134483 https://cmr.earthdata.nasa.gov/search/concepts/C2231551793-CEOS_EXTRA.umm_json These data represent locations of wind turbines found within Wyoming as of August 2012. We assigned each wind turbine to a wind farm and, in these data, provide information about each turbine’s potential megawatt output, rotor diameter, hub height, rotor height, the status of the land ownership where the turbine exists, the county each turbine is located in, wind farm power capacity, the number of units currently associated with each wind farm, the wind turbine manufacturer and model, the wind farm developer, the owner of the wind farm, the current purchaser of power from the wind farm, the year the wind farm went online, and the status of its operation. Some of the attributes are estimates based on the information we found via the American Wind Energy Association and other on-line reports. The locations are derived from National Agriculture Imagery Program (2009 and 2012) true color aerial photographs and have a positional accuracy of approximately +/-5 meters. These data will provide a planning tool for wildlife- and habitat-related projects underway at the U.S. Geological Survey’s Fort Collins Science Center and other government and non-government organizations. Specifically, we will use these data to support quantifying disturbances of the landscape as related to wind energy as well as to quantify indirect disturbances to flora and fauna. This data set represents an update to a previous version by O’Donnell and Fancher (2010). proprietary USGS_FRESC_Columbia_Basin_sagebrush_1.0 Current Distribution of Sagebrush and Associated Vegetation in the Columbia Basin and Southwestern Regions CEOS_EXTRA STAC Catalog 1999-01-01 2003-12-31 -124.70596, 29.755955, -101.33477, 50.921524 https://cmr.earthdata.nasa.gov/search/concepts/C2231551919-CEOS_EXTRA.umm_json A new regional dataset was produced using decision tree classifier and other techniques to model landcover. Multi-season satellite imagery (Landsat ETM+, 1999-2003) and digital elevation model (DEM) derived datasets (e.g. elevation, landform, aspect, etc.) were utilized to derive rule sets for the various landcover classes. Eleven mapping areas, each characterized by similar ecological and spectral characteristics, were modeled independently of one another. An internal validation for modeled classes was performed on a withheld 20% of the sample data to assess model performance. Results of the validation will be presented in the final report and are not available at this time. Mapping area models were mosaicked to create the Columbia Basin Regional Dataset (Idaho, Oregon and Washington), which was subsequently combined with the Southwest Regional Gap Landcover Dataset to create the final seamless 8 state regional landcover map. The final map contains 126 Landcover classes (103 NatureServe Ecological Systems, 7 NLCD and 16 non-native vegetation classes) and has a minimum mapping unit (MMU) of approximately 1 acre. proprietary USGS_GEOGLAM_Algeria USGS Group on Earth Observations (GEO) Global Agricultural Monitoring (GLAM) Algeria USGS_LTA STAC Catalog 1972-01-01 -8.75, 18.5, 12, 37 https://cmr.earthdata.nasa.gov/search/concepts/C1220567881-USGS_LTA.umm_json The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. (Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/) proprietary USGS_GEOGLAM_Argentina USGS Group on Earth Observations (GEO) Global Agricultural Monitoring (GLAM) Argentina USGS_LTA STAC Catalog 1972-01-01 -66.5, -41, -53.5, -22 https://cmr.earthdata.nasa.gov/search/concepts/C1220567882-USGS_LTA.umm_json The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. (Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/) proprietary @@ -13995,23 +14285,66 @@ USGS_GFOI_Thailand USGS Global Forest Observations Initiative (GFOI) Thailand US USGS_GFOI_Vietnam USGS Global Forest Observations Initiative (GFOI) Vietnam USGS_LTA STAC Catalog 1972-01-01 102.5, 9, 109, 23 https://cmr.earthdata.nasa.gov/search/concepts/C1220567924-USGS_LTA.umm_json The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to: foster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC). proprietary USGS_GFOI_Zambia USGS Global Forest Observations Initiative (GFOI) Zambia USGS_LTA STAC Catalog 1972-01-01 22.06547, -18.062311, 33.57422, -11.178402 https://cmr.earthdata.nasa.gov/search/concepts/C1220567929-USGS_LTA.umm_json The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to: foster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC). proprietary USGS_GLCC_1.2 Global Land Cover Characterization Program USGS_LTA STAC Catalog 1992-04-01 1993-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220566586-USGS_LTA.umm_json The Global Land Cover Characterization Project was established to meet science data requirements identified by the International Geosphere and Biosphere Programme (IGBP), and the U. S. Global Change Research Program. The overall goal is to produce flexible large-area land cover databases to meet evolving requirements of the earth science research community. The project was implemented by the United States Geological Survey/EROS Data Center (EDC), the University of Nebraska-Lincoln (UNL), and the Joint Research (JRC) of European Commission. This effort is part of the National Aeronautic's and Space Administration (NASA) Earth Observing System Pathfinder Program. Funding for the project was provided by the USGS, NASA, the U.S. Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), U.S. Forest Service (USFS) , and the United Nations Environment Programme. The data base has been adopted by the International Geosphere-Biosphere Programme Data and Information System office (IGBP-DIS) to fill its requirement for a global 1-km land cover data set. [Summary provided by the USGS.] proprietary +USGS_GLOBAL_CRUST Global Crustal Database CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231549336-CEOS_EXTRA.umm_json In 1988, work was started on a global database intended to characterize the Earth's crust. Today, this database has over 10,000 entries, covering a large portion of the Earth's surface. The primary data source is from published literature detailing the results of seismic refraction profiles, although some unpublished results have been used as well, especially in Russia and China. From these seismic profiles, we extract a 1-D seismic velocity model (Vp and Vs if available) for a specific latitude and longitude. The 1-D model includes the thickness and seismic velocity for each crustal layer as well as annotations of sedimentary layers, velocity gradients, and the moho depth. Other crustal parameters are added to each point to create a complete image of the Earth's crust. [Summary provided by the USGS.] proprietary +USGS_GLSC_GreatLakesCopepods Free-living and Parasitic Copepods of the Laurentian Great Lakes: Keys and Details on Individual Species CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551007-CEOS_EXTRA.umm_json "We intend that this website provide individuals interested in copepod and branchiuran crustaceans of the Great Lakes with the best taxonomic information currently available, a brief introduction to the known distributions and ecology of the various species, and some of the most relevant literature. This product reflects our belief that the taxonomy of even ""difficult"" groups can be made understandable, interesting, and informative, especially in this digital age. Most of the information on the copepod fauna of the Great Lakes can be accessed directly from the Main Menu. To access information on identification nuances, distribution, life history, ecology, and synonymies for each species, there are two routes available. You can go to the Species List of Major Groups and Distribution Within the Great Lakes and in the table click on the name of the species in which you are interested, or you can click on the species name within the key when you reach the end of the identification process. Photographs, drawings, and text can be printed by placing the cursor over the object or page of interest, right-clicking, and then selecting the appropriate option from the drop down menu." proprietary USGS_IndianapolisMetroStreams Benthic-invertebrate, fish-community, and streambed-sediment-chemistry data for steams in the Indianapolis metropolitan area, Indiana, 2009-2012 CEOS_EXTRA STAC Catalog 2009-01-01 2012-12-31 -86.4, 39.5, -86, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231550453-CEOS_EXTRA.umm_json Aquatic-biology and sediment-chemistry data were collected at seven sites on the White River and at six tributary sites in the Indianapolis metropolitan area of Indiana during the period 2009 through 2012. Data collected included benthic-invertebrate and fish-community information and concentrations of metals, insecticides, herbicides, and semivolatile organic compounds adsorbed to streambed sediments. A total of 120 benthic-invertebrate samples were collected, of which 16 were replicate samples. A total of 26 fish-community samples were collected in 2010 and 2012. Thirty streambed-sediment chemistry samples were collected in 2009 and 2011, of which four were concurrent duplicate samples. proprietary USGS_JECAM_Canada_South_Nation USGS JECAM Canada South Nation USGS_LTA STAC Catalog 2011-05-25 -78.12, 43.62, -72.9, 46.99 https://cmr.earthdata.nasa.gov/search/concepts/C1220567544-USGS_LTA.umm_json Joint Experiment for Crop Assessment and Monitoring The overarching goal of JECAM is to reach a convergence of approaches, develop monitoring and reporting protocols and best practices for a variety of global agricultural systems. JECAM will enable the global agricultural monitoring community to compare results based on disparate sources of data, using various methods, over a variety of global cropping systems. It is intended that the JECAM experiments will facilitate international standards for data products and reporting, eventually supporting the development of a global system of systems for agricultural crop assessment and monitoring. The JECAM initiative is developed in the framework of GEO Global Agricultural Monitoring (GEOSS Task AG0703 a) and Agricultural Risk Management (GEOSS Task AG0703 b). proprietary +USGS_KATRINA_COASTAL_IMPACT_LIDAR Hurricane Katrina Impact Studies: Pre- and Post-Storm 3D Topography CEOS_EXTRA STAC Catalog 1970-01-01 -89, 30, -87, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2231548501-CEOS_EXTRA.umm_json In a cooperative research program, the USGS, NASA and the US Army Corps of Engineers (USACE) are using airborne laser mapping systems to survey coastal areas before and after hurricanes. As the aircraft flies along the coast, a laser altimeter (lidar) scans a several hundred meter wide swath of the earth's surface acquiring an estimate of ground elevation approximately every square meter. The elevation data from different flights can be compared to determine the patterns and magnitudes of coastal change (erosion, overwash, etc.) and the loss (or gain) of buildings and infrastructure. Results come from two lidar systems, the USACE's Compact Hydrographic Airborne Rapid Total Survey (CHARTS) and NASA's Experimental Advanced Airborne Research Lidar (EAARL). [Summary provided by the USGS.] proprietary +USGS_Katrina_Coastal_Impact Hurricane Katrina Impact Studies CEOS_EXTRA STAC Catalog 2005-08-29 -94, 29, -86, 36 https://cmr.earthdata.nasa.gov/search/concepts/C2231758498-CEOS_EXTRA.umm_json Hurricane Katrina made landfall as a category 4 storm in Plaquemines Parish, LA on August 29, 2005. The U.S. Geological Survey (USGS), NASA, the U.S. Army Corps of Engineers, and the University of New Orleans are cooperating in a research project investigating coastal change that occurred as a result of Hurricane Katrina. Aerial video, still photography, and laser altimetry surveys of post-storm beach conditions were collected August 31 and September 1, 2005 for comparison with earlier data. The comparisons will show the nature, magnitude, and spatial variability of coastal changes such as beach erosion, overwash deposition, and island breaching. These data will also be used to further refine predictive models of coastal impacts from severe storms. The data are being made available to local, state, and federal agencies for purposes of disaster recovery and erosion mitigation. proprietary +USGS_MAP_MF-2336_1.0 Geologic map of the Cape Mendocino, Eureka, Garberville, & southwestern part of the Hayfork 30 X 60 Quadrangles and Adjacent Offshore Area, Northern California CEOS_EXTRA STAC Catalog 1970-01-01 -125.029884, 39.982655, -123, 41.017353 https://cmr.earthdata.nasa.gov/search/concepts/C2231548610-CEOS_EXTRA.umm_json This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:100,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (ceghmf.ps, ceghmf.pdf, ceghmf.txt), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:100,000 or smaller. This is the pre-release version of the report. The accompanying text file mf2336.rev contains version numbers for each part of the data set. This report consists of a set of geologic map database files (Arc/ Info coverages) and supporting text and plotfiles. In addition, the report includes two sets of plotfiles (PostScript and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described in the explanatory pamphlets (ceghdesc and ceghdb). The base map layer used in the preparation of the geologic map plotfiles was downloaded from the web (www.gisdatadepot.com) as Digital Raster Graphic files of scale-stable versions of the USGS 1:100,000 topographic maps and coverted to TIFF images which were then converted to GRIDs. These grids contain no database information other than position, and are included for reference only. The base maps used were the Cape Mendocino (1989 edition), Eureka (1987 edition), Garberville (1979 edition), Hayfork (1978 edition) 1:100,000 topographic maps, which all have a 50-meter contour interval. The bathymetry maps were converted from the Coast and Geodetic Survey hydrographic chart 1308 N-12, 1969. proprietary +USGS_MAP_MF-2349_1.0 Geologic map and map database of the Spreckels 7.5-minute quadrangle, Monterey County, California CEOS_EXTRA STAC Catalog 1970-01-01 -121.75, 36.62, -121.62, 36.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231552246-CEOS_EXTRA.umm_json This database and accompanying plot files depict the distribution of geologic materials and structures at a scale of 1:24,000. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be used to accurately identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (skmf.txt, skmf.pdf, or skmf.ps), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller. This report consists of a set of geologic map database files (Arc/Info coverages) and supporting text and plotfiles. In addition, the report includes two sets of plotfiles (PostScript and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described below: >ARC/INFO Resultant Description of Coverage >export file Coverage >----------- ----------- -------------------------------- >sk-geol.e00 sk-geol/ Polygon and line coverage showing > faults, depositional contacts, and > rock units in the map area. > >sk-strc.e00 sk-strc/ Point and line coverage showing > strike and dip information and fold axes. > >sk-lnds.e00 sk-lnds/ Point and line coverage showing arrows > indicating landslide directions as well > as the locations of wells and springs > not included in the topographic base map. ASCII text files, including explanatory text, ARC/INFO key files, PostScript and PDF plot files, and a ARC Macro Language file for conversion of ARC export files into ARC coverages: >skmf.ps A PostScript plot file of the pamphlet > containing detailed unit descriptions > and geological information, a description > of the digital files associated with the > publication, plus references cited. > >skmf.pdf A PDF version of mamf.ps. > >skmf.txt A text-only file containing an unformatted > version of skmf.ps. > >import.aml ASCII text file in ARC Macro Language to > convert ARC export files to ARC coverages > in ARC/INFO. > >skmap.ps A PostScript plottable file containing > an image of the geologic map and base > maps at a scale of 1:24,000, along with > a simple map key. > >skmap.pdf A PDF file containing an image of the > geologic map and base maps at a scale > of 1:24,000, along with a simple map key. Base maps Base Map layers used in the preparation of the geologic map plotfiles were derived from published digital maps (Aitken, 1997) obtained from the U.S. Geological Survey Geologic Division Website for the Western Region (http://wrgis.wr.usgs.gov). Please see the website for more detailed information about the original databases. Because the base map digital files are already available at the website mentioned above, they are not included in the digital database package. proprietary +USGS_MAP_MF-2352_Version 1.0 Geologic map of the Tetilla Peak quadrangle, Santa Fe and Sandoval Counties, New Mexico CEOS_EXTRA STAC Catalog 1970-01-01 -106.25, 35.5, -106.125, 35.625 https://cmr.earthdata.nasa.gov/search/concepts/C2231551508-CEOS_EXTRA.umm_json The purpose of this mapping was to determine the bedrock geology that would control or impact ground-water flow from the Espanola basin into the Santo Domingo basin. As it is a multi-purpose geologic map, it is suitable as the geologic layer for any variety of interdisciplinary investigations incorporating geology as a theme. This digital geologic map summarizes all available geologic information for the Tetilla Peak quadrangle located immediately southwest of Santa Fe, New Mexico. The geologic map consists of new polygon (geologic map units) and line (contact, fault, fold axis, dike, flow contact, hachure) data, as well as point data (locations for structural measurements, geochemical and geochronologic data, geophysical soundings, and water wells). The map database has been generated at 1:24,000 scale, and provides significant new geologic information for an area of the southern Cerros del Rio volcanic field, which sits astride the boundary of the Espanola and Santo Domingo basins of the Rio Grande rift. The quadrangle includes the west part of the village of La Cienega along its eastern border and includes the southeasternmost part of the Cochiti Pueblo reservation along its northwest side. The central part of the quadrangle consists of Santa Fe National Forest and Bureau of Land Management lands, and parts of several Spanish-era land grants. Interstate 25 cuts through the southern half of the quadrangle between Santa Fe and Santo Domingo Pueblo. Canada de Santa Fe, a major river tributary to the Rio Grande, cuts through the quadrangle, but there is no dirt or paved road along the canyon bottom. A small abandoned uranium mine (the La Bajada mine) is found in the bottom of the Canada de Santa Fe about 3 km east of the La Bajada fault zone; it has been partially reclaimed. The surface geology of the Tetilla Peak quadrangle consists predominantly of a thin (1-2 m generally, locally as thick as 10? m) layer of windblown surficial deposits that has been reworked colluvially. Locally, landslide, fluvial, and pediment deposits are also important. These colluvial deposits mantle the principal bedrocks units, which are (from most to least common): (1) basalts, basanites, andesite, and trachyte of the Pliocene (2.7-2.2 Ma) Cerros del Rio volcanic field; (2) unconsolidated deposits of the Santa Fe Group, mainly along the western border, in the hanging wall of the La Bajada fault zone, but locally extending 2-3 km east under the Cerros del Rio volcanic field; (3) older Tertiary volcanic and sedimentary rocks (Abiquiu?, Espinaso, and Galisteo Formations); (4) intrusive rocks of the Cerrillos intrusive center that are roughly coeval with the Espinaso volcanic rocks; and (5) Mesozoic sedimentary rocks ranging in age from the Upper Triassic Chinle Formation to the Upper Cretaceous Mancos Shale. GEOSPATIAL DATAFILES AND OTHER FILES INCLUDED IN THIS DATA SET: Map political location: Santa Fe and Sandoval Counties, New Mexico Compilation scale: 1:24,000 Geology mapped: 1996-1998 >tepk_geol: geologic units, faults, dikes, volcanic flow boundaries >tepk_struct: bearing and attitude measurements of structural features >tepk_bed: attitude measurements of geologic units >tepk_chem: geochemical and geochronologic data by sample >tepk_amt: audio-magneto-telluric (AMT) geophysical sample data >tepk_wells: water well locations >tepk_marker: cartographic decorations (bar and ball symbol, etc.) >color524.shd: ArcInfo shadeset used to color geology polygons >geoscamp1.mrk: ArcInfo markerset used to plot geologic symbols >geoscamp1.lin: ArcInfo lineset used to plot geologic line symbols >tepk_base.tif,.tfw: 1:24,000-scale topographic base proprietary +USGS_MAP_MF-2354_Version 1.0 Geologic map of the Chewelah 30' x 60' quadrangle, Washington and Idaho CEOS_EXTRA STAC Catalog 1963-07-01 1989-10-09 -117.9, 41.8, -117, 42.31 https://cmr.earthdata.nasa.gov/search/concepts/C2231550449-CEOS_EXTRA.umm_json The data set for the Chewelah 30' X 60' quadrangle has been jointly prepared by the U.S. Geological Survey Mineral Resource Program, the Southern California Areal Mapping Project (SCAMP), and the Washington Division of Geology and Earth Resources, as part of an ongoing effort to utilize a Geographical Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Data Base of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the Chewelah 30' X 60' quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, it can be used for mineral resource evaluation studies, animal and plant habitat studies, and soil studies in the Colville and Kaniksu National Forests. The database is not suitable for site-specific geologic evaluations. This data set maps and describes the geology of the Chewelah 30' X 60' quadrangle, Washington and Idaho. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) a point coverage containing site-specific geologic structural data, (3) two coverages derived from 1:100,000 Digital Line Graphs (DLG); one of which represents topographic data, and the other, cultural data, (4) two line coverages that contain cross-section lines and unit-label leaders, respectively, and (5) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, and two cross sections, and on a separate sheet, a Correlation of Map Units (CMU) diagram, an abbreviated Description of Map Units (DMU), modal diagrams for granitic rocks, an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of the Readme text-file and expanded Description of Map Units (DMU), and (3) this metadata file. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. The map was compiled from geologic maps of eight 1:48,000 15' quadrangle blocks, each of which was made by mosaicing and reducing the four constituent 7.5' quadrangles. These 15' quadrangle blocks were mapped chiefly at 1:24,000 scale, but the detail of the mapping was governed by the intention that it was to be compiled at 1:48,000 scale. The compilation at 1:100,000 scale entailed necessary simplification in some areas and combining of some geologic units. Overall, however, despite a greater than two times reduction in scale, most geologic detail found on the 1:48,000 maps is retained on the 1:100,000 map. Geologic contacts across boundaries of the eight constituent quadrangles required minor adjustments, but none significant at the final 1:100,000 scale. The geologic map was compiled on a base-stable cronoflex copy of the Chewelah 30' X 60' topographic base and then scribed. The scribe guide was used to make a 0.007 mil-thick blackline clear-film, which was scanned at 1200 DPI by Optronics Specialty Company, Northridge, California. This image was converted to vector and polygon GIS layers and minimally attributed by Optronics Specialty Company. Minor hand-digitized additions were made at the USGS. Lines, points, and polygons were subsequently edited at the USGS by using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:100,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum. Data package contents: >chew_geo.e00 Contacts, faults, geologic unit labels >chew_pts.e00 Attitudes and their dip values. Dip values plotted > as annotation. >chew_xs.e00 lines of cross sections >chew_ldr.e00 unit label leaders >chew_hyps.e00 Topography >chew_trans.e00 Roads, cultural information >lines.rel.e00 Line dictionary >points.rel.e00 Point dictionary >scamp2.shd.e00 SCAMP shade set proprietary +USGS_MAP_MF-2356_1.0 Geologic map of the Jasper quadrangle, Newton and Boone Counties, Arkansas CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -93.25, 36, -93.125, 36.125 https://cmr.earthdata.nasa.gov/search/concepts/C2231550760-CEOS_EXTRA.umm_json To provide a digital geologic map database of the quadrangle that improves understanding of the regional geologic framework and its influence on the regional groundwater flow system. This digital geologic map compilation presents new polygon (i.e., geologic map unit contacts), line (i.e., fault, fold axis, and structure contour), and point (i.e., structural attitude, contact elevations) vector data for the Jasper 7 1/2' quadrangle in northern Arkansas. The map database, which is at 1:24,000-scale resolution, provides geologic coverage of an area of current hydrogeologic, tectonic, and stratigraphic interest. The Jasper quadrangle is located in northern Newton and southern Boone Counties about 20 km south of the town of Harrison. The map area is underlain by sedimentary rocks of Ordovician, Mississippian, and Pennsylvanian age that were mildly deformed by a series of normal and strike-slip faults and folds. The area is representative of the stratigraphic and structural setting of the southern Ozark Dome. The Jasper quadrangle map provides new geologic information for better understanding groundwater flow paths in and adjacent to the Buffalo River watershed. The current map database incorporates geologic data from: (1) early geologic mapping (1906) by Purdue and Miser and (2) more recent field mapping (1995-1998) by M. R. Hudson. Buffalo National River, under the auspices of the National Park Service, occupies the central part of the map area. >FILES INCLUDED WITH THIS DATA SET: >jsp24k: geology polygon coverage >jsppnt: strike/dip point locations and data >jspcontrol: field elevation control points >jspcontour: structure contours on the top of the Boone Formation >geoscamp1.lin: geologic line symbols >geoscamp1.mrk: geologic marker symbols >fnt037: font used with geoscamp1.mrk >wpgcmykg.shd: shadeset used to color polygons in jsp24k coverage >fnt027: font containing geologic age symbols proprietary +USGS_MAP_MF-2359_1.0 Geologic Map of the Clifton Quadrangle, Mesa County, Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -108.5, 39, -108.375, 39.125 https://cmr.earthdata.nasa.gov/search/concepts/C2231553308-CEOS_EXTRA.umm_json To update earlier small-scale geologic mapping, and to provide sufficient geologic information for land-use decisions. 1:24,000-scale geologic mapping in the Clifton 7.5' quadrangle, in support of the USGS Colorado River/I-70 Corridor Cooperative Geologic Mapping Project, provides interpretations of the Quaternary stratigraphy and geologic hazards in this area of the Grand Valley. The Clifton 1:24,000 quadrangle is in Mesa County in western Colorado. Because the map area is dominated by various surficial deposits, the map depicts 16 different Quaternary units. Five prominent river terraces are present in the quadrangle containing gravels deposited by the Colorado River. The map area contains a large landslide deposit on the southern slopes of Mount Garfield. The landslide developed in the Mancos Shale and contains large blocks of the overlying Mesaverde Group. In addition, the landslide is a source of debris flows that have closed I-70 in the past. The major bedrock unit in the quadrangle is the Mancos Shale of Upper Cretaceous age. The map is accompanied by text containing unit descriptions, and sections on geologic hazards (including landslides, piping, gullying, expansive soils, and flooding), and economic geology (including sand and gravel). A table indicates what map units are susceptible to a given hazard. Approximately 20 references are cited at the end of the report. Map political location: Mesa County, Colorado Compilation scale: 1:24,000 Geology mapped in 1996 to 1998. Compilation completed March 1999. DATASETS INCLUDED IN THIS GEOSPATIAL DATABASE: > clifpoly: geology polygons, contacts, and other linear features > clifline: line of cross-section A-A' > clifpnt: point features - bedding attitudes, drillholes proprietary +USGS_MAP_MF2329_1.0 Map showing inventory and regional susceptibility for Holocene debris flows and related fast moving landslides in the conterminous United States CEOS_EXTRA STAC Catalog 1928-01-01 1999-12-31 -127.6, 31.8, -69.5, 48.8 https://cmr.earthdata.nasa.gov/search/concepts/C2231554010-CEOS_EXTRA.umm_json These data are intended for geographic display and analysis at the national level, and for large regional areas. It is not intended for hazard evaluation or other site-specific work, and should not be used for such. It can be used to determine where debris flow processes may be a problem and where additional information and investigation are warranted. Although the digital form of the data removes the constraint imposed by the scale of a paper map, the detail and accuracy inherent in map scale are also present in the digital data. The fact that this database was edited at a scale of 1:2,500,000 means that higher resolution information is not present in the data. Plotting at scales larger than 1:2,500,000 will not yield greater real detail, and it may reveal fine-scale irregularities below the intended resolution of the database. Similarly, where this database is used in combination with other data of higher resolution, the resolution of the combined output will be limited by the lower resolution of these data. No responsibility is assumed by the U.S. Geological Survey in the use of these data. Debris flows, debris avalanches, mud flows and lahars are fast-moving landslides that occur in a wide variety of environments throughout the world. They are particularly dangerous to life and property because they move quickly, destroy objects in their paths, and can strike with little warning. The purpose of this map is to show where debris flows have occurred in the conterminous United States and where these slope movements might be expected in the future. proprietary +USGS_MASSBAY Massachusetts Bay Circulation and Effluent Modeling CEOS_EXTRA STAC Catalog 1989-10-01 1992-12-31 -71.104, 41.685, -69.82, 42.66 https://cmr.earthdata.nasa.gov/search/concepts/C2231549081-CEOS_EXTRA.umm_json Understanding the circulation of water in Massachusetts and Cape Cod Bays is of critical importance for determining how nutrients, sediment, contaminants and other water-borne materials are transported. Numerical circulation models represent a powerful tool to build understanding of transport processes in these bays, as well as for synthesis, scenario testing and prediction. The U.S. Geological Survey has developed a three-dimensional model of circulation in Massachusetts Bay driven by tides, wind, river runoff, surface heating and cooling and remote forcing from the Gulf of Maine. The circulation calculated from this model was used as input to the HydroQual water quality model. The USGS is currently using the model in a Regional Marine Research Program in the Gulf of Maine funded study of sources, transport and nutrient environment of red tide populations in the western gulf. Together with investigators from WHOI and UNH, this work seeks to characterize the physical transport mechanisms that influence the distribution and fate of toxic Alexandrium cells in this region, and the processes by which cells are transported to Massachusetts Bay. The ability of the regional model to represent the movement of fresh water from the Kennebec and Androscoggin rivers will be determined. Over the next three years, the USGS will be developing a regional sediment transport model by interfacing existing surface wave, bottom boundary layer and sediment erosion models into the current hydrodynamic model. Current and suspended sediment data from the long-term mooring (as well as other sites) will be used for calibration and verification. When the new outfall comes online, additional hydrodynamic model runs in Massachusetts Bay will be performed to test the ability of the model to simulate the effects of the relocated effluent discharge. proprietary +USGS_MF-2323_1.0 Extent of Pleistocene Lakes in the Western Great Basin CEOS_EXTRA STAC Catalog 1999-01-01 1999-12-31 -121.319, 36.934, -113.445, 42.973 https://cmr.earthdata.nasa.gov/search/concepts/C2231552513-CEOS_EXTRA.umm_json The purpose of this map is to show the differences between the extents of late Pleistocene pluvial lakes and older, larger lakes caused by much higher effective moisture during past glacial-pluvial episodes. During the Pliocene to middle Pleistocene, pluvial lakes in the western Great Basin repeatedly rose to levels much higher than those of the well-documented late Pleistocene pluvial lakes, and some presently isolated basins were connected. Sedimentologic, geomorphic, and chronologic evidence at sites shown on the map indicates that Lakes Lahontan and Columbus-Rennie were as much as 70 m higher in the early-middle Pleistocene than during their late Pleistocene high stands. Lake Lahontan at its 1400-m shoreline level would submerge present-day Reno, Carson City, and Battle Mountain, and would flood other now-dry basins. To the east, Lakes Jonathan (new name), Diamond, Newark, and Hubbs also reached high stands during the early-middle(?) Pleistocene that were 25-40 m above their late Pleistocene shorelines; at these very high levels, the lakes became temporarily or permanently tributary to the Humboldt River and hence to Lake Lahontan. Such a temporary connection could have permitted fish to migrate from the Humboldt River southward into the presently isolated Newark Valley and from Lake Lahontan into Fairview Valley. The timing of drainage integration also provides suggested maximum ages for fish to populate the basins of Lake Diamond and Lake Jonathan. Reconstructing and dating these lake levels also has important implications for paleoclimate, tectonics, and drainage evolution in the western Great Basin. For example, shorelines in several basins form a stair-step sequence downward with time from the highest levels, thought to have formed at about 650 ka, to the lowest, formed during the late Pleistocene. This descending sequence indicates progressive drying of pluvial periods, possibly caused by uplift of the Sierra Nevada and other western ranges relative to the western Great Basin. However, these effects cannot account for the extremely high lake levels during the early middle Pleistocene; rather, these high levels were probably due to a combination of increased effective moisture and changes in the size of the Lahontan drainage basin. proprietary USGS_MOJAVE_CLIM Climate History of the Mojave Desert Region, 1892 - 1996 CEOS_EXTRA STAC Catalog 1892-01-01 1996-12-31 -117.96, 34.12, -114.8, 37.23 https://cmr.earthdata.nasa.gov/search/concepts/C2231550943-CEOS_EXTRA.umm_json The Climate History of the Mojava Desert Region provides an overview of regional climate variations including precipitation and temperature information. To evaluate climate variation, weather data was compiled from 48 long-term weather stations across the Mojave Desert. The stations are in western Arizona, eastern California, southern Nevada, and southwest Utah. The primary data set consists of about 1.2 and 1.8 million daily observations of precipitation and temperature, respectively. These data were collected mainly at weather stations staffed by volunteers (NOAA, 1986). Some of the raw data were purchased in electronic form from EarthInfo, Inc. who obtained it from the National Climate Data Center (NCDC), Asheville, North Carolina. These data do not contain the entire record of a particular station, as the available electronic record typically begins in 1948. To evaluate climate variation, the longest possible record is necessary. Thus, the USGS obtained from the NCDC the complete National Weather Service reports on microfiche for the four-state region of the Mojave Desert. These data were entered into the computer manually, producing a record of precipitation beginning in 1892. [Summary provided by the USGS.] proprietary +USGS_Map-MF-2377_1.0 Generalized Geologic Map of Part of the Upper Animas River Watershed and Vicinity, Silverton, Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -107.875, 37.75, -107.5, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231552017-CEOS_EXTRA.umm_json This map data was compiled for the purpose of comparing multiple Animas River Watershed Abandoned Mine Lands Project datasets such as geophysical, biologic, remote sensing, and geochemical datasets in a geologic context. This dataset represents geology compiled for the upper Animas River Watershed near Silverton, Colorado. The source data used are derived from 1:24,000, 1:20,000, 1:48,000 and 1:250,000-scale geologic maps by geologists who have worked in this area since the early 1960's. This product consists of seven vector coverages. These separate coverages include the geology, faults, veins, andesite dikes, dacite dikes, rhyolite dikes, and San Juan Caldera topographic margin. proprietary +USGS_Map-MF-2387_1.0 Geologic Map and Digital Database of Hidden Hills and Vicinity, Mohave County, Northwestern Arizona CEOS_EXTRA STAC Catalog 1970-01-01 -113.759, 36.245, -113.492, 36.506 https://cmr.earthdata.nasa.gov/search/concepts/C2231552810-CEOS_EXTRA.umm_json The geologic map of Hidden Hills and vicinity covers part of the Arizona Strip north of Grand Canyon and several large tributary canyons that make up the western part of Arizona's Grand Canyon. The map is part of a cooperative U.S. Geological Survey, National Park Service, and Bureau of Land Management project to provide geologic information for areas within the newly established Grand Canyon-Parashant National Monument. This map fills in one of the remaining areas where uniform quality geologic mapping was needed. The geologic information will be useful for future resource management studies for federal, state, and private agencies. This digital map database is compiled from unpublished data and new mapping by the authors and represents the general distribution of surficial and bedrock geology in the mapped area. Together with the accompanying pamphlet, it provides current information on the geologic structure and stratigraphy of the area. The database delineates map units that are identified by age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution of the database to 1:31,680 or smaller. proprietary +USGS_Map-MF-2388_1.0 Generalized Surficial Geologic Map of the Pueblo 1 degree x 2 degrees Quadrangle, Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -106, 38, -104, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231552005-CEOS_EXTRA.umm_json The report may be used for land-use planning (e.g., selecting land-fill sites, greenbelts, avoiding geologic hazards), for finding aggregate resources (crushed rock, sand, and gravel), and for study of geomorphology and Quaternary geology. The report identifies geologic hazards (e.g., landslides, swelling soils, heaving bedrock, and flooding) if they are known to be located in, or characteristic of, mapped units. Surficial deposits in the quadrangle are evidence of depositional events of the Quaternary Period (the most recent 1.8 million years). Some events such as floods are familiar to persons living in the area, while others preceded human occupation. The latter include glaciation, probable large earthquakes, protracted drought, and widespread deposition of sand and silt by wind. At least twice in the past 200,000 years (most recently from about 30,000 to 12,000 years ago) global cooling caused glaciers to form on Pikes Peak and in the high parts of the Sangre de Cristo Mountains. Some glaciers advanced down valleys, deeply eroded the bedrock, and deposited moraines (map units tbk, tbg, tbj, tbi) and deposited outwash (ggq, gge), in the Wet Mountain Valley. On the plains (east part of map area), eolian sand (es), stabilized dune sand (ed), and loess (elb) are present and in places contain buried paleosols, which indicate sand dune deposition alternating with periods of stabilized landscape during which soils developed. Fifty-three types of surficial geologic deposits and residual materials of Quaternary age are described in a pamphlet and located on a map of the greater Pueblo area, in part of the Front Range, in the Wet and Sangre de Cristo Mountains, and on the plains east of Colorado Springs and Pueblo. Deposits formed by landslides, wind, and glaciers, as well as colluvium, residuum, alluvium, and others are described in terms of predominant grain size, mineral or rock composition (e.g., gypsiferous, calcareous, granitic, andesitic), thickness, and other physical characteristics. Origins and ages of the deposits and geologic hazards related to them are noted. Many lines drawn between units on our map were placed by generalizing contacts on published maps. However, in 1997-1999 we mapped new boundaries as well. The map was projected to the UTM projection. This large map area extends from near Salida (on the west edge), eastward about 107 mi (172 km), and from Antero Reservoir and Woodland Park on the north edge to near Colorado City at the south edge (68 mi; 109 km). Compilation scale: 1:250,000. Map is available in digital and print-on-demand paper formats. Deposits are described in terms of predominant grain size, mineralogic and lithologic composition, general thickness, and geologic hazards, if any, and relevant geologic historical information and paleosoil information, if any. Fifty-three map units of deposits include alluvium, colluvium, residuum, eolian deposits, periglacial/disintegrated deposits, tills, landslide units, glaciofluvial units, and a diamicton. A bedrock map unit depicts large areas of mostly bare bedrock. The physical properties of materials were compiled from published soil and geologic maps and reports, our field observations, and from earth science journal articles. Selected deposits in the field were checked for conformity to descriptions of map units by the Quaternary geologist who compiled the surficial geologic map units. >puebpoly: polygon coverage containing geologic unit contacts and labels. >puebline: arc coverage containing faults. >puebpnt: point coverage containing point locations of decorative > bar-and-ball symbols for faults. >geol_sfo.lin: This lineset file defines geologic line types in the > geologically themed coverages. >geoscamp2.mrk: This markerset file defines the geologic markers in the > geologically themed coverages. >color524.shd: This shadeset file defines the cmyk values of colors > assigned to polygons in the geologically themed coverages. proprietary +USGS_Map_MF-2326_1.0 Geologic map of the Palisade Quadrangle, Mesa County, Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -108.375, 39, -108.25, 39.125 https://cmr.earthdata.nasa.gov/search/concepts/C2231550131-CEOS_EXTRA.umm_json This map has been prepared to provide the first detailed view of the Palisade 1:24,000-scale quadrangle. Previous geologic mapping that encompassed the map area was at scales of 1:100,000 and 1: 250,000. The Palisade area is an important agricultural region of Colorado, fruit orchards were first established in the area in the late 19th century. In addition, the Palisade quadrangle is undergoing rapid growth, as is the rest of the Grand Valley. Because of this rapid growth, the recognition of geologic hazards is important. The map depicts many surficial units associated with geologic hazards. The map is accompanied by a separate leaflet containing a section on geologic hazards (including landslides, piping, gullying, expansive soils, and flooding). A table indicates what map units are susceptible to a given hazard. The map will be of interest to town and county officials, land- use planners, as well as the general public. The Palisade 1:24,000 quadrangle is in Mesa County in western Colorado. Because the map area is dominated by various surficial deposits, the map depicts 22 different Quaternary units. Two prominent river terraces are present in the quadrangle containing gravels deposited by the Colorado River. The map area contains many mass movement deposits. Extensive landslide deposits are present along the eastern part of the quadrangle. These massive landslides originate on the flanks of Grand Mesa, in the Green River and Wasatch Formations, and flow west onto the Palisade quadrangle. In addition, large areas of the eastern and southern parts of the map are covered by extensive pediment surfaces. These pediment surfaces are underlain by debris flow deposits also originating from Grand Mesa. Material in these deposits consists of mainly subangular basalt cobbles and boulders and indicate that these debris flow deposits have traveled as much as 10 km from their source area. The pediment surfaces have been divided into 5 age classes based on their height above surrounding drainages. Two common bedrock units in the map area are the Mancos Shale and the Mesaverde Group both of Upper Cretaceous age. The Mancos shale is common in low lying areas near the western map border. The Mesaverde Group forms prominent sandstone cliffs in the north-central map area. The map is accompanied by a separate pamphlet containing unit descriptions, a section on geologic hazards (including landslides, piping, gullying, expansive soils, and flooding), and a section on economic geology (including sand and gravel, and coal). A table indicates what map units are susceptible to a given hazard. Approximately twenty references are cited at the end of the report. Map political location: Mesa County, Colorado Compilation scale: 1:24,000 Geology mapped in 1996 and 1997 proprietary USGS_Map_MF-2330 Bituminous coal production in the Appalachian basin--Past, present, and future CEOS_EXTRA STAC Catalog 1830-01-01 1996-12-31 -97, 39, -86, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2231549532-CEOS_EXTRA.umm_json Maps Provide an overview of coal production from the Appalachian basin, by county. This report on Appalachian basin coal production consists of four maps and associated graphs and tables, with links to the basic data that were used to construct the maps. Plate 1 shows the time (year) of maximum coal production, by county. For illustration purposes, the years of maximum production are grouped into decadal units. Plate 2 shows the amount of coal produced (tons) during the year of maximum coal production for each county. Plate 3 illustrates the cumulative coal production (tons) for each county since about the beginning of the 20th century. Plate 4 shows 1996 annual production by county. During the current (third) cycle of coal production in the Appalachian basin, only seven major coal-producing counties (those with more than 500 million tons cumulative production), including Greene County, Pa.; Boone, Kanawha, Logan, Mingo, and Monongalia Counties, W. Va.; and Pike County, KY., exhibit a general increase in coal production. Other major coal-producing counties have either declined to a small percentage of their maximum production or are annually maintaining a moderate level of production. In general, the areas with current high coal production have large blocks of coal that are suitable for mining underground with highly efficient longwall methods, or are occupied by very large scale, relatively low cost surface mining operations. The estimated cumulative production for combined bituminous and anthracite coal is about 100 billion tons or less for the Appalachian basin. In general, it is anticipated that the remaining resources will be progressively of lower quality, will cost more to mine, and will become economical only as new technologies for extraction, beneficiation, and consumption are developed, and then only if prices for coal increase. proprietary +USGS_Map_MF-2331_1.0 Geologic Map of the Silt Quadrangle, Garfield County, Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -107.75, 39.5, -107.625, 39.625 https://cmr.earthdata.nasa.gov/search/concepts/C2231549606-CEOS_EXTRA.umm_json To update and reinterpret earlier geologic mapping, and to provide sufficient geologic information for land-use decisions. New 1:24,000-scale geologic mapping in the Silt 7.5' quadrangle, in support of the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping Project, provides new interpretations of the stratigraphy, structure, and geologic hazards in the area of the southwest flank of the White River uplift, the Grand Hogback, and the eastern Piceance Basin. The Wasatch Formation was subdivided into three formal members, the Shire, Molina, and Atwell Gulch Members. Also a sandstone unit within the Shire Member was broken out. The Mesaverde Group consists of the upper Williams Fork Formation and the lower Iles Formation. Members for the Iles Formation consist of the Rollins Sandstone, the Cozzette Sandstone, and the Corcoran Sandstone Members. The Cozzette and Corcoran Sandstone Members were mapped as a combined unit. Only the upper part of the Upper Member of the Mancos Shale is exposed in the quadrangle. From the southwestern corner of the map area toward the northwest, the unfaulted early Eocene to Paleocene Wasatch Formation and underlying Mesaverde Group gradually increase in dip to form the Grand Hogback monocline that reaches 45-75 degree dips to the southwest (section A-A'). The shallow west- northwest-trending Rifle syncline separates the northern part of the quadrangle from the southern part along the Colorado River. Geologic hazards in the map area include erosion, expansive soils, and flooding. Erosion includes mass wasting, gullying, and piping. Mass wasting involves any rock or surficial material that moves downslope under the influence of gravity, such as landslides, debris flows, or rock falls, and is generally more prevalent on steeper slopes. Locally, where the Grand Hogback is dipping greater than 60 degrees and the Wasatch Formation has been eroded, leaving sandstone slabs of the Mesa Verde Group unsupported over vertical distances as great as 500 m, the upper part of the unit has collapsed in landslides, probably by a process of beam-buckle failure. In the source area of these landslides strata are overturned and dip shallowly to the northeast. Landslide deposits now armor Pleistocene pediment surfaces and extend at least 1 km into Cactus Valley. Gullying and piping generally occur on more gentle slopes. Expansive soils and expansive bedrock are those unconsolidated materials or rocks that swell when wet and shrink when dry. Most floods are restricted to low-lying areas. Several gas-producing wells extract methane from coals from the upper part of the Iles Formation. Map political location: Garfield County, Colorado Compilation scale: 1:24,000 Geology mapped in 1992 to 1996. Compilation completed March 1997. proprietary +USGS_Map_MF-2337_1.0 Digital geologic map and map database of parts of Marin, San Francisco, Alameda, Contra Costa, and Sonoma Counties, California CEOS_EXTRA STAC Catalog 1999-01-01 1999-12-31 -123, 37.7, -122.2, 38.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231548662-CEOS_EXTRA.umm_json This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:62,500) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (mageo.txt, mageo.pdf, or mageo.ps), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:62,500 or smaller. The databases in this report were compiled in ARC/INFO, a commercial Geographic Information System (Environmental Systems Research Institute, Redlands, California), with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files are in either GRID (ARC/INFO raster data) format or COVERAGE (ARC/INFO vector data) format. Coverages are stored in uncompressed ARC export format (ARC/INFO version 7.x). ARC/INFO export files (files with the .e00 extension) can be converted into ARC/INFO coverages in ARC/INFO (see below) and can be read by some other Geographic Information Systems, such as MapInfo via ArcLink and ESRI's ArcView (version 1.0 for Windows 3.1 to 3.11 is available for free from ESRI's web site: http://www.esri.com ). The digital compilation was done in version 7.1.1 of ARC/INFO with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The geologic map information was digitized from stable originals of the geologic maps at 1:62,500 scale. The author manuscripts (pen on mylar) were scanned using a Altek monochrome scanner with a resolution of 800 dots per inch. The scanned images were vectorized and transformed from scanner coordinates to projection coordinates with digital tics placed by hand at quadrangle corners. The scanned lines were edited interactively by hand using ALACARTE, color boundaries were tagged as appropriate, and scanning artifacts visible at 1:24,000 were removed. Revisions: 8/31/99 This is the pre-release version of the report. There have been no revisions to any part of the report. Data Revision List > File Report Version Last Update > Last Updated > > mamap.ps 1.0 > maexpl.ps 1.0 > mageo.ps 1.0 > mamap.pdf 1.0 > maexpl.pdf 1.0 > mageo.pdf 1.0 > ma-geol.e00 1.0 > ma-strc.e00 1.0 > ma-blks.e00 1.0 > ma-altr.e00 1.0 > ma-quad.e00 1.0 > ma-corr.e00 1.0 > ma-so.e00 1.0 > ma-terr.e00 1.0 > mageo.txt 1.0 > mafig1.tif 1.0 > mafig2.tif 1.0 > madb.ps 1.0 > madb.pdf 1.0 > madb.txt 1.0 > import.aml 1.0 > mageol.met 1.0 Reviews_Applied_to_Data: This report has undergone two scientific peer reviews, one digital database review, one review for conformity with geologic names policy, and review of the plotfiles for conformity with USGS map standards. Related_Spatial_and_Tabular_Data_Sets: This report consists of a set of geologic map database files (Arc/Info coverages) and supporting text and plotfiles. In addition, the report includes two sets of plotfiles (PostScript and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described below: > ARC/INFO Resultant Description of Coverage > export file Coverage > ----------- ----------- -------------------------------- > ma-geol.e00 ma-geol/ Polygon and line coverage showing faults, > depositional contacts, and rock units > in the map area. > > ma-strc.e00 ma-strc/ Point and line coverage showing strike and dip > information and fold axes. > > ma-blks.e00 ma-blks/ Point coverage showing location of high-grade > blocks in Franciscan rock units. > > ma-altr.e00 ma-altr/ Polygon coverage showing areas of hydrothermal > alteration. > > ma-quad.e00 ma-quad/ Line coverage showing index map of quadrangles > in the map area. Lines and annotation only. > > ma-corr.e00 ma-corr/ Polygon and line coverage of the correlation > table for the units in this map database. > This database is not geospatial. > > ma-so.e00 ma-so/ Line coverage showing sources of data index > map for this map database. > > ma-terr.e00 ma-terr/ Polygon and line coverage of the index map of > tectonostratigraphic terranes in the map area. > (Terranes are described in mageo.txt, > mageo.ps, or mageo.pdf). ASCII text files, including explanatory text, ARC/INFO key files, PostScript and PDF plot files, and a ARC Macro Language file for conversion of ARC export files into ARC coverages: > mageo.ps A PostScript plot file of a report containing > detailed unit descriptions and geological > information, plus sources of data and references > cited, with two figures. > > mageo.pdf A PDF version of mageo.ps. > > mageo.txt A text-only file containing an unformatted > version of mageo.ps without figures. > > mafig1.tif A TIFF file of Figure 1 from mageo.ps > > mafig2.tif A TIFF file of Figure 2 from mageo.ps > > madb.ps A PostScript plot file of a pamphlet containing > detailed information about the contents and > availability of this report. > > madb.pdf A PDF version of madb.ps. > > madb.txt A text-only file containing an unformatted > version of madb.ps. > > import.aml ASCII text file in ARC Macro Language to convert > ARC export files to ARC coverages in ARC/INFO. > > mamap.ps A PostScript plottable file containing an image > of the geologic map and base maps at a scale of > 1:62,500, along with a simple map key. > > maexpl.ps A PostScript plot file containing an image of > the explanation sheet, including terrane map, > index maps, correlation chart, and unit > descriptions. > > mamap.pdf A PDF file containing an image of the geologic > map and base maps at a scale of 1:62,500, along > with a simple map key. > > maexpl.pdf A PDF file containing an image of the > explanation sheet, including terrane map, index > maps, correlation chart, and unit descriptions. Base maps Base Map layers used in the preparation of the geologic map plotfiles were derived from published digital maps (Aitken, 1997) obtained from the U.S. Geological Survey Geologic Division Website for the Western Region (http://wrgis.wr.usgs.gov). Please see the website for more detailed information about the original databases. Because the base map digital files are already available at the website mentioned above, they are not included in the digital database package. proprietary +USGS_Map_MF-2341_1.0 Geologic map of the Rifle Falls quadrangle, Garfield County, Colorado CEOS_EXTRA STAC Catalog 1992-01-01 1998-12-31 -107.75, 39.625, -107.625, 39.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231550390-CEOS_EXTRA.umm_json New 1:24,000-scale geologic map of the Rifle Falls 7.5' quadrangle, in support of the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping Project, provides new interpretations of the stratigraphy, structure, and geologic hazards in the area of the southwest flank of the White River uplift. Bedrock strata include the Upper Cretaceous Iles Formation through Ordovician and Cambrian units. The Iles Formation includes the Cozzette Sandstone and Corcoran Sandstone Members, which are undivided. The Mancos Shale is divided into three members, an upper member, the Niobrara Member, and a lower member. The Lower Cretaceous Dakota Sandstone, the Upper Jurassic Morrison Formation, and the Entrada Sandstone are present. Below the Upper Jurassic Entrada Sandstone, the easternmost limit of the Lower Jurassic and Upper Triassic Glen Canyon Sandstone is recognized. Both the Upper Triassic Chinle Formation and the Lower Triassic(?) and Permian State Bridge Formation are present. The Pennsylvanian and Permian Maroon Formation is divided into two members, the Schoolhouse Member and a lower member. All the exposures of the Middle Pennsylvanian Eagle Evaporite intruded into the Middle Pennsylvanian Eagle Valley Formation, which includes locally mappable limestone beds. The Middle and Lower Pennsylvanian Belden Formation and the Lower Mississippian Leadville Limestone are present. The Upper Devonian Chaffee Group is divided into the Dyer Dolomite, which is broken into the Coffee Pot Member and the Broken Rib Member, and the Parting Formation. Ordovician through Cambrian units are undivided. The southwest flank of the White River uplift is a late Laramide structure that is represented by the steeply southwest-dipping Grand Hogback, which is only present in the southwestern corner of the map area, and less steeply southwest-dipping older strata that flatten to nearly horizontal attitudes in the northern part of the map area. Between these two is a large-offset, mid-Tertiary(?) Rifle Falls normal fault, that dips southward placing Leadville Limestone adjacent to Eagle Valley and Maroon Formations. Diapiric Eagle Valley Evaporite intruded close to the fault on the down-thrown side and presumably was injected into older strata on the upthrown block creating a blister-like, steeply north-dipping sequence of Mississippian and older strata. Also, removal of evaporite by either flow or dissolution from under younger parts of the strata create structural benches, folds, and sink holes on either side of the normal fault. A prominent dipslope of the Morrison-Dakota-Mancos part of the section forms large slide blocks that form distinctly different styles of compressive deformation called the Elk Park fold and fault complex at different parts of the toe of the slide. The major geologic hazard in the area consist of large landslides both associated with dip-slope slide blocks and the steep slopes of the Eagle Valley Formation and Belden Formation in the northern part of the map. Significant uranium and vanadium deposits were mined prior to 1980. proprietary +USGS_Map_MF-2342_1.0 Geologic map and map database of the Oakland metropolitan area, Alameda, Contra Costa, and San Francisco Counties, California CEOS_EXTRA STAC Catalog 1970-01-01 -122.4, 37.6, -122, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231550741-CEOS_EXTRA.umm_json This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:24,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (oakmf.ps, oakmf.pdf, oakmf.txt), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller. The databases in this report were compiled in ARC/INFO, a commercial Geographic Information System (Environmental Systems Research Institute, Redlands, California), with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files are in either GRID (ARC/INFO raster data) format or COVERAGE (ARC/INFO vector data) format. Coverages are stored in uncompressed ARC export format (ARC/INFO version 7.x). ARC/INFO export files (files with the .e00 extension) can be converted into ARC/INFO coverages in ARC/INFO (see below) and can be read by some other Geographic Information Systems, such as MapInfo via ArcLink and ESRI's ArcView (version 1.0 for Windows 3.1 to 3.11 is available for free from ESRI's web site: http://www.esri.com ). The digital compilation was done in version 7.1.1 of ARC/INFO with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The geologic map information was digitized from stable originals of the geologic maps at 1:62,500 and 1:24,000 scale. The author manuscripts (pen on mylar) were scanned using a Altek monochrome scanner with a resolution of 800 dots per inch. The scanned images were vectorized and transformed from scanner coordinates to projection coordinates with digital tics placed by hand at quadrangle corners. The scanned lines were edited interactively by hand using ALACARTE, color boundaries were tagged as appropriate, and scanning artifacts visible at 1:24,000 were removed. proprietary +USGS_Map_MF-2343_1.0 Geologic Map and Digital Database of the Upper Parashant Canyon and Vicinity, Mohave County, Northwestern Arizona CEOS_EXTRA STAC Catalog 1970-01-01 -113.5, 36.2, -113.2, 36.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231550938-CEOS_EXTRA.umm_json The geologic map of the upper Parashant Canyon area covers part of the Colorado Plateau and several large tributary canyons that make up the western part of Arizona's Grand Canyon. The map is part of a cooperative U.S. Geological Survey and National Park Service project to provide geologic information for areas within the newly established Grand Canyon/Parashant Canyon National Monument. Most of the Grand Canyon and parts of the adjacent plateaus have been geologically mapped; this map fills in one of the remaining areas where uniform quality geologic mapping was needed. The geologic information presented may be useful in future related studies as to land use management, range management, and flood control programs for federal and state agencies, and private concerns. This digital map database is compiled from unpublished data and new mapping by the authors, represents the general distribution of surficial and bedrock geology in the mapped area. Together with the accompanying pamphlet, it provides current information on the geologic structure and stratigraphy of the area. The database dilineate map units that are identified by age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution of the database to 1:31,680 or smaller. This report consists of a set of geologic map database files (ARC/ INFO coverages) and supporting text and plot files. In addition, the report includes two sets of plot files (Post Script and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described in the explanatory pamphlets (para.eps, para.pdf, or para.txt). The base layer used in the preparation of the geologic map plot files was derived from four Digital Raster Graphic versions of standard USGS 7.5' quadrangles. These raster images where converted to Grid format in ARC/INFO, trimmed and seamed together, then converted to a GeoTIFF image. The resultant TIFF image was combined with geologic data to produce the final map image in Illustrator 8.0. proprietary +USGS_Map_MF-2347_1.0 Generalized Surficial Geologic Map of the Denver 1 degree x 2 degree Quadrangle, Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -106, 39, -104, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231551427-CEOS_EXTRA.umm_json The map and descriptions offer information that may be used for: land-use planning (e.g. selecting land fill sites, greenbelts, avoiding geologic hazards), for finding aggregate resources (crushed rock, sand, and gravel), for study of geomorphology and Quaternary geology. Geologic hazards (e.g., landslides, swelling soils, heaving bedrock, and flooding) known to be located in, or characteristic of some mapped units, were identified. Surficial deposits in the quadrangle partially record depositional events of the Quaternary Period (the most recent 1.8 million years). Some events such as floods are familiar to persons living in the area, while other recorded events are pre-historical. The latter include glaciation, probable large earthquakes, protracted drought, and widespread deposition of sand and silt by wind. At least twice in the past 200,000 years (most recently about 30,000 to 12,000 years ago) global cooling caused glaciers to form along the Continental Divide. The glaciers advanced down valleys in the Front Range, deeply eroded the bedrock, and deposited moraines (map units tbg, tbj) and outwash (ggq, gge). On the plains (east part of map), eolian sand (es), stabilized dune sand (ed), and loess (elb) are present and in places contain buried paleosols. These deposits indicate that periods of sand dune deposition alternated with periods of stabilized dunes and soil formation. Thirty-nine types of surficial geologic deposits and residual materials of Quaternary age are described and mapped in the greater Denver area, in part of the Front Range, and in the piedmont and plains east of Denver, Boulder, and Castle Rock. Descriptions appear in the pamphlet that accompanies the map. Landslide deposits, colluvium, residuum, alluvium, and other deposits or materials are described in terms of predominant grain size, mineral or rock composition (e.g., gypsiferous, calcareous, granitic, andesitic), thickness of deposits, and other physical characteristics. Origins and ages of the deposits and geologic hazards related to them are noted. Many lines between geologic units on our map were placed by generalizing contacts on published maps. However, in 1997-1999 we mapped new boundaries, as well. The map was projected to the UTM projection. This large map area extends from the Continental Divide near Winter Park and Fairplay ( on the west edge), eastward about 107 mi (172 km); and extends from Boulder on the north edge to Woodland Park at the south edge (68 mi; 109 km). Compilation scale: 1:250,000. Map is available in digital and print-on-demand paper formats. Deposits are described in terms of predominant grain size, mineralogic and lithologic composition, general thickness, and geologic hazards, if any, relevant geologic historical information and paleosoil information, if any. Thirty- nine map units of deposits include 5 alluvium types, 15 colluvia, 6 residua, 3 types of eolian deposits, 2 periglacial/disintegrated deposits, 3 tills, 2 landslide units, 2 glaciofluvial units, and 1 diamicton. An additional map unit depicts large areas of mostly bare bedrock. The physical properties of the surficial materials were compiled from published soil and geologic maps and reports, our field observations, and from earth science journal articles. Selected deposits in the field were checked for conformity to descriptions of map units by the Quaternary geologist who compiled the surficial geologic map units. FILES INCLUDED IN THIS DATA SET: >denvpoly: polygon coverage containing geologic unit contacts and labels. >denvline: arc coverage containing faults. >geol_sfo.lin: This lineset file defines geologic line types in the > geologically themed coverages. >geoscamp2.mrk: This markerset file defines the geologic markers in the > geologically themed coverages. >color524.shd: This shadeset file defines the cmyk values of colors > assigned to polygons in the geologically themed coverages. proprietary +USGS_Map_MF-2361_1.0 Geologic map of the Eagle quadrangle, Eagle County, Colorado CEOS_EXTRA STAC Catalog 1997-01-01 1997-12-31 -106.875, 39.625, -106.75, 39.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231552216-CEOS_EXTRA.umm_json This map was funded by the National Cooperative Geologic Program as part of the geologic mapping studies conducted along the I-70 urban corridor. This corridor is experiencing rapid urban growth and geologic mapping is needed to aid in land-use planning in order to address, avoid, and mitigate known and potential geologic hazards. The Eagle quadrangle covers an area that straddles the Eagle River and Interstate 70 (I-70) and it includes the town of Eagle, Colo., which is located in the southwestern part of the quadrangle, just south of I-70 and the Eagle River, about 37 km west of Vail, Colo. The map area is part of the I-70 urban corridor, which is experiencing rapid and escalating urban growth. Geologic mapping along this corridor is needed for ongoing land-use planning. A variety of rocks and deposits characterize the map area and areas nearby. Sedimentary rocks present in the map area range in age from Pennsylvanian rocks, which were deposited in the ancestral Eagle basin during the formation of the ancestral Rocky Mountains, to Late Cretaceous rocks that were deposited just prior to the formation of the present Rocky Mountains. The Pennsylvanian rocks in the map area include a thick sequence of evaporitic rocks (Eagle Valley Evaporite). These evaporitic rocks are commonly complexly folded throughout the southern part of the quadrangle where they are exposed. In general, in the central and northern parts of the quadrangle, the sedimentary rocks overlying the evaporite dip gently to moderately northward. Consequently, the youngest sedimentary rocks (Late Cretaceous rocks) are exposed dipping gently to the north in the northern part of the quadrangle; landslide complexes are widespread along the northerly dipping, dip slopes in shaly rocks of the Cretaceous sequence in the northeastern part of the map area. During the Early Miocene, basaltic volcanism formed extensive basaltic flows that mantled the previously deformed and eroded sedimentary rocks. Erosional remnants of the basaltic flows are preserved in the southeastern, west-central, and north-central parts of the map area. Some of these basaltic flows are faulted and downdropped in a manner that suggests they were downdropped in areas where large volumes of the underlying evaporitic rocks were removed from the subsurface, beneath the basaltic rocks, by dissolution or flowage of the evaporite in the subsurface. Quaternary and late Tertiary(?) surficial deposits in the map area consist mainly of Quaternary alluvium and colluvium, late and middle Pleistocene terrace gravels of the Eagle River, Miocene(?) gravel remnants of the ancestral Eagle River and its tributaries, and Pleistocene to recent mass movement deposits that include landslides and debris flows. Potential geologic hazards in the map area include landslides, debris flows, rockfalls, local flooding, ground subsidence, and expansive and corrosive soils. Map political location: Eagle County, Colorado Compilation scale: 1:24,000 Geology mapped in 1997. GEOSPATIAL DATA FILES INCLUDED IN THIS DATA SET: eaglepy: polygon coverage containing geologic unit contacts and labels. eagleln: arc coverage containing fold axes and other line entities. eaglept: point coverage containing bedding attitude measurements and other point entities. eaglepit: polygon coverage containing gravel pits. proprietary +USGS_Map_MF-2363_1.0 Geologic map of the Grand Junction quadrangle, Mesa County, Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -108.625, 39, -108.5, 39.125 https://cmr.earthdata.nasa.gov/search/concepts/C2231552505-CEOS_EXTRA.umm_json "To update and reinterpret earlier geologic mapping, and provide sufficient geologic information for land-use decisions for private land and for areas managed by the Bureau of Land Management and the National Park Service. Use of these data at scales greater than 1:24,000 would be inappropriate because mapping was performed at that scale. This 1:24,000-scale geologic map of the Grand Junction 7.5' quadrangle, in support of the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping Project, provides new interpretations of the stratigraphy, structure, and geologic hazards in the area of the junction of the Colorado River and the Gunnison River. Bedrock strata include the Upper Cretaceous Mancos Shale through the Lower Jurassic Wingate Sandstone units. Below the Mancos Shale, which floors the Grand Valley, the Upper and Lower(?)Cretaceous Dakota Formation and the Lower Cretaceous Burro Canyon Formation hold up much of the resistant northeast- dipping monocline along the northeast side of the Uncompahgre uplift. The impressive sequence of Jurassic strata below include the Brushy Basin, Salt Wash, and Tidwell Members of the Upper Jurassic Morrison Formation, the Middle Jurassic Wanakah Formation and informal ""board beds"" unit and Slick Rock Member of the Entrada Formation, and the Lower Jurassic Kayenta Formation and Wingate Sandstone. The Upper Triassic Chinle Formation and Early Proterozoic meta-igneous gneiss and migmatitic meta- sedimentary rocks, which are exposed in the Colorado National Monument quadrangle to the west, do not crop out here. The monoclinal dip slope of the northeastern margin of the Uncompahgre uplift is apparently a Laramide structural feature. Unlike the southwest-dipping, high-angle reverse faults in the Proterozoic basement and s-shaped fault- propagation folds in the overlying strata found in the Colorado National Monument 7.5' quadrangle along the front of the uplift to the west, the monocline in the map area is unbroken except at two localities. One locality displays a small asymmetrical graben that drops strata to the southwest. This faulted character of the structure dies out to the northwest into an asymmetric fault-propagation fold that also drops strata to the southwest. Probably both parts of this structure are underlain by a northeast-dipping high-angle reverse fault. The other locality displays a second similar asymmetric fold. No evidence of post-Laramide tilting or uplift exists here, but the antecedent Unaweep Canyon, only 30 km to the south-southwest of the map area, provides clear evidence of Late Cenozoic, if not Pleistocene, uplift. The major geologic hazards in the area include large landslides associated with the dip-slope-underlain, smectite-rich Brushy Basin Member of the Morrison Formation and overlying Dakota and Burro Canyon Formations. Active landslides affect the southern bank of the Colorado River where undercutting by the river and smectitic clays in the Mancos trigger landslides. The Wanakah, Morrison, and Dakota Formations and the Mancos Shale create a significant hazard to houses and other structures by containing expansive smectitic clay. In addition to seasonal spring floods associated with the Colorado and Gunnison Rivers, a serious flash flood hazard associated with sudden summer thunderstorms threatens the intermittent washes that drain the dip slope of the monocline. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology. Geospatial data files of this data set: > gj24k: geology polygons, contacts, faults > gjpnt: point data representing bedding attitudes > gjline: line representing location of the cross section, > and fold axes Symbolsets used for plotting in ArcInfo: > wpgcmykg.shd: shadeset > geol_sfo.lin: lineset > geoscamp1.mrk: markerset" proprietary +USGS_Map_MF-2364_1.0 Geologic Map and Digital Database of the House Rock Quadrangle, Coconino County, Northern Arizona CEOS_EXTRA STAC Catalog 1970-01-01 -112.127, 36.624, -111.998, 36.751 https://cmr.earthdata.nasa.gov/search/concepts/C2231553373-CEOS_EXTRA.umm_json This geologic map is part of a cooperative project between the U.S. Geological Survey and the Kaibab National Forest Service to provide geologic information for the Paradine Plains Cactus (Pediocactus pardinei Benson, 1957) Conservation Assessment and Strategy conducted by the Kaibab National Forest, Williams, Arizona. The map area includes part of House Rock Valley and part of the Kaibab Plateau, sub-physiographic provinces of the Colorado Plateau. This part of the Colorado Plateau was not previously mapped in adequate geologic detail. This map completes one of several remaining areas where uniform quality geologic mapping was needed. The geologic information in this report may be useful to future biological studies, land management, range management, and flood control programs for all federal, state, and private agencies. The map area is in the North Kaibab Ranger District of the Kaibab National Forest and the Arizona Strip Field Office of the Bureau of Land Management (BLM). The nearest settlement is Jacob Lake about 8 km (5 mi) west of the map area (fig. 1). Elevations range from about 2,305 m (7,560 ft) on the Kaibab Plateau in the northwest corner of the map area to about 1,555 m (5,100 ft) in House Rock Valley in the east-central edge of the map area. Primary vehicle access is by U.S. Highway 89A in the northern part of the map area. Four-wheel-drive roads access most of the map area. Dirt roads are not passable in winter snow conditions. The Bureau of Land Management Arizona Strip Field Office in St. George, Utah, manages the public lands, and the North Kaibab Ranger District in Fredonia, Arizona manages the U.S. National Forest system land. Other lands include one quarter of a section belonging to the State of Arizona, about 0.7 of a section of private land, and about 1.5 sections within the BLM-administered Paria Canyon-Vermilion Cliffs Wilderness Area (U.S. Department of the Interior, 1993). The private land is in House Rock Valley near State Highway 89A. Lower elevations within upper House Rock Valley support a sparse growth of cactus, grass, and a variety of desert shrubs. Sagebrush, grass, cactus, cliffrose bush, pinion pine trees, juniper trees, ponderosa pine, and oak trees thrive at elevations above 1,830 m (6,000 ft). Surface runoff in the map area drains eastward toward the Colorado River through House Rock Valley and into Marble Canyon of the Colorado River at Mile 17 (17 miles downstream from Lees Ferry, Arizona). proprietary +USGS_Map_MF-2366_1.0 Geologic Map and Digital Database of the Cane Quadrangle, Coconino County, Northern Arizona CEOS_EXTRA STAC Catalog 1970-01-01 -112.127, 36.499, -111.998, 36.626 https://cmr.earthdata.nasa.gov/search/concepts/C2231552953-CEOS_EXTRA.umm_json This geologic map is part of a cooperative project between the U.S. Geological Survey and the Kaibab National Forest Service to provide geologic information for the Paradine Plains Cactus (Pediocactus pardinei B,W. Benson) Conservation Assessment and Strategy conducted by the Kaibab National Forest, Williams, Arizona. The map area includes part of House Rock Valley and part of the Kaibab Plateau, sub- physiographic provinces of the Colorado Plateau. This part of the Colorado Plateau was not previously mapped in adequate geologic detail. This map completes one of several remaining areas where uniform quality geologic mapping was needed. The geologic information in this report may be useful to land management, range management, and flood control programs for all federal and state agencies, and private affairs. This digital map database is compiled from unpublished data and new mapping by the authors, represents the general distribution of surficial and bedrock geology in the mapped area. Together with the accompanying pamphlet, it provides current information on the geologic structure and stratigraphy of the area. The database delineate map units thatare identified by age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution of the database to 1:24,000 or smaller. This report consists of a set of geologic map database files (ARC/ INFO coverages) and supporting text and plot files. In addition, the report includes two sets of plot files (Post Script and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described in the explanatory pamphlets (canegeo.doc, canegeo. pdf, or canegeo.txt). The base layer used in the preparation of the geologic map plot files was derived from a Digital Raster Graphic of a standard USGS 7.5' quadrangle. This raster image was converted to Grid format in ARC/INFO, trimmed and rotated, then converted to a GeoTIFF image. The resultant TIFF image was combined with geologic data to produce the final map layout in Illustrator 8.0. proprietary +USGS_Map_MF-2367_1.0 Geologic Map and Digital Database of the House Rock Spring Quadrangle, Coconino County, Northern Arizona CEOS_EXTRA STAC Catalog 1970-01-01 -112.127, 36.749, -111.998, 36.876 https://cmr.earthdata.nasa.gov/search/concepts/C2231553061-CEOS_EXTRA.umm_json The digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the House Rock Spring area. Together with the accompanying text, it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age, lithology, and geomorphology following the spatial resolution (scale) of the database to 1:24,000. The content and character of the database, as well as three methods of obtaining the database, are described below. This digital map database is compiled from unpublished data and new mapping by the authors, represents the general distribution of surficial and bedrock geology in the mapped area. Together with the accompanying pamphlet, it provides current information on the geologic structure and stratigraphy of the area. The database delineate map units that are identified by age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution of the database to 1:24,000 or smaller. This report consists of a set of geologic map database files (ARC/ INFO coverages) and supporting text and plot files. In addition, the report includes two sets of plot files(Post Script and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described in the explanatory pamphlets (hrsgeo.doc, hrsgeo. pdf, or hrsgeo.txt). The base layer used in the preparation of the geologic map plot files was derived from a Digital Raster Graphic version of a standard USGS 7.5' quadrangle. This raster image was converted to Grid format in ARC/INFO, trimmed and converted to a GeoTIFF image. The resultant TIFF image was combined with geologic data to produce the final map image in Illustrator 8.0. proprietary +USGS_Map_MF-2368_1.0 Geologic Map and Digital Database of Part of the Uinkaret Volcanic Field, Mohave County, Northwestern Arizona CEOS_EXTRA STAC Catalog 1970-01-01 -113.257, 36.246, -112.994, 36.504 https://cmr.earthdata.nasa.gov/search/concepts/C2231550460-CEOS_EXTRA.umm_json The geologic map of the Uinkaret volcanic field area covers part of the Colorado Plateau and several large tributary canyons that make up the western part of Arizona's Grand Canyon. The map is part of a cooperative U.S. Geological Survey and National Park Service project to provide geologic information for areas within the newly established Grand Canyon/Parashant Canyon National Monument. Most of the Grand Canyon and parts of the adjacent plateaus have been geologically mapped; this map fills in one of the remaining areas where uniform quality geologic mapping was needed. The geologic information presented may be useful in future related studies as to land use management, range management, and flood control programs for federal and state agencies, and private concerns. This digital map database is compiled from unpublished data and new mapping by the authors, represents the general distribution of surficial and bedrock geology in the mapped area. Together with the accompanying pamphlet, it provides current information on the geologic structure and stratigraphy of the area. The database dilineate map units that are identified by age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution of the database to 1:31,680 or smaller. This report consists of a set of geologic map database files (ARC/ INFO coverages) and supporting text and plot files. In addition, the report includes two sets of plot files (Post Script and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described in the explanatory pamphlets (uink.eps, uink.pdf, or uink.txt). The base layer used in the preparation of the geologic map plot files was derived from four Digital Raster Graphic versions of standard USGS 7.5' quadrangles. These raster images where converted to Grid format in ARC/INFO, trimmed and seamed together, then converted to a GeoTIFF image. The resultant TIFF image was combined with geologic data to produce the final map image in Illustrator 9.0. proprietary +USGS_Map_MF-2369_1.0 Geologic Map of the Vail West quadrangle, Eagle County, Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -106.5, 39.625, -106.375, 39.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231550380-CEOS_EXTRA.umm_json This map was funded by and is a product of the National Cooperative Geologic Mapping Program. This corridor is experiencing rapid urban growth. Geologic mapping is needed to aid in land development planning in order to address, avoid, or mitigate known and potential geologic hazards. This new 1:24,000-scale geologic map of the Vail West 7.5' quadrangle, as part of the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping Project, provides new interpretations of the stratigraphy, structure, and geologic hazards in the area on the southwest flank of the Gore Range. Bedrock strata include Miocene tuffaceous sedimentary rocks, Mesozoic and upper Paleozoic sedimentary rocks, and undivided Early(?) Proterozoic metasedimentary and igneous rocks. Tuffaceous rocks are found in fault-tilted blocks. Only small outliers of the Dakota Sandstone, Morrison Formation, Entrada Sandstone, and Chinle Formation exist above the redbeds of the Permian-Pennsylvanian Maroon Formation and Pennsylvanian Minturn Formation, which were derived during erosion of the Ancestral Front Range east of the Gore fault zone. In the southwestern area of the map, the proximal Minturn facies change to distal Eagle Valley Formation and the Eagle Valley Evaporite basin facies. The Jacque Mountain Limestone Member, previously defined as the top of the Minturn Formation, cannot be traced to the facies change to the southwest. Abundant surficial deposits include Pinedale and Bull Lake Tills, periglacial deposits, earth-flow deposits, common diamicton deposits, common Quaternary landslide deposits, and an extensive, possibly late Pliocene landslide deposit. Landscaping has so extensively modified the land surface in the town of Vail that a modified land-surface unit was created to represent the surface unit. Laramide movement renewed activity along the Gore fault zone, producing a series of northwest-trending open anticlines and synclines in Paleozoic and Mesozoic strata, parallel to the trend of the fault zone. Tertiary down-to-the-northeast normal faults are evident and are parallel to similar faults in both the Gore Range and the Blue River valley to the northeast; presumably these are related to extensional deformation that occurred during formation of the northern end of the Rio Grande rift system in Colorado. In the southwestern part of the map area, a diapiric(?) exposure of the Eagle Valley Evaporite exists and chaotic faults and folds suggest extensive dissolution and collapse of overlying bedrock, indicating the presence of a geologic hazard. Quaternary landslides are common and indicate that landslide hazards are widespread in the area, particularly where old slide deposits are disturbed by construction. The late Pliocene(?) landslide that consists largely of a smectitic upper Morrison Formation matrix and boulders of Dakota Sandstone is readily reactivated. Debris flows are likely to invade low-standing areas within the towns of Vail and West Vail where tributaries of Gore Creek issue from the mountains on the north side of the valley. DATASETS INCLUDED IN THIS GEOSPATIAL DATABASE: > vwpoly: geologic polygons, contacts, faults, marker beds, and intra-unit scarps > vwline: fold axes, concealed linear features, limits of abundant chert fragments in the Maroon Formation, and cross-section lines > vwpoint: bedding and foliation attitudes, and miscellaneous point data proprietary +USGS_Map_MF-2371 Geologic map of the Silver Lake quadrangle, Cowlitz County, Washington CEOS_EXTRA STAC Catalog 1970-01-01 -122.875, 46.25, -122.749, 46.375 https://cmr.earthdata.nasa.gov/search/concepts/C2231553299-CEOS_EXTRA.umm_json This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:24,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the Oregon State Geologist. Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits of the Silver lake 7.5 minute quadrangle. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller. proprietary +USGS_Map_MF-2372_1.0 Hydrostructural Maps of the Death Valley Regional Flow System, Nevada and California CEOS_EXTRA STAC Catalog 1970-01-01 -118, 35, -115, 38.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231552955-CEOS_EXTRA.umm_json These maps (maps A and B) were prepared in support of a regional three-dimensional ground-water model currently being constructed by the U.S. Geological Survey (USGS) for the DVRFS. The maps identify regional geologic structures whose possible hydrologic significance merits their inclusion in the HFM for the DVRFS. The locations of principal faults and structural zones that may influence ground-water flow were compiled in support of a three-dimensional ground-water model for the Death Valley regional flow system (DVRFS), which covers 80,000 square km in southwestern Nevada and southeastern California. Faults include Neogene extensional and strike-slip faults and pre-Tertiary thrust faults. Emphasis was given to characteristics of faults and deformed zones that may have a high potential for influencing hydraulic conductivity. These include: (1) faulting that results in the juxtaposition of stratigraphic units with contrasting hydrologic properties, which may cause ground-water discharge and other perturbations in the flow system; (2) special physical characteristics of the fault zones, such as brecciation and fracturing, that may cause specific parts of the zone to act either as conduits or as barriers to fluid flow; (3) the presence of a variety of lithologies whose physical and deformational characteristics may serve to impede or enhance flow in fault zones; (4) orientation of a fault with respect to the present-day stress field, possibly influencing hydraulic conductivity along the fault zone; and (5) faults that have been active in late Pleistocene or Holocene time and areas of contemporary seismicity, which may be associated with enhanced permeabilities. The faults shown on maps A (Structural Framework, Neogene Basins, and Potentiometric Surface) and B (Structural Framework, Earthquake Epicenters, and Potential Zones of Enhanced Hydraulic Conductivity) are largely from Workman and others (in press), and fit one or more of the following criteria: (1) faults that are more than 10 km in map length; (2) faults with more than 500 m of displacement; and (3) faults in sets that define a significant structural fabric that characterizes a particular domain of the DVRFS. The following fault types are shown: Neogene normal, Neogene strike-slip, Neogene low-angle normal, pre-Tertiary thrust, and structural boundaries of Miocene calderas. We have highlighted faults that have late Pleistocene to Holocene displacement (Piety, 1996). Areas of thick Neogene basin-fill deposits (thicknesses 1-2 km, 2-3 km, and >3 km) are shown on map A, based on gravity anomalies and depth-to-basement modeling by Blakely and others (1999). We have interpreted the positions of faults in the subsurface, generally following the interpretations of Blakely and others (1999). Where geophysical constraints are not present, the faults beneath late Tertiary and Quaternary cover have been extended based on geologic reasoning. Nearly all of these concealed faults are shown with continuous solid lines on maps A and B, in order to provide continuous structures for incorporation into the hydrogeologic framework model (HFM). Map A also shows the potentiometric surface, regional springs (25-35 degrees Celsius, D'Agnese and others, 1997), and cold springs (Turner and others, 1996). A composite base map is included based upon published 83-m DEM data from USGS 1:250,000-scale quadrangles, as well as road lines and political boundaries from published USGS 1:100,000-scale DLG data. The 1:100,000-scale data were generalized to 1:250,000 scale for inclusion with the 1:250,000-scale database. Additional coverages include a ground-water model area coverage, and text labels for structural features. Files necessary for printing the map are also included such as text fonts, linesets, shadesets, projection files, and AML files. These files are all explained in the included README.txt file. proprietary +USGS_Map_MF-2373_1.0 Geologic maps and structure sections of the southwestern Santa Clara Valley and southern Santa Cruz Mountains, Santa Clara and Santa Cruz Counties, California CEOS_EXTRA STAC Catalog 1988-01-01 1997-12-31 -122, 36.998, -121.548, 37.252 https://cmr.earthdata.nasa.gov/search/concepts/C2231553047-CEOS_EXTRA.umm_json This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:24,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be substituted for comprehensive maps that systematically identify and classify landslide hazards. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (scvmf.ps, scvmf.pdf, scvmf.txt), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller. proprietary +USGS_Map_MF-2381-A_1.0 Geologic Map of the Death Valley Ground-water Model Area, Nevada and California CEOS_EXTRA STAC Catalog 1970-01-01 -118, 35, -115, 38.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231554614-CEOS_EXTRA.umm_json This digital geologic and tectonic database of the Death Valley ground-water model area, as well as its accompanying geophysical maps, are compiled at 1:250,000 scale. The map compilation presents new polygon, line, and point vector data for the Death Valley region. The map area is enclosed within a 3 degree X 3 degree area along the border of southern Nevada and southeastern California. In addition to the Death Valley National Park and Death Valley-Furnace Creek fault systems, the map area includes the Nevada Test Site, the southwest Nevada volcanic field, the southern end of the Walker Lane (from southern Esmeralda County, Nevada, to the Las Vegas Valley shear zone and Stateline fault system in Clark County, Nevada), the eastern California shear zone (in the Cottonwood and Panamint Mountains), the eastern end of the Garlock fault zone (Avawatz Mountains), and the southern basin and range (central Nye and western Lincoln Counties, Nevada). This geologic map improves on previous geologic mapping in the area by providing new and updated Quaternary and bedrock geology, new interpretation of mapped faults and regional structures, new geophysical interpretations of faults beneath the basins, and improved GIS coverages. The basic geologic database has tectonic interpretations imbedded within it through attributing of structure lines and unit polygons which emphasize significant and through-going structures and units. An emphasis has been put on features which have important impacts on ground-water flow. Concurrent publications to this one include a new isostatic gravity map (Ponce and others, 2001), a new aeromagnetic map (Ponce and Blakely, 2001), and contour map of depth to basement based on inversion of gravity data (Blakely and Ponce, 2001). This map compilation was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the Department of Energy in conjunction with the U. S. Geological Survey and National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants off of the Nevada Test Site and Yucca Mountain high-level waste repository. The geologic compilation and tectonic interpretations contained within this database will serve as the basic framework for the flow model. The database also represents a synthesis of many sources of data compiled over many years in this geologically and tectonically significant area. proprietary +USGS_Map_MF-2381-C_1.0 Isostatic Gravity Map of the Death Valley Ground-water Model Area, Nevada and California CEOS_EXTRA STAC Catalog 1970-01-01 -118, 35, -115, 38.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231555211-CEOS_EXTRA.umm_json An isostatic gravity map of the Death Valley groundwater model area was prepared from over 40,0000 gravity stations as part of an interagency effort by the U.S. Geological Survey and the U.S. Department of Energy to help characterize the geology and hydrology of southwest Nevada and parts of California. This dataset was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the U.S. Department of Energy in conjunction with the U. S. Geological Survey and U.S. National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants out of the Nevada Test Site and Yucca Mountain high-level waste repository. proprietary USGS_Map_MF-2381-D_1.0 Aeromagnetic Map of the Death Valley Ground-water Model Area, Nevada and California CEOS_EXTRA STAC Catalog 1970-01-01 -118, 35, -115, 38.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231554157-CEOS_EXTRA.umm_json An aeromagnetic map of the Death Valley groundwater model area was prepared from published aeromagnetic surveys as part of an interagency effort by the U.S. Geological Survey and the U.S. Department of Energy to help characterize the geology and hydrology of southwest Nevada and parts of California. This dataset was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the U.S. Department of Energy in conjunction with the U. S. Geological Survey and U.S. National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants off of the Nevada Test Site and Yucca Mountain high-level waste repository. proprietary +USGS_Map_MF-2381-E_1.0 Map Showing Depth to Pre-Cenozoic Basement in the Death Valley Ground-water Model Area, Nevada and California CEOS_EXTRA STAC Catalog 1970-01-01 -118, 35, -115, 38.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231554277-CEOS_EXTRA.umm_json A depth to basement map of the Death Valley groundwater model area was prepared using over 40,0000 gravity stations as part of an interagency effort by the U.S. Geological Survey and the U.S. Department of Energy to help characterize the geology and hydrology of southwest Nevada and parts of California. This dataset was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the U.S. Department of Energy in conjunction with the U. S. Geological Survey and U.S. National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants off of the Nevada Test Site and Yucca Mountain high-level waste repository. proprietary +USGS_Map_MF-2385_1.0 Map and map database of susceptibility to slope failure by sliding and earthflow in the Oakland area, California CEOS_EXTRA STAC Catalog 1970-01-01 -122.375, 37.625, -122, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231551540-CEOS_EXTRA.umm_json Mitigation is superior to post-disaster response in reducing the billions of dollars in losses resulting from U.S. natural disasters, and information that predicts the varying likelihood of geologic hazards can help public agencies improves the necessary decision making on land use and zoning. Accordingly, this map was created to increase the resistance of one urban area, metropolitan Oakland, California, to land sliding. Prepared in a geographic information system from a statistical model, the map estimates the relative likelihood of local slopes to fail by two processes common to this area of diverse geology, terrain, and land use. Map data that predict the varying likelihood of land sliding can help public agencies make informed decisions on land use and zoning. This map, prepared in a geographic information system from a statistical model, estimates the relative likelihood of local slopes to fail by two processes common to an area of diverse geology, terrain, and land use centered on metropolitan Oakland. The model combines the following spatial data: (1) 120 bedrock and surficial geologic-map units, (2) ground slope calculated from a 30-m digital elevation model, (3) an inventory of 6,714 old landslide deposits (not distinguished by age or type of movement and excluding debris flows), and (4) the locations of 1,192 post-1970 landslides that damaged the built environment. The resulting index of likelihood, or susceptibility, plotted as a 1:50,000-scale map, is computed as a continuous variable over a large area (872 km2) at a comparatively fine (30 m) resolution. This new model complements landslide inventories by estimating susceptibility between existing landslide deposits, and improves upon prior susceptibility maps by quantifying the degree of susceptibility within those deposits. Susceptibility is defined for each geologic-map unit as the spatial frequency (areal percentage) of terrain occupied by old landslide deposits, adjusted locally by steepness of the topography. Susceptibility of terrain between the old landslide deposits is read directly from a slope histogram for each geologic-map unit, as the percentage (0.00 to 0.90) of 30-m cells in each one-degree slope interval that coincides with the deposits. Susceptibility within landslide deposits (0.00 to 1.33) is this same percentage raised by a multiplier (1.33) derived from the comparative frequency of recent failures within and outside the old deposits. Positive results from two evaluations of the model encourage its extension to the 10-county San Francisco Bay region and elsewhere. A similar map could be prepared for any area where the three basic constituents, a geologic map, a landslide inventory, and a slope map, are available in digital form. Added predictive power of the new susceptibility model may reside in attributes that remain to be explored-among them seismic shaking, distance to nearest road, and terrain elevation, aspect, relief, and curvature. proprietary USGS_NAWQA_HG_DEP Atmospheric Deposition of Mercury in the Boston Area CEOS_EXTRA STAC Catalog 1970-01-01 -78, 40, -70, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231550487-CEOS_EXTRA.umm_json Atmospheric deposition has been found to be the dominant source of mercury (Hg) in New England's aquatic environment (Krabbenhoft and others, 1999; Northeast States for Coordinated Air Use Management (NESCAUM) and others, 1998). Little is known about atmospheric mercury deposition in urban areas because most atmospheric monitoring to date has been done in rural areas. Preliminary water, sediment, and fish tissue data, collected by U.S. Geological Survey's New England Coastal Basins (NECB) study as part of the National Water Quality Assessment (NAWQA) program, shows elevated concentrations of mercury in the Boston metropolitan area. The NECB Mercury Deposition Network is a four-site, 2-year data collection effort by the USGS to help define the levels of mercury in precipitation and identify how atmospheric mercury may be contributing to mercury in the aquatic ecosystem. [Summary provided by the USGS.] proprietary USGS_NEIC_NEARRT Current and Near Real Time Earthquake Data from the USGS/National Earthquake Information Center (NEIC) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551913-CEOS_EXTRA.umm_json The National Earthquake Information Center (NEIC of the U.S. Geological Survey provides current earthquake information and data including interactive earthquake maps, near real time earthquake data, fast moment and broadband solutions, and lists of earthquakes for the past 3 weeks. Current earthquake information and data are located at: http://earthquake.usgs.gov/ Near real time earthquake data is located at: http://earthquake.usgs.gov/ Archives of past earthquakes can be found at: http://earthquake.usgs.gov/earthquakes/eqinthenews/ proprietary +USGS_NHD_CATCH National Hydrography Dataset Catchment Delineations CEOS_EXTRA STAC Catalog 1970-01-01 -170, 17, -46, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2231554271-CEOS_EXTRA.umm_json Topographically-based catchments will be delineated for all stream-reach segments of the National Hydrography Dataset (NHD) within the entire conterminous United States. The NHD is a digital hydrographic dataset produced by the USGS, in cooperation with the U.S. Environmental Protection Agency (USEPA), that shows streams, lakes, ponds, and wetlands for the Nation at an initial scale of 1:100,000. This effort is being supported by the USEPA and USGS and is intended to benefit a wide variety of water-quality and stream-flow studies across the nation. The catchment-delineation technique is the same as that developed for use in the New England SPARROW model. The New England SPARROW model was the first to utilize the detail of the National Hydrography Dataset (NHD) as the underlying stream-reach network. Final products for this project will be the completion of NHD catchment delineations for the conterminous United States, which will be part of the NHDPlus project to be completed and made available in 2006. proprietary USGS_NPS_AcadiaAccuracy_Final Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -75.262726, 43.99941, -68.044304, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231554200-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). Thematic accuracy requirements of the VMP specify 80% accuracy for each map class (theme) that represents National Vegetation Classification System (NVCS) associations (vegetation communities). The UMESC selected 728 field sites, all within Acadia National Park fee and easement lands, for a thematic accuracy assessment (AA) to the vegetation map. The sites were randomly generated, stratified to map class themes that represent NVCS natural/semi-natural vegetation communities using VMP standards. Certain modifications to the process were necessary to accommodate logistical challenges. Local botanists collected field data for 724 of the sites during the 1999 field season. Thematic AA used 688 sites. Sites not used for the analysis were due to the elimination of an entire map class because of irreconcilable classification concepts (19 sites), or to other reasons including unmanageable error with GPS coordinate, duplicate site location, and incomplete field data (17 sites). Regardless of their use in the analysis, all 724 AA sites collected are represented in the Accuracy Assessment Site Spatial Database. proprietary USGS_NPS_AcadiaFieldPlots_Final Acadia National Park Vegetation Mapping Project - Field Plot Points CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -68.65603, 44.017136, -68.045715, 44.404953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549568-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of mapping and classifying the vegetation, vegetation sample plots were collected and analyzed, identifying 53 National Vegetation Classification System natural/semi-natural associations (vegetation communities). Local botanists, via contract with The Nature Conservancy, collected 179 vegetation plot samples at Acadia National Park (NP) during the 1997-1999 field seasons. Maine Natural Areas Program performed ordination analysis using the field plot data and other existing vegetation data of the area. Vegetation communities of Acadia NP are defined and described at the local and global scale. All 179 vegetation plot samples are represented in the Vegetation Field Plot Spatial Database with selected data fields from the Project's PLOTS database. proprietary USGS_NPS_AcadiaParkBoundary_Final Acadia National Park Vegetation Mapping Project - Park Boundary CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -68.944374, 43.99941, -68.02303, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231550835-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of the mapping project, various spatial database boundary coverages were either produced or modified from their original source. These boundary coverages are: 1) Project Boundary, 2) Map Data Boundary, 3) Park Boundary, and 4) Quad Boundary. The spatial coverages are projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. proprietary USGS_NPS_AcadiaSpatialVeg_Final Acadia National Park Vegetation Mapping Project - Spatial Vegetation Data CEOS_EXTRA STAC Catalog 1997-05-27 1997-05-28 -69, 43.99574, -67.99682, 44.50385 https://cmr.earthdata.nasa.gov/search/concepts/C2231552959-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey (USGS) Upper Midwest Environmental Sciences Center (UMESC) has produced the Vegetation Spatial Database Coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). The vegetation map is of Acadia National Park (NP) and extended environs, providing 99,693 hectares (246,347 acres) of map data. Of this coverage, 52,872 hectares (130,650 acres) is non-vegetated ocean, bay, and estuary (53% of coverage). Acadia NP comprises 19,276 hectares (47,633 acres) of the total data coverage area (19%, 40% not counting ocean and estuary data). Over 7,120 polygons make up the coverage, each with map class description and, for vegetation classes, physiognomic feature information. The spatial database provides crosswalk information to all National Vegetation Classification System (NVCS) floristic and physiognomic levels, and to other established classification systems (NatureServe's U.S. Terrestrial Ecological System Classification, Maine Natural Community Classification, and the USGS Land Use and Land Cover Classification). This mapping project has identified 53 NVCS associations (vegetation communities) at Acadia National Park through analyses of vegetation sample data. These associations are represented in the map coverage with 33 map classes. With all vegetation types, land use classes, and park specific categories combined, 57 map classes define the ground features within the project area (58 classes including the class for no map data). Each polygon within the spatial database map is identified with one of these map classes. In addition, physiognomic modifiers are added to map classes representing vegetation to describe the vegetation structure within a polygon (density, pattern, and height). The spatial database was produced from the interpretation of spring 1997 1:15,840-scale color infrared aerial photographs. The standard minimum mapping unit (MMU) applied is 0.5 hectares (1.25 acres). The interpreted data were transferred and automated using base maps produced from USGS digital orthophoto quadrangles. The finished spatial database is a single seamless coverage, projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. The estimated overall thematic accuracy for vegetation map classes is 80%. proprietary +USGS_NSHMP National Seismic Hazard Maps from the USGS National Seismic Hazard Mapping Project CEOS_EXTRA STAC Catalog 1970-01-01 170, 18, -65, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231550531-CEOS_EXTRA.umm_json The National Seismic Hazard Mapping Project (NSHMP) provides online maps. The hazard maps depict probabilistic ground motions and spectral response with 10%, 5%, and 2% probabilities of exceedance (PE) in 50 years. These maps correspond to return times of approximately 500, 1000, and 2500 years, respectively. The maps are based on the assumption that earthquake occurrence is Poissonian, so that the probability of occurrence is time-independent. The maps cover all of the U.S. including Hawaii and Alaska along with other pertinent information related to earthquake hazards. proprietary +USGS_NWRC_LA_LandChange_1932-2010 Land Area Change in Coastal Louisiana from 1932 to 2010 CEOS_EXTRA STAC Catalog 1932-01-01 2010-12-31 -94, 29, -89, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2231549617-CEOS_EXTRA.umm_json The analyses of landscape change presented in this dataset use historical surveys, aerial data, and satellite data to track landscape changes in coastal Louisiana. Persistent loss and gain data are presented for 1932-2010. The U.S. Geological Survey (USGS) analyzed landscape changes in coastal Louisiana by determining land and water classifications for 17 datasets. These datasets include survey data from 1932, aerial data from 1956, and Landsat Multispectral Scanner System (MSS) and Thematic Mapper (TM) data from the 1970s to 2010. proprietary +USGS_OF99-535_1.0 Middle Pliocene Paleoenvironmental Reconstruction: PRISM2 CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231553168-CEOS_EXTRA.umm_json As part of the USGS Global Change Research effort, the PRISM (Pliocene Research, Interpretation and Synoptic Mapping) Project has documented the characteristics of middle Pliocene climate on a global scale. The middle Pliocene was selected for detailed study because it spans the transition from relatively warm global climates when glaciers were absent or greatly reduced in the Northern Hemisphere to the generally cooler climates of the Pleistocene with expanded Northern Hemisphere ice sheets and prominent glacial-interglacial cycles. The purpose of this report is to document and explain the PRISM2 mid Pliocene reconstruction. The PRISM2 reconstruction consists of a series of 28 global scale data sets (Table 1) on a 2° latitude by 2° longitude grid. As such, it is the most complete and detailed global reconstruction of climate and environmental conditions older than the last glacial. PRISM2 evolved from a series of studies that summarized conditions at a large number of marine and terrestrial sites and areas (eg. Cronin and Dowsett, 1991; Poore and Sloan, 1996). The first global reconstruction of mid Pliocene climate (PRISM1) was based upon 64 marine sites and 74 terrestrial sites and included data sets representing annual vegetation and land ice, monthly sea surface temperature (SST) and sea-ice, sea level and topography on a 2°x2° grid (Dowsett et al. (1996) and Thompson and Fleming (1996)). The current reconstruction (PRISM2) is a revision of PRISM1 that incorporates several important differences: 1) Additional sites were added to the marine portion of the reconstruction to improve previous coverage. Sites from the Mediterranean Sea and Indian Ocean are incorporated for the first time in PRISM2. 2) All Pliocene sea surface temperature (SST) estimates were recalculated based upon a new core top calibration to the Reynolds and Smith (1995) adjusted optimum interpolation (AOI) SST data set. This reduced some of the problems previously encountered when different fossil groups were calibrated to different modern climatologies (Climate / Long Range Investigation Mapping and Predictions [CLIMAP], Goddard Institute for Space Sciences [GISS], Advanced Very High Resolution Radiometer [AVHRR], etc.). 3) PRISM2 uses a +25m rise in sea level for the Pliocene (PRISM1 used +35m), in keeping with much new data that has become available. 4) Although the change in global ice volume between PRISM1 and PRISM2 is minor, PRISM2 uses model results from Prentice (personal communication) to guide the areal and topographic distribution of Antarctic ice. This results in a more realistic Antarctic ice configuration in tune with the +25m sea level rise. proprietary USGS_OFR-03-13 Cascadia Tsunami Deposit Database CEOS_EXTRA STAC Catalog 1970-01-01 -130, 36, -116, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2231550569-CEOS_EXTRA.umm_json Abstract The Cascadia Tsunami Deposit Database contains data on the location and sedimentological properties of tsunami deposits found along the Cascadia margin. Data have been compiled from 52 studies, documenting 59 sites from northern California to Vancouver Island, British Columbia that contain known or potential tsunami deposits. Bibliographical references are provided for all sites included in the database. Cascadia tsunami deposits are usually seen as anomalous sand layers in coastal marsh or lake sediments. The studies cited in the database use numerous criteria based on sedimentary characteristics to distinguish tsunami deposits from sand layers deposited by other processes, such as river flooding and storm surges. Several studies cited in the database contain evidence for more than one tsunami at a site. Data categories include age, thickness, layering, grainsize, and other sedimentological characteristics of Cascadia tsunami deposits. The database documents the variability observed in tsunami deposits found along the Cascadia margin. proprietary USGS_OFR-97-792 500,000 Year-old Stable Isotopic Record from Devils Hole, USGS OFR-97-792 CEOS_EXTRA STAC Catalog 1970-01-01 -116.3, 36.42, -116.3, 36.42 https://cmr.earthdata.nasa.gov/search/concepts/C2231554597-CEOS_EXTRA.umm_json Devils Hole is a tectonically formed cave developed in the discharge zone of a regional aquifer in south-central Nevada. (See Riggs, et al., 1994.) The walls of this subaqueous cavern are coated with dense vein calcite which provides an ideal material for precise uranium-series dating via thermal ionization mass spectrometry (TIMS). Devils Hole Core DH-11 is a 36-cm-long core of vein calcite from which we obtained an approximately 500,000-year-long continuous record of paleotemperature and other climatic proxies. Data from this core were recently used by Winograd and others (1997) to discuss the length and stability of the last four interglaciations. These data are given in table 1 (http://pubs.usgs.gov/of/1997/ofr97-792/) These records have provided information that has posed several challenges to the orbital theory of the causation of the Pleistocene glaciations, suggested insights regarding the duration of current Holocene climate, provided a new chronology for the Vostok, Antarctica, ice core paleotemperature record, and yielded insights on the age of the groundwater in the principal aquifer of southern Nevada (http://pubs.usgs.gov/of/2002/ofr02-266/) Carbon and oxygen stable isotopic ratios were measured on 285 samples cut at regular intervals inward from the free face of the core (as reported in Winograd et al. ,1992, and in Coplen et al., 1994). Table 1 lists only 284 samples because a sample taken at 114.28 mm was eliminated when post-1994 reanalysis of its delta 18O value indicated an error in the earlier determination. Carbon isotopic ratios are reported in per mill relative to VPDB, defined by assigning a delta 13C of +1.95 per mill to the reference material NBS 19 calcite. Oxygen isotopic ratios are reported relative to VSMOW reference water on a scale normalized such that SLAP reference water is -55.5 per mill relative to VSMOW reference water. The oxygen isotopic fractionation factors employed in this determination are those listed in Coplen and others (1983). The delta 18O value of the isotopic reference material NBS 19 on this scale is +28.65 per mill. The ± 1 sd (standard deviation) error for the delta 18O and delta 13C analyses is ±0.07 and 0.05 per mill, respectively. Ages were estimated by linear interpolation between age control points taken at key intervals in the core and analyzed by TIMS 230Th-234U-238U dating. The age estimates in Table 1 are based on the original 21 control points (see Table 2 in Ludwig, et al., 1992, and Figure 2 in Winograd, et al., 1992) as well as for the recently obtained TIMS age of 143.8±0.9 ka (2 sd analytical error) at 51.5 mm (Winograd, et al., 1997). The later sample was taken specifically for additional control in a critical portion of the core. Errors in the ages vary but are bounded by the errors in the appropriate control points. (See Table 2 in Ludwig, et al., 1992.) proprietary USGS_OFR00-45_1.0 Bedrock Geologic Map of the Hubbard Brook Experimental Forest, Grafton County, New Hampshire, USGS/OFR 00-45 CEOS_EXTRA STAC Catalog 1998-01-01 2000-12-31 -71.875, 43.875, -71.625, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2231550787-CEOS_EXTRA.umm_json Our mapping study was funded by the USGS Toxic Substances Hydrology Program and was undertaken for the following reasons: 1) to ascertain whether the area might have a greater number of mappable lithologic units than shown on Barton's (1997) map, and to verify the stratigraphically higher formations shown on the map; 2) to have sufficient data to draw geologic cross- sections through the Mirror Lake research site; 3) to gather more data on brittle fracture distribution and orientation; and 4) to assess the degree to which the subsurface lithologies, ductile structures, and fractures observed at the two Mirror Lake well fields correlate with the geology of the surrounding region. The bedrock geology of the Hubbard Brook Experimental Forest, Grafton County, New Hampshire is described in this report of new field investigation. The database includes contacts of bedrock geologic units, faults, folds, and other structural geologic information, as well as the base maps on which the mapped geological features are registered. This report supersedes Barton (1997). Data were originally collected in UTM coordinates, zone 19, NAD 1927, and reprojected to geographic coordinates (Lat/Long), NAD 1983. The database is accompanied by two large format color maps, a readme.txt file, and a explanatory pamphlet. proprietary USGS_OFR00-462 Archive of Chirp Subbottom Data Collected During USGS Cruise MGNM 00014, Central South Carolina, 13-30 March, 2000, USGS/OFR 00-462 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -79.17, 33.25, -78.5, 33.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231554800-CEOS_EXTRA.umm_json In November 1999, the U. S. Geological Survey, in cooperation with Coastal Carolina University, began a program to produce geologic maps of the nearshore regime off northern South Carolina, utilizing high resolution sidescan sonar, interferometric (direct phase methods) swath bathymetry, and seismic subbottom profiling systems. The study areas extends from the ~7m isobath to about 10km offshore (water depths <12m). The goals of the investigation are to determine regional scale sand resource availability needed for planned beach nourishment programs, to investigate the roles that the inner shelf morphology and geologic framework play in the evolution of this coastal region, and to provide baseline geologic maps for use in proposed biologic habitat studies. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS MGNM 00014 cruise. The coverage is the nearshore of central South Carolina. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary USGS_OFR00-463 Archive of Boomer Subbottom Data Collected During USGS Cruise MGNM 00014, Central South Carolina, 13-30 March, 2000, USGS, OFR 00-463 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -79.17, 33.25, -78.5, 33.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231555400-CEOS_EXTRA.umm_json In November 1999, the U. S. Geological Survey, in cooperation with Coastal Carolina University, began a program to produce geologic maps of the nearshore regime off northern South Carolina, utilizing high resolution sidescan sonar, interferometric (direct phase methods) swath bathymetry, and seismic subbottom profiling systems. The study areas extends from the ~7m isobath to about 10km offshore (water depths <12m). The goals of the investigation are to determine regional scale sand resource availability needed for planned beach nourishment programs, to investigate the roles that the inner shelf morphology and geologic framework play in the evolution of this coastal region, and to provide baseline geologic maps for use in proposed biologic habitat studies. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS MGNM 00014 cruise. The coverage is the nearshore of central South Carolina. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary USGS_OFR00-467 Archive of Boomer Subbottom Data Collected During USGS Cruise DIAN 96040, Long Island, NY Inner Shelf -- Fire Island, NY 4-24 September, 1996, USGS/OFR 00-467 CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -73.51, 40.51, -72.95, 40.7 https://cmr.earthdata.nasa.gov/search/concepts/C2231553644-CEOS_EXTRA.umm_json In 1995, the USGS Woods Hole Field Center in Cooperation with the U.S. Army Corps of Engineers, began a program designed to map the seafloor offshore of the New York-New Jersey metropolitan area; the most heavily populated, and one of the most impacted coastal regions of the United States. The ultimate goal of this program is to provide an overall synthesis of the sea floor environment, including surficial sediment texture, subsurface geometry, and anthropogenic impact (e.g. ocean dumping, trawling, channel dredging), through the use and analysis of sidescan-sonar and subbottom mapping techniques. This regional synthesis will support a wide range of management decisions and will provide a basis for further process-oriented investigations. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS DIAN 96040 cruise. The coverage is the nearshore of Long Island, NY in the vicinity of Fire Island. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary +USGS_OFR00-494 High-Resolution Marine Seismic Reflection Data From the San Francisco Bay Area, USGS/OFR 00-494 CEOS_EXTRA STAC Catalog 1970-01-01 -122.77, 37.49, -121.68, 38.16 https://cmr.earthdata.nasa.gov/search/concepts/C2231549874-CEOS_EXTRA.umm_json "Marine seismic reflection data are used to image and map sedimentary and structural features of the seafloor and subsurface. These data are useful in mapping faults (such as the San Andreas and Hayward Faults) where they pass under the waters of the San Francisco Bay, and in assessing other submarine geologic characteristics and features. Particularattention was devoted to investigating the offshore confluence of the San Andreas and San Gregorio fault zones. These data were collected under the auspices of the auspices of the Central California/San Francisco Bay Earthquake Hazards Project of the Western Coastal and Marine Geology Program. Further information concerning the objectives and efforts of this project may be found at: ""http://walrus.wr.usgs.gov/earthquakes/cencal/"" This report consists of two-dimensional marine seismic reflection profile data from the San Francisco Bay area. These data were acquired between 1993 and 1997 with the Research Vessels David Johnston and Robert Gray. The data are available in a variety of formats, including binary, postscript and GIF image. Binary data are in Society of Exploration Geologists (SEG) SEG-Y format and may be downloaded for further processing or display. Reference maps and GIF images othe profiles may be viewed with your Web browser. Seismic reflection profiles are acquired by means of an acoustic source (usually generated electromagnetically or with compressed air), and a hydrophone or hydrophone array. Both elements are typically towed in the waterbehind a survey vessel. The sound source emits a short acoustic pulse, which propogates through the water and sediment columns. The acoustic energy is reflected at density boundaries (such as the seafloor or sediment layers beneath the seafloor), and detected at the hydrophone. As the vessel moves, this process is repeated at intervals ranging between 0.5 and 20 meters depending on the source type. In this way a two-dimensional image of the geologic structure beneath the ship track is constructed." proprietary +USGS_OFR00-495_1.0 Geologic Data Sets for Weights-of-evidence Analysis in Northeast Washington--1. Geologic Raster Data, USGS/OFR 00-495 CEOS_EXTRA STAC Catalog 1998-01-01 1998-12-31 -120, 48, -117, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231550193-CEOS_EXTRA.umm_json This dataset contains the combination of geology data (geologic units, faults, folds, and dikes) from 6 1;100,000 scale digital coverages in eastern Washington (Chewelah, Colville, Omak, Oroville, Nespelem, Republic). The data was converted to an Arc grid in ArcView using the Spatial Analyst extension. proprietary USGS_OFR0047 Coal-Bearing Regions and Structural Sedimentary Basins of China and Adjacent Seas CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 66.41994, 14.52108, 144.76797, 54.84727 https://cmr.earthdata.nasa.gov/search/concepts/C2231553346-CEOS_EXTRA.umm_json The purpose of this dataset is to give geologists and other scientists a spatial database of coal-bearing regions in China. This data set will be utilized in energy research and cartographic projects. The data set covers of coal-bearing regions, coal fields, structural sedimentary basins, major coal mine production, and other commodities in China. Procedures_Used: The coal-bearing regions were digitized from the Energy Mineral Resource Map of China and Adjacent Seas, published in 1992 by the Geological Publishing House, Chinese Institute of Geology and Mineral Resources Information and Institute of Mineral Deposits of Chinese Academy of Geological Sciences, utilizing ARC/INFO. proprietary USGS_OFR0047_coal_bearing Coal-Bearing Regions and Structural Sedimentary Basins of China and Adjacent Seas: Coal-bearing regions, USGS OFR 00-47 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 66.41994, 14.52108, 144.76797, 54.84727 https://cmr.earthdata.nasa.gov/search/concepts/C2231550879-CEOS_EXTRA.umm_json The purpose of this dataset is to give geologists and other scientists a spatial database of coal-bearing regions in China. This data set will be utilized in energy research and cartographic projects. This dataset is a collection of coal-bearing regions located in The Peoples Republic of China. Included in this dataset are the age and name of the coal-bearing regions. Procedures_Used: The coal-bearing regions were digitized from the Energy Mineral Resource Map of China and Adjacent Seas, published in 1992 by the Geological Publishing House, Chinese Institute of Geology and Mineral Resources Information and Institute of Mineral Deposits of Chinese Academy of Geological Sciences, utilizing ARC/INFO. proprietary USGS_OFR0047_coal_type Coal-Bearing Regions and Structural Sedimentary Basins of China and Adjacent Seas: Coal field locations CEOS_EXTRA STAC Catalog 2000-03-07 2000-03-07 66.41994, 14.52108, 144.76797, 54.84727 https://cmr.earthdata.nasa.gov/search/concepts/C2231552530-CEOS_EXTRA.umm_json The purpose of this dataset is to give geologists and other scientists a spatial database of coal fields in China. This data set will be utilized in energy research and cartographic projects. This dataset is a collection of coal field locations in The Peoples Republic of China. Included in this dataset is the rank of the coal in each field. Procedures_Used: The coal types were digitized from the Energy Mineral Resource Map of China and Adjacent Seas, published in 1992 by the Geological Publishing House, Chinese Institute of Geology and Mineral Resources Information and Institute of Mineral Deposits of Chinese Academy of Geological Sciences, utilizing ARC/INFO. proprietary @@ -14019,128 +14352,316 @@ USGS_OFR0047_commodities Coal-Bearing Regions and Structural Sedimentary Basins USGS_OFR0047_mines Coal-Bearing Regions and Structural Sedimentary Basins of China and Adjacent Seas: Major coal mine locations CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 66.41994, 14.52108, 144.76797, 54.84727 https://cmr.earthdata.nasa.gov/search/concepts/C2231551833-CEOS_EXTRA.umm_json The purpose of this dataset is to give geologists and other scientists a spatial database of major coal mines and their production in China. This data set will be utilized in energy research and cartographic projects. This dataset is a collection of major coal mine production located in The Peoples Republic of China. Included in this dataset are the locations of major coal mines and the approximate annual amount of coal mined annually in millions of metric tons. Procedures_Used: The major coal mine production points were digitized from the Coalfield Prediction Map of China, utilizing ARC/INFO. proprietary USGS_OFR0047_sed_basins Coal-Bearing Regions and Structural Sedimentary Basins of China and Adjacent Seas: Structural sedimentary basins CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 66.41994, 14.52108, 144.76797, 54.84727 https://cmr.earthdata.nasa.gov/search/concepts/C2231555011-CEOS_EXTRA.umm_json The purpose of this dataset is to give geologists and other scientists a spatial database of structural sedimentary basins in China. This data set will be utilized in energy research and cartographic projects. This dataset is a collection of structural sedimentary basin locations in The Peoples Republic of China. Included in this dataset are the age and name of each structural sedimentary basin. Procedures_Used: The structural sedimentary basins were digitized from the Energy Mineral Resource Map of China and Adjacent Seas, published in 1992 by the Geological Publishing House, Chinese Institute of Geology and Mineral Resources Information and Institute of Mineral Deposits of Chinese Academy of Geological Sciences, utilizing ARC/INFO. proprietary USGS_OFR00503cellarbndry Boundary of the Cellar Dirt DisposalSite (Historic Area Remediation Site, Offshore New York), USGS OFR 00-503 CEOS_EXTRA STAC Catalog 1996-01-01 2000-12-31 -73.90012, 40.349976, -73.81226, 40.433605 https://cmr.earthdata.nasa.gov/search/concepts/C2231552887-CEOS_EXTRA.umm_json The purpose of this project is to map the surficial geology of the sea floor of Historic Area Remediation Site (HARS) and changes in surficial characteristics over time. This GIS project presents multibeam and other data in a digital format for analysis and display by scientists, policy makers, managers and the general public. This data set includes the boundaries of the Cellar Dirt Disposal site, located offshore of New York and New Jersey. proprietary +USGS_OFR00503dredgesite Locations of Dredged Material Placed in the Historic Area Remediation Site Offshore of New York 1996-2000, USGS OFR 00-503 CEOS_EXTRA STAC Catalog 1996-01-01 2000-12-31 -73.90012, 40.349976, -73.81226, 40.433605 https://cmr.earthdata.nasa.gov/search/concepts/C2231554757-CEOS_EXTRA.umm_json The purpose of this project is to map the surficial geology of the sea floor of Historic Area Remediation Site (HARS) and changes in surficial characteristics over time. This GIS project presents multibeam and other data in a digital format for analysis and display by scientists, policy makers, managers and the general public. This data set includes the location and volume of material placed on the sea floor in the Historic Area Remediation Site between November 1996 and April 2000, extracted from records maintained by the U.S. Army Corps of Engineers. These data are maintained in a system called DAN-NY (Disposal Analysis System NY). DAN-NY includes data from Inspector Logs (data recorded by inspectors on the barges), and more recently data acquired by NYDISS (New York Disposal Surveillance System). The NYDISS automatically records the location of the barge when placement begins and ends. For material placed between November 1996 and November 1998, the plotted locations are from Inspector Logs. For the material placed between November 1998 and April 2000, the placement location was determined by NYDISS. This Open-File Report utilizes the location (latitude and longitude), date of placement, and volume of material in the scow from these data bases. proprietary USGS_OFR00503epabndry Boundaries of the Historic Area Remediation Site, Offshore New York, USGS OFR 00-503 CEOS_EXTRA STAC Catalog 1996-01-01 2000-12-31 -73.90012, 40.349976, -73.81226, 40.433605 https://cmr.earthdata.nasa.gov/search/concepts/C2231553189-CEOS_EXTRA.umm_json The purpose of this project is to map the surficial geology of the sea floor of Historic Area Remediation Site (HARS) and changes in surficial characteristics over time. This GIS project presents multibeam and other data in a digital format for analysis and display by scientists, policy makers, managers and the general public. This project presents maps of the sea floor in GIS format of the Historic Area Remedition Site (HARS), located offshore of New York and New Jersey. The data were collected with a multibeam sea floor mapping system on surveys conducted November 23 - December 3, 1996, October 26 - November 11, 1998, and April 6 - 30, 2000. The maps show sea floor topography, shaded relief, and backscatter intensity (a measure of sea floor texture and roughness) at a spatial resolution of 3 m/pixel, and locations of dredged material placed on the sea floor. The sea floor of the HARS, approximately 9 square nautical miles in area, is being remediated by placing at least a one-meter of clean dredged material on top of the existing surface sediments that exhibit varying degrees degradation resulting from previous disposal of dredged and other material. Comparison of the topography and backscatter intensity from the three surveys show changes in topography and surficial sediment properties resulting from placement of dredged material in 1996 and 1997 prior to designation of the HARS, as well as placement of material for remediation of the HARS. This study is carried out cooperatively by the U.S. Geological Survey and the U.S. Army Corps of Engineers. proprietary USGS_OFR01-122_Version 1.0, April 20, 2001 Digital Data for Construction Material Sources Reported by the Arizona Department of Transportation in 1977 for Maricopa County, Arizona, USGS OFR 01-122 CEOS_EXTRA STAC Catalog 1977-01-01 1977-12-31 -113.4, 32.5, -111.1, 34 https://cmr.earthdata.nasa.gov/search/concepts/C2231549782-CEOS_EXTRA.umm_json "U.S. Geological Survey scientists desired to have the data presented in the Source in a digital format to use in GIS and spreadsheet software programs for aggregate models and aggregate assessment. The data set marpits1 is an ArcInfo coverage of point features representing pit locations and attribution data captured from an atlas of map sheets and pit data sheets titled ""A Materials Inventory of Maricopa County [Arizona]"" by the Arizona Highway Department (AHD), now named the Arizona Department of Transportation (ADOT), hereafter referred to as the 'Source'. Pit locations were represented by point symbols in the Source map sheets. Points were digitized from the Source map sheets. Selected attribute data were collected from the Source pit data and map sheets. In the Source introduction it states: ""The pit location maps show the location of all pits bearing Materials Services serial numbers. Other sources are not shown. The plotted locations are as close as possible to the true location as the scale of the map will allow."" The point attribute data, captured from the Source pit data sheets are ""designed to show test results (sieve analysis, plasticity index, and abrasion) for the usable material within each ADOT pit."" The digital editor and digital compilers of the GIS data set made certain adjustments to the data to make them complete and usable in a GIS. These adjustments include adding points locations for records in the accompanying Source pit data sheets where no point representation existed on the Source map sheets, adding attribution data to the furthest extent possible for points on the Source map sheets without entries in the accompanying Source pit data sheets, appending a letter to the pit number of repeated (duplicate) pit numbers to make them unique and correspond one-to-one with a record in the Source pit data sheets, and adding a '-999' to represent 'No data' or 'No observation' for blank entries in the pit data sheets. Table 3 in the Open-File Report text describes the actions taken to insure data consistency and uniqueness of the individual points. An accompanying ArcInfo arc coverage called marbase of the generalized Maricopa County boundary, and generalized major roadways and generalized major hydrography of Maricopa County has been included to give a general reference of pit location in proximity to natural and man-made features." proprietary USGS_OFR01-123 Deep-towed Chirp Profiles of the Blake Ridge Collapse Structure Collected on Aboard the R/V Cape Hatteras in 1992 and 1995, USGS OFR 01-123 CEOS_EXTRA STAC Catalog 1992-01-01 1995-12-31 -76.03, 31.76, -75.36, 32.03 https://cmr.earthdata.nasa.gov/search/concepts/C2231550204-CEOS_EXTRA.umm_json "This study was completed as part of an ongoing project in the field of natural gas hydrate research. Natural gas hydrates are an ice-like crystalline combination of water and gas, most commonly methane. The data included in this report were collected in an effort to understand a site where we believe large quantities of methane, approximately 4% of the present atmospheric total, was released from seafloor sediments. This site is known as the Blake Ridge collapse structure, located 300 km off the South Carolina coast at approximately 2600 m of water depth. This CD-ROM contains copies of the navigation and deep-towed chirp subbottom data collected aboard the R/V Cape Hatteras on cruises 92023 and 95023 in 1992 and 1995 respectively. This CD-ROM is (Compact Disc-Read Only Memory UDF (Universal Disc Format) CD-ROM Standard (ISO 9660 equivalent). The HTML documentation is written utilizing some HTML 4.0 enhancements. The disk should be viewable by all WWW browsers but may not properly format on some older WWW browsers. Also, some links to USGS collaborators and other agencies are available on this CD-ROM. These links are only accessible if access to the Internet is available during browsing of the CD-ROM. On cruise 92023, 58 km of deep-towed chirp data were recorded on 4 lines and broken into a total of 8 files. 78 square kilometers of sidescan mosaic and approximately 1000 km of air gun single channel seismic reflection data were recorded as well but are not achived on this report. On cruise 95023, 100km of deep- towed chirp data were recorded on 5 lines and broken into 18 files. 152 square kilometers of sidescan mosaic and 244.3 km of GI gun single channel seismic reflection were also recorded but are not archived on this report. The archived Chirp subbottom data are in standard Society of Exploration Geologists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded for processing with software such as Seismic Unix or SIOSEIS. The subbottom data were recorded on the ISIS data acquisition system in QMIPS format. Chirp subbottom channel extracted from raw QMIPS format sonar files and converted to 16-bit Int. SEG-Y format using the program QMIPSTOSEGY. Even though the data are in SEG-Y format, it is not the conventional time series data (e.g. voltages or pressures), but rather instantaneous amplitude or envelope detected and therefore all of the amplitudes are positive (though not simply rectified). Seismic reflection profiles are acquired by means of an acoustic source (usually generated electromagnetically or with compressed air), and a hydrophone or hydrophone array. Both elements are typically towed in the water behind a survey vessel, or some cases, mounted on side of the hull. The sound source emits a short acoustic pulse, which propagates through the water and sediment columns. The acoustic energy is reflected at density boundaries (such as the seafloor or sediment layers beneath the seafloor), and detected at the hydrophone. As the vessel moves, this process is repeated at intervals ranging between 0.5 and 20 meters depending on the source type. In this way, a two-dimensional image of the geologic structure beneath the ship track is constructed. For more information concerning seismic reflection profiling at the USGS Woods Hole ""http://woodshole.er.usgs.gov/operations/sfmapping/""" proprietary +USGS_OFR01-131_Version 1.0 Geologic Map of the San Bernardino North 7.5' Quadrangle, San Bernardino County, California, USGS OFR 01-131 CEOS_EXTRA STAC Catalog 1974-01-01 1981-12-31 -117.37509, 34.124985, -117.24991, 34.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549362-CEOS_EXTRA.umm_json The data set for the San Bernardino North 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) a part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographical Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the San Bernardino North 7.5' quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, itcan be used for groundwater studies in the San Bernardino basin, and for mineral resource evaluation studies, animal and plant habitat studies, and soil studies in the San Bernardino National Forest. The database is not suitable for site-specific geologic evaluations. This data set maps and describes the geology of the San Bernardino North 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of this Readme (including the metadata file as an appendix), Description of Map Units (DMU), and the graphic produced by the PostScript plot file. The geologic map covers a part of the southwestern San Bernardino Mountains and the northwestern San Bernardino basin. Granitic and metamorphic rocks underlie most of the mountain area, and a complex array of Quaternary deposits fill the basin. These two areas are separated by strands of the seismically active San Andreas Fault. Bedrock units in the San Bernardino Mountains are dominate by large Cretaceous and Jurassic granitic bodies, ranging in composition from onzogranite to monzodiorite, and include lesser Triassic monzonite. The younger of these granitic rocks intrude a complex assemblage of gneiss, marble, and granitic rock of probable early Mesozoic age; the relationship between these metemorphic rocks and the Triassic rocks is unknown. Spanning the Pleistocene in age, large and small alluvial bodies emerge from the San Bernardino Mountains, and and fill the San Bernardino basin. In the southwestern part of the quadrangle, Cajon Wash carries sediments from both the San Bernardino and San Gabriel Mountains, and Lytle Creek heads in the eastern San Gabriel Mountains. Limite bedrock areas showing through the Quaternary sediments of the basin consist exclusively of Mesozoic Pelona Schist locally intruded by Tertairy dikes. Youthful-appearing fault scarps discontinuously mark the traces of the San Andreas Fault along the southern edge of the San Bernardino Mountains. Unnamed Tertiary sedimentary rocks are bounded by two strands of the fault between Badger Canyon and the east edge of the quadrangle. Young and old high-angle faults cut bedrock units within the San Bernardino Mountains, and the buried, seismically active San Jacinto Fault traverses the southwestern part of the quadrangle. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. This digital Open-File map superceeds an older analog Open-File map of the quadrangle, and includes extensive new data on the Quaternary deposits, and revises some fault and bedrock distribution within the San Bernardino Mountains. The digital map was compiled on a base-stable cronoflex copy of the San Bernardino North 7.5' topographic base and then scribed. This scribe guide was used to make a 0.007 mil blackline clear-film, which was scanned at 1200 DPI by Optronics Specialty Company, Northridge, California; minor hand-digitized additions were made at the USGS. Lines, points, and polygonswere subsequently edited at the USGS using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum. proprietary +USGS_OFR01-132_Version 1.0 Geologic Map of the Fifteenmile Valley 7.5' Quadrangle, San Bernardino County, California, USGS OFR 00-132 CEOS_EXTRA STAC Catalog 1997-01-01 2000-12-31 -117.12509, 34.374985, -116.99991, 34.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231548788-CEOS_EXTRA.umm_json The data set for the Fifteenmile Valley 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographical Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the Fifteenmile Valley 7.5' quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, it can be used for mineral resource evaluation studies, animal and plant habitat studies, and soil studies in the San Bernardino National Forest. The database is not suitable for site-specific geologic evaluations. This data set maps and describes the geology of the Fifteenmile Valley 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Descriptionof Map Units (DMU), an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of this Readme (including the metadata file as an appendix), Description of Map Units (DMU), and a screen graphic of the plot produced by the PostScript plot file. The geologic map covers the northernmost part of the San Bernardino Mountain and the southern Granite Mountains. These two bedrock areas are separated by the wide, alluviated Fifteenmile Valley. Bedrock units in the San Bernardino Mountains are dominated by large Cretaceous granitic bodies ranging in composition from monzogranite to gabbro, and include lesser Triassic monzonite. The Granite Mountains are underlain chiefly by large Triassic monzonite bodies, and in the western part, by Cretaceous and possibly Jurassic monzogranite to monzodiorite. Spanning the Pleistocene in age, large alluvial fans, flank the north side of the San Bernardino Mountains, and are dominated by debris flow deposits. The central part of Fifteenmile Valley is covered by fine grained alluvial material deposited by streams flowing into Rabbit Lake and an unnamed dry lake in the northwestern part of the quadrangle. Young, south dipping reverse faults, some with moderately to well eroded fault scarps, discontinuously flank the northern edge of the San Bernardino Mountains. Young and old high-angle faults are mapped within both the San Bernardino and Granite Mountains. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. The map was compiled on a base-stable cronoflex copy of the Fifteenmile Valley 7.5' topographic base and then scribed. This scribe guide was used to make a0.007 mil blackline clear-film, which was scanned at 1200 DPI by Optronics Specialty Company, Northridge, California; minor hand-digitized additions were madeat the USGS. Lines, points, and polygons were subsequently edited at the USGS using standard ARC/INFO commands. Digitizing and editing artifacts significan enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum. proprietary USGS_OFR01-142_1 Digital Database of Mining-related Features at Selected Historic and Active Phosphate Mines in Idaho, USGS OFR 01-142 CEOS_EXTRA STAC Catalog 1998-01-01 1998-12-31 -112.17872, 42.30539, -111.11463, 43.107204 https://cmr.earthdata.nasa.gov/search/concepts/C2231551080-CEOS_EXTRA.umm_json This is a spatial database that delineates mining-related features in areas of historic and active phosphate mining in the core of the southeastern Idaho phosphate resource area. The data has varying degrees of accuracy and attribution detail. The breakdown of areas by type of activity at active mines is detailed; however, the disturbed areas at many of the closed or inactive mines are not subdivided into specific categories detailing the type of activity that occurred. Nineteen phosphate mine sites are included in the study. A total of 5,728 hc (14,154 ac), or more than 57 km2 (22 mi2), of phosphate mining-related surface disturbance are documented in the spatial coverage of the core of the southeast Idaho phosphate resource area. The study includes 4 active phosphate minebsDry Valley, Enoch Valley, Rasmussen Ridge, and Smoky Canyobnand 15 historic phosphate minebsBallard, Champ, Conda, Diamond Gulch, Gay, Georgetown Canyon, Henry, Home Canyon, Lanes Creek, Maybe Canyon, Mountain Fuel, Trail Canyon, Rattlesnake Canyon, Waterloo, and Wooley Valley. Spatial data on the inactive historic mines is relatively up-to-date; however, spatially described areas for active mines are based on digital maps prepared in early 1999. The inactive Gay mine has the largest total area of disturbance: 1,917 hc (4,736 ac) or about 19 km2 (7.4 mi2). It encompasses over three times the disturbance area of the next largest mine, the Conda mine with 607 hc (1,504 ac), and it is nearly four times the area of the Smoky Canyon mine, the largest of the active mines with 497 hc (1,228 ac). The wide range of phosphate mining-related surface disturbance features (approximately 80) were reduced to 13 types or features used in this studbyadit and pit, backfilled mine pit, facilities, mine pit, ore stockpile, railroad, road, sediment catchment, tailings or tailings pond, topsoil stockpile, water reservoir, and disturbed land (undifferentiated). In summary, the spatial coverage includes polygons totaling 1,114 hc (2,753 ac) of mine pits, 272 hc (671 ac) of backfilled mine pits, 1,570 hc (3,880 ac) of waste dumps, 26 hc (64 ac) of ore stockpiles, and 44 hc (110 ac) of tailings or tailings ponds. Areas of undifferentiated phosphate mining-related land disturbances, called bed land,b site-specific studies to delineate distinct mine features will allow modification of this preliminary spatial database. proprietary +USGS_OFR01-153_Version 1.0, February 13, 2001 Heavy Minerals from the Palos Verdes Margin, Southern California: Data and Factor Analysis, USGS OFR 00-153 CEOS_EXTRA STAC Catalog 1992-01-01 1992-12-31 -118.44776, 33.67033, -118.14747, 33.8569 https://cmr.earthdata.nasa.gov/search/concepts/C2231550564-CEOS_EXTRA.umm_json Heavy or high-density minerals in the 63-250-um (micron) size fraction (very fine and fine sand) were analyzed from beach and offshore sites to determine the areal and temporal mineralogic distributions and the relation of those distributions to the deposit affected by effluent discharged from the Los Angeles County Sanitation District sewage system. Heavy or high-density minerals in the 63-250-_m (micron) size fraction (very fine and fine sand) were analyzed from 36 beach and offshore sites (38 samples) of the Palos Verdes margin to determine the areal and temporal mineralogic distributions and the relation of those distributions to the deposit affected by material discharged from the Los Angeles County Sanitation District sewage system (Lee, 1994) (Figure 1). Data presented here were tabulated for a report to the Department of Justice (Wong, 1994). The results of the data analysis are discussed in Wong (in press). proprietary USGS_OFR01-157 Archive of Water Gun Subbottom Data Collected During USGS Cruise SEAX 95007 New York Bight, 7-25 May, 1995 CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -73.9, 40.36, -73.49, 40.58 https://cmr.earthdata.nasa.gov/search/concepts/C2231551639-CEOS_EXTRA.umm_json Beginning in 1995, the USGS, in cooperation with the U.S Army Corps of Engineers (USACE), New York District, began a program to generate reconnaissance maps of the sea floor offshore of the New York-New Jersey metropolitan area, one of the most populated coastal regions of the United States. The goal of this mapping program is to provide a regional synthesis of the sea-floor environment, including a description of sedimentary environments, sediment texture, seafloor morphology, and geologic history to aid in understanding the impacts of anthropogenic activities, such as ocean dumping. This mapping effort differs from previous studies of this area by obtaining digital, sidescan sonar images that cover 100 percent of the sea floor. This investigation was motivated by the need to develop an environmentally acceptable solution for the disposal of dredged material from the New York - New Jersey Port, by the need to identify potential sources of sand for renourishment of the southern shore of Long island, and by the opportunity to develop a better understanding of the transport and long-term fate of contaminants by investigations of the present distribution of materials discharged into the New York Bight over the last 100+ years (Schwab and others, 1997). This DVD-ROM contains copies of the navigation and field Water Gun subbottom data collected aboard the R/V Seaward Explorer, from 7-25 May, 1995. The coverage is in the New York Bight area. This DVD-ROM (Digital Versatile Disc-Read Only Memory) has been produced in accordance with the UDF (Universal Disc Format) DVD-ROM Standard (ISO 9660 equivalent) and is therefore capable of being read on any computing platform that has appropriate DVD-ROM driver software installed. Access to the data and information contained on this DVD-ROM was developed using the HyperText Markup Language (HTML) utilized by the World Wide Web (WWW) project. Development of the DVD-ROM documentation and user interface in HTML allows a user to access the information by using a variety of WWW information browsers to facilitate browsing and locating information and data. To access the information contained on this disk with a WWW client browser, open the file'index.htm' at the top level directory of this DVD-ROM with your selected browser. The HTML documentation is written utilizing some HTML 4.0 enhancements. The disk should be viewable by all WWW browsers but may not properly format on some older WWW browsers. Also, some links to USGS collaborators and other agencies are available on this DVD-ROM. These links are only accessible if access to the Internet is available during browsing of the DVD-ROM. proprietary +USGS_OFR01-173_Version 1.0 Geologic Map of the Devore 7.5' quadrangle, San Bernardino County, California, USGS, OFR 00-173 CEOS_EXTRA STAC Catalog 1974-01-01 1981-12-31 -117.50009, 34.124985, -117.37491, 34.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231552829-CEOS_EXTRA.umm_json The data set for the Devore 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographical Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the Devore 7.5' quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, it can be used for groundwater studies in the San Bernardino basin, and for mineral resource evaluation studies, animal and plant habitat studies, and soil studies in the San Bernardino National Forest. The database is not suitable for site-specific geologic evaluations. This data set maps and describes the geology of the Devore 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of this Readme (including the metadata file as an appendix), Description of Map Units (DMU), and the graphic produced by the PostScript plot file. The Devore quadrangle straddles part of the boundary between two major physiographic provinces of California, the Transverse Ranges Province to the north and the Peninsular Ranges Province to the south. The north half of the quadrangle includes the eastern San Gabriel Mountains and a small part of the western San Bernardino Mountains, both within the east-central part of the Transverse Ranges Province. South of the Cucamonga and San Andreas Fault zones, the extensive alluviated area in the south half of the quadrangle lies within the upper Santa Ana River Valley, and represents the northernmost part of the Peninsular Ranges Province. There are numerous active faults within the quadrangle, including right-lateral strike-slip faults of the San Andreas Fault system, which dominate the younger structural elements, and separate the San Gabriel from the San Bernardino Mountains. The active San Jacinto Fault zone projects toward the quadrangle from the southeast, but its location is poorly constrained not only within the quadrangle, but for at least several kilometers to the southeast. As a result, the interrelation between it, the Glen Helen Fault, and the probable easternmost part of the San Gabriel Fault is intrepretive. Thrust faults of the Cucamonga Fault zone along the south margin of the San Gabriel Mountains, represent the rejuvinated eastern end of a major old fault zone that bounds the south side of the western and central Transverse Ranges (Morton and Matti, 1993). Rejuvenation of this old fault zone, including the Cucamonga Fault zone, is apparently in response to compression in the eastern San Gabriel Mountains resulting from initiation of right-lateral slip on the San Jacinto Fault zone in the Peninsular Ranges.The structural grain within the San Gabriel Mountains, as defined by basement rocks, is generally east striking. Within the Devore quadrangle, these basement rocks include a Paleozoic (?) schist, quartzite, and marble metasedimentary sequence, which occurs as discontinuous lenses and septa within Cretaceous granitic rocks. Most of the granitic rocks are of tonalitic composition, and much of them are mylonitic. South of the granitic rocks is a complex assemblage of Proterozoic (?) metamorphic rocks, at least part of which is metasedimentary. The assemblage was metamorphosed to upper amphibolite and lower granulite grade, and subsequently remetamorphosed to a lower metamorphic grade. It is also intensely deformed by mylonitization which is characterized by an east striking, north dipping foliation, and by a pronounced lineation that plunges shallowly east and west. East of Lytle Creek and west of the San Andreas Fault zone, the predominant basement lithology is Mesozoic Pelona Schist, which consists mostly of greenschist grade metabasalt and metagraywacke. Intruding the Pelona Schist, between Lytle Creek and Cajon Canyon, is the granodiorite of Telegraph Peak of Oligocene age (May and Walker, 1989). East of the San Andreas Fault in the San Bernardino Mountains, basement rocks consist of amphibolite grade gneiss and schist intermixed with concordant and discordant tonalitic rock and pegmatite. Tertiary conglomerate and sandstone occur in the Cucamonga Fault zone and in a zone 200 to 700 m wide between strands of the San Andreas Fault zone and localized thrust faults northeast of the San Andreas. Most of the conglomerate and sandstone within the Cucamonga Fault zone is overturned forming the north limb of an overturned syncline. Clasts in the conglomerate are not derived from any of the basement rocks in the eastern San Gabriel Mountains. Clasts in the conglomerate and sandstone northeast of the San Andreas Fault zone do not appear to be locally derived either. The south half of the quadrangle is dominated by the large symmetrical alluvial-fan emanating from the canyon of Lytle Creek, and by the complex braided stream sediments of Lytle Creek and Cajon Wash. The San Andreas Fault is restricted to a relatively narrow zone marked by a pronounced scarp that is especially well exposed near the east margin of the quadrangle. Two poorly exposed, closely spaced, north-dipping thrust faults northeast of the San Andreas Fault have dips that appear to range from 55? to near horizontal. These hallower dips probably are the result of rotation of initially steeper fault surfaces by downhill surface creep. Between the San Andreas and Glen Helen Fault zones, there are several faults that have north facing scarps, the largest of which are the east striking Peters Fault and the northwest striking Tokay Hill Fault. The Tokay Hill Fault is at least in part a reverse fault. Scarps along both faults are youthful appearing. The Glen Helen Fault zone along the west side of Cajon Creek, is well defined by a pronounced scarp from the area north of Interstate 15, south through Glen Helen Regional Park; an elongate sag pond is located within the park. The large fault zone along Meyers Canyon, between Penstock and Lower Lytle Ridges, is probably the eastward extension of the San Gabriel Fault zone that is deformed into a northwest orientation due to compression in the eastern San Gabriel Mountains (Morton and Matti, 1993). At the south end of Sycamore Flat, this fault zone consists of three discreet faults distributed over a width of 300 m. About 2.5 km northwest of Sycamore Flats, it consists of a 300 m wide shear zone. At the north end of Penstock Ridge, the fault zone has bifurcated into four strands, which at the northwest corner of the quadrangle are distributed over a width of about one kilometer. From the northern part of Sycamore Flat, for a distance of nearly 5 km northwestward, a northeast dipping reverse fault is located along the east side of the probable San Gabriel Fault zone. This youthful reverse fault has locally placed the Oligocene granodiorite of Telegraph Peak over detritus derived from the granodiorite. The Lytle Creek Fault, which is commonly considered the western splay of the San Jacinto Fault zone, is located on the west side of Lytle Creek. Lateral displacement on the Lytle Creek Fault has offset parts of the old Lytle Creek channel; this offset gravel-filled channel is best seen at Texas Hill, near the mouth of Lytle Creek, where the gravel was hydraulic mined for gold in the 1890s. The Cucamonga Fault zone consists of a one kilometer wide zone of northward dip-ping thrust faults. Most splays of this fault zone dip north 25 to 35. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. This digital Open-File map supercedes an older analog Open-File map of the quadrangle, and includes extensive new data on the Quaternary deposits, and revises some fault and bedrock distribution within the San Gabriel Mountains. The digital map was compiled on a base-stable cronoflex copy of the Devore 7.5 deg. topographic base and then scribed. This scribe guide was used to make a 0.007 mil blackline clear-film, from which lines and point were hand digitized. Lines, points, and polygons were subsequently edited at the USGS using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum. proprietary +USGS_OFR01-227_1.0 Geologic Map Database of the Washington DC Area Featuring Data From Three 30' X 60' Quadrangles: Frederick, Washington West, and Fredericksburg, USGS OFR 01-227 CEOS_EXTRA STAC Catalog 1988-01-01 2001-12-31 -78, 38, -77, 39.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231550463-CEOS_EXTRA.umm_json Geology was researched and compiled for use in studies of ecosystem health, environmental impact, soils, groundwater, land use, tectonics, crustal genesis, sedimentary provenance, and any others that could benefit from geographically referenced geological data. The Washington DC Area geologic map database (DCDB) provides geologic map information of areas to the NW, W, and SW of Washington, DC to various professionals and private citizens who have uses for geologic data. Digital, geographically referenced, geologic data is more versatile than traditional hard copy maps, and facilitates the examination of relationships between numerous aspects of the geology and other types of data such as: land-use data, vegetation characteristics, surface water flow and chemistry, and various types of remotely sensed images. The DCDB was created by combining Arc/Info coverages, designing a Microsoft (MS) Access database, and populating this database. Proposed improvements to the DCDB include the addition of more geochemical, structural, and hydrologic data. Data are provided in several common GIS formats and MS Access database files. The geologic data themes included are bedrock, surficial, faults and fold axes, neat line, structural data, and sinkholes; the base themes are political boundaries, roads, elevation contours, and hydrography. Data were originally collected in UTM coordinates, zone 18, NAD 1927, and projected to geographic coordinates (Lat/Long), NAD 1983. The data base is accompanied by large format color maps, a readme.txt file, and a explanatory PDF pamphlet. proprietary +USGS_OFR01-290_1.0 Geologic map and digital database of the San Rafael Mtn. 7.5- minute Quadrangle, Santa Barbara County, California, USGS OFR 01-290 CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 -119.875, 34.624985, -119.74991, 34.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231549269-CEOS_EXTRA.umm_json This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:24,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, mineral and energy resources, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be substituted for comprehensive maps that systematically identify and classify landslide hazards. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (srm_expl.txt, srm_expl.pdf), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller. The databases in this report were compiled in ARC/INFO, a commercial Geographic Information System (Environmental Systems Research Institute, Redlands, California), with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files are COVERAGE (ARC/INFO vector data) format. Coverages are stored in uncompressed ARC export format (ARC/INFO version 8.0. 2). ARC/INFO export files (files with the .e00 extension) can be converted into ARC/INFO coverages in ARC/INFO (see below) and can be read by some other Geographic Information Systems, such as MapInfo via ArcLink and ESRI's ArcView (version 1.0 for Windows 3.1 to 3.11 is available for free from ESRI's web site: http://www.esri.com.) The digital compilation was done in version 7.2.1 of ARC/INFO with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The geologic map information was digitized from stable originals of the geologic maps at 1:24,000 scale. The author manuscripts (pen on mylar) were scanned using a Altek monochrome scanner with a resolution of 800 dots per inch. The scanned images were vectorized and transformed from scanner coordinates to projection coordinates with digital tics placed by hand at quadrangle corners. The scanned lines were edited interactively by hand using ALACARTE, color boundaries were tagged as appropriate, and some scanning artifacts visible at 1:24,000 were removed. This report consists of a set of geologic map database files (Arc/Info coverages) and supporting text and plotfiles. In addition, the report includes two sets of plotfiles (PostScript and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Open-File Report. These files are described in the explanatory pamphlets (srm.ps, srm.pdf, and srm.txt). The base map layers used in the preparation of the geologic map plotfiles were scanned from a scale-stable version of the USGS 1:24,000 topographic maps of the San Rafael Mtn. (1959, photorevised 1988) 7.5-minute quadrangle. The map has a 40 foot contour interval. proprietary +USGS_OFR01-293_Version 1.0 Geologic Map of the Telegraph Peak 7.5 min. quadrangle, San Bernardino County, California, USGS OFR 01-293 CEOS_EXTRA STAC Catalog 1974-01-01 1981-12-31 -117.62509, 34.249985, -117.49991, 34.375 https://cmr.earthdata.nasa.gov/search/concepts/C2231548656-CEOS_EXTRA.umm_json The data set for the Telegraph 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) and the California Division of Mines as part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographical Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the Telegraph 7.5' quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, the U.S. Forest Service, San Bernardino National Forest, may use the map and database as a basic geologic data source for soil studies, mineral resource evaluations, road building, biological surveys, and general forest management. The database is not suitable for site-specific geologic evaluations at scales greater than 1:24,000 (1 in = 2,000 ft). This data set maps and describes the geology of the Telegraph 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a double precision map coverage containing geologic contacts and units, (2) a coverage containing site-specific structural data, (3) a coverage containing geologic-unit label leaders and their associated attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of this Readme (including the metadata file as an appendix), Description of Map Units (DMU), and the graphic produced by the PostScript plot file. The Telegraph Peak quadrangle is located in the eastern San Gabriel Mountains part of the Transverse Ranges Province of southern California. The generally east-striking structural grain characteristic of the crystalline rocks of much of the San Gabriel Mountains is apparent, but not well developed in the Telegraph Peak quadrangle. Here, the east-striking structural grain is somewhat masked by the northwest-striking grain associated with the San Andreas Fault zone. Faults within the quadrangle include northwest-striking, right-lateral strike-slip faults of the San Andreas system. The active San Andreas Fault, located in the northern part of the quadrangle, dominates the younger structural elements. North of the San Andreas Fault is the inactive Cajon Valley Fault that was probably an early strand of the San Andreas system. It was active during deposition of the middle Miocene Cajon Valley Formation. South of the San Andreas, the Punchbowl Fault, which is probably a long-abandoned segment of the San Andreas Fault (Matti and Morton, 1993), has a sinuous trace apparently due to compression in the eastern San Gabriel Mountains that post-dates displacement on the fault. The Punchbowl Fault separates two major subdivisions of the Mesozoic Pelona Schist and is left-laterally offset by a northeast-striking fault in the northwestern part of the quadrangle. Within the Punchbowl Fault zone is a thin layer of highly deformed basement rock, which is clearly not part of the Pelona Schist. To the southeast, in the Devore quadrangle, this included basement rock attains a thickness of several hundred feet. Along strike to the northwest, Tertiary sedimentary rocks are included within the fault zone. South of the Punchbowl Fault are several arcuate (in plan) faults that are part of an antiformal schuppen-like fault complex of the eastern San Gabriel Mountains. Most of these arcuate faults are reactivated and deformed older faults, and probably include the eastern part of the San Gabriel Fault. The Vincent Thrust of late Cretaceous or early Tertiary age separates the Pelona Schist in the lower plate from a heterogeneous basement complex in the upper plate. Immediately above the Vincent Thrust is a variable thickness of mylonitic rock generally interpreted as a product of displacement on the thrust. The upper plate includes two Paleozoic units, a schist and gneiss sequence and a schist, quartzite, and marble metasedimentary sequence. Both sequences are thrust over the Mesozoic Pelona Schist along the Vincent Thrust, and intruded by Tertiary (late Oligocene) granitic rocks, granodiorite of Telegraph Peak, that also intrude the Vincent Thrust. The Pelona Schist consists mostly of greenschist to amphibolite metamorphic grade meta-basalt (greenschist and amphibolite) and meta-graywacke (siliceous and white mica schist), with minor impure quartzite and marble, in which all primary structures have been destroyed and all layering transposed. Cretaceous granitic rocks, chiefly tonalite, intrude the schist and gneiss sequence, but not the Pelona Schist or the Vincent Thrust. North of the San Andreas Fault, bedrock units consist of undifferentiated Cretaceous tonalite, here informally named tonalite of Circle Mountain, with some included small boldies of gneiss and marble. These basement rocks are the westward continuation of rocks of the San Bernardino Mountains and not rocks of the San Gabriel Mountains south of the San Andreas Fault. Also north of the San Andreas Fault are the Oligocene Vaqueros Formation, middle Miocene Cajon Valley Formation, and Pliocene rocks of Phelan Peak. The latter two formations are divided into several conglomerate and arkosic sandstone subunits. In the northeastern corner of the quadrangle, the rocks of Phelan Peak are unconformably overlain by the Quaternary Harold Formation and Shoemaker Gravel. Quaternary units ranging from early Pleistocene to recent are mapped, and represent alluvial fan, landslide, talus, and wash environments. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. This digital Open-File map supercedes an older analog Open-File map of the quadrangle, and includes extensive new data on the Quaternary deposits, and revises some fault and bedrock distribution within the San Gabriel Mountains. The digital map was compiled on a base-stable cronoflex copy of the Telegraph 7.5' topographic base and then scribed. This scribe guide was used to make a 0.007 mil blackline clear-film, from which lines and point were hand digitized. Lines, points, and polygons were subsequently edited at the USGS using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum. proprietary +USGS_OFR01-308_Version 1.0 Hawaii Beach Monitoring Program: Beach Profile Data for Maui and Oahu, Hawaii, USGS OFR 01-308 CEOS_EXTRA STAC Catalog 1994-08-03 1999-12-31 -158.29727, 20.571814, -155.95554, 21.746782 https://cmr.earthdata.nasa.gov/search/concepts/C2231549926-CEOS_EXTRA.umm_json "This data set is intended for scientific research of beach morphology and volume changes. Biannual beach profiles were collected at 42 Oahu and 36 Maui Locations between August 1994 and August 1999. Surveys were conducted at approximately summer-winter intervals and extend from landward of the active beach to about -4 meters water depth. Profile data on this CDROM are presented in both Microsoft EXCEL 97/98 & 5.0/95 Workbook (.xls) format and comma separated value (.csv) format. Graphical representation of the surveys (x vs. z and x vs. y) are presented in EXCEL format only. Site descriptions, including beach location, directions to site, GPS information, and a description of Reference Points used, are available in both EXCEL and ADOBE ACROBAT .pdf format. Cross-shore beach profile data were collected as a component of the Hawaii Coastal Erosion Study, a cooperative effort by U.S. Geological Survey and University of Hawaii in order to document seasonal and longer-term variations in beach volume and behavior. The overall objectives of the Hawaii Coastal Erosion Study are to document the recent history of shoreline change in Hawaii and to determine the primary factor(s) responsible for coastal erosion in low-latitude environments for the purpose of predicting future changes and to provide quality scientific data that is useful to other scientists, planners, engineers, and coastal managers. The overall strategy consists of first quantifying the magnitude and location of serious erosion problems followed by close monitoring of coastal change in critical areas. Bi-annual beach profiles have been collected at over 40 critical beach sites on the islands of Oahu and Maui. Once sufficient background information is analyzed and key problems are defined, field sites will be selected for detailed process- oriented studies (both physical and biological) to gain an understanding of the complex relationships between reef carbonate production, sediment dispersal, and the interaction of man-made structures with sediment movement along the shore. Information derived from this project will be used to develop general guidelines for sediment production, transport, and deposition of low- latitude coasts. Planned major products include a comprehensive atlas of coastal hazards, journal articles and reports presenting results of our studies, and a ""living"" database of shoreline history and changes based on results of the beach profile monitoring and softcopy photogrammetric analysis." proprietary +USGS_OFR01-30_1.0 Geologic Map and Digital Database of the Porcupine Wash 7.5 minute Quadrangle, Riverside County, California, USGS OFR 01-30 CEOS_EXTRA STAC Catalog 1973-01-01 2000-12-31 -115.87509, 33.749985, -115.74991, 33.875 https://cmr.earthdata.nasa.gov/search/concepts/C2231552435-CEOS_EXTRA.umm_json The data set for the Porcupine Wash quadrangle has been prepared by the Southern California Areal Mapping Project (SCAMP), a cooperative project sponsored jointly by the U.S. Geological Survey and the California Division of Mines and Geology. The Porcupine Wash data set represents part of an ongoing effort to create a regional GIS geologic database for southern California. This regional digital database, in turn, is being developed as a contribution to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The Porcupine Wash database has been prepared in cooperation with the National Park Service as part of an ongoing project to provide Joshua Tree National Park with a geologic map base for use in managing Park resources and developing interpretive materials. The digital geologic map database for the Porcupine Wash quadrangle has been created as a general-purpose data set that is applicable to land-related investigations in the earth and biological sciences. Along with geologic map databases in preparation for adjoining quadrangles, the Porcupine Wash database has been generated to further our understanding of bedrock and surficial processes at work in the region and to document evidence for seismotectonic activity in the eastern Transverse Ranges. The database is designed to serve as a base layer suitable for ecosystem and mineral resource assessment and for building a hydrogeologic framework for Pinto Basin. This data set maps and describes the geology of the Porcupine Wash 7.5 minute quadrangle, Riverside County, southern California. The quadrangle, situated in Joshua Tree National Park in the eastern Transverse Ranges physiographic and structural province, encompasses parts of the Hexie Mountains, Cottonwood Mountains, northern Eagle Mountains, and south flank of Pinto Basin. It is underlain by a basement terrane comprising Proterozoic metamorphic rocks, Mesozoic plutonic rocks, and Mesozoic and Mesozoic or Cenozoic hypabyssal dikes. The basement terrane is capped by a widespread Tertiary erosion surface preserved in remnants in the Eagle and Cottonwood Mountains and buried beneath Cenozoic deposits in Pinto Basin. Locally, Miocene basalt overlies the erosion surface. A sequence of at least three Quaternary pediments is planed into the north piedmont of the Eagle and Hexie Mountains, each in turn overlain by successively younger residual and alluvial deposits. The Tertiary erosion surface is deformed and broken by north-northwest-trending, high-angle, dip-slip faults and an east-west trending system of high-angle dip- and left-slip faults. East-west trending faults are younger than and perhaps in part coeval with faults of the northwest-trending set. The Porcupine Wash database was created using ARCVIEW and ARC/INFO, which are geographical information system (GIS) software products of Environmental Systems Research Institute (ESRI). The database consists of the following items: (1) a map coverage showing faults and geologic contacts and units, (2) a separate coverage showing dikes, (3) a coverage showing structural data, (4) a scanned topographic base at a scale of 1:24,000, and (5) attribute tables for geologic units (polygons and regions), contacts (arcs), and site-specific data (points). The database, accompanied by a pamphlet file and this metadata file, also includes the following graphic and text products: (1) A portable document file (.pdf) containing a navigable graphic of the geologic map on a 1:24,000 topographic base. The map is accompanied by a marginal explanation consisting of a Description of Map and Database Units (DMU), a Correlation of Map and Database Units (CMU), and a key to point-and line-symbols. (2) Separate .pdf files of the DMU and CMU, individually. (3) A PostScript graphic-file containing the geologic map on a 1:24,000 topographic base accompanied by the marginal explanation. (4) A pamphlet that describes the database and how to access it. Within the database, geologic contacts , faults, and dikes are represented as lines (arcs), geologic units as polygons and regions, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum and link it to other tables (.rel) that provide more detailed geologic information. Map nomenclature and symbols Within the geologic map database, map units are identified by standard geologic map criteria such as formation-name, age, and lithology. The authors have attempted to adhere to the stratigraphic nomenclature of the U.S. Geological Survey and the North American Stratigraphic Code, but the database has not received a formal editorial review of geologic names. Special symbols are associated with some map units. Question marks have been added to the unit symbol (e.g., QTs?, Prpgd?) and unit name where unit assignment based on interpretation of aerial photographs is uncertain. Question marks are plotted as part of the map unit symbol for those polygons to which they apply, but they are not shown in the CMU or DMU unless all polygons of a given unit are queried. To locate queried map-unit polygons in a search of database, the question mark must be included as part of the unit symbol. Geologic map unit labels entered in database items LABL and PLABL contain substitute characters for conventional stratigraphic age symbols: Proterozoic appears as 'Pr' in LABL and as '<' in PLABL, Triassic appears as 'Tr' in LABL and as '^' in PLABL. The substitute characters in PLABL invoke their corresponding symbols from the GeoAge font group to generate map unit labels with conventional stratigraphic symbols. proprietary +USGS_OFR01-311_Version 1.0 Geologic Map of the Cucamonga Peak 7.5' Quadrangle, San Bernardino County, California, USGS OFR 01-311 CEOS_EXTRA STAC Catalog 1974-01-01 1981-12-31 -117.62509, 34.124985, -117.49991, 34.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231553911-CEOS_EXTRA.umm_json The data set for the Cucamonga Peak 7.5' quadrangle has been prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as part of an ongoing effort to develop a regional geologic framework of southern California, andto utilize a Geographical Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the Cucamonga Peak 7.5' quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, the U.S. Forest Service and the San Bernardino National Forest may use the map and data base as a basic geologic data source for soil studies, mineral resource evaluations, road building, biologicalsurveys, and general forest management. The Cucamonga Peak database is not suitablefor site-specific geologic evaluations at scales greater than 1:24,000 (1in = 2,000 ft.). This data set maps and describes the geology of the Cucamonga Peak 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the database consists of the following items: (1) a map coverage containing geologic contacts and units, (2) a coverage containing site-specific structural data, (3) a coverage containing geologic-unit label leaders and their associated attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of this Readme (including the metadata file as an appendix) and the graphic produced by the PostScript plot file. The Cucamonga Peak quadrangle includes part of the boundary between two major physiographic provinces of California, the Transverse Ranges Province to the north and the Peninsular Ranges Province to the south. The north part of the quadrangle isin the eastern San Gabriel Mountains, and the southern part includes an extensive quaternary alluvial-fan complex flanking the upper Santa Ana River valley, the northernmost part of the Peninsular Ranges Province. Thrust faults of the active Cucamonga Fault zone along the the south margin of the San Gabriel Mountains are the rejuvenated eastern terminus of a major old fault zone that bounds the south side of the western and central Transverse Ranges (Morton and Matti, 1993). Rejuvenation of this old fault zone, including the Cucamonga Fault zone, is apparently in response to compression in the eastern San Gabriel Mountains resulting from initiation of right-lateral slip on the San Jacinto Fault zone in the Peninsular Ranges. Within the northern part of the quadrangle are several arcuate-in-plan faults that are part of an antiformal, schuppen-like fault complex of the eastern San Gabriel Mountains. Most of these arcuate faults are reactivated and deformed older faults that probably include the eastern part of the San Gabriel Fault. The structural grain within the San Gabriel Mountains, as defined by basement rocks, is generally east striking. Within the Cucamonga Peak quadrangle, these basement rocks include a Paleozoic schist and gneiss sequence which occurs as large, continuous and discontinuous bodies intruded by Cretaceous granitic rocks. Most of the granitic rocks are of tonalitic composition, and many are mylonitic. South of the granitic rocks is a comple assemblage of Proterozoic(?) metamorphic rocks, at least part of which is metasedimentary. This assemblage is intruded by Cretaceous tonalite on its north side, and by charnockitic rocks near the center of the mass. The charnockitic rocks are in contact with no other Cretaceous granitic rocks. Consequently, their relative position in the intrusive sequence is unknown. The Proterozoic(?) assemblage was metamorphosed to upper amphibolite and lower granulite grade, and subsequently to a lower metamorphic grade. It is also intensely deformed by mylonitization characterized by an east-striking, north-dipping foliation, and by a pronounced subhorizontal lineation that plunges shallowly east and west. The southern half of the quadrangle is dominated by extensive, symmetrical alluvial-fan complexes, particularly two emanating from Day and Deer Canyons. Other Quaternary units ranging from early Pleistocene to recent are mapped, and represent alluvial-fan, landslide, talus, and wash environments. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. This digital Open-File map supercedes an older analog Open-File map of the quadrangle, and includes extensive new data on the Quaternary deposits, and revises some fault and bedrock distribution within the San Gabriel Mountains. The digital map was compiled on a base-stable cronoflex copy of the Cucamonga Peak 7.5' topographic base and then scribed. This scribe guide was used to make a 0.007 mil blackline clear-film, from which lines and point were hand digitized. Lines, points, and polygons were subsequently edited at the USGS using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum. proprietary USGS_OFR01-318 Coal Geology, Land Use, and Human Health in the Peoples Republic of China CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 87.6347, 21.6875, 131.238, 49.3578 https://cmr.earthdata.nasa.gov/search/concepts/C2232411625-CEOS_EXTRA.umm_json The purpose of this dataset is to give geologists and other scientists a spatial database of major coal mines and their production in China. This data set will be utilized in energy research and cartographic projects. This data set consists of several databases on coal mining activities in China: The ESRI ArcView shapefiles are the coverages of political boundaries, counties, provinces, cities, urban areas, airfields, roads, railroads, electrical power network, river networks, ecoregions, population density, dental fluorosis prevalence rates, elevated flouride sources, ore deposits, oil and gas fields, coal-bearing age units, coal fields, coal mine production and rank, and geology (scale 1:10,000,000). proprietary USGS_OFR01-318_fl_pollution Coal Geology, Land Use, and Human Health in the Peoples Republic of China: Sources of Fluorosis Pollution CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 73.6302, 18.2277, 134.654, 53.5493 https://cmr.earthdata.nasa.gov/search/concepts/C2232411636-CEOS_EXTRA.umm_json To describe areal extents of environmental pollution sources of fluorosis affected areas in China. This dataset will be used to provide support for the assessment and analysis of coal quality and potential inventories and risks to human health in China. Map representing environmental pollution sources of fluorsis affected areas in the People's Republic of China. This coverage was developed as part of the World Coal Quality Inventory (WoCQI) project by the Energy Resources Team, U.S. Geological Survey. proprietary USGS_OFR01-318_fluorosis Coal Geology, Land Use, and Human Health in the Peoples Republic of China: Prevalence of Endemic Fluorosis CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 73.7811, 18.1613, 130.573, 49.2412 https://cmr.earthdata.nasa.gov/search/concepts/C2231554691-CEOS_EXTRA.umm_json To describe areal extents and rates of endemic flourosis in China. This dataset will be used to provide support for the assessment and analysis of coal quality and potential inventories and risks to human health in China. Map representing prevalence rates of endemic fluorosis in the People's Republic of China. This coverage was developed as part of the World Coal Quality Inventory (WoCQI) project by the Energy Resources Team, U.S. Geological Survey. proprietary USGS_OFR01-318_power_lines Coal Geology, Land Use, and Human Health in the Peoples Republic of China: Power line locations CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 98.1689, 21.1354, 131.748, 49.0443 https://cmr.earthdata.nasa.gov/search/concepts/C2231551642-CEOS_EXTRA.umm_json The purpose of this dataset is to give geologists and other scientists a spatial database of the electrical power line network in China. This data set will be utilized in energy research and cartographic projects. This dataset contains the distribution, type, and power handling capacity of power line networks in the People's Republic of China. proprietary USGS_OFR01-318_power_stations Coal Geology, Land Use, and Human Health in the Peoples Republic of China: Power station locations CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 98.1806, 21.1179, 131.755, 49.0413 https://cmr.earthdata.nasa.gov/search/concepts/C2231550040-CEOS_EXTRA.umm_json The purpose of this dataset is to give geologists and other scientists a spatial database of electrical power stations in China. This data set will be utilized in energy research and cartographic projects. This dataset contains locations, type, and power generation capacity of powerplants/powerstations in the People's Republic of China. proprietary USGS_OFR01-318coal_mines Coal Geology, Land Use, and Human Health in the Peoples Republic of China: Coal Mine Locations CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 87.6347, 21.6875, 131.238, 49.3578 https://cmr.earthdata.nasa.gov/search/concepts/C2231550697-CEOS_EXTRA.umm_json The purpose of this dataset is to give geologists and other scientists a spatial database of major coal mines and their production in China. This data set will be utilized in energy research and cartographic projects. This dataset is a collection of major coal mine locations and production data for The Peoples Republic of China. Included in this dataset are the locations of major coal mines, Coal Mining Administrations, and the approximate annual amount of coal and type mined annually in millions of metric tons. Procedures_Used: The major coal mine production points were digitized utilizing ARC/VIEW from page-size maps found in the U.S. Environmental Protection Agency Report ERP 430-R-96-005. Revisions: none Reviews_Applied_to_Data: None proprietary +USGS_OFR01-31_Version 1.0 Geologic Map and Digital Database of the Conejo Well 7.5 Minute Quadrangle, Riverside County, California, USGs OFR 01-31 CEOS_EXTRA STAC Catalog 1973-01-01 2000-12-31 -115.75009, 33.749985, -115.62491, 33.875 https://cmr.earthdata.nasa.gov/search/concepts/C2231552761-CEOS_EXTRA.umm_json The data set for the Conejo Well quadrangle has been prepared by the Southern California Areal Mapping Project (SCAMP), a cooperative project sponsored jointly by the U.S. Geological Survey and the California Division of Mines and Geology. The Conejo Well data set represents part of an ongoing effort to create a regional GIS geologic database for southern California. This regional digital database, in turn, is being developed as a contribution to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The Conejo Well database has been prepared in cooperation with the National Park Service as part of an ongoing project to provide Joshua Tree National Park with a geologic map base for use in managing Park resources and developing interpretive materials. The digital geologic map database for the Conejo Well quadrangle has been created as a general-purpose data set that is applicable to land-related investigations in the earth and biological sciences. Along with geologic map databases in preparation for adjoining quadrangles, the Conejo Well database has been generated to further our understanding of bedrock and surficial processes at work in the region and to document evidence for seismotectonic activity in the eastern Transverse Ranges. The database is designed to serve as a base layer suitable for ecosystem and mineral resource assessment and for building a hydrogeologic framework for Pinto Basin. This data set maps and describes the geology of the Conejo Well 7.5 minute quadrangle, Riverside County, southern California. The quadrangle, situated in Joshua Tree National Park in the eastern Transverse Ranges physiographic and structural province, encompasses part of the northern Eagle Mountains and part of the south flank of Pinto Basin. It is underlain by a basement terrane comprising Proterozoic metamorphic rocks, Mesozoic plutonic rocks, and Mesozoic and Mesozoic or Cenozoic hypabyssal dikes. The basement terrane is capped by a widespread Tertiary erosion surface preserved in remnants in the Eagle Mountains and buried beneath Cenozoic deposits in Pinto Basin. Locally, Miocene basalt overlies the erosion surface. A sequence of at least three Quaternary pediments is planed into the north piedmont of the Eagle Mountains, each in turn overlain by successively younger residual and alluvial deposits. The Tertiary erosion surface is deformed and broken by north-northwest-trending, high-angle, dip-slip faults in the Eagle Mountains and an east-west trending system of high-angle dip- and left-slip faults. In and adjacent to the Conejo Well quadrangle, faults of the northwest-trending set displace Miocene sedimentary rocks and basalt deposited on the Tertiary erosion surface and Pliocene and (or) Pleistocene deposits that accumulated on the oldest pediment. Faults of this system appear to be overlain by Pleistocene deposits that accumulated on younger pediments. East-west trending faults are younger than and perhaps in part coeval with faults of the northwest-trending set. The Conejo Well database was created using ARCVIEW and ARC/INFO, which are geographical information system (GIS) software products of Environmental Systems Research Institute (ESRI). The database consists of the following items: (1) a map coverage showing faults and geologic contacts and units, (2) a separate coverage showing dikes, (3) a coverage showing structural data, (4) a point coverage containing line ornamentation, and (5) a scanned topographic base at a scale of 1:24,000. The coverages include attribute tables for geologic units (polygons and regions), contacts (arcs), and site-specific data (points). The database, accompanied by a pamphlet file and this metadata file, also includes the following graphic and text products: (1) A portable document file (.pdf) containing a navigable graphic of the geologic map on a 1:24,000 topographic base. The map is accompanied by a marginal explanation consisting of a Description of Map and Database Units (DMU), a Correlation of Map and Database Units (CMU), and a key to point-and line-symbols. (2) Separate .pdf files of the DMU and CMU, individually. (3) A PostScript graphic-file containing the geologic map on a 1:24,000 topographic base accompanied by the marginal explanation. (4) A pamphlet that describes the database and how to access it. Within the database, geologic contacts , faults, and dikes are represented as lines (arcs), geologic units as polygons and regions, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum and link it to other tables (.rel) that provide more detailed geologic information. Map nomenclature and symbols Within the geologic map database, map units are identified by standard geologic map criteria such as formation-name, age, and lithology. The authors have attempted to adhere to the stratigraphic nomenclature of the U.S. Geological Survey and the North American Stratigraphic Code, but the database has not received a formal editorial review of geologic names. Special symbols are associated with some map units. Question marks have been added to the unit symbol (e.g., QTs?, Jmi?) and unit name where unit assignment based on interpretation of aerial photographs is uncertain. Question marks are plotted as part of the map unit symbol for those polygons to which they apply, but they are not shown in the CMU or DMU unless all polygons of a given unit are queried. To locate queried map-unit polygons in a search of database, the question mark must be included as part of the unit symbol. In some polygons, multiple units crop out in individual domains that are too small or too intricately intermingled to distinguish at 1:24,000, or for which relations are not well documented. For these polygons, unit symbols are combined using plus (+) signs (e.g., Qyaos + Qyas2) in the LABL and PLABL items. Geologic map unit labels entered in database items LABL and PLABL contain substitute characters for conventional stratigraphic age symbols: Proterozoic appears as 'Pr' in LABL and as '<' in PLABL, Triassic appears as 'Tr' in LABL and as '^' in PLABL. The substitute characters in PLABL invoke their corresponding symbols from the GeoAge font group to generate map unit labels with conventional stratigraphic symbols. proprietary USGS_OFR01-321_Version 1.0 Chromite Deposits in Stillwater Complex, Sweet Grass Co., MT: A Digital Database for the Geologic Map of the East Slope of Iron Mountain, USGS OFR 01-321 CEOS_EXTRA STAC Catalog 1955-01-01 1955-12-31 -110.06, 45.4, -110.05, 45.41 https://cmr.earthdata.nasa.gov/search/concepts/C2231550178-CEOS_EXTRA.umm_json This data set was developed to provide a geologic map GIS of the east slope of Iron Mountain, Sweet Grass County, Montana for use in future spatial analysis by a variety of users. These data can be printed in a variety of ways to display various geologic features or used for digital analysis and modeling. This database is not meant to be used or displayed at any scale larger than 1:3077 (for example, 1:2000 or 1:1500). The digital geologic map of the east slope of Iron Mountain, Sweet Grass County, Montana was prepared from preliminary digital datasets digitized by Optronics Specialty Co., Inc. from a paper copy of plate 10 from UGSG Bulletin 1015-D (Howland, 1955).The files were prepared and transformed to the Montana State Plane South projection by Helen Z. Kayser (Information Systems Support, Inc.). Further editing and attributing was performed by Lorre A. Moyer in 2001. The resulting spatial digital database can be queried in many ways to produce a variety of derivative geologic maps. This GIS consists of two major ArcInfo datasets: one line and polygon file (ironmtn) containing geologic contacts and structures (lines) and geologic map rock units (polygons), and one point file (ironmtnp) containing structural data. proprietary USGS_OFR01-411_1 Chemical Composition of Samples Collected from Waste Rock Dumps and Other Mining-Related Features at Selected Phosphate Mines USGS OFR 01-411 CEOS_EXTRA STAC Catalog 1999-01-01 2000-12-31 -112.1294, 40.1008, -110.5823, 43.0326 https://cmr.earthdata.nasa.gov/search/concepts/C2231553432-CEOS_EXTRA.umm_json Chemical Composition of Samples Collected from Waste Rock Dumps and Other Mining-Related Features at Selected Phosphate Mines in Southeastern Idaho, Western Wyoming, and Northern Utah The sampling effort was undertaken as a reconnaissance and does not constitute a characterization of mine wastes. Twenty-five samples were collected from waste rock dumps, 2 from stockpiles, and 1 each from slag, tailings, mill shale, and an outcrop. All samples were analyzed for a suite of major, minor, and trace elements. This text file contains chemical analyses for 31 samples collected from various phosphate mine sites in southeastern Idaho (25), northern Utah (2), and western Wyoming (4). proprietary +USGS_OFR01139_Version 1.0 Geochemical Analysis of Soils and Sediments, Coeur d'Alene Drainage Basin, Idaho: Sampling, Analytical Methods, and Results, USGS OFR 00-139 CEOS_EXTRA STAC Catalog 1993-01-01 2000-12-31 -116.73, 47.47, -115.72, 47.58 https://cmr.earthdata.nasa.gov/search/concepts/C2231549496-CEOS_EXTRA.umm_json This report was prepared to document the chemical composition of sediments and soils in the Coeur d'Alene (CdA) drainage basin in northern Idaho. These compositions are of interest because of the potential for human and wildlife health impacts from high metal contents of some sediments and soils from over 100 years of mining activity. This report presents the results of over 1100 geochemical analyses of samples of soil and sediment from the Coeur d'Alene (CdA) drainage basin in northern Idaho. The location (in 3 dimensions) and a lithological description of each sample is included with the laboratory analytical data. Methods of sample location, collection, preparation, digestion and geochemical analysis are described. Five different laboratories contributed geochemical data for this report and the quality control procedures used by each laboratory are described. Comparison of the analytical accuracy and precision of each laboratory is given by comparing analyses of standard reference materials and of splits of CdA samples. These geochemical data are presented in seven MS Excel tables and seven dBase4 tables. The seven dBase4 files allow users to more easily import these geochemical data into a GIS. Only one of these seven tables includes geospatial data AppendixB. However, in AppendixB there is a Site ID column that will allow users to link or join the matching Site Id columns in the six associated lithologic and geochemical tables. Due to format constraints of dBase4, the column names (headers) had to be modified to a maximum of only ten ASCII characters. As a result, some of the dBase4 column header names can be rather cryptic. To assist dBase files users, this ten digit dBase4 column name is also found directly under the more descriptive column names found in the MS Excel tables packaged with this report. Additional formatting requirements such as changing the below detection limit symbol (<) to a negative symbol (-) were used to accurately display the data the dBase4 format. This dataset consists of the following MS Excel 2000 spreadsheets and equivalent dBase4 files: AppendixB.xls, AppendixB.dbf: sample location and site description AppendixC.xls, AppendixC.dbf: lithologic descriptions of samples AppendixD.xls, AppendixD.dbf: USGS EDXRF Data AppendixE.xls, AppendixE.dbf: EWU 4-acid ICP-MS, ICP-AES, and FAA Data AppendixF.xls, AppendixF.dbf: CHEMEX nitric/aqua regia ICP-AES Data AppendixG.xls, AppendixG.dbf: XRAL 4-acid ICP-AES Data AppendixH.xls, AppendixH.dbf: ACZ microwave assisted nitric acid ICP-AES Data proprietary +USGS_OFR_00-376_1.0 Geologic map and database of the Roseburg 30 x 60 minute quadrangle, Douglas and Coos Counties, Oregon, USGS OFR 00-376 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -123.99993, 42.995632, -122.99986, 43.504375 https://cmr.earthdata.nasa.gov/search/concepts/C2231548720-CEOS_EXTRA.umm_json This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:100,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the Oregon State Geologist. Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, largely compiled from new mapping by the authors, represents the general distribution of bedrock and surficial deposits of the Roseburg 30 x 60 minute quadrangle along the southeastern margin of the Oregon Coast Range and its tectonic boundary with Mesozoic terranes of the Klamath Mountains. Together with the accompanying text files as PDF (rb_geol.pdf), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps is 1:24,000, but the Quaternary contacts and structural data have been much simplified for the 1:100,000-scale map and database. The spatial resolution (scale) of the database is 1:100,000 or smaller. proprietary +USGS_OFR_00135_Version 1.0 Digital geologic map of the Coeur d'Alene 1:100,000 quadrangle, Idaho and Montana CEOS_EXTRA STAC Catalog 2000-10-05 2000-10-05 -117, 47.5, -116, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2231550217-CEOS_EXTRA.umm_json This data set was developed to provide geologic map GIS of the Coeur d'Alene 1:100,000 quadrangle for use in future spatial analysis by a variety of users. These data can be printed in a variety of ways to display various geologic features or used for digital analysis and modeling. This database is not meant to be used or displayed at any scale larger than 1:100,000 (e.g. 1:62,500 or 1:24,000). The digital geologic map of the Coeur d'Alene 1:100,000 quadrangle was compiled from preliminary digital datasets [Athol, Coeur d'Alene, Kellogg, Kingston, Lakeview, Lane, and Spirit Lake 15-minute quadrangles] prepared by the Idaho Geological Survey from A. B. Griggs (unpublished field maps), supplemented by Griggs (1973) and by digital data from Bookstrom and others (1999) and Derkey and others (1996). The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps. This GIS consists of two major Arc/Info data sets: one line and polygon file (cda100k) containing geologic contacts and structures (lines) and geologic map rock units (polygons), and one point file (cda100kp) containing structural data. proprietary USGS_OFR_00142 Archive of Boomer and Sparker Subbottom Data Collected During USGS Cruise DIAN 97032 Long Island, NY, Inner Shelf-Fire Island, NY, September 24-October 19, 1997 CEOS_EXTRA STAC Catalog 1997-09-24 1997-10-19 -72.83, 40.64, -71.79, 41.08 https://cmr.earthdata.nasa.gov/search/concepts/C2231548465-CEOS_EXTRA.umm_json This project will generate reconnaissance maps of the sea floor offshore of the New York - New Jersey metropolitan area -- the most heavily populated, and one of the most impacted coastal regions of the United States. The surveys will provide an overall synthesis of the sea floor environment, including seabed texture and bed forms, Quaternary strata geometry, and anthropogenic impact (e.g., ocean dumping, trawling, channel dredging). The goal of this project is to survey the offshore area, the harbor, and the southern shore of Long Island, providing a regional synthesis to support a wide range of management decisions and a basis for further process-oriented investigations. The project is conducted cooperatively with the U.S. Army Corps of Engineer (USACE). This CD-ROM contains digital high resolution seismic reflection data collected during the USGS Diane G 97032 cruise. The coverage is the nearshore of Long Island, NY in the vicinity of Fire Island. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary +USGS_OFR_00145_Version 1.0 Digital Geologic Map of the Butler Peak 7.5' Quadrangle, San Bernardino County, California CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -117.12509, 34.249985, -116.99991, 34.375004 https://cmr.earthdata.nasa.gov/search/concepts/C2231549734-CEOS_EXTRA.umm_json The data set for the Butler Peak quadrangle has been prepared by the Southern California Areal Mapping Project (SCAMP), a cooperative project sponsored jointly by the U.S. Geological Survey and the California Division of Mines and Geology, as part of an ongoing effort to utilize a Geographical Information System (GIS) format to create a regional digital geologic database for southern California. This regional database is being developed as a contribution to the National Geologic Map Data Base of the National Cooperative Geologic Mapping Program of the USGS. Development of the data set for the Butler Peak quadrangle has also been supported by the U.S. Forest Service, San Bernardino National Forest. The digital geologic map database for the Butler Peak quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. For example, the U.S. Forest Service, San Bernardino National Forest, is using the database as part of a study of an endangered plant species that shows preference for particular rock type environments. The Butler Peak database is not suitable for site-specific geologic evaluations at scales greater than 1:24,000 (1 in = 2,000 ft). This data set maps and describes the geology of the Butler Peak 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage showing geologic contacts and units,(2) a scanned topographic base at a scale of 1:24,000, and (3) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map on a 1:24,000 topographic base accompanied by a Description of Map Units (DMU), a Correlation of Map Units (CMU), and a key to point and line symbols; (2) PDF files of the DMU and CMU, and of this Readme, and (3) this metadata file. The geologic map data base contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. The map was created by transferring lines from the aerial photographs to a 1:24,000 mylar orthophoto-quadrangle and then to a base-stable topographic map. This map was then scribed, and a .007 mil, right-reading, black line clear film made by contact photographic processes.The black line was scanned and auto-vectorized by Optronics Specialty Company, Northridge, CA. The non-attributed scan was imported into ARC/INFO, where the database was built. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum and link it to other tables (.rel) that provide more detailed geologic information. proprietary +USGS_OFR_0014_version 1.0 Geologic datasets for weights of evidence analysis in northeast Washington--4. Mineral industry activity in Washington, 1985-1997. CEOS_EXTRA STAC Catalog 1985-01-01 1997-12-31 -123, 45, -117, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231551581-CEOS_EXTRA.umm_json This dataset was developed to provide mineral resource data for the region of northeast WA for use in future spatial analysis by a variety of users. This database is not meant to be used or displayed at any scale larger than 1:24,000 This report is a tabular presentation of mineral activities for mining and exploration in Washington during 1985 to 1997. The data may be incomplete as it depended on published data or data volunteered by operators. proprietary USGS_OFR_00152 Archive of Chirp Subbottom Data Collected During USGS Cruise DIAN 97032, Long Island, NY Inner Shelf -- Fire Island, NY, 25 September-19 October, 1997 CEOS_EXTRA STAC Catalog 1997-10-19 1997-10-25 -73.53, 40.5, -72.82, 40.78 https://cmr.earthdata.nasa.gov/search/concepts/C2231550197-CEOS_EXTRA.umm_json This project will generate reconnaissance maps of the sea floor offshore of the New York - New Jersey metropolitan area -- the most heavily populated, and one of the most impacted coastal regions of the United States. The surveys will provide an overall synthesis of the sea floor environment, including seabed texture and bed forms, Quaternary strata geometry, and anthropogenic impact (e.g., ocean dumping, trawling, channel dredging). The goal of this project is to survey the offshore area, the harbor, and the southern shore of Long Island, providing a regional synthesis to support a wide range of management decisions and a basis for further process-oriented investigations. The project is conducted cooperatively with the U.S. Army Corps of Engineer (USACE). This CD-ROM contains digital high resolution seismic reflection data collected during the USGS DIAN 97032 cruise. The coverage is the nearshore of Long Island, NY in the vicinity of Fire Island. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary USGS_OFR_00153 Archive of Boomer Subbottom Data Collected During USGS Cruise ATSV 99044 Myrtle Beach, South Carolina, 29 October - 12 November, 1999 CEOS_EXTRA STAC Catalog 1999-10-29 1999-11-12 -79.1, 33.4, -78.6, 33.8 https://cmr.earthdata.nasa.gov/search/concepts/C2231549843-CEOS_EXTRA.umm_json In November 1999, the U. S. Geological Survey, in cooperation with Coastal Carolina University, began a program to produce geologic maps of the nearshore regime off northern South Carolina, utilizing high resolution sidescan sonar, interferometric (direct phase methods) swath bathymetry, and seismic subbottom profiling systems. The study areas extends from the ~7m isobath to about 10km offshore (water depths <12m). The goals of the investigation are to determine regional scale sand resource availability needed for planned beach nourishment programs, to investigate the roles that the inner shelf morphology and geologic framework play in the evolution of this coastal region, and to provide baseline geologic maps for use in proposed biologic habitat studies. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS ATSV 99044 cruise. The coverage is the nearshore of the northern South Carolina. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary +USGS_OFR_00175_Version 1.0 Geologic Map and Digital Database of the Cougar Buttes 7.5' Quadrangle, San Bernardino County, California CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -116.87509, 34.374985, -116.74991, 34.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231554454-CEOS_EXTRA.umm_json The data set for the Cougar Buttes quadrangle has been prepared by the Southern California Areal Mapping Project (SCAMP), a cooperative project sponsored jointly by the U.S. Geological Survey and the California Division of Mines and Geology, as part of an ongoing effort to utilize a Geographical Information System (GIS) format to create a regional digital geologic database for southern California. This regional database is being developed as a contribution to the National Geologic Map Data Base of the National Cooperative Geologic Mapping Program of the USGS. Development of the data set for the Cougar Buttes quadrangle has also been supported by the Mojave Water Agency and U.S. Forest Service, San Bernardino National Forest. The digital geologic map database for the Cougar Buttes quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. In cooperation with the Water Resources Division of the U.S. Geological Survey, we have used our mapping in the Cougar Buttes and adjoining quadrangles together with well log data to develop a hydrogeologic framework for the basin. In an effort to understand surficial processes and to provide a base suitable for ecosystem assessment, we have differentiated surficial veneers on piedmont and pediment surfaces and distinguished the various substrates found beneath these veneers. Currently, the geologic database for the Cougar Buttes quadrangle is being applied in groundwater investigations in the Lucerne Valley basin (USGS, Water Resources Division), in biological species studies of the Cushenbury Canyon area (U.S. Forest Service, San Bernardino National Forest), and in the study of soils on various Quaternary landscape surfaces on the north piedmont of the San Bernardino Mountains (University of New Mexico). The Cougar Buttes database is not suitable for site-specific geologic evaluations at scales greater than 1:24,000 (1 in = 2,000 ft). This data set maps and describes the geology of the Cougar Buttes 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage showing geologic contacts and units, (2) a separate coverage layer showing structural data, (3) a scanned topographic base at a scale of 1:24,000, and (4) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). The data base is accompanied by a readme file and this metadata file. In addition, the data set includes the following graphic and text products: (1) A portable document file (.pdf) containing a browse-graphic of the geologic map on a 1:24,000 topographic base. The map is accompanied by a marginal explanation consisting of a Description of Map Units (DMU), a Correlation of Map Units (CMU), and a key to point and line symbols. (2) Separate .pdf files of the DMU and CMU, individually. (3) A PostScript graphic plot-file containing the geologic map on a 1:24,000 topographic base accompanied by the marginal explanation. (4) A pamphlet that summarizes the late Cenozoic geology of the Cougar Buttes quadrangle. The geologic map data base contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs, including low-altitude color and black-and-white photographs and high-altitude infrared photographs. The map was created by transferring lines from the aerial photographs to a 1:24,000 topographic base via a mylar orthophoto-quadrangle or by using a PG-2 plotter. The map was then scribed, scanned, and imported into ARC/INFO, where the database was built. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum and link it to other tables (.rel) that provide more detailed geologic information. proprietary USGS_OFR_00177 Archive of Sidescan-Sonar Data and DGPS Navigation Data Collected During USGS Cruise ATSV 99045 North Carolina Coast, NC, 7-27 October, 1999 CEOS_EXTRA STAC Catalog 1999-10-07 1999-10-27 -76.96, 34.6, -75.53, 36.58 https://cmr.earthdata.nasa.gov/search/concepts/C2231553610-CEOS_EXTRA.umm_json In 1999, the USGS began developing a cooperative mapping program in North Carolina, with collaborators at the North Carolina Geological Survey (NCGS), and academic institutions. The goal of the program is to develop a refined understanding of the regional geological framework and non-living resources of the North Carolina coastal area, including the emerged and submerged portions of the Coastal Plain. The USGS Coastal and Marine Geology Program is focusing on nearshore morphologic evolution (using LIDAR), short-term shoreline change (with SWASH), and with the present cruise, collecting data on the geologic framework of the shoreface and inner continental shelf. The goal of the inner shelf mapping program is to provide a regional synthesis of the seafloor environment, including a description of sedimentary environments, sediment texture, seafloor morphology, and shallow stratigraphy to aid in understanding the long- and short-term evolution of the coastal system, the form and distribution of sand and gravel resources, and to provide a basis for sediment dynamics studies. proprietary +USGS_OFR_00192_Version 1.0 Geologic Map of the Christian Quadrangle, Alaska CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -147, 67, -144, 68 https://cmr.earthdata.nasa.gov/search/concepts/C2231549713-CEOS_EXTRA.umm_json Digital representation of geologic mapping facilitates the presentation and analysis of earth-science data. Digital maps may be displayed at any scale or projection, however the geologic data in this coverage is not intended for use at a scale larger than 1:250,000. This data set represents reconnaissance geologic mapping of the Christian quadrangle, Alaska. It is used to create the mapsheet in USGS OFR 00-192, which shows bedrock and surficial deposits of the 1:250,000 scale Christian quadrangle in northern Alaska. proprietary +USGS_OFR_00222_1.0 Geologic Map Database of the El Mirage Lake Area, San Bernardino and Los Angeles Counties, California CEOS_EXTRA STAC Catalog 1999-01-01 1999-12-31 -117.690704, 34.49907, -117.50146, 34.73541 https://cmr.earthdata.nasa.gov/search/concepts/C2231549328-CEOS_EXTRA.umm_json This geologic map database for the El Mirage Lake area describes geologic materials for the dry lake, parts of the adjacent Shadow Mountains and Adobe Mountain, and much of the piedmont extending south from the lake upward toward the San Gabriel Mountains. This area lies within the western Mojave Desert of San Bernardino and Los Angeles Counties, southeastern California (see Fig. 1). The area is traversed by a few paved highways that service the community of El Mirage, and by numerous dirt roads that lead to outlying properties. An off-highway vehicle area established by the Bureau of Land Management encompasses the dry lake and much of the land north and east of the lake. The physiography of the area consists of the dry lake, flanking mud and sand flats and alluvial piedmonts, and a few sharp craggy mountains. This digital geologic map database, intended for use at 1:24,000-scale, describes and portrays the rock units and surficial deposits of the El Mirage Lake area. The map database was prepared to aid in a water-resource assessment of the area by providing surface geologic information with which deepergroundwater-bearing units may be understood. The area mapped covers the Shadow Mountains SE and parts of the Shadow Mountains, Adobe Mountain, and El Mirage 7.5-minute quadrangles (see Fig. 2). The map includes detailed geology of surface and bedrock deposits, which represent a significant update from previous bedrock geologic maps by Dibblee (1960) and Troxel and Gunderson (1970), and the surficial geologic map of Ponti and Burke (1980); it incorporates a fringe of the detailed bedrock mapping in the Shadow Mountains by Martin (1992). The map data were assembled as a digital database using ARC/INFO to enable wider applications than traditional paper-product geologic maps and to provide for efficient meshing with other digital data bases prepared by the U.S. Geological Survey's Southern California Areal Mapping Project. [Summary provided by the USGS.] proprietary USGS_OFR_00241 Archive of Boomer and Sparker Subbottom Data Collected During USGS Cruise DIAN 97011, Long Island, NY Inner Shelf -- Fire Island, New York, 5-26 May, 1997 CEOS_EXTRA STAC Catalog 1997-05-08 1997-05-26 -73.53, 40.51, -72.82, 40.78 https://cmr.earthdata.nasa.gov/search/concepts/C2231550971-CEOS_EXTRA.umm_json In 1995, the USGS Woods Hole Field Center in Cooperation with the U.S. Army Corps of Engineers, began a program designed to map the seafloor offshore of the New York-New Jersey metropolitan area; the most heavily populated, and one of the most impacted coastal regions of the United States. The ultimate goal of this program is to provide an overall synthesis of the sea floor environment, including surficial sediment texture, subsurface geometry, and anthropogenic impact (e.g. ocean dumping, trawling, channel dredging), through the use and analysis of sidescan-sonar and subbottom mapping techniques. This regional synthesis will support a wide range of management decisions and will provide a basis for further process-oriented investigations. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS DIAN 97011 cruise. The coverage is the nearshore of Long Island, NY in the vicinity of Fire Island. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary USGS_OFR_00242 Archive of Chirp Data Collected During USGS Cruise DIAN 97011 Long Island, NY Inner Shelf -- Fire Island, 5-26 May, 1997 CEOS_EXTRA STAC Catalog 1997-05-08 1997-05-26 -73.53, 40.5, -72.82, 40.78 https://cmr.earthdata.nasa.gov/search/concepts/C2231550466-CEOS_EXTRA.umm_json In 1995, the USGS Woods Hole Field Center in Cooperation with the U.S. Army Corps of Engineers, began a program designed to map the seafloor offshore of the New York-New Jersey metropolitan area; the most heavily populated, and one of the most impacted coastal regions of the United States. The ultimate goal of this program is to provide an overall synthesis of the sea floor environment, including surficial sediment texture, subsurface geometry, and anthropogenic impact (e.g. ocean dumping, trawling, channel dredging), through the use and analysis of sidescan-sonar and subbottom mapping techniques. This regional synthesis will support a wide range of management decisions and will provide a basis for further process-oriented investigations. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS DIAN 97011 cruise. The coverage is the nearshore of Long Island, NY in the vicinity of Fire Island. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary USGS_OFR_00273 Archive of Chirp Subbottom Data Collected During USGS Cruise MGNM 99023, Southern Long Island, NY Inner Shelf and Hudsen Shelf Valley, 26-31 July, 1999 CEOS_EXTRA STAC Catalog 1999-07-26 1999-07-31 -73.5, 39.7, -72.4, 40.8 https://cmr.earthdata.nasa.gov/search/concepts/C2231553287-CEOS_EXTRA.umm_json In 1995, the USGS Woods Hole Field Center, in Cooperation with the U.S. Army Corps of Engineers, began a program designed to map the seafloor offshore of the New York-New Jersey metropolitan area, the most heavily populated, and one of the most impacted coastal regions of the United States. The ultimate goal of this program is to provide an overall synthesis of the sea floor environment, including surficial sediment texture, subsurface geometry, and anthropogenic impact (e.g. ocean dumping, trawling, channel dredging). Sidescan sonar and high-resoluion seismic reflection profiling have been used to map the region north of about 42o10' and west of about 73o15'. The Hudson Shelf Valley, a shallow topographic feature that cuts across the shelf from offshore of New York to the shelf edge, was mapped using multibeam. This regional synthesis will support a wide range of management decisions and will provide a basis for further process-oriented investigations. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS MGNM 99023 cruise. The coverage is the nearshore of Southern Long Island and the Hudson Shelf Valley. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary +USGS_OFR_00304 Georeferenced Sea-Floor Mapping and Bottom Photography in Long Island Sound, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1952-01-01 -73.0991, 41.0167, -72.7566, 41.2256 https://cmr.earthdata.nasa.gov/search/concepts/C2231552915-CEOS_EXTRA.umm_json This Open File Report (00-304) consists of over 30 digital data sets representing GIS layers assessing the benthic communities and spatial seafloor structures of the Long Island Sound. These data sets are made available as a USGS Open File Report and correspond to research arctiles published in a thematic section of the Journal of Coastal Research (Vol. 16 (3), 2000). proprietary USGS_OFR_00304_BENTHOS Detailed analysis of 35 most common species found in Long Island Sound benthic communities, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1983-01-01 1983-12-31 -73.6418, 40.9067, -71.863, 41.3536 https://cmr.earthdata.nasa.gov/search/concepts/C2231550411-CEOS_EXTRA.umm_json This GIS layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. This GIS layer provides the location where samples from Pellegrino and Hubbard were summarized to provide detailed analysis of 35 common species found in Long Island Sound benthic communities. proprietary +USGS_OFR_00304_BUZAS M.A. Buzas benthic foraminiferal samples (1965) from Long Island Sound, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1965-01-01 1965-12-31 -73.7, 40.8917, -72.4811, 41.2778 https://cmr.earthdata.nasa.gov/search/concepts/C2231550485-CEOS_EXTRA.umm_json The purpose of this layer is to disseminate a digital version of the location of samples collected and analyzed by M. A. Buzas in 1965. This GIS layer contains a point overlay showing the the distribution of benthic foraminiferal samples collected in 1965 by M. A. Buzas in Long Island Sound. Sediment samples were washed on a 0.062 mm sieve to separate the foraminifera from the silt and clay. Foraminifera were picked from the fraction retained on the sieve and individually identified and counted with a binocular microscope using reflected light. The foraminifera data and navigation were entered into a flat-file database (Excel) and inported into Mapview for graphical analysis. The files were subsequently exported into MIDMIF files, and converted into shape files with the Arc utility MIFSHAPE.EXE. proprietary +USGS_OFR_00304_CPERFLOC Locations of sediment samples with Clostridium perfringens in Long Island Sound, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1970-01-01 -73.7469, 40.8405, -72.2128, 41.2919 https://cmr.earthdata.nasa.gov/search/concepts/C2231551771-CEOS_EXTRA.umm_json The purpose of this layer is to disseminate a digital version of the location of samples containing Clostridium perfringens, and concentrations of Clostridium perfringens in those samples. This GIS layer contains a point layer showing the location of surficial sediment samples in Long Island Sound containing Clostridium perfringens and the concentration of Clostridium perfringens in those samples. Grab samples were frozen at sea, and freeze-dried in the lab. Analyses for Clostridium perfringens were performed by Biological Analytical Labs of North Kingston, RI, according to methods described by Emerson and Cabelli (1982) and Bisson and Cabelli (1979). Data consisting of station navigation and Clostridium perfringens concentrations in the surficial sediments were inmported as text files. proprietary +USGS_OFR_00304_CST27 Medium resolution shoreline for the Long Island Sound study area, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1994-01-01 1994-12-31 -74.4422, 40.2499, -71.4471, 41.5513 https://cmr.earthdata.nasa.gov/search/concepts/C2231551911-CEOS_EXTRA.umm_json This data layer provides a medium resolution coastline for the Long Island Sound Study Area in OFR 00-304. NOAA's Medium Resolution Digital Vector Shoreline is a high quality, GIS-ready, general-use digital vector data set created by the Strategic Environmental Assessments (SEA) Division of NOAA's Office of Ocean esources Conservation and Assessment. The coastlines are compiled from the NOAA coast charts. The specified section of NOAA's medium resolution shoreline was downloaded from their website. That file was clipped to include the are of interest for the Long Island Sound studies. NOAA's Medium Resolution Digital Vector Shoreline was compiled from hundreds of NOAA coast charts and comproses over 75,000 nautical miles of coastline. The portion contained here is part of the EC80_04 - Chincoteague Inlet Virginia to Block Island Sound Rhode Island data layer, which is part of the Atlantic East-Coast Section. proprietary USGS_OFR_00304_FRANZ Benthic community sediment samples in Long Island Sound collected by D. Franz (1976), USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1976-01-01 1976-12-31 -72.0362, 41.2705, -71.8871, 41.3376 https://cmr.earthdata.nasa.gov/search/concepts/C2231551421-CEOS_EXTRA.umm_json This GIS layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. This GIS layer provides the location where samples were taken in a survey conducted by D. Franz (1976). proprietary +USGS_OFR_00304_GRAVITY Free-air gravity of Long Island and Block Island Sounds, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1975-01-01 1975-12-31 -73.7099, 40.8512, -71.5563, 41.3909 https://cmr.earthdata.nasa.gov/search/concepts/C2231554660-CEOS_EXTRA.umm_json The purpose is to disseminate the only existing free-air gravity information in digital form to the research community, and to facilitate modern geophysical and environmental studies of the Long Island and Block Island Sounds. This GIS layer contains an interpretive layer represented by contour lines (2-mgal intervals) of the free-air gravity of Long Island and Block Island Sounds. proprietary +USGS_OFR_00304_LISGRABS Long Island Sound metals sample distribution locations, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1970-01-01 -73.7469, 40.8363, -72.042, 41.3379 https://cmr.earthdata.nasa.gov/search/concepts/C2231550528-CEOS_EXTRA.umm_json The purpose of this datalayer is to disseminate a digital version of the map showing the locations of surficial samples used in the analysis of metal distributions in Long Island Sound. This GIS layer contains a point overlay showing the location of surficial samples used in the analysis of metal distributions in Long Island Sound. Attribute information containing the chemical analysis values are also included in the data layer. proprietary +USGS_OFR_00304_LISTEX Distribution of Surficial Sediments in Long Island Sound, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 2000-07-21 2000-07-21 -74.0491, 40.5031, -71.8185, 41.4348 https://cmr.earthdata.nasa.gov/search/concepts/C2231549629-CEOS_EXTRA.umm_json The purpose is to disseminate a digital version of a regional map showing the distribution of surficial sediments in Long Island Sound. Grain size is the most basic attribute of sediment texture, and texture controls many benthic ecological and chemical processes. This GIS layer contains an computer generated model of the distribution of surficial sediments in Long Island Sound. proprietary +USGS_OFR_00304_LISTOC Distribution of Total Organic Carbon (TOC) in Long Island Sound, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1992-06-01 1998-03-31 -74.131, 40.5037, -71.8412, 41.4201 https://cmr.earthdata.nasa.gov/search/concepts/C2231552639-CEOS_EXTRA.umm_json The purpose of this layer is to disseminate a digital version of the regional total organic carbon distribution in Long Island Sound. This GIS layer contains a polygon overlay showing the distribution of Total Organic Carbon (TOC) in the sediments of Long Island Sound. These data, which represent the only regional total organic carbon study of Long Island Sound, were originally published in USGS Open-File Report 98-502. proprietary USGS_OFR_00304_MARINET Depth of the Marine Transgressive Surface in Long Island Sound, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 2000-07-21 2000-07-21 -73.7629, 40.0083, -72.03531, 41.002747 https://cmr.earthdata.nasa.gov/search/concepts/C2231552881-CEOS_EXTRA.umm_json The purpose is to disseminate a digital version of a regional map showing the marine transgressive surface in Long Island Sound. This GIS layer contains an interpretive layer represented by contour lines showing the marine transgressive surface in Long Island Sound. proprietary USGS_OFR_00304_MCCALL Benthic community samples in Long Island Sound collected by P.L. McCall (1975), USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1975-01-01 1975-12-31 -73.0808, 40.983, -72.6382, 41.2474 https://cmr.earthdata.nasa.gov/search/concepts/C2231550498-CEOS_EXTRA.umm_json This GIS layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. This GIS layer provides the location where samples were taken in a survey conducted by P.L. McCall (1975). proprietary +USGS_OFR_00304_MOSAREA Extent of the area covered by the sidescan sonar mosaic from the study area off New London, CT, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1992-01-01 2000-12-31 -72.1383, 41.2512, -72.0307, 41.3143 https://cmr.earthdata.nasa.gov/search/concepts/C2231550556-CEOS_EXTRA.umm_json This GIS layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. This GIS layer shows the extent of the area covered by the sidescan sonar mosaic from the study area off New London, CT. proprietary USGS_OFR_00304_NLBENTHS Benthic communities in the New London sidescan mosaic study area, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1992-01-01 2000-12-31 -72.1253, 41.2587, -72.0423, 41.3065 https://cmr.earthdata.nasa.gov/search/concepts/C2231553379-CEOS_EXTRA.umm_json This data layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. The original studies were conducted to describe the benthic communities in Long Island Sound; the corresponding data layer is presented to show the available species richness data in eastern Long Island Sound. This data layer depicts benthic communities found in the New London sidescan sonar mosaic study area. proprietary +USGS_OFR_00304_NLMOSINT Interpretation of the sidescan sonar mosaic from the study area off New London, CT, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1992-01-01 2000-12-31 -72.1383, 41.2512, -72.0307, 41.3143 https://cmr.earthdata.nasa.gov/search/concepts/C2231552755-CEOS_EXTRA.umm_json "This data layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. Mapping was performed on a sidescan sonar survey. This survey was processed at 3,479-scale utilizing the U.S.G.S. Mini Image Processing system (MIPS) in an Equatorial Mercator Projection. Processing included bottom, ratio, and radiometric corrections; sectioning the survey area; ""Geoming"" individual map sections; ""stenciling"" and ""mosaicing""; and building the final image. The shading convention for this mosaic is that dark tones are interpreted as fine sediment (fine sand, silt and clay); and light tones are interpreted as coarse sediment. Rough and ""grainy"" patches are interpreted as glacial drift or bedrock outcrops.The image files contained here have been modified, using Arc/Info software, from the three original TIFFs delivered by University of Rhode Island. The images were converted to grids, geo-referenced, and individually reclassified in a manner similar to linear stretching to account for variations in gray scales among the three sections of the mosaic. The grids were then converted back to TIFF format with world files in Latitude/Longitude decimal degrees (no projection). Pixel size is approximately 0.8 meters. The original studies were conducted to describe the benthic communities in Long Island Sound; the corresponding data layer is presented to show the extent of the sidescan sonar mosaic off New London, in eastern Long Island Sound, and the distribution of habitats on the mosaic. This data layer is an interpretation of the sidescan sonar mosaic from the study area off New London, CT." proprietary +USGS_OFR_00304_PARKER F. L. Parker benthic foraminiferal samples (1952) in Long Island Sound, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1952-01-01 1952-12-31 -75.9367, 41.0617, -72.305, 41.26 https://cmr.earthdata.nasa.gov/search/concepts/C2231555079-CEOS_EXTRA.umm_json The purpose of this layer is to disseminate a digital version of the location of samples collected and analyzed by F. L. Parker in 1952 in Long Island Sound. This GIS layer contains a point overlay showing the the distribution of benthic foraminiferal samples collected in 1952 by F. L. Parker. proprietary USGS_OFR_00304_PELLEGRI Benthic community samples collected by Pellegrino and Hubbard (1983) in Long Island Sound, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1983-01-01 1983-12-31 -73.6418, 40.9067, -71.863, 41.3536 https://cmr.earthdata.nasa.gov/search/concepts/C2231553749-CEOS_EXTRA.umm_json This data layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. This data layer provides the location where samples were taken in a survey conducted by P. Pellegrino and W. Hubbard (1983). Sediment samples were collected by grab sampler and were wet sieved to remove the mud fraction. Coarse fractions were stored in formalin until individual species specimens could be identified and counted with a binocular microscope under reflected light. proprietary USGS_OFR_00304_REIDETAL Benthic community samples collected by Reid, et al (1979) in Long Island Sound, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1979-01-01 1979-12-31 -73.7967, 40.7741, -71.9298, 41.3555 https://cmr.earthdata.nasa.gov/search/concepts/C2231554034-CEOS_EXTRA.umm_json This data layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. This GIS layer provides the location where samples were taken in a survey conducted by R.N. Reid, et al (1979). proprietary USGS_OFR_00304_SANDERS Benthic community samples collected by H. L. Sanders (1956) in Long Island Sound, USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1956-01-01 1956-12-31 -73.0991, 41.0167, -72.7566, 41.2256 https://cmr.earthdata.nasa.gov/search/concepts/C2231551951-CEOS_EXTRA.umm_json This data layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management. This GIS layer provides the location where samples were taken in a survey conducted by H.L. Sanders (1956). proprietary +USGS_OFR_00304_TOCPNT1 Location of Long Island Sound samples with Total Organic Carbon (TOC), USGS OFR 00-304 CEOS_EXTRA STAC Catalog 1992-06-01 1998-03-31 -73.7482, 40.8367, -72.126, 41.3439 https://cmr.earthdata.nasa.gov/search/concepts/C2231553922-CEOS_EXTRA.umm_json The purpose of this data layer is to disseminate a digital version of the map showing the locations of surficial total organic carbon sampling stations in Long Island Sound. This GIS layer contains a point overlay showing the location of samples with Total Organic Carbon (TOC). This layer shows the distribution of samples used in the creation of the TOC polygon layer, listoc. proprietary +USGS_OFR_00351_1.0 Geologic map and database of the Salem East and Turner 7.5 minute quandrangles, Marion County, Oregon: A digital database, USGS OFR 00-351 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -123.00002, 44.874996, -122.87471, 44.999996 https://cmr.earthdata.nasa.gov/search/concepts/C2231549933-CEOS_EXTRA.umm_json "This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:24,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the Oregon State Geologist. Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits of the Salem East and Turner 7.5 minute quadrangles. A previously published adjacent geologic map and database by Tolan, Beeson, and Wheeler (1999) contains a text file (geol.txt or geol.ps), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller. The Salem East and Turner 7.5-minute quadrangles are situated in the center of the Willamette Valley near the western margin of the Columbia River Basalt Group (CRBG) distribution. The terrain within the area is of low to moderate relief, ranging from about 150 to almost 1,100-ft elevation. Mill Creek flows northward from the Stayton basin (Turner quadrangle) to the northern Willamette Valley (Salem East quadrangle) through a low that dissects the Columbia River basalt that forms the Salem Hills on the west and the Waldo Hills to the east. Approximately eight flows of CRBG form a thickness of up to 700 in these two quadrangles. The Ginkgo intracanyon flow that extends from east to west through the south half of the Turner quadrangle is exposed in the hills along the southeast part of the quadrangle. The major emphasis of this study was to identify and map CRBG units within the Salem East and Turner Quadrangles and to utilize this detailed CRBG stratigraphy to identify and characterize structural features. Water well logs were used to provide better subsurface stratigraphic control. Three other quadrangles (Scotts Mills, Silverton, and Stayton NE) in the Willamette Valley have been mapped in this way (Tolan and Beeson, 1999). The databases in this report were compiled in ARC/INFO, a commercial Geographic Information System (Environmental Systems Research Institute, Redlands, California), with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files are in either GRID (ARC/ INFO raster data) format or COVERAGE (ARC/INFO vector data) format. Coverages are stored in uncompressed ARC export format (ARC/INFO version 7.x). ARC/INFO export files (files with the .e00 extension) can be converted into ARC/INFO coverages in ARC/INFO (see below) and can be read by some other Geographic Information Systems, such as MapInfo via ArcLink and ESRI's ArcView (version 1.0 for Windows 3.1 to 3.11 is available for free from ESRI's web site: ""http://www.esri.com""). The digital compilation was done in version 7.1.1 of ARC/INFO with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The geologic map information was digitized from stable originals of the geologic maps at 1:24,000 scale. The author manuscripts (pen on mylar and pen on paper) were scanned using a Anatek rasterizing color scanner with a resolution of 600 and 400 dots per inch. The scanned images were vectorized and transformed from scanner coordinates to projection coordinates with digital tics placed by hand at quadrangle corners. The scanned lines were edited interactively by hand using ALACARTE, color boundaries were tagged as appropriate, and scanning artifacts visible at 1:24,000 were removed." proprietary +USGS_OFR_00356_Version 1.0 Geologic map of the Wildcat Lake 7.5' Quadrangle Kitsap and Mason Counties, Washington, USGS OFR 00-356 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -122.875, 47.5, -122.75, 47.625 https://cmr.earthdata.nasa.gov/search/concepts/C2231549341-CEOS_EXTRA.umm_json Digital representation of geologic mapping facilitates the presentation and analysis of earth-science data. Digital maps may be displayed at any scale or projection, however, the geologic data in this coverage is not intended for use at a larger scale. This data set represents reconnaissance geologic mapping of the Wildcat Lake 7.5' Quadrangle, Kitsap and Mason Counties, Washington. It is used to create the map sheet in USGS OFR 00-356 , which shows bedrock, surficial, and structural geology of the Wildcat Lake Quadrangle. This data was hand digitized in ARC/Info from an unfolded paper 1:24,000 scale compilation map. The arcs and polygons were attributed. For the purposes of distribution, the coverage has been converted to an interchange format file using the ARC/Info export command. proprietary +USGS_OFR_00359_Version 1.0 Geologic Map and Digital Database of the Apache Canyon 7.5' Quadrangle, Ventura and Kern Counties, California, USGS OFR 00-359 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -119.37509, 34.749985, -119.24991, 34.875 https://cmr.earthdata.nasa.gov/search/concepts/C2231555330-CEOS_EXTRA.umm_json The data set for the Apache Canyon quadrangle has been prepared by the Southern California Areal Mapping Project (SCAMP), a cooperative project sponsored jointly by the U.S. Geological Survey and the California Division of Mines and Geology, as part of an ongoing effort to utilize a Geographical Information System (GIS) format to create a regional digital geologic database for southern California. This regional database is being developed as a contribution to the National Geologic Map Data Base of the National Cooperative Geologic Mapping Program of the USGS. The digital geologic map database for the Apache Canyon quadrangle has been created as a general-purpose data set that is applicable to other land-related investigations in the earth and biological sciences. The Apache Canyon database is not suitable for site-specific geologic evaluations at scales greater than 1:24,000 (1 in = 2,000 ft). This data set maps and describes the geology of the Apache Canyon 7.5' quadrangle, Ventura and Kern Counties, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage showing geologic contacts, faults and units, (2) a separate coverage layer showing structural data, (3) an additional point coverage which contains bedding data, (4) a point coverage containing sample localities, (5) a scanned topographic base at a scale of 1:24,000, and (6) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). The data base is accompanied by a readme file and this metadata file. In addition, the data set includes the following graphic and text products: (1) A jpg file (.jpg) containing a browse-graphic of the geologic map on a 1:24,000 topographic base. The map is accompanied by a marginal explanation consisting of a List of Map Units, a Correlation of Map Units, and a key to point and line symbols. (2) A .pdf file of a geologic explanation pamphlet that includes a Description of Map Units. (3) Two postScript graphic plot-files: one containing the geologic map on a 1:24,000 topographic base and the other, three accompanying structural cross sections. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. The map was created by transferring lines and point data from the aerial photographs to a 1:24,000 topographic base by using a PG-2 plotter. The map was scribed, scanned, and imported into ARC/INFO, where the database was built. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum and link it to other tables (.rel) that provide more detailed geologic information. proprietary USGS_OFR_00366 Archive of Chirp Data Collected During USGS Cruise DIAN 97011 Long Island, NY Inner Shelf -- Fire Island, 5-26 May, 1997, USGS OFR 00-306 CEOS_EXTRA STAC Catalog 1997-05-08 1997-05-26 -73.53, 40.5, -72.82, 40.78 https://cmr.earthdata.nasa.gov/search/concepts/C2231554741-CEOS_EXTRA.umm_json In 1995, the USGS Woods Hole Field Center, in cooperation with the U.S. Army Corps of Engineers, began a program designed to map the seafloor offshore of the New York-New Jersey metropolitan area; the most heavily populated, and one of the most impacted coastal regions of the United States. The ultimate goal of this program is to provide an overall synthesis of the sea floor environment, including surficial sediment texture, subsurface geometry, and anthropogenic impact (e.g. ocean dumping, trawling, channel dredging), through the use and analysis of sidescan-sonar and subbottom mapping techniques. This regional synthesis will support a wide range of management decisions and will provide a basis for further process-oriented investigations. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS DIAN 97011 cruise. The coverage is the nearshore of Long Island, NY in the vicinity of Fire Island. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary USGS_OFR_00396 Archive of Water Gun Subbottom Data Collected During USGS Cruise SEAX 96004, New York Bight, 1 May - 9 June, 1996, USGS OFR 00-396 CEOS_EXTRA STAC Catalog 1996-05-01 1996-06-09 -73.84, 40.35, -73.03, 40.67 https://cmr.earthdata.nasa.gov/search/concepts/C2231548714-CEOS_EXTRA.umm_json Beginning in 1995, the USGS, in cooperation with the U.S Army Corps of Engineers (USACE), New York District, began a program to generate reconnaissance maps of the sea floor offshore of the New York-New Jersey metropolitan area, one of the most populated coastal regions of the United States. The goal of this mapping program is to provide a regional synthesis of the sea-floor environment, including a description of sedimentary environments, sediment texture, seafloor morphology, and geologic history to aid in understanding the impacts of anthropogenic activities, such as ocean dumping. This mapping effort differs from previous studies of this area by obtaining digital, sidescan sonar images that cover 100 percent of the sea floor. This investigation was motivated by the need to develop an environmentally acceptable solution for the disposal of dredged material from the New York - New Jersey Port, by the need to identify potential sources of sand for renourishment of the southern shore of Long Island, and by the opportunity to develop a better understanding of the transport and long-term fate of contaminants by investigations of the present distribution of materials discharged into the New York Bight over the last 100+ years (Schwab and others, 1997). This DVD-ROM contains copies of the navigation and field Water Gun subbottom data collected aboard the R/V Seaward Explorer, from 1 May - 9 June, 1996. This DVD-ROM (Digital Versatile Disc-Read Only Memory) has been produced in accordance with the UDF (Universal Disc Format) DVD-ROM Standard (ISO 9660 equivalent) and is therefore capable of being read on any computing platform that has appropriate DVD-ROM driver software installed. Access to the data and information contained on this DVD-ROM was developed using the HyperText Markup Language (HTML) utilized by the World Wide Web (WWWW). Development of the DVD-ROM documentation and user interface in HTML allows a user to access the information by using a variety of WWW browsers to facilitate browsing and locating information and data. To access the information contained on this disk with a WWW client browser, open the file'index.htm' at the top level directory of this DVD-ROM with your selected browser. The HTML documentation is written utilizing some HTML 4.0 enhancements. The disk should be viewable by all WWW browsers but may not properly format on some older WWW browsers. Also, some links to USGS collaborators and other agencies are available on this DVD-ROM. These links are only accessible if access to the Internet is available during browsing of the DVD-ROM. proprietary USGS_OFR_0040 Archive of Chirp Subbottom Data Collected During USGS Cruise ATSV 99044 Myrtle Beach, South Carolina, 29 October - 12 November, 1999, USGS OFR 00-40 CEOS_EXTRA STAC Catalog 1999-10-29 1999-11-12 -79.1, 33.4, -78.6, 33.8 https://cmr.earthdata.nasa.gov/search/concepts/C2231554911-CEOS_EXTRA.umm_json In November 1999, the U. S. Geological Survey, in cooperation with Coastal Carolina University, began a program to produce geologic maps of the nearshore regime off northern South Carolina, utilizing high resolution sidescan sonar, interferometric (direct phase methods) swath bathymetry, and seismic subbottom profiling systems. The study areas extends from the ~7m isobath to about 10km offshore (water depths <12m). The goals of the investigation are to determine regional scale sand resource availability needed for planned beach nourishment programs, to investigate the roles that the inner shelf morphology and geologic framework play in the evolution of this coastal region, and to provide baseline geologic maps for use in proposed biologic habitat studies. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS ATSV 99044 cruise. The coverage is the nearshore of the northern South Carolina. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary +USGS_OFR_00409_Digital Version 1.0 Digital Geologic Map of Arizona: A Digital Database Derived from the 1983 Printing of the Wilson, Moore, and Cooper 1:500,000-scale Map, USGS OFR 00-409 CEOS_EXTRA STAC Catalog 2000-01-01 2000-01-01 -115, 31.25, -108.75, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2231549678-CEOS_EXTRA.umm_json This database was developed to provide a GIS of the geologic map of the State of Arizona for use at a scale of 1:500,000 or smaller. This GIS is intended for use in future spatial analysis by a variety of users. The geologic unit descriptions for this map may be updated to reflect more current description of structures and the geochronology of the map units. This database is not meant to be used or displayed at any scale larger than 1:500,000 (e.g., 1:100,000 or 1:24,000) The Geologic Map of Arizona was compiled at a scale of 1:500,000 by Eldred D. Wilson, Richard T. Moore and John R. Cooper, in 1969 and reprinted in 1977, 1981, and 1983. Comparison of an acetate copy of the 1983 map with existing paper copies of earlier maps shows some updating of the original by 1983. This 1983 acetate was scanned and vectorized by Optronics Specialty Co., Inc. in 1998, and put into an Arc/Info geographic information system (GIS). The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps. This GIS database consists of 4 Arc/Info data sets: one line and polygon file (azgeol) containing geologic contacts and structures (lines) and geologic map rock units (polygons), one line file (azfold) containing the folds and crater boundaries, one point file (azptfeat) containing geologic features, cinder cones and diatremes. one point file (azptdec) containing decorations, and proprietary USGS_OFR_011_version 1.0 Dataset of Aggregate Producers in New Mexico, USGS Open File Report 011 CEOS_EXTRA STAC Catalog 1997-06-01 1999-06-01 -109.5, 31.33, -103, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2231552952-CEOS_EXTRA.umm_json This data set was developed as part of a larger effort by the U.S. Geological Survey to provide plottable locations of aggregate producers for National Atlas and for aggregate research. This data set contains latitudes, longitudes, and other descriptive data for aggregate producers in New Mexico that are believed to have been active during the period 1997-1999. The data in this compilation were derived from U.S. Geological Survey files, U.S. Bureau of Land Management files, contact with producers, and reports from the New Mexico Bureau of Mines and Mineral Resources, the New Mexico Bureau of Mine Inspection, and the New Mexico Mining and Mineral Division. This dataset includes 2 tables: Table 1 contains crushed stone operations and table 2 contains sand and gravel operations. This data set consists of one Excel 97 spreadsheet file (NMsandg2.xls) which contains information about Sand Gravel operations in New Mexico and one Excel 97 spreadsheet file NMcstn1.xls) which contains information about Crushed Stone operations in New Mexico. The files are also included in DIF format under the same filenames, but with the .DIF extension. proprietary +USGS_OFR_02-266 Ice Core Depth-Age Relation for Vostok (delD) and Dome Fuji (del18O) Records Based on the Devils Hole Paleotemperature Chronology, USGS Open File Report 02-266 CEOS_EXTRA STAC Catalog 1970-01-01 -116.3, -78.5, 40, 36.42 https://cmr.earthdata.nasa.gov/search/concepts/C2231548530-CEOS_EXTRA.umm_json This report presents the data for the Vostok - Devils Hole chronology, termed V-DH chronology, for the Antarctic Vostok ice core record. This depth - age relation is based on a join between the Vostok deuterium profile (delD) and the stable oxygen isotope ratio (del18O) record of paleotemperature from a calcitic core at Devils Hole, Nevada, using the algorithm developed by Landwehr and Winograd (2001). Both the control points defining the V-DH chronology and the numeric values for the chronology are given. In addition, a plausible chronology for a deformed bottom portion of the Vostok core developed with this algorithm is presented. Landwehr and Winograd (2001) demonstrated the broader utility of their algorithm by applying it to another appropriate Antarctic paleotemperature record, the Antarctic Dome Fuji ice core del18O record. Control points for this chronology are also presented in this report but deemed preliminary because, to date, investigators have published only the visual trace and not the numeric values for the Dome Fuji del18O record. The total uncertainty that can be associated with the assigned ages is also given. proprietary +USGS_OFR_02005 Multibeam Mapping of the West Florida Shelf, Gulf of Mexico, USGS OFR 02-005 CEOS_EXTRA STAC Catalog 2001-09-03 2001-10-12 -86.71667, 28, -84.583336, 31.083334 https://cmr.earthdata.nasa.gov/search/concepts/C2231554018-CEOS_EXTRA.umm_json "Our objective was to map the region between the 50 to 150-m isobaths south from the eastern edge of De Soto Canyon as far as Steamboat Lumps using a state-of-the-art multibeam mapping system (MBES). The cruise used a Kongsberg Simrad EM1002 MBES, the latest generation of high-resolution mapping systems. The EM1002 produces both geodetically accurate georeferenced bathymetry and coregistered, calibrated, acoustic backscatter. Acoustic backscatter is the intensity of an acoustic pulse that is backscattered off the seafloor back to the transducer. The signal can give an indication of the type of material exposed on the ocean floor (i.e. rock vs. mud). These data should prove extremely useful in relating dominant species groups (which display highly specific biotope affinities) to the geomorphology (e.g., reef flattop, forereef crest, reef wall, reef base, circum-reef talus zone, circum-reef, high-reflectivity sediment apron, etc.). These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) or other software to display bathymetry and backscatter data of the West Florida Shelf, Gulf of Mexico. This report provides multibeam bathymetry and acoustic backscatter data, along with images for parts of the sea floor. These data were obtained through a multibeam sonar survey of the West Florida Shelf, Gulf of Mexico. Data are provided in ASCII and ArcInfo GRID formats. Information for USGS Coastal and Marine Geology related activities are online at ""http://walrus.wr.usgs.gov/infobank/m/m201gm/html/m-2-01-gm.meta.html""" proprietary +USGS_OFR_02006 Multibeam mapping of the Pinnacles region, Gulf of Mexico, USGS OFR 02-006 CEOS_EXTRA STAC Catalog 2000-06-02 2000-07-28 -88.537, 29.154, -87.367, 29.629 https://cmr.earthdata.nasa.gov/search/concepts/C2231553636-CEOS_EXTRA.umm_json "Our objective was to map as large an area of the outer shelf deep reefs off Alabama-Mississippi as the project budget allowed using a state-of-the-art multibeam mapping system. The cruise used a Kongsberg Simrad EM1002, the latest generation of high-resolution multibeam mapping systems (HRMBS). The EM1002 produces both accurate georeferenced bathymetry and coregistered, calibrated, acoustic backscatter. These data should prove extremely useful in relating dominant species groups (which display highly specific biotope affinities) to the geomorphology (e.g., reef flattop, forereef crest, reef wall, reef base, circumreef talus zone, circum-reef, high-reflectivity sediment apron). The mapping is the first phase of a two-phase study of the Pinnacles area. The second year of this study (FY01) will concentrate on measuring the currents in and around the reefs as well as continued census of the fish populations. These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) or other software to display bathymetry and backscatter data of the West Florida Shelf, Gulf of Mexico. This report provides multibeam bathymetry and acoustic backscatter data, along with images for parts of the sea floor. These data were obtained through a multibeam sonar survey of the Pinnacles region, northern Gulf of Mexico. Data are provided in ASCII and ArcInfo GRID formats. Information for USGS Coastal and Marine Geology related activities are online at ""http://walrus.wr.usgs.gov/infobank/m/m201gm/html/m-2-01-gm.meta.html""" proprietary +USGS_OFR_0205 Multibeam Mapping of the West Florida Shelf, Gulf of Mexico, USGS OFR 02-05 CEOS_EXTRA STAC Catalog 2001-09-03 2001-10-12 -86.71667, 28, -84.583336, 31.083334 https://cmr.earthdata.nasa.gov/search/concepts/C2231550230-CEOS_EXTRA.umm_json "The objective was to map the region between the 50 to 150-m isobaths south from the eastern edge of De Soto Canyon as far as Steamboat Lumps using a state-of-the-art multibeam mapping system (MBES). The cruise used a Kongsberg Simrad EM1002 MBES, the latest generation of high-resolution mapping systems. The EM1002 produces both geodetically accurate georeferenced bathymetry and coregistered, calibrated, acoustic backscatter. Acoustic backscatter is the intensity of an acoustic pulse that is backscattered off the seafloor back to the transducer. The signal can give an indication of the type of material exposed on the ocean floor (i.e. rock vs. mud). These data should prove extremely useful in relating dominant species groups (which display highly specific biotope affinities) to the geomorphology (e.g., reef flattop, forereef crest, reef wall, reef base, circum-reef talus zone, circum-reef, high-reflectivity sediment apron, etc.). These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) or other software to display bathymetry and backscatter data of the West Florida Shelf, Gulf of Mexico. This report provides multibeam bathymetry and acoustic backscatter data, along with images for parts of the sea floor. These data were obtained through a multibeam sonar survey of the West Florida Shelf, Gulf of Mexico. Data are provided in ASCII and ArcInfo GRID formats. Information for USGS Coastal and Marine Geology related activities are online at ""http://walrus.wr.usgs.gov/infobank/m/m201gm/html/m-2-01-gm.meta.html""" proprietary +USGS_OFR_0206 Multibeam mapping of the Pinnacles region, Gulf of Mexico, USGS OFR 02-06 CEOS_EXTRA STAC Catalog 2000-06-02 2000-07-28 -88.537, 29.154, -87.367, 29.629 https://cmr.earthdata.nasa.gov/search/concepts/C2231550560-CEOS_EXTRA.umm_json "The objective was to map as large an area of the outer shelf deep reefs off Alabama-Mississippi as the project budget allowed using a state-of-the-art multibeam mapping system. The cruise used a Kongsberg Simrad EM1002, the latest generation of high-resolution multibeam mapping systems (HRMBS). The EM1002 produces both accurate georeferenced bathymetry and coregistered, calibrated, acoustic backscatter. These data should prove extremely useful in relating dominant species groups (which display highly specific biotope affinities) to the geomorphology (e.g., reef flattop, forereef crest, reef wall, reef base, circumreef talus zone, circum-reef, high-reflectivity sediment apron). The mapping is the first phase of a two-phase study of the Pinnacles area. The second year of this study concentrated on measuring the currents in and around the reefs as well as continued census of the fish populations. These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) or other software to display bathymetry and backscatter data of the West Florida Shelf, Gulf of Mexico. This report provides multibeam bathymetry and acoustic backscatter data, along with images for parts of the sea floor. These data were obtained through a multibeam sonar survey of the Pinnacles region, northern Gulf of Mexico. Data are provided in ASCII and ArcInfo GRID formats. Information for USGS Coastal and Marine Geology related activities are online at: ""http://walrus.wr.usgs.gov/infobank/m/m201gm/html/m-2-01-gm.meta.html""" proprietary +USGS_OFR_02110_1.0 Mines and Mineral Occurrences of Afghanistan, USGS OFR 02-110 CEOS_EXTRA STAC Catalog 2002-01-01 2002-12-31 60.45, 29.25, 75, 38.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552209-CEOS_EXTRA.umm_json This data set was compiled due to interest in Afghanistan and anticipated continuing interest as post-war aid and reconstruction begin. This data set contains latitudes, longitudes, commodity, and limited geologic data for metallic and nonmetallic mines, deposits, and mineral occurrences of Afghanistan. The data in this compilation were derived from published literature and data files of members of the USGS National Industrial Minerals project. This data set consists of one table with 17 fields and over 1000 sites. This data set consists of one Excel 98 spreadsheet file, OF02110.xls. Data fields include location, deposit, commodity, and geologic data for mineral deposits, mines and occurrences. proprietary +USGS_OFR_0221_Version 1.0 Geologic Map of the Corona South 7.5' Quadrangle, Riverside and Orange Counties, California, USGS OFR 02-21 CEOS_EXTRA STAC Catalog 2002-01-01 2002-12-31 -117.62509, 33.749985, -117.49991, 33.875 https://cmr.earthdata.nasa.gov/search/concepts/C2231553180-CEOS_EXTRA.umm_json The data set for the Corona South 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographic Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. This data set maps and describes the geology of the Corona South 7.5' quadrangle, Riverside and Orange Counties, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) a coverage containing structural data, (3) a coverage containing geologic unit annotation and leaders, and (4) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) a postscript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), and a key for point and line symbols, and (2) PDF files of the Readme (including the metadata file as an appendix), and the graphic produced by the Postscript plot file. The Corona South quadrangle is located near the northern end of the Peninsular Ranges Province. Diagonally crossing the quadrangle is the northern end of the Elsinore Fault zone, a major active right-lateral strike-slip fault zone of the San Andreas Fault system. East of the fault zone is the Perris block and to the west the Santa Ana Mountains block. Basement in the Perris block part of the quadrangle is almost entirely Cretaceous volcanic rocks and granitic rocks of the Cretaceous Peninsular Ranges batholith. Three small exposures of very low metamorphic grade siliceous rocks correlated on the basis of lithology with Mesozoic age rocks are located near the eastern edge of the quadrangle. Exposures of batholithic rocks is restricted to mostly granodiorite of the Cajalco pluton that underlies extensive areas to the east and north. There are limited amounts of undifferentiated granitic rock and one small body of gabbro. The most extensive basement rocks are volcanic shallow intrusives and extrusives of the Estelle Mountain volcanics. The volcanics, predominantly latite and rhyolite, are quarried as a source of crushed rock. West of the Elsinore Fault zone is a thick section of Bedford Canyon Formation of Jurassic age. This unit consists of incipiently metamorphosed marine sedimentary rocks consisting of argillite, slate, graywacke, impure quartzite, and small pods of limestone. Bedding and other primary sedimentary structures are commonly preserved and tight folds are common. Incipiently developed transposed layering, S1, is locally well developed. Included within the siliceous rocks are small outcrops of fossiliferous limestone than contain a fauna indicating the limestone formed in a so-called black smoker environment. Unconformably overlying and intruding the Bedford Canyon Formation is the Santiago Peak Volcanics of Cretaceous age. These volcanics consist of basaltic andesite, andesite, dacite, rhyolite, breccia and volcanoclastic rocks. Much of the unit has been hydrothermally altered; the alteration was contemporaneous with the volcanism. A minor occurrence of serpentine and associated silica-carbonate rock occurs in association with the volcanics. Sedimentary rocks of late Cretaceous and Paleogene age and a few Neogene age rocks occur within the Elsinore Fault zone. Marine sandstone of the middle Miocene Topanga Formation occurs within the fault zone southeast of Corona. Underlying the Topanga Formation is the nonmarine undivided Sespe and Vaqueros Formation that are predominantly sandstone. Sandstone, siltstone, and conglomerate of the marine and nonmarine Paleocene Silverado Formation extends essentially along the entire length of the fault zone in the quadrangle. Clay beds in the Silverado Formation have been an important source of clay. In the northwest corner of the quadrangle is a thick, faulted, sedimentary section that ranges in age from Cretaceous to early Pliocene-Miocene. Emanating from the Santa Ana Mountains is an extensive alluvial fan complex that underlies Corona and the surrounding valleys. This fan complex includes both Pleistocene and Holocene age deposits. The Elsinore Fault zone at the base of the Santa Ana Mountains splays in the northwestern part of the quadrangle; beyond the quadrangle boundary the name Elsinore Fault is generally not used. The southern splay takes a more western trend and to the west of the quadrangle is termed the Whittier Fault, a major active fault. The eastern splay continues on strike along the east side of the Chino (Puente) Hills north of the quadrangle where it is termed the Chino Fault. The Chino Fault appears to have very limited displacement. The geologic map data base contains original U.S. Geological Survey data generated by detailed field observation recorded on 1:24,000 scale aerial photographs. The map was created by transferring lines from the aerial photographs to a 1:24,000 scale topographic base. The map was digitized and lines, points, and polygons were subsequently edited using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units are polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum. proprietary +USGS_OFR_0222_Version 1.0 Geologic Map of the Corona North 7.5' Quadrangle Riverside and San Bernardino Counties, California, USGS OFR 02-22 CEOS_EXTRA STAC Catalog 2002-01-01 2002-12-31 -117.62509, 33.874985, -117.49991, 34 https://cmr.earthdata.nasa.gov/search/concepts/C2231552842-CEOS_EXTRA.umm_json The data set for the Corona North 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographic Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. This data set maps and describes the geology of the Corona North 7.5' quadrangle, Riverside and San Bernardino Counties, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) a coverage containing structural data, (3) a coverage containing geologic unit annotation and leaders, and (4) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) a postscript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), and a key for point and line symbols, and (2) PDF files of the Readme (including the metadata file as an appendix), and the graphic produced by the Postscript plot file. The Corona North quadrangle is located near the northern end of the Peninsular Ranges Province. All but the southeastern tip of the quadrangle is within the Perris block, a relatively stable, rectangular in plan area located between the Elsinore and San Jacinto fault zones. The southeastern tip of the quadrangle is barely within the Elsinore fault zone. The quadrangle is underlain by Cretaceous plutonic rocks that are part of the composite Peninsular Ranges batholith. These rocks are exposed in a triangular-shaped area bounded on the north by the Santa Ana River and on the south by Temescal Wash, a major tributary of the Santa Ana River. A variety of mostly silicic granitic rocks occur in the quadrangle, and are mainly of monzogranite and granodioritic composition, but range in composition from micropegmatitic granite to gabbro. Most rock units are massive and contain varying amounts of meso- and melanocratic equant-shaped inclusions. The most widespread granitic rock is monzogranite of the Cajalco pluton, a large pluton that extends some distance south of the quadrangle. North of Corona is a body of micropegmatite that appears to be unique in the batholith rocks. Diagonally bisecting the quadrangle is the Santa Ana River. North of the Santa Ana River alluvial deposits are dominated by the distal parts of alluvial fans emanating from the San Gabriel Mountains north of the quadrangle. Widespread areas of the fan deposits are covered by a thin layer of wind blown sand. Alluvial deposits in the triangular-shaped area between the Santa Ana River and Temescal Wash are quite varied, but consist principally of locally derived older alluvial fan deposits. These deposits rest on remnants of older, early Quaternary or late Tertiary age, nonmarine sedimentary deposits that were derived from both local sources and sources as far away as the San Bernardino Mountains. These deposits in part were deposited by an ancestral Santa Ana River. Older are a few scattered remnants of late Tertiary (Pliocene) marine sandstone that include some conglomerate lenses. Clasts in the conglomerate include siliceous volcanic rocks exotic to this part of southern California. This sandstone was deposited as the southeastern-most part of the Los Angeles sedimentary marine basin and was deposited along a rocky shoreline developed in the granitic rocks, much like the present day shoreline at Monterey, California. Most of the sandstone and granitic paleoshoreline features have been removed by quarrying and grading in the area of Porphyry north to Highway 91. Excellent exposures in highway road cuts still remain on the north side of Highway 91 just east of the 91-15 interchange and on the east side of U.S. 15 just north of the interchange. South of Temescal Wash is a series of both younger and older alluvial fan deposits emanating from the Santa Ana Mountains to the southeast. In the immediate southwest corner of the quadrangle is a small exposure of sandstone and pebble conglomerate of the Sycamore Canyon member of the Puente Formation of early Pliocene and Miocene age and sandstone and conglomerate of undivided Sespe and Vaqueros Formations of early Miocene, Oligocene, and late Eocene age. The geologic map data base contains original U.S. Geological Survey data generated by detailed field observation recorded on 1:24,000 scale aerial photographs. The map was created by transferring lines from the aerial photographs to a 1:24,000 scale topographic base. The map was digitized and lines, points, and polygons were subsequently edited using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units are polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum. proprietary USGS_OFR_2001_0497_1.0 Databases and Simplified Geology for Mineralized Areas, Claims, Mines, and Prospects in Wyoming CEOS_EXTRA STAC Catalog 1970-01-01 -111.05, 40.99, -104.05, 45.01 https://cmr.earthdata.nasa.gov/search/concepts/C2231552911-CEOS_EXTRA.umm_json This data release contains mineral resource data for metallic and nonmetallic mineral sites in the State of Wyoming. Along with resource data is additional data, such as mineralized areas and mining districts; mine, prospect and commodity information; claim density by section; county boundaries; quadrangles; and simplified geology. All the data are provided in both spreadsheet format (Microsoft Excel) and in formats for two commonly used Geographic Information Systems (GIS) software packages (MapInfo and ESRI's ArcView). Not only does GIS software allow the data to be shown as layers in map views that can be displayed with various geographic and geologic data, but the data can be queried and analyzed relative to data in any of the layers. [Summary provided by the USGS.] proprietary +USGS_OFR_2001_164 Earthquake Ground-Motion Amplification in Southern California CEOS_EXTRA STAC Catalog 1970-01-01 -121, 33, -117, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553205-CEOS_EXTRA.umm_json The two most important factors influencing the level of earthquake ground motion at a site are the magnitude and distance of the earthquake. The map available here shows the influence of a third important factor, the site effect: conditions at a particular location can increase (amplify) or decrease the level of shaking that is otherwise expected for a given magnitude and distance. Combining information about site effects with where and how often earthquakes of various magnitudes are likely to occur should provide improved assessments of seismic hazard. [Summary provided by the USGS.] proprietary +USGS_OFR_2002_002 Geological Framework Data from Long Island Sound, 1981-1990: A Digital Data Release CEOS_EXTRA STAC Catalog 1970-01-01 -75, 40, -70, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2231553651-CEOS_EXTRA.umm_json Since 1980 the Coastal and Marine Geology Program of the U.S. Geological Survey and Connecticut Department of Environmental Protection have conducted a joint program of cooperative geologic research in Long Island Sound and its vicinity. As part of this program, a highly successful regional-scale study of theSeismic reflection acquisition illustration geologic framework was completed. Reconnaissance high-resolution seismic reflection data were collected and used to establish the basic stratigraphy within the Sound and to map the major geologic units (Needell and Lewis, 1984; Lewis and Needell, 1987; Needell and others, 1987); field verification of the geologic interpretations of the seismic profiles was primarily accomplished with vibratory cores (Williams, 1981; Thomas, 1985; Neff and others, 1989). These interpretations were in turn used to produce basin-wide syntheses of the late Quaternary depositional history (Lewis and Stone, 1991; Stone and others, 1998; Lewis and DiGiacomo-Cohen, 2000). Unfortunately, the original seismic records and core logs were generated only in analog form. These unique paper documents, which are still under demand for industrial applications and academic research, are fragile and have become ragged from frequent use. The purpose of this report is to preserve these data by converting the seismic profiles, core descriptions, and ancillary reports into digital form, and to organize these files into a product that can be more readily accessed and disseminated. Not all of the existing high-resolution seismic-reflection surveys, collected in Long Island Sound through cooperatives with the U.S. Geological Survey and the Connecticut Department of Environmental Protection, have been incorporated into this report. These surveys, whose records are still in need of preprocessing and annotation, generally cover smaller areas along the Connecticut coast and were originally intended to provide additional detail to the larger, more regional data sets presented herein. The digital release of the omitted data sets is planned as part of a future product. proprietary +USGS_OFR_2002_206 Environmental Atlas of the Lake Pontchartrain Basin CEOS_EXTRA STAC Catalog 1970-01-01 -91, 29, -89, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2231554508-CEOS_EXTRA.umm_json The story of Lake Pontchartrain and its surrounding Basin is a fascinating saga. Created at the end of the last Ice Age, this estuary is much more than just a magnificent natural resource. It has provided humans with sources of food, as well as a means of communication, transportation and commerce. These and a host of other benefits have supported the growth of New Orleans and the surrounding communities. Today, the 1632 km2 (630 mi2) Lake Pontchartrain is the centerpiece of the 12,173 km2 (4,700 mi2) Pontchartrain drainage basin or watershed. The Basin encompasses land in 16 Louisiana parishes and 4 Mississippi counties. This vast ecological system includes lakes, rivers, bayous, forest, swamps and marshes. It is habitat for countless species of fish, birds, mammals, reptiles and plants. It is also the most densely populated portion of Louisiana with almost 1.5 million people residing immediately around the Lake. The history of the environmental quality of the Pontchartrain Basin demonstrates that no resource should be taken for granted or exploited. As the population grew in the Twentieth Century, use and, unfortunately, abuse of this nationally important estuary also grew. By the second half of the Twentieth Century, Pontchartrain's environmental quality had deteriorated to a point that many believed unrecoverable. Responsibility and stewardship are necessary for natural resource protection, restoration and preservation. Recognizing these needs, area citizens began the SAVE OUR LAKE movement that led to the creation of the Lake Pontchartrain Basin Foundation in 1989. The Foundation's mission is to coordinate the overall restoration and preservation of the entire Lake Pontchartrain Basin ecosystem. The Environmental Atlas of the Lake Pontchartrain Basin will become one of our tools to help accomplish that mission. The Environmental Atlas of the Lake Pontchartrain Basin is more than a summary of Pontchartrain's ecology. It presents information about geology, land cover, types of shorelines, biological resources, flow patterns, significant storms, growth trends and more. This Atlas is more of a directory to the Basin's environment. Hopefully, it will become an easily understandable reference for students and the public as well as a readily used source for professionals. [Summary provided by the USGS.] proprietary +USGS_OFR_2003_080 New Jersey Aeromagnetic and Gravity Maps and Data CEOS_EXTRA STAC Catalog 1970-01-01 -75.69, 38.8, -73.78, 41.47 https://cmr.earthdata.nasa.gov/search/concepts/C2231551303-CEOS_EXTRA.umm_json "Aeromagnetic anomalies are due to variations in the Earth's magnetic field caused by the uneven distribution of magnetic minerals (primarily magnetite) in the rocks that make up the upper part of the Earth's crust. The features and patterns of the aeromagnetic anomalies can be used to delineate details of subsurface geology including the locations of buried faults, magnetite-bearing rocks, and the thickness of surficial sedimentary rocks (which are generally non-magnetic). This information is valuable for mineral exploration, geologic mapping, and environmental studies. The New Jersey aeromagnetic map in this report is constructed from grids that combine aeromagnetic data (see data processing details) collected in eight separate aeromagnetic surveys flown between 1950 and 1979. The data from these surveys are of varying quality. The design and specifications (terrain clearance, flight line separation, flight direction, analog/digital recording, navigation, and reduction procedures) may vary between surveys depending on the purpose of the project and the technology of that time. All of the pre-1976 data are available only on hand-contoured analog maps and had to be digitized. These maps were digitized along flight-line/contour-line intersections, which is considered to be the most accurate method of recovering the original data. Digitized data are available as USGS Open File Report 99-557. All surveys have been continued to 304.8 meters (1000 feet) above ground and then blended or merged together. The merging of grids and production of images were created using a PC version of Geosoft/OASIS montaj software. An index map and data table gives an overview of the original surveys and summarizes the specifications of the surveys. The resulting grid has a data interval of 500 m and can be downloaded. A color-shaded relief image of the grid is shown on the opening page of this web report. This grid is an interim product. Considerable editing of digital flight line data was undertaken for survey 3144 to reduce leveling inconsistencies between adjacent flight lines, most notably in the southern part of the state. Anomaly resolution is only fair in the northern portion of this survey, which was flown at one-mile flight line separation, where the source rocks are at or near the surface. In these areas of this survey where the anomalies run roughly parallel to the flight lines, the gridding process produces a 'string of pearls' effect. Improved resolution can only be rectified by new surveys with more closely spaced flight lines. Heavy strike filtering in the direction of the flight lines was necessary to reduce flight line striping for two digital surveys (5004 and 6027). Where local high-resolution surveys were not available, in either digital or digitized format, we used aeromagnetic data collected by the National Uranium Resource Evaluation (NURE) program of the U.S. Department of Energy, which are available in digital format and together cover the entire state. However, because magnetic surveying was not the primary objective in the design of the NURE surveys, these data are subject to certain limitations. Although the NURE surveys were flown at elevations close to the reduction datum level, the spacing between flight lines generally ranged from 4.8 to 9.6 km (3 to 6 mile). In some areas of the U.S., detailed NURE surveys were flown with a finer line spacing, usually at a 0.4 km (0.25 mile) interval. In New Jersey, the NURE program flew the Reading Prong (5004) at this interval. This New Jersey aeromagnetic compilation is one part of a national digital compilation by the U.S. Geological Survey. Certain characteristics are common to all of the State compilations. Whereas surveys are typically flown either at a constant elevation above sea level or draped to a constant mean terrain clearance, the standard selected for this national compilation is a survey elevation of 305 m (1000 ft) above mean terrain. All of the surveys used in the New Jersey compilation were flown at either 122 m (400 ft) or 152 m (500 ft) above terrain. To conform to the national standard, the entire State grid was analytically continued upward to 305 m (1000 ft) above ground (Hildenbrand, 1983). This aeromagnetic compilation supercedes a prior report (Snyder, 1992) releasing the same data as three separate grids on 5.25"" floppies. The same data have since been reprocessed to produce better results. This project was supported by the Mineral Resource and Geologic Mapping Programs of the USGS. Thanks to USGS colleagues Pat Hill and Robert Kucks for their assistance in preparing this report. [Summary provided by the USGS.]" proprietary USGS_OFR_2003_090_1.0 Databases and Simplified Geology for Mineralized Areas, Claims, Mines and Prospects in Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -109.41, 36.64, -101.69, 41.36 https://cmr.earthdata.nasa.gov/search/concepts/C2231550976-CEOS_EXTRA.umm_json This data release contains mineral resource data for metallic and nonmetallic mineral sites in the State of Colorado. Along with the resource data, there is additional data, such as mineralized areas and mining districts; mine, prospect and commodity information; claim density by section; county boundaries; quadrangles; and simplified geology. All the geographic data are provided in formats for two commonly used Geographic Information Systems (GIS) software packages (MapInfo and ESRI?s ArcView). Not only does GIS software allow the data to be shown as layers in ?map? views that can be displayed with various geographic and geologic data, but the data can be queried and analyzed relative to data in any of the layers. Free shareware, ArcExplorer, is provided with this report so users may display the data in ?map? views and query the various datasets (Appendix A) without requiring a GIS program such as Arc/Info1, ArcView1, or MapInfo1. Additional data, such as original and unedited mine and prospect files, bibliography and references, and text are provided in appropriate formats such as in spreadsheets (Microsoft Excel), or documents (text, WordPerfect, or Microsoft Word). [Summary provided by the USGS.] proprietary +USGS_OFR_2003_095_1.1 Map and Data for Quaternary Faults and Folds in Oregon CEOS_EXTRA STAC Catalog 1970-01-01 -126.29038, 41.47393, -116.71798, 46.417915 https://cmr.earthdata.nasa.gov/search/concepts/C2231551564-CEOS_EXTRA.umm_json The map shows faults and folds in the state of Oregon that exhibit evidence of Quaternary deformation, and includes data on timing of most recent movement, sense of movement, slip rate, and continuity of surface expression. The primary purpose of this compilation is for use in earthquake-hazard evaluations. Paleoseismic studies, which evaluate the history of surface faulting or deformation along structures with evidence of Quaternary movement, provide a long-term perspective that augments the short historic records of seismicity in many regions. Published or publicly available data are the primary sources of data used to compile this report. [Summary provided by the USGS.] proprietary +USGS_OFR_2003_096_1.0 Geologic Map of the Valjean Hills 7.5' Quadrangle, San Bernardino County, California CEOS_EXTRA STAC Catalog 2003-01-01 2003-12-31 -116.126366, 35.624027, -115.99844, 35.750973 https://cmr.earthdata.nasa.gov/search/concepts/C2231551946-CEOS_EXTRA.umm_json This data set maps and describes the geology of the Valjean Hills 7.5' quadrangle, San Bernardino County, California. proprietary +USGS_OFR_2003_102_1.0 Geologic Map and Digital Database of the Romoland 7.5' Quadrangle, Riverside County, California CEOS_EXTRA STAC Catalog 1970-01-01 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2231552936-CEOS_EXTRA.umm_json The Geologic Map and Digital Database of the Romoland 7.5' Quadrangle, Riverside County, California report contains a digital geologic map database of the Romoland 7.5' quadrangle, Riverside County, California that includes: 1. ARC/INFO version 7.2.1 coverages of the various elements of the geologic map. 2. A Postscript file to plot the geologic map on a topographic base, and containing a Correlation of Map Units diagram (CMU), a Description of Map Units (DMU), and an index map. 3. Portable Document Format (.pdf) files of: a. This Readme; includes in Appendix I, data contained in rom_met.txt b. The same graphic as plotted in 2 above. Test plots have not produced precise 1:24,000-scale map sheets. Adobe Acrobat page size setting influences map scale. The Correlation of Map Units and Description of Map Units is in the editorial format of USGS Geologic Investigations Series (I-series) maps but has not been edited to comply with I-map standards. Within the geologic map data package, map units are identified by standard geologic map criteria such as formationname, age, and lithology. Where known, grain size is indicated on the map by a subscripted letter or letters following the unit symbols as follows: lg, large boulders; b, boulder; g, gravel; a, arenaceous; s, silt; c, clay; e.g. Qyfa is a predominantly young alluvial fan deposit that is arenaceous. Multiple letters are used for more specific identification or for mixed units, e.g., Qfysa is a silty sand. In some cases, mixed units are indicated by a compound symbol; e.g., Qyf2sc. [Summary provided by the USGS.] proprietary +USGS_OFR_2003_103_1.0 Geologic Map and Digital Database of the Bachelor Mountain 7.5' Quadrangle, Riverside County, California CEOS_EXTRA STAC Catalog 1970-01-01 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2231553086-CEOS_EXTRA.umm_json The Geologic Map and Digital Database of the Bachelor Mountain 7.5' Quadrangle, Riverside County, California contains a digital geologic map database of the Bachelor Mountain 7.5 - quadrangle, Riverside County, California that includes: 1. ARC/INFO (Environmental Systems Research Institute, http://www.esri.com) version 7.2.1 coverages of the various elements of the geologic map. 2. A Postscript file to plot the geologic map on a topographic base, and containing a Correlation of Map Units diagram (CMU), a Description of Map Units (DMU), and an index map. 3. Portable Document Format (.pdf) files of: a. This Readme; includes in Appendix I, data contained in bch_met.txt b. The same graphic as plotted in 2 above. Test plots have not produced precise 1:24,000- scale map sheets. Adobe Acrobat page size setting influences map scale. The Correlation of Map Units and Description of Map Units is in the editorial format of USGS Geologic Investigations Series (I-series) maps but has not been edited to comply with I-map standards. Within the geologic map data package, map units are identified by standard geologic map criteria such as formationname, age, and lithology. Where known, grain size is indicated on the map by a subscripted letter or letters following the unit symbols as follows: lg, large boulders; b, boulder; g, gravel; a, arenaceous; s, silt; c, clay; e.g. Qyfa is a predominantly young alluvial fan deposit that is arenaceous. Multiple letters are used for more specific identification or for mixed units, e.g., Qfysa is a silty sand. In some cases, mixed units are indicated by a compound symbol; e.g., Qyf2sc. [Summary provided by the USGS.] proprietary USGS_OFR_2003_108_1.0 Coastal Vulnerability Assessment of Gulf Islands To Sea-Level Rise CEOS_EXTRA STAC Catalog 1970-01-01 -90, 27, -85, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2231551754-CEOS_EXTRA.umm_json A coastal vulnerability index (CVI) was used to map the relative vulnerability of the coast to future sea-level rise within Gulf Islands National Seashore (GUIS) in Mississippi and Florida. The CVI ranks the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level rise, shoreline change rates, mean tidal range and mean wave height. The rankings for each variable were combined and an index value calculated for 1-minute grid cells covering the park. The CVI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural vulnerability to the effects of sea-level rise. The Gulf Islands in Mississippi and Florida consist of stable and washover dominated portions of barrier beach backed by wetland and marsh. The areas likely to be most vulnerable to sea-level rise are those with the highest occurrence of overwash, the highest rates of shoreline change, the gentlest regional coastal slope, and the highest rates of relative sea-level rise. The CVI provides an objective technique for evaluation and long-term planning by scientists and park managers. [Summary provided by the USGS.] proprietary USGS_OFR_2003_120 Bathymetry and selected perspective views of 6 reef and coastal areas in Northern Lake Michigan CEOS_EXTRA STAC Catalog 1970-01-01 -89, 42, -85, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231551744-CEOS_EXTRA.umm_json "Bathymetry and selected perspective views of 6 reef and coastal areas in Northern Lake Michigan involves applying state of the art laser technology and derivative imagery to map the detailed morphology and of principal lake trout spawning sites on reefs in Northern Lake Michigan and to provide a geologic interpretation. One objective was to identify the presence of ideal spawning substrate: shallow, ""clean"" gravel/cobble substrate, adjacent to deeper water. This study is a pilot collaborative effort with the US Army Corps of Engineers SHOALS (Scanning Hydrographic Operational Airborne Lidar Survey) program. The high-definition maps are integrated with known and developing data on fisheries, as well as limited substrate sedimentology information and underlying Paleozoic carbonate rocks. [Summary provided by the USGS.]" proprietary +USGS_OFR_2003_135 Geologic database for digital geology of California, Nevada, and Utah?An application of the North American Data Model CEOS_EXTRA STAC Catalog 1970-01-01 -125, 36, -109, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2231551929-CEOS_EXTRA.umm_json The USGS is creating an integrated national database for digital state geologic maps that includes stratigraphic, age, and lithologic information. The majority of the conterminous 48 states have digital geologic base maps available, often at scales of 1:500,000. This product is a prototype, and is intended to demonstrate the types of derivative maps that will be possible with the national integrated database. This database permits the creation of a number of types of maps via simple or sophisticated queries, maps that may be useful in a number of areas, including mineral-resource assessment, environmental assessment, and regional tectonic evolution. This database is distributed with three main parts: a Microsoft Access 2000 database containing geologic map attribute data, an Arc/Info (Environmental Systems Research Institute, Redlands, California) Export format file containing points representing designation of stratigraphic regions for the Geologic Map of Utah, and an ArcView 3.2 (Environmental Systems Research Institute, Redlands, California) project containing scripts and dialogs for performing a series of generalization and mineral resource queries. [Summary provided by the USGS.] proprietary +USGS_OFR_2003_150 Geophysical Surveys of Bear Lake, Utah-Idaho, September, 2002 CEOS_EXTRA STAC Catalog 1970-01-01 -112, 41, -111, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2231549315-CEOS_EXTRA.umm_json The objectives of the September, 2002 Geophysical Surveys of Bear Lake, Utah-Idaho operations, preliminarily reported here, were (1) to compile a detailed bathymetric map of the lake using swath-mapping techniques, in order to provide baseline data for a variety of applications and studies, and (2) to complete a sidescan-sonar survey of the lake, providing a nearly complete acoustic image of the lake floor. Limited amounts of subbottom acoustic-reflection data (chirp) were also collected, along with samples of lake-floor sediments representative of different kinds of backscatter patterns. These surveys followed an earlier subbottom acoustic-reflection survey (1997), using boomer and 3.5 kHz systems (S. M. Colman, unpublished data). Past seismic-reflection work has indicated that faults secondary to the east-side master fault cut the lake floor. These faults were among the primary targets of the sidescan-sonar survey. Preliminary interpretation of the data suggests that the morphology of the fault scarps on the lake floor are too subtle to be imaged by the sidescan-sonar system. However, some segments of the East Bear Lake fault at the foot of the steep eastern margin of the lake, are visible in the sidescan-sonar images. The other main targets of the sidescan-sonar survey were possible springs discharging at the lake floor. Discharge from such springs may be necessary to explain the chemistry and mineralogy of the lake sediments. A number of structures that appear to be related to spring discharge were observed in the sidescan-sonar images, and sediments at some of these features were sampled. [Summary provided by the USGS.] proprietary +USGS_OFR_2003_225_1.0 Generalized Lithology and Lithogeochemical Character of Near-Surface Bedrock in the New England Region CEOS_EXTRA STAC Catalog 1970-01-01 -73.73, 40.9, -66.93, 47.42 https://cmr.earthdata.nasa.gov/search/concepts/C2231548779-CEOS_EXTRA.umm_json This geographic information system (GIS) data layer shows the dominant lithology and geochemical, termed lithogeochemical, character of near-surface bedrock in the New England region covering the states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. The bedrock units in the map are generalized into groups based on their lithological composition and, for granites, geochemistry. Geologic provinces are defined as time-stratigraphic groups that share common features of age of formation, geologic setting, tectonic history, and lithology. This data set incorporates data from digital maps of two NAWQA study areas, the New England Coastal Basin (NECB) and the Connecticut, Housatonic, and Thames River Basins (CONN) areas and extends data to cover the states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. The result is a regional dataset for the lithogeochemical characterization of New England (the layer named NE_LITH). Polygons in the final coverage are attributed according to state, drainage area, geologic province, general rock type, lithogeochemical characteristics, and specific bedrock map unit. [Summary provided by the USGS.] proprietary USGS_OFR_2003_230_1.1 Digital depth horizon compilations of the Alaskan North Slope and adjacent arctic regions CEOS_EXTRA STAC Catalog 1970-01-01 -180, 55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231548492-CEOS_EXTRA.umm_json The Digital depth horizon compilations of the Alaskan North Slope and adjacent arctic regions file report contains data that has been digitized and combined to create four detailed depth horizon grids spanning the Alaskan North Slope and adjacent offshore areas. These map horizon compilations were created to aid in petroleum system modeling and related studies. [Summary provided by the USGS.] proprietary +USGS_OFR_2003_235 High-resolution seismic-reflection surveys in the nearshore of outer Cape Cod, Massachusetts CEOS_EXTRA STAC Catalog 1970-01-01 -73.68, 41.06, -69.75, 43.07 https://cmr.earthdata.nasa.gov/search/concepts/C2231549794-CEOS_EXTRA.umm_json The U.S. Geological Survey (USGS) Woods Hole Field Center (WHFC), in cooperation with the USGS Water Resources Division conducted high-resolution seismic-reflection surveys along the nearshore areas of outer Cape Cod, Massachusetts from Chatham to Provincetown, Massachusetts. The objectives of this investigation were to determine the stratigraphy of the nearshore in relation to the Quaternary stratigraphy of outer Cape Cod by correlating units between the nearshore and onshore and to define the geologic framework of the region. [Summary provided by the USGS.] proprietary +USGS_OFR_2003_236_1.0 National Geochronological Database CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231549399-CEOS_EXTRA.umm_json The National Geochronological Data Base (NGDB) was established by the United States Geological Survey (USGS) to collect and organize published isotopic (also known as radiometric) ages of rocks in the United States. The NGDB (originally known as the Radioactive Age Data Base, RADB) was started in 1974. A committee appointed by the Director of the USGS was given the mission to investigate the feasibility of compiling the published radiometric ages for the United States into a computerized data bank for ready access by the user community. A successful pilot program, which was conducted in 1975 and 1976 for the State of Wyoming, led to a decision to proceed with the compilation of the entire United States. For each dated rock sample reported in published literature, a record containing information on sample location, rock description, analytical data, age, interpretation, and literature citation was constructed and included in the NGDB. The NGDB was originally constructed and maintained on a mainframe computer, and later converted to a Helix Express relational database maintained on an Apple Macintosh desktop computer. The NGDB and a program to search the data files were published and distributed on Compact Disc-Read Only Memory (CD-ROM) in standard ISO 9660 format as USGS Digital Data Series DDS-14 (Zartman and others, 1995). As of May 1994, the NGDB consisted of more than 18,000 records containing over 30,000 individual ages, which is believed to represent approximately one-half the number of ages published for the United States through 1991. Because the organizational unit responsible for maintaining the database was abolished in 1996, and because we wanted to provide the data in more usable formats, we have reformatted the data, checked and edited the information in some records, and provided this online version of the NGDB. This report describes the changes made to the data and formats, and provides instructions for the use of the database in geographic information system (GIS) applications. The data are provided in *.mdb (Microsoft Access), *.xls (Microsoft Excel), and *.txt (tab-separated value) formats. We also provide a single non-relational file that contains a subset of the data for ease of use. [Summary provided by the USGS.] proprietary USGS_OFR_2003_241_1.0 Contaminated Sediments Database for Long Island Sound and the New York Bight CEOS_EXTRA STAC Catalog 1956-01-01 1997-12-31 -74.99, 38.49333, -71, 41.44219 https://cmr.earthdata.nasa.gov/search/concepts/C2231551092-CEOS_EXTRA.umm_json The Contaminated Sediments Database for Long Island Sound and the New York Bight provides a compilation of published and unpublished sediment texture and contaminant data. This report provides maps of several of the contaminants in the database as well as references and a section on using the data to assess the environmental status of these coastal areas. The database contains information collected between 1956-1997; providing an historical foundation for future contaminant studies in the region. [Summary provided by the USGS.] proprietary USGS_OFR_2003_247_1.0 A Digital Geological Map Database For the State of Oklahoma CEOS_EXTRA STAC Catalog 1970-01-01 -103, 33, -94, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2231550225-CEOS_EXTRA.umm_json This report consists of a compilation of twelve digital geologic maps provided in ARC/INFO interchange (e00) format for the state of Oklahoma. The source maps consisted of nine USGS 1:250,000-scale quadrangle maps and three 1:125,000 scale county maps. This publication presents a digital composite of these data intact and without modification across quadrangle boundaries to resolve geologic unit discontinuities. An ESRI ArcView shapefile formatted version and Adobe Acrobat (pdf) plot file of the compiled digital map are also provided. [Summary provided by the USGS.] proprietary +USGS_OFR_2003_265 Grand Canyon Riverbed Sediment Changes, Experimental Release of September 2000 - A Sample Data Set CEOS_EXTRA STAC Catalog 2000-08-28 2000-09-18 -112.09242, 36.08593, -111.47837, 36.93602 https://cmr.earthdata.nasa.gov/search/concepts/C2231552397-CEOS_EXTRA.umm_json An experimental water release from the Glen Canyon Dam into the Colorado River above Grand Canyon was conducted in September 2000 by the U.S. Bureau of Reclamation. The U.S. Geological Survey (USGS) conducted sidescan sonar surveys between Glen Canyon Dam (mile -15) and Diamond Creek (mile 220), Arizona (mile designations after Stevens, 1998) to determine the sediment characteristics of the Colorado River bed before and after the release. The first survey (R3-00-GC, 28 Aug to 5 Sep 2000) was conducted before the release when the river was at its Low Summer Steady Flow (LSSF) of 8,000 cfs. The second survey (R4-00-GC, 10 to 18 Sep 2000) was conducted immediately after the September 2000 experimental release when the average daily flow was as high as 30,800 cfs as measured below Glen Canyon Dam (Figure 2). Riverbed sediment properties interpreted from the sidescan sonar images include sediment type and sandwaves; overall changes in these properties between the two surveys were calculated. Sidescan sonar data from the USGS surveys were processed for segments of the Colorado River from Glen Canyon Dam (mile -15) to Phantom Ranch (mile 87.7, Figure 3). The surveys targeted pools between rapids that are part of the Grand Canyon Monitoring and Research Center (GCMRC http://www.gcmrc.gov/) physical sciences study. Maps interpreted from the sidescan sonar images show the distribution of sediment types (bedrock, boulders, pebbles or cobbles, and sand) and the extent of sandwaves for each of the pre- and post-flow surveys. The changes between the two surveys were calculated with spatial arithmetric and had properties of fining, coarsening, erosion, deposition, and the appearance or disappearance of sandwaves. This report describes GIS spatial data files for this project and provides examples of the data from the Colorado River near mile 2 below the confluence of the Paria and Colorado Rivers. The complete data set includes sidescan sonar images and interpreted map files for each of the pre- and post-flow surveys and the changes between the segments of rivers. [Summary provided by the USGS.] proprietary USGS_OFR_2003_267 Catalog of Earthquake Hypocenters at Alaskan Volcanoes: January 1 through December 31, 2002 CEOS_EXTRA STAC Catalog 2002-01-01 2002-12-31 -170, 51, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231552354-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO), a cooperative program of the U.S. Geological Survey, the Geophysical Institute of the University of Alaska Fairbanks, and the Alaska Division of Geological and Geophysical Surveys, has maintained seismic monitoring networks at historically active volcanoes in Alaska since 1988 (Power and others, 1993; Jolly and others, 1996; Jolly and others, 2001; Dixon and others, 2002). The primary objectives of this program are the seismic monitoring of active, potentially hazardous, Alaskan volcanoes and the investigation of seismic processes associated with active volcanism. This catalog presents the basic seismic data and changes in the seismic monitoring program for the period January 1, 2002 through December 31, 2002. Appendix G contains a list of publications pertaining to seismicity of Alaskan volcanoes based on these and previously recorded data. The AVO seismic network was used to monitor twenty-four volcanoes in real time in 2002. These include Mount Wrangell, Mount Spurr, Redoubt Volcano, Iliamna Volcano, Augustine Volcano, Katmai Volcanic Group (Snowy Mountain, Mount Griggs, Mount Katmai, Novarupta, Trident Volcano, Mount Mageik, Mount Martin), Aniakchak Crater, Mount Veniaminof, Pavlof Volcano, Mount Dutton, Isanotski Peaks, Shishaldin Volcano, Fisher Caldera, Westdahl Peak, Akutan Peak, Makushin Volcano, Great Sitkin Volcano, and Kanaga Volcano (Figure 1). Monitoring highlights in 2002 include an earthquake swarm at Great Sitkin Volcano in May-June; an earthquake swarm near Snowy Mountain in July-September; low frequency (1-3 Hz) tremor and long-period events at Mount Veniaminof in September-October and in December; and continuing volcanogenic seismic swarms at Shishaldin Volcano throughout the year. Instrumentation and data acquisition highlights in 2002 were the installation of a subnetwork on Okmok Volcano, the establishment of telemetry for the Mount Veniaminof subnetwork, and the change in the data acquisition system to an EARTHWORM detection system. AVO located 7430 earthquakes during 2002 in the vicinity of the monitored volcanoes. This catalog includes: (1) a description of instruments deployed in the field and their locations; (2) a description of earthquake detection, recording, analysis, and data archival systems; (3) a description of velocity models used for earthquake locations; (4) a summary of earthquakes located in 2002; and (5) an accompanying UNIX tar-file with a summary of earthquake origin times, hypocenters, magnitudes, and location quality statistics; daily station usage statistics; and all HYPOELLIPSE files used to determine the earthquake locations in 2002. [Summary provided by the USGS.] proprietary +USGS_OFR_2003_85_1.0 Nearshore Benthic Habitat GIS for the Channel Islands National Marine Sanctuary and Southern California State Fisheries Reserves Volume 1 CEOS_EXTRA STAC Catalog 1970-01-01 -122, 33, -119, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231551552-CEOS_EXTRA.umm_json The nearshore benthic habitat of the Santa Barbara coast and Channel Islands supports diverse marine life that is commercially, recreationally, and intrinsically valuable. Some of these resources are known to be endangered including a variety of rockfish and the white abalone. Agencies of the state of California and the United States have been mandated to preserve and enhance these resources. Data from sidescan sonar, bathymetry, video and dive observations, and physical samples are consolidated in a geographic information system (GIS). The GIS provides researchers and policymakers a view of the relationship among data sets to assist scienctific research and to help with economic and social policy-making decisions regarding this protected environment. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1007_1.0 Desert Landforms and Surface Processes in the Mojave National Preserve and Vicinity CEOS_EXTRA STAC Catalog 1970-01-01 -117.96, 34.12, -114.8, 37.23 https://cmr.earthdata.nasa.gov/search/concepts/C2231551091-CEOS_EXTRA.umm_json Landscape features in the Mojave National Preserve are a product of ongoing processes involving tectonic forces, weathering, and erosion. Long-term climatic cycles (wet and dry periods) have left a decipherable record preserved as landform features and sedimentary deposits. This website provides and introduction to climate-driven desert processes influencing landscape features including stream channels, alluvial fans, playas (dry lakebeds), dunes, and mountain landscapes. Bedrock characteristics, and the geometry of past and ongoing faulting, fracturing, volcanism, and landscape uplift and subsidence influence the character of processes happening at the surface. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1008_1.0 Geophysical Terranes of the Great Basin and Parts of Surrounding Provinces CEOS_EXTRA STAC Catalog 1970-01-01 -170, 25, -85, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231552043-CEOS_EXTRA.umm_json This study of geophysical terranes within and surrounding the Great Basin of the western United States integrates geophysical and geologic data to provide new insights on basement composition and structure at local, intermediate, and regional scales. Potential field (gravity and magnetic) studies are particularly useful to define the location, depth, and extent of buried basement sources and fundamental structural or compositional boundaries. They especially serve in imaging the subsurface in areas of extensive Cenozoic cover or where surface outcrops may be detached from the deeper crust. Identifying buried compositional or structural boundaries has applications, for example, in tectonic and earthquake hazard studies as they may reflect unmapped or buried faults. In many places, such features act as guides or barriers to fluid or magma flow or form favorable environments for mineralization and are therefore important to mineral, groundwater, and geothermal studies. This work serves in assessing the potential for undiscovered mineral deposits and provides important long-term land-use planning information. The primary component of this report is a set of geophysical maps with anomalies that are labeled and keyed to tables containing information on the anomaly and its source. Maps and data tables are provided in a variety of formats (tab delimited text, Microsoft Excel, PDF, and ArcGIS) for readers to review and download. The PDF formatted product allows the user to easily move between features on the maps and their entries in the tables, and vice-versa. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1009 Geology of the Ugashik-Mount Peulik Volcanic Center, Alaska CEOS_EXTRA STAC Catalog 1970-01-01 -170, 50, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231552364-CEOS_EXTRA.umm_json The Ugashik-Mount Peulik volcanic center, 550 km southwest of Anchorage on the Alaska Peninsula, consists of the late Quaternary 5-km-wide Ugashik caldera and the stratovolcano Mount Peulik built on the north flank of Ugashik. The center has been the site of explosive volcanism including a caldera-forming eruption and post-caldera dome-destructive activity. Mount Peulik has been formed entirely in Holocene time and erupted in 1814 and 1845. A large lava dome occupies the summit crater, which is breached to the west. A smaller dome is perched high on the southeast flank of the cone. Pyroclastic-flow deposits form aprons below both domes. One or more sector-collapse events occurred early in the formation of Mount Peulik volcano resulting in a large area of debris-avalanche deposits on the volcano's northwest flank. The Ugashik-Mount Peulik center is a calcalkaline suite of basalt, andesite, dacite, and rhyolite, ranging in SiO2 content from 51 to 72 percent. The Ugashik-Mount Peulik magmas appear to be co-genetic in a broad sense and their compositional variation has probably resulted from a combination of fractional crystallization and magma-mixing. The most likely scenario for a future eruption is that one or more of the summit domes on Mount Peulik are destroyed as new magma rises to the surface. Debris avalanches and pyroclastic flows may then move down the west and, less likely, east flanks of the volcano for distances of 10 km or more. A new lava dome or series of domes would be expected to form either during or within some few years after the explosive disruption of the previous dome. This cycle of dome disruption, pyroclastic flow generation, and new dome formation could be repeated several times in a single eruption. The volcano poses little direct threat to human population as the area is sparsely populated. The most serious hazard is the effect of airborne volcanic ash on aircraft since Mount Peulik sits astride heavily traveled air routes connecting the U.S. and Europe to Asia. Activity of the type described could produce eruption columns to heights of 15 km and result in significant amounts of ash 250-300 km downwind. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1010_1.0 Effect of Structural Heterogeneity and Slip Distribution on Coseismic Vertical Displacement from Rupture on the Seattle Fault CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231551656-CEOS_EXTRA.umm_json "Workshops in 2001 and 2002 were convened to determine critical issues in the development of tsunami inundation maps for the Puget Sound region. The Tsunami Inundation Mapping Effort (TIME) is conducted under the multi-agency National Tsunami Hazard Mitigation Program (NTHMP). The Puget Sound Tsunami/Landslide Workshop in 2001 focused on integrated tsunami research involving a wide range of research studies and tsunami hazard mitigation issues. The 2002 Puget Sound Tsunami Sources workshop (González et al., 2003) made specific recommendations for tsunami source modeling and improving our state of knowledge for sources in the Puget Sound region. One of the recommendations stated in González et al. (2003) is ""Develop methods to assess the sensitivity of coastal areas to tsunami inundation, based on multiple simulations that reflect the possible range of variations in the source parameters."" Tsunami inundation models rely heavily on the imposed initial conditions which, for an earthquake source, is the coseismic vertical displacement field. For example, Koshimura et al. (2002) use the geologic uplift observations (Buknam et al., 1992) to constrain the slip distribution for the event that occurred 1100 years ago, resulting in an average slip of 3.7 m and a magnitude of 7.6. Walsh et al. (2003) develop a tsunami inundation map for Elliot Bay based on a M 7.3 earthquake and the geologic uplift observations from the 1100 y.b.p. event as in Koshimura et al. (2002), though they use a constant fault dip of 60° rather than different dips for deep and shallow segments. The objective of this report is to examine how coseismic vertical displacement from a smaller M 6.5 Seattle Fault earthquake (as in Hartzell et al., 2002) is affected by structural heterogeneity and different slip distribution patterns. The three-dimensional crustal structure of the Puget Sound region has recently been defined using shallow seismic reflection data (Pratt et al., 1997; Johnson et al., 1999) and reflection and wide-angle recordings from the large-scale SHIPS experiments (e.g., Brocher et al., 2001; ten Brink et al., 2002). The presence of a deep sedimentary basin (Seattle Basin) adjacent to the Seattle Fault has led to the question of whether structural heterogeneity has an effect on our estimate of vertical displacement for earthquake scenarios in the region. We use a three-dimensional elastic finite-element model (Yoshioka et al., 1989) to calculate vertical displacements from rupture on a two-segment (deep and shallow) Seattle fault using a heterogeneous crustal structure. Similar studies by Geist and Yoshioka (1996) and Masterlark et al. (2001) used three-dimensional, finite-element models (FEM) to study the effect of structural heterogeneity on coseismic displacement fields. Results for the Puget Sound study are compared to calculations using a homogeneous structure as assumed with conventional elastic dislocation solutions. Effects of slip distribution patterns on vertical displacement is computed using the stochastic source model adopted for tsunami studies by Geist (2002). Finally, we examine an alternate model for shallow faulting proposed by ten Brink et al. (2002) and Brocher et al. (submitted) and its effect on the vertical displacement field. [Summary provided by the USGS.]" proprietary +USGS_OFR_2004_1011_1.0 Emergency Assessment of Debris-Flow Hazards from Basins Burned by the Cedar and Paradise Fires of 2003, Southern California CEOS_EXTRA STAC Catalog 1970-01-01 -125, 32, -112, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231552021-CEOS_EXTRA.umm_json These maps present preliminary assessments of the probability of debris-flow activity and estimates of peak discharges that can potentially be generated by debris flows issuing from basins burned by the Cedar and Paradise Fires of October 2003 in southern California in response to 25-year, 10-year, and 2-year recurrence, 1-hour duration rain storms. The probability maps are based on the application of a logistic multiple regression model that describes the percent chance of debris-flow production from an individual basin as a function of burned extent, soil properties, basin gradients, and storm rainfall. The peak-discharge maps are based on application of a multiple-regression model that can be used to estimate debris-flow peak discharge at a basin outlet as a function of basin gradient, burn extent, and storm rainfall. Probabilities of debris-flow occurrence for the Cedar Fire range between 0 and 98% and estimates of debris-flow peak discharges range between 893 and 5,987 ft3/s (25 to 170 m3/s). Basins burned by the Paradise Fire show probabilities for debris-flow occurrence between 2 and 98%, and peak discharge estimates between 1,814 and 5,980 ft3/s (51 and 169 m3/s). These maps are intended to identify those basins that are most prone to the largest debris-flow events and provide critical information for the preliminary design of mitigation measures and for the planning of evacuation timing and routes. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1013_1.0 Maps Showing the Stratigraphic Framework of South Carolina's Long Bay from Little River to Winyah Bay CEOS_EXTRA STAC Catalog 1994-01-01 -81, 32.5, -78.5, 34 https://cmr.earthdata.nasa.gov/search/concepts/C2231551083-CEOS_EXTRA.umm_json South Carolina's Grand Strand is a heavily populated coastal region that supports a large tourism industry. Like most densely developed coastal communities, the potential for property damage and lost revenues associated with coastal erosion and vulnerability to severe storms is of great concern. In response to these concerns, the U.S. Geological Survey (USGS) and the South Carolina Sea Grant Consortium have chosen to focus upon the Grand Strand (the arcuate strand of beaches between the North Carolina Border and Winyah Bay, SC) and adjacent Long Bay as a portion of Phase II of the South Carolina/Georgia Coastal Erosion Study (SC/GCES). Phase I of the SC/GCES (1994 - 1999) focused upon critical areas of erosion along the central portion of the South Carolina coastline. Research conducted during Phase I began to identify how physical processes, inlet-beach interaction, framework geology and shoreline geometry combine to control patterns of erosion along the central South Carolina coast. Phase II of SC/GCES (1999 - present) was designed to gain a further understanding of the factors affecting shoreline change within northern South Carolina and Georgia. Specific goals of the Phase II study include: 1) quantifying historic shoreline change and identifying erosional hotspots; 2) mapping geologic framework and determining its role in the area's coastal evolution; and 3) calculating a sediment budget and identifying transport mechanisms within the study area. In November 1999, to address the second goal of Phase II of the SC/GCES, the USGS, Coastal Carolina University (CCU) and Scripps Institution of Oceanography (SIO) began a program to systematically map the geologic framework within the South Carolina segment of Long Bay. Data sources used to produce these maps include high-resolution sidescan-sonar, interferometric sonar swath bathymetry and sub-bottom profiling. Surface sediment samples, vibracores and video data provide groundtruth for the geophysical data. The goals of the program include determining regional-scale sand-resource availability (needed for ongoing beach nourishment projects) and investigating the role that inner-shelf morphology and geologic framework play in the evolution of this portion of coastal South Carolina. This report presents preliminary maps generated through integrated interpretation of geophysical data, which detail the geometries of Cretaceous and Tertiary continental shelf deposits, show the location and extent of paleochannel incisions, and define a regional transgressive unconformity and overlying bodies of reworked sediment. Defining the shallow sub-surface geologic framework will provide a base for future process-oriented studies and provide insight into coastal evolution. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1014 Geophysical, Sedimentological, and Photographic Data from the John Day Reservoir, Washington and Oregon CEOS_EXTRA STAC Catalog 1970-01-01 -128, 39, -118, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2231550514-CEOS_EXTRA.umm_json "Recently, concerns about declining stocks of endangered anadromous salmonids in the Columbia River basin raised the issue of restoration of riverine functions in this and other Columbia and Snake River reservoirs (ISG, 2000; Dauble and others, 2003). One option for restoration of riverine functions includes lowering water levels within selected reservoirs such as the John Day Reservoir. Questions about how much sediment has been trapped by this dam warranted a detailed study of the floor of the reservoir to assess changes that had occurred since impoundment. High-resolution geophysical mapping techniques were employed to provide, to our knowledge, the first detailed view of the floor of the reservoir since its formation. This geophysical ""road map"" in concert with bottom video images, some sediment samples, and historical data collected prior to creation of the reservoir were incorporated into a GIS. The subsequent text summarizes the techniques used in this study. It also provides a preliminary analysis of the results and a background for the GIS that accompanies this report. [Summary provided by the USGS.]" proprietary USGS_OFR_2004_1020_1.0 Coastal Vulnerability Assessment of Assateague Island National Seashore (ASIS) To Sea-Level Rise CEOS_EXTRA STAC Catalog 1970-01-01 -77, 36, -75, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231552045-CEOS_EXTRA.umm_json A coastal vulnerability index (CVI) was used to map relative vulnerability of the coast to future sea-level rise within Assateague Island National Seashore (ASIS) in Maryland and Virginia. The CVI ranks the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level rise, shoreline change rates, mean tidal range and mean wave height. Rankings for each variable were combined and an index value calculated for 1-minute grid cells covering the park. The CVI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural vulnerability to the effects of sea-level rise. The CVI provides an objective technique for evaluation and long-term planning by scientists and park managers. Assateague Island consists of stable and washover dominated portions of barrier beach backed by wetland and marsh. The areas within Assateague that are likely to be most vulnerable to sea-level rise are those with the highest occurrence of overwash and the highest rates of shoreline change. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1021_1.0 Coastal Vulnerability Assessment of Olympic National Park To Sea-Level Rise CEOS_EXTRA STAC Catalog 1970-01-01 -125, 46, -123, 48.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552357-CEOS_EXTRA.umm_json A coastal vulnerability index (CVI) was used to map the relative vulnerability of the coast to future sea-level rise within Olympic National Park (OLYM), Washington. The CVI scores the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level rise, shoreline change rates, mean tidal range and mean wave height. The rankings for each variable were combined and an index value calculated for 1-minute grid cells covering the park. The CVI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural vulnerability to the effects of sea-level rise. The CVI provides an objective technique for evaluation and long-term planning by scientists and park managers. The Olympic National Park coast consists of rocky headlands, pocket beaches, glacial-fluvial features, and sand and gravel beaches. The Olympic coastline that is most vulnerable to sea-level rise are beaches in gently sloping areas. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1026 Chemistry of Stream Sediments and Surface Waters in New England CEOS_EXTRA STAC Catalog 1970-01-01 -75, 41, -66, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2231552858-CEOS_EXTRA.umm_json This online publication portrays regional data for pH, alkalinity, and specific conductance for stream waters and a multi-element geochemical dataset for stream sediments collected in the New England states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. A series of interpolation grid maps portray the chemistry of the stream waters and sediments in relation to bedrock geology, lithology, drainage basins, and urban areas. A series of box plots portray the statistical variation of the chemical data grouped by lithology and other features. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1038 Inventory of Significant Mineral Deposit Occurrences in the Headwaters Project Area in Idaho, Western Montana, and Extreme Eastern Oregon and Washington CEOS_EXTRA STAC Catalog 1970-01-01 -126, 42, -110, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2231551664-CEOS_EXTRA.umm_json The significant mineral deposit inventory supports the U.S. Geological Survey Headwaters project, which will provide Federal land management agencies with basic geologic and mineral resource information that can be used to manage near-term mineral resource development activity. The Headwaters study is focused on areas in Idaho lying north of the Snake River plain and in western Montana where a preponderance of the lands are managed by the U.S. Forest Service. The scope of this mineral resource inventory embraces a broader geographic area that includes all of Idaho, the western half of Montana and small portions of extreme eastern Oregon and Washington. This inventory covers only significant mineral deposits. Significant deposits are those deposits where a mineral or natural material endowment occurs in such a high concentration that it is reasonable to expect that recovery was or could, in the future, be economically viable. Minimum endowments proposed by Long (personal communication) for 46 commodities have been used in this compilation. For deposits of other commodities where minimum endowments have not been established a default deposit size minimum of one million metric tons of ore has been utilized. A significant status has also been applied to deposits where a commodity or material is of a highly unusual nature. Data collection was limited to deposit attributes that reflect directly on the endowed size and location of a deposit, and ancillary information that can be used in assessing regional mineral resource potential. The data are organized in topical information categories that include name, location, deposit classification, discovery date, production and resources, surface area, development status, and source of new information. Data were extracted from a diverse array of sources that includes scientific, technical, and trade publications of public and private institutions, organizations, and associations that follow and report on scientific, business, and environmental issues in the minerals industry; company financial reports, news releases, and technical reports available at company web sites; mineral information databases maintained by Federal and state agencies involved with monitoring and regulating mining activities and compiling mining industry statistics; and oral communications with individual mining company personnel and with staff of Federal and state regulatory agencies. Several formatting conventions are used to indicate what the relative accuracy of the numerical data is believed to be. A total of 256 significant deposit sites are identified by location and deposit-type. Production and resource figures are given in both English and metric units and the approximate surface areas associated with three aspects of deposit development are expressed in acres. Of the 256 sites, 208 have some history of past or present production, of which 23 are currently producing and mining could resume at 7 others on short notice with a rise in commodity prices. Within the 208 sites are 34 placer districts and two zeolite operations wherein mining activity on a small scale occurs intermittently. There are 166 sites where the presence of a significant resource has been recognized, of which 49 have no prior history of development. Due to the presence of a significant resource, these 166 sites are candidates for consideration when addressing issues associated with management of near-term mineral development. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1039 Location, Age, and Tectonic Significance of the Western Idaho Suture Zone CEOS_EXTRA STAC Catalog 1970-01-01 -118, 43, -112, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231552012-CEOS_EXTRA.umm_json The Western Idaho Suture Zone (WISZ) represents the boundary between crust overlying Proterozoic North American lithosphere and Late Paleozoic and Mesozoic intraoceanic crust accreted during Cretaceous time. Highly deformed plutons constituted of both arc and sialic components intrude the WISZ and in places are thrust over the accreted terranes. Pronounced variations in Sr, Nd, and O isotope ratios and in major and trace element composition occur across the suture zone in Mesozoic plutons. The WISZ is located by an abrupt west to east increase in initial 87Sr/86Sr ratios, traceable for over 300 km from eastern Washington near Clarkston, east along the Clearwater River thorough a bend to the south of about 110° from Orofino Creek to Harpster, and extending south-southwest to near Ola, Idaho, where Columbia River basalts conceal its extension to the south. K-Ar and 40Ar/39Ar apparent ages of hornblende and biotite from Jurassic and Early Cretaceous plutons in the accreted terranes are highly discordant within about 10 km of the WISZ, exhibiting patterns of thermal loss caused by deformation, subsequent batholith intrusion, and rapid rise of the continental margin. Major crustal movements within the WISZ commenced after about 135 Ma, but much of the displacement may have been largely vertical, during and following emplacement of batholith-scale silicic magmas. Deformation continued until at least 85 Ma and probably until 74 Ma, progressing from south to north. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1049_1.0 Geologic and Bathymetric Reconnaissance Overview of the San Pedro Shelf Region, Southern California CEOS_EXTRA STAC Catalog 2002-01-01 2002-12-31 -118.33333, 33.46667, -117.83333, 33.78333 https://cmr.earthdata.nasa.gov/search/concepts/C2231548808-CEOS_EXTRA.umm_json This report presents a series of maps that describe the bathymetry and late Quaternary geology of the San Pedro shelf area as interpreted from seismic-reflection profiles and 3.5-kHz and multibeam bathymetric data. Some of the seismic-reflection profiles were collected with Uniboom and 120-kJ sparker during surveys conducted by the U.S. Geological Survey (USGS) in 1973 (K-2-73-SC), 1978 (S-2-78-SC), and 1979 (S-2a-79-SC). The remaining seismic-reflection profiles were collected in 2000 using Geopulse boomer and minisparker during USGS cruise A-1-00-SC. The report consists of seven oversized sheets: 1. Map of 1978 and 1979 uniboom seismic-reflection and 3.5-kHz tracklines used to map faults and folds on San Pedro Shelf. 2. Maps of multibeam shaded bathymetric relief with faults and folds, and bathymetric contours. 3. Isopach map of unconsolidated sediment, seismic-reflection profile across the San Pedro shelf, seismic-reflection profile across San Gabriel paleo-valley. 4. Seismic-reflection profiles across the Palos Verdes Fault Zone. 5. Geologic map and samples of Uniboom and 120-kJ sparker seismic-reflection profiles used to make the map. 6. Map showing thickness of uppermost (Holocene?) sediment layer. 7. Map of San Gabriel Canyon paleo-valley and associated drainage basins. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1054 Assessment of Hazards Associated with the Bluegill Landslide, South-Central Idaho CEOS_EXTRA STAC Catalog 1970-01-01 -117.59, 41.64, -110.7, 49.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231554051-CEOS_EXTRA.umm_json The Bluegill landslide, located in south-central Idaho, is part of a larger landslide complex that forms an area in the Salmon Falls Creek drainage named Sinking Canyon. The landslide is on public property administered by the U.S. Bureau of Land Management (BLM). As part of ongoing efforts to address possible public safety concerns, the BLM requested that the U.S. Geological Survey (USGS) conduct a preliminary hazard assessment of the landslide, examine possible mitigation options, and identify alternatives for further study and monitoring of the landslide. This report presents the findings of that assessment based on a field reconnaissance of the landslide on September 24, 2003, a review of data and information provided by BLM and researchers from Idaho State University, and information collected from other sources. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1058 2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory CEOS_EXTRA STAC Catalog 2002-01-01 -168, 46, -126, 76 https://cmr.earthdata.nasa.gov/search/concepts/C2231549438-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO) tracks activity at the more than 40 historically active volcanoes of the Aleutian Arc. As of December 31, 2002, 24 of these volcanoes are monitored with short-period seismometer networks. AVO's monitoring program also includes daily analysis of satellite imagery supported by occasional over flights and compilation of pilot reports, observations of local residents, and observations of mariners. In 2002, AVO responded to eruptive activity or suspect volcanic activity at 6 volcanic centers in Alaska - Wrangell, the Katmai Group, Veniaminof, Shishaldin, Emmons Lake (Hague), and Great Sitkin volcanoes. In addition to responding to eruptive activity at Alaskan volcanoes, AVO also disseminated information on behalf of the Kamchatkan Volcanic Eruption Response Team (KVERT) about activity at 5 Russian volcanoes - Sheveluch, Klyuchevskoy, Bezymianny, Karymsky, and Chikurachki. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1064 Coastal Vulnerability Assessment of Cape Hatteras National Seashore (CAHA) to Sea-Level Rise CEOS_EXTRA STAC Catalog 1970-01-01 -80, 33, -76, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231549408-CEOS_EXTRA.umm_json A coastal vulnerability index (CVI) was used to map the relative vulnerability of the coast to future sea-level rise within Cape Hatteras National Seashore (CAHA) in North Carolina. The CVI ranks the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level rise, historical shoreline change rates, mean tidal range, and mean significant wave height. The rankings for each variable were combined and an index value was calculated for 1-minute grid cells covering the park. The CVI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural vulnerability to the effects of sea-level rise. The CVI provides an objective technique for evaluation and long-term planning by scientists and park managers. Cape Hatteras National Seashore consists of stable and washover dominated segments of barrier beach backed by wetland and marsh. The areas within Cape Hatteras that are likely to be most vulnerable to sea-level rise are those with the highest occurrence of overwash and the highest rates of shoreline change. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1067 Digital Database of Selected Aggregate and Related Resources in Ada, Boise, Canyon, Elmore, Gem, and Owyhee Counties, Southwestern Idaho CEOS_EXTRA STAC Catalog 1934-01-01 2003-12-31 -117.01154, 42.29952, -115.10053, 44.17547 https://cmr.earthdata.nasa.gov/search/concepts/C2231549777-CEOS_EXTRA.umm_json "The U.S. Geological Survey (USGS) compiled a database of aggregate sites and geotechnical sample data for six counties - Ada, Boise, Canyon, Elmore, Gem, and Owyhee - in southwest Idaho as part of a series of studies in support of the Bureau of Land Management (BLM) planning process. Emphasis is placed on sand and gravel sites in deposits of the Boise River, Snake River, and other fluvial systems and in Neogene lacustrine deposits. Data were collected primarily from unpublished Idaho Transportation Department (ITD) records and BLM site descriptions, published Army Corps of Engineers (ACE) records, and USGS sampling data. The results of this study provides important information needed by land-use planners and resource managers, particularly in the BLM, to anticipate and plan for demand and development of sand and gravel and other mineral material resources on public lands in response to the urban growth in southwestern Idaho. The aggregate database combines two data sets - site information and geotechnical sample data - into an integrated spatial database with 82 unique fields. The material source site data set includes information on 680 sites, and the geotechnical data set consists of selected information from 2,723 laboratory analyses of samples collected from many, but not all, of the sites. The 680 aggregate sites are divided into six classes: sand & gravel (614); rock quarry (43); cinder quarry (9); placer tailings (8); talus (4); and mine waste rock (2). Most importantly, the aggregate database includes detailed location information allowing individual sites to be located at least within a section and most often within a small parcel of a section. Additional information includes, but is not limited to: lithology-mineralogy or geologic formation (if known); surface ownership; size; production; permitting; agency; and number of samples. Geotechnical data include: lab number and test date; field parameters including sample location, type of material, and size; and the results of geotechnical analyses - gradation (grain size distribution), Los Angeles (LA) Degradation, sand equivalent, absorption, density, and several other tests. Ninety-five percent of the 2,723 geotechnical sample records include gradation data, and 72 percent of the samples have sand equivalent data. However, LA Degradation, absorption, and bulk density data are reported only in about 30 percent of the sample records. Large volumes of geotechnical data reside in a variety of accessible but little-used archives maintained by local and county highway districts, state transportation bureaus, and federal engineering, construction and transportation agencies. Integration of good quality geotechnical lithogeochemical information, particularly in digital form suitable for geospatial analysis, can produce profoundly superior databases that may allow more accurate and reliable ""expert"" decision making and improved land use planning. The database that accompanies this report, structured for direct import into geographic information system (GIS) software, is the first step toward producing such an integrated geologic-geotechnical spatial database. [Summary provided by the USGS.]" proprietary USGS_OFR_2004_1069 A 30-Year Record of Surface Mass Balance (1966-95) and Motion and Surface Altitude (1975-95) at Wolverine Glacier, Alaska CEOS_EXTRA STAC Catalog 1966-04-01 1995-12-31 -156, 57, -144, 66 https://cmr.earthdata.nasa.gov/search/concepts/C2231554448-CEOS_EXTRA.umm_json Scientific measurements at Wolverine Glacier, on the Kenai Peninsula in south-central Alaska, began in April 1966. At three long-term sites in the research basin, the measurements included snow depth, snow density, heights of the glacier surface and stratigraphic summer surfaces on stakes, and identification of the surface materials. Calculations of the mass balance of the surface strata-snow, new firn, superimposed ice, and old firn and ice mass at each site were based on these measurements. Calculations of fixed-date annual mass balances for each hydrologic year (October 1 to September 30), as well as net balances and the dates of minimum net balance measured between time-transgressive summer surfaces on the glacier, were made on the basis of the strata balances augmented by air temperature and precipitation recorded in the basin. From 1966 through 1995, the average annual balance at site A (590 meters altitude) was -4.06 meters water equivalent; at site B (1,070 meters altitude), was -0.90 meters water equivalent; and at site C (1,290 meters altitude), was +1.45 meters water equivalent. Geodetic determination of displacements of the mass balance stake, and glacier surface altitudes was added to the data set in 1975 to detect the glacier motion responses to variable climate and mass balance conditions. The average surface speed from 1975 to 1996 was 50.0 meters per year at site A, 83.7 meters per year at site B, and 37.2 meters per year at site C. The average surface altitudes were 594 meters at site A, 1,069 meters at site B, and 1,293 meters at site C; the glacier surface altitudes rose and fell over a range of 19.4 meters at site A, 14.1 meters at site B, and 13.2 meters at site C. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1074 Flood of June 4, 2002, in the Indian Creek Basin, Linn County, Iowa CEOS_EXTRA STAC Catalog 2002-06-04 2002-06-04 -96.97, 40.05, -89.82, 43.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231549367-CEOS_EXTRA.umm_json Severe flooding occurred on June 4, 2002, in the Indian Creek Basin in Linn County, Iowa, following thunderstorm activity over east-central Iowa. The rain gage at Cedar Rapids, Iowa, recorded a 24-hour rainfall of 4.76 inches at 6:00 p.m. on June 4th. Radar indications estimated as much as 6 inches of rain fell in the headwaters of the Indian Creek Basin. Peak discharges on Indian Creek of 12,500 cubic feet per second at County Home Road north of Marion, Iowa, and 24,300 cubic feet per second at East Post Road in southeast Cedar Rapids, were determined for the flood. The recurrence interval for these peak discharges both exceed the theoretical 500-year flood as computed using flood-estimation equations developed by the U.S. Geological Survey. Information about the basin and flood history, the 2002 thunderstorms and associated flooding, and a profile of high-water marks are presented for selected reaches along Indian and Dry Creeks. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1075 Bedrock Geology and Mineral Resources of the Knoxville 1 degree x 2 degree Quadrangle, Tennessee, North Carolina, and South Carolina (Digital Version) CEOS_EXTRA STAC Catalog 1970-01-01 -90, 33, -78, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231548820-CEOS_EXTRA.umm_json The following geographic information system (GIS) data layers provide a digital format for the map plate in Bulletin 1979 (Robinson et al., 1991), Bedrock Geology and Mineral Resources of the Knoxville 1 degree x 2 degree Quadrangle, Tennessee, North Carolina, and South Carolina. This open-file report is meant to supplement Bulletin 1979. The Knoxville 1 degree x 2 degree quadrangle spans the Southern Blue Ridge physiographic province at its widest point from eastern Tennessee across western North Carolina to the northwest corner of South Carolina. The quadrangle also contains small parts of the Valley and Ridge province in Tennessee and the Piedmont province in North and South Carolina. The bedrock geology for the coverage area is provided as a polygon coverage with bedrock unit information included. Mineral resources and geologic faults are provided as point and line files, respectively, to overlay the geology coverage. Detailed geologic information is provided in the attribute tables for these files, and .avl legend files are provided. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1083 Cross-Sections and Maps Showing Double-Difference Relocated Earthquakes from 1984-2000 along the Hayward and Calaveras Faults, California CEOS_EXTRA STAC Catalog 1984-01-01 2000-12-31 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2231554080-CEOS_EXTRA.umm_json This report present a cross-section and map views of earthquakes that occurred from 1984 to 2000 in the vicinity of the Hayward and Calaveras faults in the San Francisco Bay region, California. These earthquakes came from a catalog of events relocated using the double-difference technique, which provides superior relative locations of nearby events. As a result, structures such as fault surfaces and alignments of events along these surfaces are more sharply defined than in previous catalogs. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1086_1.2 Catalog of Significant Historical Earthquakes in the Central United States CEOS_EXTRA STAC Catalog 1800-01-01 1999-12-31 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231554496-CEOS_EXTRA.umm_json The Catalog of Significant Historical Earthquakes in the Central United States use Modified Mercalli intensity assignments to estimate source locations and moment magnitude M for eighteen 19th-century and twenty early- 20th-century earthquakes in the central United States (CUS) for which estimates of M are otherwise not available. We use these estimates, and locations and M estimated elsewhere, to compile a catelog of significant historical earthquakes in the CUS. The 1811-1812 New Madrid earthquakes apparently dominated CUS seismicity in the first two decades of the 19th century. M5-6 earthquakes occurred in the New Madrid Seismic Zone in 1843 and 1878, but none have occurred since 1878. There has been persistent seismic activity in the Illinois Basin in southern Illinois and Indiana, with M > 5.0 earthquakes in 1895, 1909, 1917, 1968, and 1987. Four other M > 5.0 CUS historical earthquakes have occurred: in Kansas in 1867, in Nebraska in 1877, in Oklahoma in 1882, and in Kentucky in 1980. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1096 Ground Magnetic Data from within the Long Valley Caldera, California CEOS_EXTRA STAC Catalog 2003-07-27 2003-08-01 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2231554402-CEOS_EXTRA.umm_json The past two decades have been a period of unrest for the Long Valley caldera of eastern California. The unrest began in 1978 and continued through late 1999 and included recurring swarms of moderate earthquakes, as well as uplifting of the Resurgent Dome, which has totaled approximately 80 cm. It is believed that the seismicity is accompanied by magmatic intrusion beneath both the Resurgent Dome at a depth of about 7 km; 10 km and the South Moat Seismic Zone (SMSZ) at a depth of about 15 km (Sorey and others, 2003). Seismic surveys within the caldera's topographic boundary have indicated the seismicity beneath the northwest section of the caldera is associated with fluid injection into narrow conduits and fractures (Stroujkova and Malin, 2000). Like the dominant regional structural trend, these conduits run in a northwest-southeast direction and are only expressed at the surface by a slight topographic relief of about 3 m. Merged aeromagnetic data (Roberts and Jachens, 1999) over the caldera show a magnetic low in the west and a high in the east (Figure 3). The western part has been modeled to relate to altered, low-magnetization (about 2.5 km thick) Bishop Tuff beneath the Resurgent Dome, indicating hydrothermal alteration in the west, whereas the high in the east represents the unaltered Bishop Tuff (Williams and others, 1977). The ground magnetic survey was conducted to locate magnetic lows that might indicate altered zones reflecting conduits for hydrothermal fluid flow in the northwest portion of the caldera. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1192 Deposition, Erosion, and Bathymetric Change in South San Francisco Bay: 1858-1983 CEOS_EXTRA STAC Catalog 1858-01-01 1983-12-31 -126, 34, -120, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231553316-CEOS_EXTRA.umm_json Since the California Gold Rush of 1849, sediment deposition, erosion, and the bathymetry of South San Francisco Bay have been altered by both natural processes and human activities. Historical hydrographic surveys can be used to assess how this system has evolved over the past 150 years. The National Ocean Service (NOS) (formerly the United States Coast and Geodetic Survey (USCGS), collected five hydrographic surveys of South San Francisco Bay from 1858 to 1983. Analysis of these surveys enables us to reconstruct the surface of the bay floor for each time period and quantify spatial and temporal changes in deposition, erosion, and bathymetry. The creation of accurate bathymetric models involves many steps. Sounding data was obtained from the original USCGS and NOS hydrographic sheets and were supplemented with hand drawn depth contours. Shorelines and marsh areas were obtained from topographic sheets. The digitized soundings and shorelines were entered into a Geographic Information System (GIS), and georeferenced to a common horizontal datum. Using surface modeling software, bathymetric grids with a horizontal resolution of 50 m were developed for each of the five hydrographic surveys. Prior to conducting analyses of sediment deposition and erosion, we converted all of the grids to a common vertical datum and made adjustments to correct for land subsidence that occurred from 1934 to 1967. Deposition and erosion that occurred during consecutive periods was then computed by differencing the corrected grids. From these maps of deposition and erosion, we calculated volumes and rates of net sediment change in the bay. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1194_1.0 Chemical Analysis Of Tertiary Volcanic Rocks, Central San Juan Caldera Complex, Southwestern Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -109.41, 36.64, -101.69, 41.36 https://cmr.earthdata.nasa.gov/search/concepts/C2231552533-CEOS_EXTRA.umm_json In conjunction with integrated mapping of the Oligocene central San Juan caldera cluster, southwestern Colorado (USGS I-Map 2799, in press), all modern chemical analyses of volcanic rocks for this area determined in laboratories of the U.S. Geological Survey have been re-evaluated in terms of the stratigraphic sequence as presently understood. These include approximately 700 unpublished analyses made between 1986 and 2003, as well as all USGS analyses published since 1965 when the widespread presence of regional welded ash-flow tuffs erupted from large calderas was first recognized. All the analyses are assigned unit identifiers consistent with those used for the new geologic map; quite a few of these differ from those used on sample submittal forms and in prior USGS publications. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1196 Coastal Vulnerability Assessment of Cumberland Island National Seashore (Cuis) To Sea-Level Rise CEOS_EXTRA STAC Catalog 1970-01-01 -81.4, 29, -80, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2231552144-CEOS_EXTRA.umm_json A coastal vulnerability index (CVI) was used to map the relative vulnerability of the coast to future sea-level rise within Cumberland Island National Seashore in Georgia. The CVI ranks the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level rise, historical shoreline change rates, mean tidal range and mean significant wave height. The rankings for each input variable were combined and an index value calculated for 1-minute grid cells covering the park. The CVI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural vulnerability to the effects of sea-level rise. The CVI provides an objective technique for evaluation and long-term planning by scientists and park managers. Cumberland Island National Seashore consists of stable to washover-dominated portions of barrier beach backed by wetland, marsh, mudflat and tidal creek. The areas within Cumberland that are likely to be most vulnerable to sea-level rise are those with the lowest foredune ridge and highest rates of shoreline erosion. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1201 Hydraulic Conductivity of Near-Surface Alluvium in the Vicinity of Cattlemans Detention Basin, South Lake Tahoe, California CEOS_EXTRA STAC Catalog 1970-01-01 -121, 38, -119, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231550993-CEOS_EXTRA.umm_json Cattlemans detention basin, South Lake Tahoe, California is designed to capture and reduce urban runoff and pollutants originating from developed areas before entering Cold Creek, which is tributary to Trout Creek and to Lake Tahoe. The effectiveness of the basin in reducing sediment and nutrient loads currently is being assessed with a five-year study. Hydraulic conductivity of the alluvium near the detention basin is needed to estimate ground-water flow and subsurface nutrient transport. Hydraulic conductivity was estimated using slug tests in 27 monitoring wells that surround the detention basin. For each test, water was poured rapidly into a well, changes in water-level were monitored, and the observed changes were analyzed using the Bouwer and Rice method. Each well was tested one to four times. A total of 24 wells were tested more than once. Of the 24 wells, the differences among the tests were within 10 percent of the average. Estimated hydraulic conductivities of basin alluvium range from 0.5 to 70 feet per day with an average of 17.8 feet per day. This range is consistent with the sandy alluvial deposits observed in the area of Cattlemans detention basin. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1208 Concentrations of Polycyclic Aromatic Hydrocarbons (PAHs) and Major and Trace Elements in Simulated Rainfall Runoff From Parking Lots, Austin, Texas, 2003 CEOS_EXTRA STAC Catalog 2001-02-01 2001-02-28 -98, 30, -97, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2231552750-CEOS_EXTRA.umm_json Samples of creek bed sediment collected near seal-coated parking lots in Austin, Texas, by the City of Austin during 2001-02 had unusually elevated concentrations of polycyclic aromatic hydrocarbons (PAHs). To investigate the possibility that PAHs from seal-coated parking lots might be transported to urban creeks, the U.S. Geological Survey, in cooperation with the City of Austin, sampled runoff and scrapings from four test plots and 13 urban parking lots. The surfaces sampled comprise coal-tar-emulsion-sealed, asphalt-emulsion-sealed, unsealed asphalt, and unsealed concrete. Particulates and filtered water in runoff and surface scrapings were analyzed for PAHs. In addition, particulates in runoff were analyzed for major and trace elements. Samples of all three media from coal-tar-sealed parking lots had concentrations of PAHs higher than those from any other types of surface. The average total PAH concentrations in particulates in runoff from parking lots in use were 3,500,000, 620,000, and 54,000 micrograms per kilogram from coal-tar-sealed, asphalt-sealed, and unsealed (asphalt and concrete combined) lots, respectively. The probable effect concentration sediment quality guideline is 22,800 micrograms per kilogram. The average total PAH (sum of detected PAHs) concentration in filtered water from parking lots in use was 8.6 micrograms per liter for coal-tar-sealed lots; the one sample analyzed from an asphalt-sealed lot had a concentration of 5.1 micrograms per liter and the one sample analyzed from an unsealed asphalt lot was 0.24 microgram per liter. The average total PAH concentration in scrapings was 23,000,000, 820,000, and 14,000 micrograms per kilogram from coal-tar-sealed, asphalt-sealed, and unsealed asphalt lots, respectively. Concentrations were similar for runoff and scrapings from the test plots. Concentrations of lead and zinc in particulates in runoff frequently exceeded the probable effect concentrations, but trace element concentrations showed no consistent variation with parking lot surface type. [Summary provided by USGS.] proprietary USGS_OFR_2004_1211 Baseline and Historic Depositional Rates and Lead Concentrations, Floodplain Sediments, Lower Coeur d'Alene River, Idaho CEOS_EXTRA STAC Catalog 1970-01-01 -117.59, 41.64, -110.7, 49.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231551144-CEOS_EXTRA.umm_json Lead-rich sediments, containing at least 1000 ppm of lead (Pb), and derived mainly from discarded mill tailings in the Coeur d'Alene mining region, cover about 60 km2 of the 80-km2 floor of the main stem of the Coeur d'Alene River valley, in north Idaho. Although mill tailings have not been discarded directly into tributary streams since 1968, frequent floods continue to re-mobilize sediment from large secondary sources, previously deposited on the bed, banks, alluvial terraces, and natural levees of the river. Thus, lead-rich sediments (also enriched in iron, manganese, zinc, copper, arsenic, cadmium, antimony and mercury) continue to be deposited on the floodplain. This is hazardous to the health of resident and visiting human and wildlife populations, attracted by the river and its lateral lakes and wetlands. This report documents and compares depositional rates and lead concentrations of lead-rich sediments deposited on the bed, banks, natural levees, and flood basins of the main stem of the Coeur d'Alene River during several time-stratigraphic intervals. These intervals are defined by their stratigraphic positions relative to the base of the section of lead-rich sediments, the 1980 Mt. St. Helens volcanic-ash layer, and the sedimentary surface at the time of sampling. Four important intervals represent sediment deposition during the following time spans (younger to older): 1. Baseline, from 1980 to about 1993 (after tailings disposal to streams ended, but before any major removals of lead-rich sediments); 2. Early post-tailings-release, from about 1968 to 1980; 3. Historic floodplain-contamination, from about 1903 to 1968; and 4. Background, before the 1893 flood (the first major flood after large-scale mining and milling began upstream in 1886). Medians of baseline depositional rates and lead concentrations in levee sediments vary laterally, from 6.4 cm/10y and 3300 ppm Pb on riverbanks and levee fore-slopes to 2.8 cm/10y and 3800 ppm Pb on levee back-slope uplands. In lateral flood basins, baseline medians increase with water depth, from 2.2 cm/10y and 1900 ppm Pb in lateral marshes, to 2.9 cm/10y and 2100 ppm Pb in littoral margins of lateral lakes, and 4.0 cm/10y and 4400 ppm Pb on limnetic bottoms of lateral lakes. The median of lead concentrations in baseline sediments is 82 percent of the median for early post-tailings-release sediments, with a 69-percent probability that the two data sets represent statistically different populations. By contrast, the median of lead concentrations in baseline sediments is 57 percent of the corresponding median for historic-interval sediments, and these two data sets definitely represent statistically different populations. The area-weighted average of medians of lead concentrations in baseline sediments of all depositional settings is 2900 ppm Pb, which is 1.6 times the 1800 ppm Pb that can be lethal to waterfowl. It also is 2.9 times the 1000-ppm-Pb threshold for removal of contaminated soil from residential yards in the Coeur d'Alene mining region, and 111 times the 26-ppm median of background lead concentrations in pre-industrial floodplain sediments. During episodes of high discharge, lead-rich sediments will continue to be mobilized from large secondary sources on the bed, banks, and natural levees of the river, and will continue to be deposited on the floodplain during frequent floods. Floodplain deposition of lead-rich sediments will continue for centuries unless major secondary sources are removed or stabilized. It is therefore important to design, sequence, implement, and maintain remediation in ways that will limit recontamination. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1214 Dissolved Pesticide and Organic Carbon Concentrations Detected in Surface Waters, Northern Central Valley, California, 2001-2002 CEOS_EXTRA STAC Catalog 2001-01-01 2002-06-30 -128, 36, -120, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2231550635-CEOS_EXTRA.umm_json Field and laboratory studies were conducted to determine the effects of pesticide mixtures on Chinook salmon under various environmental conditions in surface waters of the northern Central Valley of California. This project was a collaborative effort between the U.S. Geological Survey (USGS) and the University of California. The project focused on understanding the environmental factors that influence the toxicity of pesticides to juvenile salmon and their prey. During the periods January through March 2001 and January through May 2002, water samples were collected at eight surface water sites in the northern Central Valley of California and analyzed by the USGS for dissolved pesticide and dissolved organic carbon concentrations. Water samples were also collected by the USGS at the same sites for aquatic toxicity testing by the Aquatic Toxicity Laboratory at the University of California Davis; however, presentation of the results of these toxicity tests is beyond the scope of this report. Samples were collected to characterize dissolved pesticide and dissolved organic carbon concentrations, and aquatic toxicity, associated with winter storm runoff concurrent with winter run Chinook salmon out-migration. Sites were selected that represented the primary habitat of juvenile Chinook salmon and included major tributaries within the Sacramento and San Joaquin River Basins and the Sacramento San Joaquin Delta. Water samples were collected daily for a period of seven days during two winter storm events in each year. Additional samples were collected weekly during January through April or May in both years. Concentrations of 31 currently used pesticides were measured in filtered water samples using solid-phase extraction and gas chromatography-mass spectrometry at the U.S. Geological Survey's organic chemistry laboratory in Sacramento, California. Dissolved organic carbon concentrations were analyzed in filtered water samples using a Shimadzu TOC-5000A total organic carbon analyzer. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1220 Baseline Characteristics of Jordan Creek, Juneau, Alaska CEOS_EXTRA STAC Catalog 1999-01-01 2002-10-01 -135.5, 58.2, -134.3, 58.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552720-CEOS_EXTRA.umm_json Anadromous fish populations historically have found healthy habitat in Jordan Creek, Juneau, Alaska. Concern regarding potential degradation to the habitat by urban development within the Mendenhall Valley led to a cooperative study among the City and Borough of Juneau, Alaska Department of Environmental Conservation, and the U.S. Geological Survey, that assessed current hydrologic, water-quality, and physical-habitat conditions of the stream corridor. Periods of no streamflow were not uncommon at the Jordan Creek below Egan Drive near Auke Bay stream gaging station. Additional flow measurements indicate that periods of no flow are more frequent downstream of the gaging station. Although periods of no flow typically were in March and April, streamflow measurements collected prior to 1999 indicate similar periods in January, suggesting that no flow conditions may occur at any time during the winter months. This dewatering in the lower reaches likely limits fish rearing and spawning habitat as well as limiting the migration of juvenile salmon out to the ocean during some years. Dissolved-oxygen concentrations may not be suitable for fish survival during some winter periods in the Jordan Creek watershed. Dissolved-oxygen concentrations were measured as low as 2.8 mg/L at the gaging station and were measured as low as 0.85 mg/L in a tributary to Jordan Creek. Intermittent measurements of pH and dissolved-oxygen concentrations in the mid-reaches of Jordan Creek were all within acceptable limits for fish survival, however, few measurements of these parameters were made during winter-low-flow conditions. One set of water quality samples was collected at six different sites in the Jordan Creek watershed and analyzed for major ions and dissolved nutrients. Major-ion chemistry showed Jordan Creek is calcium bicarbonate type water with little variation between sampling sites. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1221 Los Angeles and San Diego Margin High-Resolution Multibeam Bathymetry and Backscatter Data CEOS_EXTRA STAC Catalog 1970-01-01 -118.85, 33.334, -117.754, 34.029 https://cmr.earthdata.nasa.gov/search/concepts/C2231552471-CEOS_EXTRA.umm_json The U.S. Geological Survey in cooperation with the University of New Hampshire and the University of New Brunswick mapped the nearshore regions off Los Angeles and San Diego, California using multibeam echosounders. Multibeam bathymetry and co-registered, corrected acoustic backscatter were collected in water depths ranging from about 3 to 900 m offshore Los Angeles and in water depths ranging from about 17 to 1230 m offshore San Diego. Continuous, 16-m spatial resolution, GIS ready format data of the entire Los Angeles Margin and San Diego Margin are available online as separate USGS Open-File Reports. For ongoing research, the USGS has processed sub-regions within these datasets at finer resolutions. The resolution of each sub-region was determined by the density of soundings within the region. This Open-File Report contains the finer resolution multibeam bathymetry and acoustic backscatter data that the USGS, Western Region, Coastal and Marine Geology Team has processed into GIS ready formats as of April 2004. The data are available in ArcInfo GRID and XYZ formats. See the Los Angeles or San Diego maps for the sub-region locations. These datasets in their present form were not originally intended for publication. The bathymetry and backscatter have data-collection and processing artifacts. These data are being made public to fulfill a Freedom of Information Act request. Care must be taken not to confuse artifacts with real seafloor morphology and acoustic backscatter. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1228 Bottom Photographs from the Pulley Ridge Deep Coral Reef CEOS_EXTRA STAC Catalog 2003-04-01 2003-04-30 -86, 25, -82, 29 https://cmr.earthdata.nasa.gov/search/concepts/C2231550612-CEOS_EXTRA.umm_json Pulley Ridge is a series of drowned barrier islands that extend over 100 km in 60-100 m water depths. This drowned ridge is located on the Florida Platform in the southeastern Gulf of Mexico about 250 km west of Cape Sable, Florida (Halley and others, 2003). This barrier island chain formed during the initial stage of the Holocene marine transgression approximately 7000 years before present. These islands were then submerged and left abandoned near the outer edge of the Florida Platform. The southern portion of Pulley Ridge, the focus of this study, hosts zooxanthellate scleractinian corals, green, red and brown macro algae, and a mix of deep and typically shallow-water tropical fishes. This largely photosynthetic community is unique in that it thrives with only 5% of the light typically associated with shallow-water reefs with similar fauna. Several factors help to account for the existence of this unique deep-water community. First, the underlying drowned barrier island provides both elevated topography and lithified substrate for the establishment of the hardbottom community. Second, the region is dominated by the west edge of the Loop Current, which brings relatively clear and warm water to this area. Third, the ridge's position on the continental shelf places it within the thermocline which provides nutrients to the reef during upwelling (Halley and others, 2003). This report presents the still photographs acquired during the April 2003 cruise aboard the Florida Institute of Oceanography's research vessel Suncoaster. These data are just one part of a multi-year study which includes the acquisition of sidescan-sonar imagery, high-resolution bathymetry, high-resolution seismic-reflection profiles, bottom video imagery, and bottom samples. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1234 Catalog of Earthquake Hypocenters at Alaskan Volcanoes: January 1 through December 31, 2003 CEOS_EXTRA STAC Catalog 2003-01-01 2003-12-31 -180, 50, -140, 66 https://cmr.earthdata.nasa.gov/search/concepts/C2231552741-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO), a cooperative program of the U.S. Geological Survey, the Geophysical Institute of the University of Alaska Fairbanks, and the Alaska Division of Geological and Geophysical Surveys, has maintained seismic monitoring networks at historically active volcanoes in Alaska since 1988. The primary objectives of this program are the near real time seismic monitoring of active, potentially hazardous, Alaskan volcanoes and the investigation of seismic processes associated with active volcanism. This catalog presents the calculated earthquake hypocenter and phase arrival data, and changes in the seismic monitoring program for the period January 1 through December 31, 2003. The AVO seismograph network was used to monitor the seismic activity at twenty-seven volcanoes within Alaska in 2003. These include Mount Wrangell, Mount Spurr, Redoubt Volcano, Iliamna Volcano, Augustine Volcano, Katmai volcanic cluster (Snowy Mountain, Mount Griggs, Mount Katmai, Novarupta, Trident Volcano, Mount Mageik, Mount Martin), Aniakchak Crater, Mount Veniaminof, Pavlof Volcano, Mount Dutton, Isanotski Peaks, Shishaldin Volcano, Fisher Caldera, Westdahl Peak, Akutan Peak, Makushin Volcano, Okmok Caldera, Great Sitkin Volcano, Kanaga Volcano, Tanaga Volcano, and Mount Gareloi. Monitoring highlights in 2003 include: continuing elevated seismicity at Mount Veniaminof in January-April (volcanic unrest began in August 2002), volcanogenic seismic swarms at Shishaldin Volcano throughout the year, and low-level tremor at Okmok Caldera throughout the year. Instrumentation and data acquisition highlights in 2003 were the installation of subnetworks on Tanaga and Gareloi Islands, the installation of broadband installations on Akutan Volcano and Okmok Caldera, and the establishment of telemetry for the Okmok Caldera subnetwork. AVO located 3911 earthquakes in 2003. This catalog includes: (1) a description of instruments deployed in the field and their locations; (2) a description of earthquake detection, recording, analysis, and data archival systems; (3) a description of velocity models used for earthquake locations; (4) a summary of earthquakes located in 2003; and (5) an accompanying UNIX tar-file with a summary of earthquake origin times, hypocenters, magnitudes, phase arrival times, and location quality statistics; daily station usage statistics; and all HYPOELLIPSE files used to determine the earthquake locations in 2003. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1235 Distribution of Holocene Sediment in Chesapeake Bay CEOS_EXTRA STAC Catalog 1970-01-01 -78, -36, -74, 41 https://cmr.earthdata.nasa.gov/search/concepts/C2231552442-CEOS_EXTRA.umm_json "The distribution of sedimentary environments presents the limited domain of deposits from ""River Input"", the flood tide wedge of ""Atlantic Sediment"", and the extensive region of indigenous, recycled ""Coastal Erosion Sediment"" in the Chesapeake Bay littoral environment. Studies by Miller (1982, 1983, 1987) along selected reaches of the tidewater Potomac River showed that bluff retreat in the littoral environment could be measured and modeled at as much as 0.5 to 1.0 m/yr. During the September 18, 2003 Hurricane Isabel storm surge of nearly 3 m, as much as 8 to 10 m of coastal erosion was measured near some of Miller's sites. Storm-driven coastal erosion is the most extensive source of Holocene sediment in the modern Bay. Although massive amounts were eroded from the terraces and uplands during lowered sea level and cold climates, presently most sediment eroded and transported from terrace and upland source areas has been stored on slopes and alluvial bottoms of the Coastal Plain landscapes that surround the Chesapeake. [Summary provided by the USGS.]" proprietary USGS_OFR_2004_1249 A Forest Vegetation Database for Western Oregon CEOS_EXTRA STAC Catalog 1970-01-01 -124.96, 41.58, -116.06, 46.68 https://cmr.earthdata.nasa.gov/search/concepts/C2231548732-CEOS_EXTRA.umm_json Data on forest vegetation in western Oregon were assembled for 2323 ecological survey plots. All data were from fixed-radius plots with the standardized design of the Current Vegetation Survey (CVS) initiated in the early 1990s. For each site, the database includes: 1) live tree density and basal area of common tree species, 2) total live tree density, basal area, estimated biomass, and estimated leaf area; 3) age of the oldest overstory tree examined, 4) geographic coordinates, 5) elevation, 6) interpolated climate variables, and 7) other site variables. The data are ideal for ecoregional analyses of existing vegetation. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1252 Digital Files for Northeast Asia Geodynamics, Mineral Deposit Location, and Metallogenic Belt Maps, Stratigraphic Columns, Descriptions of Map Units, and Descriptions of Metallogenic Belts CEOS_EXTRA STAC Catalog 1970-01-01 60, 27, 170, 81 https://cmr.earthdata.nasa.gov/search/concepts/C2231554750-CEOS_EXTRA.umm_json This publication contains a a series of files for Northeast Asia geodynamics, mineral deposit location, and metallogenic belt maps descriptions of map units and metallogenic belts, and stratigraphic columns. This region includes Eastern Siberia, Russian Far East, Mongolia, Northeast China, South Korea, and Japan. The files include: (1) a geodynamics map at a scale of 1:5,000,000; (2) page-size stratigraphic columns for major terranes; (3) a generalized geodynamics map at a scale of 1:15,000,000; (4) a mineral deposit location map at a scale of 1:7,500,000; (5) metallogenic belt maps at a scale of 1:15,000,000; (6) detailed descriptions of geologic units with references; (7) detailed descriptions of metallogenic belts with references; and (8) summary mineral deposit and metallogenic belt tables. The purpose of this publication is to provide high-quality, digital graphic files for maps and figures, and Word files for explanations, descriptions, and references to customers and users. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1260 Channel-Morphology Data for the Tongue River and Selected Tributaries, Southeastern Montana, 2001-02 CEOS_EXTRA STAC Catalog 2001-01-01 2002-12-31 -107, 45, -105, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231548542-CEOS_EXTRA.umm_json Coal-bed methane exploration and production have begun within the Tongue River watershed in southeastern Montana. The development of coal-bed methane requires production of large volumes of ground water, some of which may be discharged to streams, potentially increasing stream discharge and sediment load. Changes in stream discharge or sediment load may result in changes to channel morphology through changes in erosion and vegetation. These changes might be subtle and difficult to detect without baseline data that indicate stream-channel conditions before extensive coal-bed methane development began. In order to provide this baseline channel-morphology data, the U.S. Geological Survey, in cooperation with the Bureau of Land Management, collected channel-morphology data in 2001-02 to document baseline conditions for several reaches along the Tongue River and selected tributaries. This report presents channel-morphology data for five sites on the mainstem Tongue River and four sites on its tributaries. Bankfull, water-surface, and thalweg elevations, channel sections, and streambed-particle sizes were measured along reaches near streamflow-gaging stations. At each site, the channel was classified using methods described by Rosgen. For six sites, bankfull discharge was determined from the stage- discharge relation at the gage for the stage corresponding to the bankfull elevation. For three sites, the step-backwater computer model HEC-RAS was used to estimate bankfull discharge. Recurrence intervals for the bankfull discharge also were estimated for eight of the nine sites. Channel-morphology data for each site are presented in maps, tables, graphs, and photographs. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1265 Hydrologic Data Summary for the St. Lucie River Estuary, Martin and St. Lucie Counties, Florida, 1998-2001 CEOS_EXTRA STAC Catalog 1998-01-01 2001-12-31 -81, 27, -80, 28 https://cmr.earthdata.nasa.gov/search/concepts/C2231549907-CEOS_EXTRA.umm_json A hydrologic analysis was made at three canal sites and four tidal sites along the St. Lucie River Estuary in southeastern Florida from 1998 to 2001. The data included for analysis are stage, 15-minute flow, salinity, water temperature, turbidity, and suspended-solids concentration. During the period of record, the estuary experienced a drought, major storm events, and high-water discharge from Lake Okeechobee. Flow mainly occurred through the South Fork of the St. Lucie River; however, when flow increased through control structures along the C-23 and C-24 Canals, the North Fork was a larger than usual contributor of total freshwater inflow to the estuary. At one tidal site (Steele Point), the majority of flow was southward toward the St. Lucie Inlet; at a second tidal site (Indian River Bridge), the majority of flow was northward into the Indian River Lagoon. Large-volume stormwater discharge events greatly affected the St. Lucie River Estuary. Increased discharge typically was accompanied by salinity decreases that resulted in water becoming and remaining fresh throughout the estuary until the discharge events ended. Salinity in the estuary usually returned to prestorm levels within a few days after the events. Turbidity decreased and salinity began to increase almost immediately when the gates at the control structures closed. Salinity ranged from less than 1 to greater than 35 parts per thousand during the period of record (1998-2001), and typically varied by several parts per thousand during a tidal cycle. Suspended-solids concentrations were observed at one canal site (S-80) and two tidal sites (Speedy Point and Steele Point) during a discharge event in April and May 2000. Results suggest that most deposition of suspended-solids concentration occurs between S-80 and Speedy Point. The turbidity data collected also support this interpretation. The ratio of inorganic to organic suspended-solids concentration observed at S-80, Speedy Point, and Steele Point during the discharge event indicates that most flocculation of suspended-solids concentration occurs between Speedy Point and Steele Point. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1269_1.0 Liquefaction-Induced Lateral Spreading in Oceano, California, During the 2003 San Simeon Earthquake CEOS_EXTRA STAC Catalog 1970-01-01 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2231553668-CEOS_EXTRA.umm_json The December 22, 2003, San Simeon, California, (M6.5) earthquake caused damage to houses, road surfaces, and underground utilities in Oceano, California. The community of Oceano is approximately 50 miles (80 km) from the earthquake epicenter. Damage at this distance from a M6.5 earthquake is unusual. To understand the causes of this damage, the U.S. Geological Survey conducted extensive subsurface exploration and monitoring of aftershocks in the months after the earthquake. The investigation included 37 seismic cone penetration tests, 5 soil borings, and aftershock monitoring from January 28 to March 7, 2004. The USGS investigation identified two earthquake hazards in Oceano that explain the San Simeon earthquake damage?site amplification and liquefaction. Site amplification is a phenomenon observed in many earthquakes where the strength of the shaking increases abnormally in areas where the seismic-wave velocity of shallow geologic layers is low. As a result, earthquake shaking is felt more strongly than in surrounding areas without similar geologic conditions. Site amplification in Oceano is indicated by the physical properties of the geologic layers beneath Oceano and was confirmed by monitoring aftershocks. Liquefaction, which is also commonly observed during earthquakes, is a phenomenon where saturated sands lose their strength during an earthquake and become fluid-like and mobile. As a result, the ground may undergo large permanent displacements that can damage underground utilities and well-built surface structures. The type of displacement of major concern associated with liquefaction is lateral spreading because it involves displacement of large blocks of ground down gentle slopes or towards stream channels. The USGS investigation indicates that the shallow geologic units beneath Oceano are very susceptible to liquefaction. They include young sand dunes and clean sandy artificial fill that was used to bury and convert marshes into developable lots. Most of the 2003 damage was caused by lateral spreading in two separate areas, one near Norswing Drive and the other near Juanita Avenue. The areas coincided with areas with the highest liquefaction potential found in Oceano. Areas with site amplification conditions similar to those in Oceano are particularly vulnerable to earthquakes. Site amplification may cause shaking from distant earthquakes, which normally would not cause damage, to increase locally to damaging levels. The vulnerability in Oceano is compounded by the widespread distribution of highly liquefiable soils that will reliquefy when ground shaking is amplified as it was during the San Simeon earthquake. The experience in Oceano can be expected to repeat because the region has many active faults capable of generating large earthquakes. In addition, liquefaction and lateral spreading will be more extensive for moderate-size earthquakes that are closer to Oceano than was the 2003 San Simeon earthquake. Site amplification and liquefaction can be mitigated. Shaking is typically mitigated in California by adopting and enforcing up-to-date building codes. Although not a guarantee of safety, application of these codes ensures that the best practice is used in construction. Building codes, however, do not always require the upgrading of older structures to new code requirements. Consequently, many older structures may not be as resistant to earthquake shaking as new ones. For older structures, retrofitting is required to bring them up to code. Seismic provisions in codes also generally do not apply to nonstructural elements such as drywall, heating systems, and shelving. Frequently, nonstructural damage dominates the earthquake loss. Mitigation of potential liquefaction in Oceano presently is voluntary for existing buildings, but required by San Luis Obispo County for new construction. Multiple mitigation procedures are available to individual property owners. These procedures typically involve either changing the physical state of the underlying sands so they cannot liquefy or building a foundation that can resist the permanent displacement of the ground. Lateral spreading, which is the major threat to underground utilities, is particularly challenging to mitigate because typically large areas are involved and sizeable volumes of soil must be prevented from moving. Procedures to prevent spreading commonly require subsurface barrier walls. Prevention of lateral spreading may also require community rather than individual efforts because of the scale and cost of these mitigation measures. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1287_1.0 Coastal Circulation and Sediment Dynamics Along West Maui, Hawaii CEOS_EXTRA STAC Catalog 1970-01-01 -161, 18, -154, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2231554927-CEOS_EXTRA.umm_json High-resolution measurements of currents, temperature, salinity and turbidity were made over the course of three months off West Maui in the summer and early fall of 2003 to better understand coastal dynamics in coral reef habitats. Measurements were made through the emplacement of a series of bottom-mounted instruments in water depths less than 11 m. The studies were conducted in support of the U.S. Geological Survey (USGS) Coastal and Marine Geology Program's Coral Reef Project. The purpose of these measurements was to collect hydrographic data to better constrain the variability in currents and water column properties such as water temperature, salinity and turbidity in the vicinity of nearshore coral reef systems over the course of a summer and early fall when coral larvae spawn. These measurements support the ongoing process studies being conducted under the Coral Reef Project; the ultimate goal is to better understand the transport mechanisms of sediment, larvae, pollutants and other particles in coral reef settings. This report, the third in a series of three, describes data acquisition, processing and analysis. Previous reports provided data and results on: Long-term measurements of currents, temperature, salinity and turbidity off Kahana (PART I), and The spatial structure of currents, temperature, salinity and suspended sediment along West Maui (PART II). [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1297 Isostatic residual gravity map of The Santa Clara Valley and vicinity, California CEOS_EXTRA STAC Catalog 1970-01-01 -123, 37, -121, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231553893-CEOS_EXTRA.umm_json This map has 2 mGal gravity contours over a topographic base at a scale of 1:100,000. It covers the southern portion of San Francisco Bay, most of the Santa Clara Valley, and the surrounding mountains. It is a companion to U.S. Geological Survey Open-File Report 03-360, Shaded Relief Aeromagnetic Map of the Santa Clara Valley and Vicinity, California by Carter W. Roberts and Robert C. Jachens. [Summary provided by USGS.] proprietary USGS_OFR_2004_1303_1.0 Bedrock Geologic Map of the Port Wing, Solon Springs, and parts of the Duluth and Sandstone 30' X 60' Quadrangles, Wisconsin CEOS_EXTRA STAC Catalog 1992-01-01 2000-12-31 -93, 45, -88, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2231549066-CEOS_EXTRA.umm_json This Open-File Report provides digital data (shapefiles and .e00 files) for the bedrock geology in the Port Wing, Solon Springs, and parts of the Duluth and Sandstone quadrangles in Wisconsin. A Miscellaneous Investigations Series map (I map) is currently in review with analogous data in paper format. This map portrays the geology of part of the Midcontinent rift system (MRS) along the southern extension of the Lake Superior syncline in northern Wisconsin. The map area contains the St. Croix horst, a rift graben filled with Mesoproterozoic rocks of the Keweenawan Supergroup that was subsequently inverted. The horst exposes about 15 - 20 km of strata that record the opening of the Midcontinent rift, its subsequent transition to a thermal subsidence basin, and eventual inversion. About 3 km of underlying Mesoproterozoic strata, including the Gogebic iron range, and about 10 km of Neoarchean rocks, exposed in the southernmost part of the map area lie to the southeast of the horst. The nearly flat-lying continental red beds of the Oronto and Bayfield Groups, the youngest strata of the Keweenawan Supergroup, overlie the volcanic rocks. A wealth of geologic data exists for the area as a result of many individual studies over the last hundred years, but much has remained unpublished in theses, dissertations, and other reports of limited availability. This map has incorporated most of that data (see list of data sources) and includes results of our investigations conducted from 1992 to 2000. Our studies were designed to fill gaps in existing data and reconcile conflicting interpretations on some aspects of the geology of the region. The purpose of this map is to complete digital coverage of quadrangles with significant exposure of rocks of the Midcontinent rift in Wisconsin and Michigan at a scale of 1:100,000. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1322_1.0 Digital Shaded-Relief Map of Venezuela CEOS_EXTRA STAC Catalog 1970-01-01 -73.41939, 0.598294, -59.75341, 12.213118 https://cmr.earthdata.nasa.gov/search/concepts/C2231554134-CEOS_EXTRA.umm_json The Digital Shaded-Relief Map of Venezuela is a composite of more than 20 tiles of 90 meter (3 arc second) pixel resolution elevation data, captured during the Shuttle Radar Topography Mission (SRTM) in February 2000. The SRTM, a joint project between the National Geospatial-Intelligence Agency (NGA) and the National Aeronautics and Space Administration (NASA), provides the most accurate and comprehensive international digital elevation dataset ever assembled. The 10-day flight mission aboard the U.S. Space Shuttle Endeavour obtained elevation data for about 80% of the world's landmass at 3-5 meter pixel resolution through the use of synthetic aperture radar (SAR) technology. SAR is desirable because it acquires data along continuous swaths, maintaining data consistency across large areas, independent of cloud cover. Swaths were captured at an altitude of 230 km, and are approximately 225 km wide with varying lengths. Rendering of the shaded-relief image required editing of the raw elevation data to remove numerous holes and anomalously high and low values inherent in the dataset. Customized ArcInfo Arc Macro Language (AML) scripts were written to interpolate areas of null values and generalize irregular elevation spikes and wells. Coastlines and major water bodies used as a clipping mask were extracted from 1:500,000-scale geologic maps of Venezuela (Bellizzia and others, 1976). The shaded-relief image was rendered with an illumination azimuth of 315� and an altitude of 65�. A vertical exaggeration of 2X was applied to the image to enhance land-surface features. Image post-processing techniques were accomplished using conventional desktop imaging software. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1335 Binational Digital Soils Map of the Ambos Nogales Watershed, Southern Arizona and Northern Sonora, Mexico CEOS_EXTRA STAC Catalog 1970-01-01 -120, 25, -105, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553731-CEOS_EXTRA.umm_json A digital map of soil parameters for the international Ambos Nogales watershed was prepared to use as input for selected soils-erosion models. The Ambos Nogales watershed in southern Arizona and northern Sonora, Mexico, contains the Nogales wash, a tributary of the Upper Santa Cruz River. The watershed covers an area of 235 km squared just under half of which is in Mexico. Preliminary investigations of potential erosion revealed a discrepancy in soils data and mapping across the United States-Mexican border due to issues including different mapping resolutions, incompatible formatting, and varying nomenclature and classification systems. To prepare a digital soils map appropriate for input to a soils-erosion model, the historical analog soils maps for Nogales, Ariz., were scanned and merged with the larger-scale digital soils data available for Nogales, Sonora, Mexico using a geographic information system. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1345_1 Modeling of the Climax Stock and Related Plutons Based on the Inversion of Magnetic data, Southwest Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -116.26802, 37.149956, -115.917786, 37.31486 https://cmr.earthdata.nasa.gov/search/concepts/C2231553092-CEOS_EXTRA.umm_json The raster grid, model1, represents the elevation of the surface of the Climax and Gold Meadows Stocks. The elevation was generated by inverse modeling of the pseudogravity anomaly. This raster grid was created to model depth to the granitoid body that crops out at the Climax and Gold Meadows Stocks. Because granitic bodies may have hydrologic properties different from those of rocks they intrude, knowledge of their three-dimensional distribution in the subsurface is important for analyzing the southward flow of ground water into Yucca flat. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1352 Digital Engineering Aspects of Karst Map CEOS_EXTRA STAC Catalog 1984-01-01 1984-12-31 -178, 10, -16, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231553390-CEOS_EXTRA.umm_json These data are digital facsimiles of the original 1984 Engineering Aspects of Karst map by Davies and others. This data set was converted from a printed map to a digital GIS coverage to provide users with a citable national scale karst data set to use for graphic and demonstration purposes until new, improved data are developed. These data may be used freely with proper citation. Because it has been converted to GIS format, these data can be easily projected, displayed and queried for multiple uses in GIS. The karst polygons of the original map were scanned from the stable base negatives of the original, vectorized, edited and then attributed with unit descriptions. All of these processes potentially introduce small errors and distortions to the geography. The original map was produced at a scale of 1:7,500,000; this coverage is not as accurate, and should be used for broad-scale purposes only. It is not intended for any site-specific studies. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1038_1.0 Geologic Shaded Relief Map of Venezuela CEOS_EXTRA STAC Catalog 1970-01-01 -73.81, -0.11, -58.91, 12.92 https://cmr.earthdata.nasa.gov/search/concepts/C2231551683-CEOS_EXTRA.umm_json The geologic shaded relief map of Venezuela was created by direct digitization of geologic and hydrologic data north of the Orinoco River from a 1:500,000 scale paper map set. These data were integrated with a digital geologic map of the Venezuela Guayana Shield, also derived from 1:500,000 scale paper maps. Fault type information portrayed on the map, including unlabeled fault types, are as depicted in the original data sources. Geologic polygons were attributed for age, name, and lithologic type following the Lexico Estratigrafico de Venezuela. Significant revisions to the geology of the Cordillera de la Costa were incorporated based on new, detailed (1:25,000 scale) geologic mapping. Geologic polygons and fold and fault lines were draped over a shaded relief image produced by processing 90 m (3-arc second) radar interferometric data obtained by the space shuttle radar topography mission (SRTM). Values for null-data areas inherent in the SRTM data set were filled by interpolation based on surrounding data cells. The digital elevation model data was hill-shaded using an illumination direction of 315 degrees at an angle of 65 degrees above the horizon to produce the shaded relief image. The map projection used is equidistant conic, with latitudes 4 and 9 degrees north as standard parallels, and longitude 66 degrees west as the central meridian. The data contained in this map compilation primarily was derived from 1:500,000 scale maps and arranged for presentation and use at the scale of 1:750,000. Users may zoom in to view greater detail at larger scale; however, the authors make no guarantee of the accuracy of the map representation at scales larger than 1:750,000. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1063 Hydrologic Monitoring of Landslide-Prone Coastal Bluffs near Edmonds and Everett, Washington, 2001?2004 CEOS_EXTRA STAC Catalog 2001-01-01 2004-12-31 -123, 47.3, -122, 48.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231548776-CEOS_EXTRA.umm_json In 2001, a cooperative monitoring effort between the U.S. Geological Survey (USGS), the Burlington Northern Santa Fe Railway (BNSF), BNSF's geotechnical consultant, Shannon and Wilson, Inc., and the Washington Department of Transportation was begun to determine whether near-real-time monitoring of rainfall and shallow subsurface hydrologic conditions could be used to anticipate landslide activity on the bluffs. Monitoring currently occurs at two sites-one near Edmonds, Washington, and the other near Everett, Washington. During initial planning, the USGS proposed to evaluate the monitoring results at the end of 3 years. This report summarizes site conditions, methods, system reliability, data, and scientific results, and identifies possible future directions for development of monitoring and early warning of impending landslide activity. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1067_1.0 Landslide Hazards at La Conchita, California CEOS_EXTRA STAC Catalog 1970-01-01 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2231549807-CEOS_EXTRA.umm_json On January 10, 2005, a landslide struck the community of La Conchita in Ventura County, California, destroying or seriously damaging 36 houses and killing 10 people. This was not the first destructive landslide to damage this community, nor is it likely to be the last. This open file report describes the field observations and provides a description of the La Conchita area and its landslide history, a comparison of the 1995 and 2005 landslides, and a discussion. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1069 Coastal Change Rates and Patterns: Kaloko-Honokohau National Historical Park, Hawaii CEOS_EXTRA STAC Catalog 1950-01-01 2002-12-31 -161, 19, -154, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2231554427-CEOS_EXTRA.umm_json A collaborative project between the U.S. Geological Survey's Coastal and Marine Geology Program and the National Park Service (NPS) has been developed to create an inventory of geologic resources for National Park Service lands on the Big Island of Hawai?i. The NPS Geologic Resources Inventories are recognized as essential for the effective management, interpretation, and understanding of vital park resources. In general, there are three principal components of the inventories: geologic bibliographies, digital geologic maps, and geologic reports. The geologic reports are specific to each individual park and include information on the geologic features and processes that are important to the management of park resources, including ecological, cultural and recreational resources. This report summarizes a component of the geologic inventory concerned specifically with characterizing the coastal geomorphology of the beach system within Kaloko-Honokohau National Historical Park (NHP) and describes an analysis that utilizes georeferenced and orthorectified aerial photography to understand the spatial and temporal trends in shoreline change from 1950 to 2002. In addition, spatial patterns of beach change were examined and a beach stability map was developed. Both the shoreline change rates and the beach stability map are designed to help Park personnel effectively manage the valuable park resources within the context of understanding natural changes to the KAHO beach system. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1070_1.0 Molokai Benthic Habitat Mapping CEOS_EXTRA STAC Catalog 1970-01-01 -161, 18, -154, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2231549432-CEOS_EXTRA.umm_json The detailed high-resolution map layer provided here documents habitat characterization of a critical coral reef in Hawai'i. Integration of the aerial imagery, SHOALS bathymetry, and field observations made it possible to create detailed thematic maps reaching depths of 35 m (120 ft). This depth range encompasses the base of the Moloka'i forereef, and is deeper than can be mapped with standard optical remote sensing instruments. These maps can be used as stand-alone or in a GIS to provide useful information to scientists, managers and the general public. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1132_1.0 Ground-Magnetic Studies of the Amargosa Desert Region, California and Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2231555068-CEOS_EXTRA.umm_json High-resolution aeromagnetic surveys of the Amargosa Desert region, California and Nevada, exhibit a diverse array of magnetic anomalies reflecting a wide range of mid- and upper-crustal lithologies. In most cases, these anomalies can be interpreted in terms of exposed rocks and sedimentary deposits. More difficult to explain are linear magnetic anomalies situated over lithologies that typically have very low magnetizations. Aeromagnetic anomalies are observed, for example, over thick sections of Quaternary alluvial deposits and spring deposits associated with past or modern ground-water discharge in Ash Meadows, Pahrump Valley, and Furnace Creek Wash. Such deposits are typically considered nonmagnetic. To help determine the source of these aeromagnetic anomalies, we conducted ground-magnetic studies at five areas: near Death Valley Junction, at Point of Rocks Spring, at Devils Hole, at Fairbanks Spring, and near Travertine Springs. Depth-to-source calculations show that the sources of these anomalies lie within the Tertiary and Quaternary sedimentary section. We conclude that they are caused by discrete volcanic units lying above the pre-Tertiary basement. At Death Valley Junction and Travertine Springs, these concealed volcanic units are probably part of the Miocene Death Valley volcanic field exposed in the nearby Greenwater Range and Black Mountains. The linear nature of the aeromagnetic anomalies suggests that these concealed volcanic rocks are bounded and offset by near-surface faults. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1135_1.0 Modified Mercalli Intensity Maps for the 1906 San Francisco Earthquake Plotted in ShakeMap Format CEOS_EXTRA STAC Catalog 1906-04-18 1906-04-18 -124, 34, -120, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231554244-CEOS_EXTRA.umm_json This website presents Modified Mercalli Intensity maps for the great San Francisco earthquake of April 18, 1906. These new maps combine two important developments. First, we have re-evaluated and relocated the damage and shaking reports compiled by Lawson (1908). These reports yield intensity estimates for more than 600 sites and constitute the largest set of intensities ever compiled for a single earthquake. Second, we use the recent ShakeMap methodology to map these intensities. The resulting MMI intensity maps are remarkably detailed and eloquently depict the enormous power and damage potential of this great earthquake. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1144 Huminite Reflectance Measurements of Paleocene and Upper Cretaceous Coals from Borehole Cuttings, Zavala and Dimmit Counties, South Texas CEOS_EXTRA STAC Catalog 1970-01-01 -107.31, 25.19, -92.85, 37.14 https://cmr.earthdata.nasa.gov/search/concepts/C2231553355-CEOS_EXTRA.umm_json The reflectance of huminite in 19 cuttings samples was determined in support of ongoing investigations into the coal bed methane potential of subsurface Paleocene and Upper Cretaceous coals of South Texas. Coal cuttings were obtained from the Core Research Center of the Bureau of Economic Geology, The University of Texas at Austin. Geophysical logs, mud-gas logs, driller's logs, completion cards, and scout tickets were used to select potentially coal-bearing sample suites and to identify specific sample depths. Reflectance measurements indicate coals of subbituminous rank are present in a wider area in South Texas than previously recognized. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1148_1.0 Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004 CEOS_EXTRA STAC Catalog 1970-01-01 -80.82, 39.43, -74.41, 42.56 https://cmr.earthdata.nasa.gov/search/concepts/C2231550432-CEOS_EXTRA.umm_json Recent construction for Interstate Highway 99 (I?99) exposed pyrite and associated Zn-Pb sulfide minerals beneath a >10-m thick gossan to oxidative weathering along a 40-60-m deep roadcut through a 270-m long section of the Ordovician Bald Eagle Formation at Skytop, near State College, Centre County, Pennsylvania. Nearby Zn-Pb deposits hosted in associated sandstone and limestone in Blair and Centre Counties were prospected in the past; however, these deposits generally were not viable as commercial mines. The pyritic sandstone from the roadcut was crushed and used locally as road base and fill for adjoining segments of I?99. Within months, acidic (pH<3), metal-laden seeps and runoff from the exposed cut and crushed sandstone raised concerns about surface- and ground-water contamination and prompted a halt in road construction and the beginning of costly remediation. Mineralized sandstones from the cut contain as much as 34 wt. % Fe, 28 wt. % S, 3.5 wt. % Zn, 1% wt. Pb, 88 ppm As, and 32 ppm Cd. A composite of <2 mm material sampled from the cut face contains 8.1 wt. % total sulfide S, 0.6 wt. % sulfate S, and is net acidic by acid-base accounting (net neutralization potential ?234 kg CaCO3/t). Primary sulfide minerals include pyrite, marcasite, sphalerite (2 to 12 wt. % Fe) and traces of chalcopyrite and galena. Pyrite occurs in mm- to cm-scale veinlets and disseminated grains in sandstone, as needles, and in a locally massive pyrite-cemented breccia along a fault. Inclusions (<10 ?m) of CdS and Ni-Co-As minerals in pyrite and minor amounts of Cd in sphalerite (0.1 wt. % or less) explain the primary source of trace metals in the rock and in associated secondary minerals and seepage. Wet/dry cycles associated with intermittent rainfall promoted oxidative weathering and dissolution of primary sulfides and their oxidation products. Resulting sulfate solutions evaporated during dry periods to form intermittent ?blooms? of soluble, yellow and white efflorescent sulfate salts (copiapite, melanterite, and halotrichite) on exposed rock and other surfaces. Salts coating the cut face incorporated Fe, Al, S, and minor Zn. They readily dissolved in deionized water in the laboratory to form solutions with pH <2.5, consistent with field observations. In addition to elevated dissolved Fe and sulfate concentrations (>1,000 mg/L), seep waters at the base of the cut contain >100 mg/L dissolved Zn and >1 mg/L As, Co, Cu, and Ni. Lead is relatively immobile (<10 ?g/L in seep waters). The salts sequester metals and acidity between rainfall events. Episodic salt dissolution then contributes pulses of contamination including acid to surface runoff and ground water. The Skytop experience highlights the need to understand dynamic interactions of mineralogy and hydrology in order to avoid potentially negative environmental impacts associated with excavation in sulfidic rocks. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1153_1.0 Multibeam Bathymetry and Backscatter Data: Northeastern Channel Islands Region, Southern California CEOS_EXTRA STAC Catalog 2004-08-06 2004-08-15 -119.72, 33.88, -119.03, 34.33 https://cmr.earthdata.nasa.gov/search/concepts/C2231553010-CEOS_EXTRA.umm_json The U.S. Geological Survey (USGS) in cooperation with the Minerals Management Service (MMS) conducted multibeam mapping in the eastern Santa Barbara Channel and northeastern Channel Islands region from August 8 to15, 2004 aboard the R/V Maurice Ewing. The survey was directed and funded by the Minerals Management Service, which is interested in maps of hard bottom habitats, particularly natural outcrops, that support reef communities in areas affected by oil and gas activity. The maps are also useful to biologists studying fish that use the platforms and the sea floor beneath them as habitat. The survey collected bathymetry and corrected, co-registered acoustic backscatter using a Kongsberg Simrad EM1002 multibeam echosounder that was mounted on the hull of the R/V Maurice Ewing. Three main regions were mapped during the survey including: (1) the Eastern Santa Barbara Channel adjacent to an area previously mapped with multibeam-sonar by the Monterey Bay Aquarium Research Institute (see the MBARI Santa Barbara Basin Multibeam Survey web page), (2) the Footprint area south of Anacapa Island, which has been studied extensively by rockfish biologists and is considered a good site for a marine protected area, and (3) part of the submarine canyons along the continental slope south of Port Hueneme. These data will be used to support a number of new and ongoing projects including, habitat mapping, shelf and slope processes, and offshore hazards and resources. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1164_1.0 An Assessment of Volcanic Threat and Monitoring Capabilities in the United States: Framework for a National Volcano Early Warning System CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231551822-CEOS_EXTRA.umm_json A National Volcano Early Warning System NVEWS is being formulated by the Consortium of U.S. Volcano Observatories (CUSVO) to establish a proactive, fully integrated, national-scale monitoring effort that ensures the most threatening volcanoes in the United States are properly monitored in advance of the onset of unrest and at levels commensurate with the threats posed. Volcanic threat is the combination of hazards (the destructive natural phenomena produced by a volcano) and exposure (people and property at risk from the hazards). The United States has abundant volcanoes, and over the past 25 years the Nation has experienced a diverse range of the destructive phenomena that volcanoes can produce. Hazardous volcanic activity will continue to occur, and because of increasing population, increasing development, and expanding national and international air traffic over volcanic regions the exposure of human life and enterprise to volcano hazards is increasing. Fortunately, volcanoes exhibit precursory unrest that if detected and analyzed in time allows eruptions to be anticipated and communities at risk to be forewarned with reliable information in sufficient time to implement response plans and mitigation measures. In the 25 years since the cataclysmic eruption of Mount St. Helens, scientific and technological advances in volcanology have been used to develop and test models of volcanic behavior and to make reliable forecasts of expected activity a reality. Until now, these technologies and methods have been applied on an ad hoc basis to volcanoes showing signs of activity. However, waiting to deploy a robust, modern monitoring effort until a hazardous volcano awakens and an unrest crisis begins is socially and scientifically unsatisfactory because it forces scientists, civil authorities, citizens, and businesses into playing catch up with the volcano, trying to get instruments and civil-defense measures in place before the unrest escalates and the situation worsens. Inevitably, this manner of response results in our missing crucial early stages of the volcanic unrest and hampers our ability to accurately forecast events. Restless volcanoes do not always progress to eruption; nevertheless, monitoring is necessary in such cases to minimize either over-reacting, which costs money, or under-reacting, which may cost lives. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1176 Flooding of the Androscoggin River during December 18-19, 2003, in Canton, Maine CEOS_EXTRA STAC Catalog 2003-12-18 2003-12-19 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C2231550802-CEOS_EXTRA.umm_json The Androscoggin River flooded the town of Canton, Maine in December 2003, resulting in damage to and (or) evacuation of 44 homes. Streamflow records at the U.S. Geological Survey (USGS) streamflow-gaging stations at Rumford (USGS station identification number 01054500) and Auburn (01059000) were used to estimate the peak streamflow for the Androscoggin in the town of Canton for this flood (December 18-19, 2003). The estimated peak flood streamflow at Canton was approximately 39,800 ft3/s, corresponding to an estimated recurrence interval of 4.4 years; however, an ice jam downstream from Canton Point on December 18-19 obstructed river flow resulting in a high-water elevation commensurate with an open-water flood approximately equal to a 15-year event. The high water-surface elevations attained during the December 18-19 flood event in Canton were higher than the expected open-water flood water-surface elevations; this verified the assumption that the water-surface elevation was augmented due to the downstream ice jam. The change in slope of the riverbed from upstream of Canton to the impoundment at the downstream corporate limits, and the river bend near Stevens Island are principal factors in ice-jam formation near Canton. The U.S. Army Corps of Engineers Ice Jam Database indicates five ice-jam-related floods (including December 2003) for the town of Canton: March 13, 1936; January 1978; March 12, 1987; January 29, 1996; and December 18-19, 2003. There have been more ice-jam-related flood events in Canton than these five documented events, but the exact number and nature of ice jams in Canton cannot be determined without further research. proprietary +USGS_OFR_2005_1201 Estimated Water Use in Puerto Rico, 2000 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -67.08, 17.88, -65.45, 18.64 https://cmr.earthdata.nasa.gov/search/concepts/C2231551011-CEOS_EXTRA.umm_json Water-use data were compiled for the 78 municipios of the Commonwealth of Puerto Rico for 2000. Five offstream categories were considered: public-supply water withdrawals, domestic self-supplied water use, industrial self-supplied withdrawals, crop irrigation water use, and thermoelectric power fresh water use. Two additional categories also were considered: power generation instream use and public wastewater treatment return-flows. Fresh water withdrawals for offstream use from surface- and ground-water sources in Puerto Rico were estimated at 617 million gallons per day. The largest amount of fresh water withdrawn was by public-supply water facilities and was estimated at 540 million gallons per day. Fresh surface- and ground-water withdrawals by domestic self-supplied users was estimated at 2 million gallons per day and the industrial self-supplied withdrawals were estimated at 9.5 million gallons per day. Withdrawals for crop irrigation purposes were estimated at 64 million gallons per day, or approximately 10 percent of all offstream fresh water withdrawals. Saline instream surface-water withdrawals for cooling purposes by thermoelectric power facilities was estimated at 2,191 million gallons per day, and instream fresh water withdrawals by hydroelectric facilities at 171 million gallons per day. Total discharge from public wastewater treatment facilities was estimated at 211 million gallons per day. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1203_1.0 Magnetic Properties of Sediments in Cores BL96-1, -2, and -3 from Bear Lake, Utah and Idaho CEOS_EXTRA STAC Catalog 1970-01-01 -117.59, 36.74, -108.79, 49.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231550298-CEOS_EXTRA.umm_json As part of an ongoing study to derive records of past environmental change from lake sediments in the western United States, a set of three cores was collected from Bear Lake, Utah, in 1996. The three cores, BL96-1, -2, and -3, form an east-west profile and are located in about 50, 40 , and 30 m of water, respectively. The cores range in length from 4 m to 5 m, but because sediments thin markedly to the west (Colman, 2005) the maximum age of sediments penetrated increases from east to west. Together the cores provide a record from the last glacial period through the Holocene. This report presents magnetic property data acquired from these cores. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1253_1.0 Major- and Trace-Element Concentrations in Soils from Two Continental-Scale Transects of the United States and Canada CEOS_EXTRA STAC Catalog 1970-01-01 -140, 23, -50, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2231554514-CEOS_EXTRA.umm_json This report contains major- and trace-element concentration data for soil samples collected from 265 sites along two continental-scale transects in North America. One of the transects extends from northern Manitoba to the United States-Mexico border near El Paso, Tex. and consists of 105 sites. The other transect approximately follows the 38th parallel from the Pacific coast of the United States near San Francisco, Calif., to the Atlantic coast along the Maryland shore and consists of 160 sites. Sampling sites were defined by first dividing each transect into approximately 40-km segments. For each segment, a 1-km-wide latitudinal strip was randomly selected; within each strip, a potential sample site was selected from the most representative landscape within the most common soil type. At one in four sites, duplicate samples were collected 10 meters apart to estimate local spatial variability. At each site, up to four separate soil samples were collected as follows: (1) material from 0-5 cm depth; (2) O horizon, if present; (3) a composite of the A horizon; and (4) C horizon. Each sample collected was analyzed for total major- and trace-element composition by the following methods: (1) inductively coupled plasma-mass spectrometry (ICP-MS) and inductively coupled plasma-atomic emission spectrometry (ICPAES) for aluminum, antimony, arsenic, barium, beryllium, bismuth, cadmium, calcium, cerium, cesium, chromium, cobalt, copper, gallium, indium, iron, lanthanum, lead, lithium, magnesium, manganese, molybdenum, nickel, niobium, phosphorus, potassium, rubidium, scandium, silver, sodium, strontium, sulfur, tellurium, thallium, thorium, tin, titanium, tungsten, uranium, vanadium, yttrium, and zinc; (2) cold vapor- atomic absorption spectrometry for mercury; (3) hydride generation-atomic absorption spectrometry for antimony and selenium; (4) coulometric titration for carbonate carbon; and (5) combustion for total carbon and total sulfur. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1307 Contribution of Atmospheric Deposition to Pesticide Loads in Surface Water Runoff CEOS_EXTRA STAC Catalog 2001-01-01 2004-12-31 -126, 35, -120, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2231549636-CEOS_EXTRA.umm_json A 3.5-year study was conducted to determine the signifcance of atmospheric deposition to the pesticide concentrations in runoff. Both wet and dry atmospheric depostion were collected at six sites in the central San Joaquin Valley, California. Wet deposition samples were collected during individual rain events and dry deposition samples were collected for periods ranging from three weeks to four months. Each sample was analyzed for 41 currently used pesticides and 23 transformation products, including the oxygen analogs of nine organophosphorus (OP) insecticides. Ten compounds in rainfall and 19 in dry deposition were detected in at least 50% of the samples. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1315 Hawaiian Volcano Observatory Seismic Data, January to December 2004 CEOS_EXTRA STAC Catalog 2004-01-01 2004-12-31 -157, 18, -154, 21 https://cmr.earthdata.nasa.gov/search/concepts/C2231548689-CEOS_EXTRA.umm_json The Hawaiian Volcano Observatory (HVO) summary presents seismic data gathered during the year. The seismic summary is offered without interpretation as a source of preliminary data. It is complete in the sense that most data for events of Me1.5 routinely gathered by the Observatory are included. The HVO summaries have been published in various forms since 1956. Summaries prior to 1974 were issued quarterly, but cost, convenience of preparation and distribution, and the large quantities of data dictated an annual publication beginning with Summary 74 for the year 1974. Summary 86 (the introduction of CUSP at HVO) includes a description of the seismic instrumentation, calibration, and processing used in recent years. Beginning with 2004, summaries will simply be identified by the year, rather than Summary number. The present summary includes background information on the seismic network and processing to allow use of the data and to provide an understanding of how they were gathered. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1317 Compressional and Shear Wave Velocity Versus Depth in the San Francisco Bay Area, California: Rules for USGS Bay Area Velocity Model 05.0.0 CEOS_EXTRA STAC Catalog 1970-01-01 -128, 35, -118, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231549065-CEOS_EXTRA.umm_json This report summarizes and documents empirical compressional wave velocity (Vp) versus depth relationships for several important rock types in northern California used in constructing the new USGS Bay Area Velocity Model 05.0.0 (http://www.sf06simulation.org/). These rock types include the Jurassic and Cretaceous Franciscan Complex (metagraywacke and greenstones), serpentinites, Cretaceous Salinian and Sierra granites and granodiorites, Jurassic and Cretaceous Great Valley Sequence, and older Cenozoic sedimentary rocks (including the La Honda basin). Similar relations for less volumetrically important rocks are also developed for andesites, basalts, gabbros, and Sonoma Volcanics. For each rock type I summarize and plot the data used to develop the velocity versus depth relationships. These plots document the existing constraints on the proposed relationships. This report also presents a new empirical Vp versus depth relation derived from hundreds of measurements in USGS 30-m vertical seismic profiles (VSPs) for Holocene and Plio-Quaternary deposits in the San Francisco Bay area. For the upper 40 m (0.04 km) these mainly Holocene deposits, can be approximated by Vp (km/s) = 0.7 + 42.968z - 575.8z2 + 2931.6z3 - 3977.6z4, where z is depth in km. In addition, this report provides tables summarizing these VSP observations for the various types of Holocene and Plio-Quaternary deposits. In USGS Bay Area Velocity Model 05.0.0 these compressional wave velocity (Vp) versus depth relationships are converted to shear wave velocity (Vs) versus depth relationships using recently proposed empirical Vs versus Vp relations. Density is calculated from Vp using Gardner's rule and relations for crystalline rocks proposed by Christensen and Mooney (1995). Vs is then used to calculate intrinsic attenuation coefficients for shear and compressional waves, Qs and Qp, respectively. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1319 Biogenic Silica Measurements in Cores Collected from Bear Lake, Utah and Idaho CEOS_EXTRA STAC Catalog 1970-01-01 -117.59, 36.74, -96.06, 49.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231555243-CEOS_EXTRA.umm_json The overall goal of our research on Bear Lake is to create records of past climate change for the region, including changes in precipitation (rain and snow) patterns during the last 10,000 years and longer. As part of the project, we are attempting to determine how the size of Bear Lake has varied in the past in order to assess the possibility of future flooding and drought. We also seek to understand human influences on sediment deposition, chemistry, and life in the lake. Evidence of past conditions comes from sediments deposited in the lake, so reconstructions of past conditions require accurate dating of the sediments. The study includes the upper Bear River watershed as well as Bear Lake. The Bear River is the largest river in the Great Basin and the source of the majority of water flowing into the Great Salt Lake. In this region, wet periods may produce flooding along the course of the Bear River and around Great Salt Lake, while dry periods, or droughts, may affect water availability for ecosystems, as well as for agricultural, industrial, and residential use. Diatoms are one of the most sensitive indicators of environments in many lakes. In addition to species compositions and abundances (Moser and Kimball, 2005), total diatom productivity commonly varies considerably with changes in limnological conditions. Biogenic silica preserved in sediments is an index of total diatom productivity and, thus, is an indirect proxy for paleolimnology (for example, Colman and others, 1995; Johnson and others, 2001). In this paper, we present the results of biogenic silica analyses of two cores taken in Bear Lake, Utah, and discuss preliminary paleolimnologic conclusions based on these data. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1329 Ground-Water Reconnaissance of the Bijou Creek Watershed, South Lake Tahoe, California, June-October 2003 CEOS_EXTRA STAC Catalog 2003-06-01 2003-10-31 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2232411614-CEOS_EXTRA.umm_json A ground-water reconnaissance study of the Bijou Creek watershed in South Lake Tahoe, California was done during the summer and early fall of 2003. This study provides basic hydrologic data for a region in the Lake Tahoe Basin in which a continuing loss of lake clarity is occurring in the nearshore zone of Lake Tahoe. Wells, springs, and a surface-water site were located and basic hydrologic data were collected. Water levels were measured and water samples were collected and analyzed for nutrients. Measurements of water temperature, specific conductance, and pH were made at all ground-water sites where possible and at one surface-water site. Organic nitrogen plus ammonia, ammonia, and biologically-available iron concentrations generally were greater in the ground water in the Bijou Creek watershed than those observed in ground water elsewhere in the Lake Tahoe Basin. Nitrate concentrations were similar in the two groups. Phosphorus and orthophosphate concentrations generally were lower in the ground water of the Bijou Creek watershed compared to ground water from elsewhere in the Lake Tahoe Basin. Specific conductance and pH of ground water were similar between the Bijou Creek watershed and the Lake Tahoe Basin, but the temperature of ground water was generally greater in the Bijou Creek watershed. Nitrate concentrations appeared to increase over time at one of two long-term ground-water sites. Orthophosphate concentration decreased while specific conductance increased at one of the two sites, but no trend was detected at the other site for either parameter. No trends were detected for phosphorus, biologically-available iron, water temperature, or pH at either of the long-term sites. Trends in ammonia and organic nitrogen plus ammonia concentrations were not evaluated because a majority of the values were below the method detection limits. There were no obvious spatial distribution patterns for nutrient concentrations or field parameters in the Bijou Creek watershed. The altitude of the ground-water table above sea level generally increased with increasing distance from Lake Tahoe. The altitude of the ground-water table was greater than the altitude of the surface of Lake Tahoe except at one ground-water site which is influenced by a cone of depression around a nearby production well. Ground water in the Bijou Creek watershed discharges to Lake Tahoe and may contribute to the higher than normal turbidity in the area. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1333 Estimates of Ground-Water Recharge Based on Streamflow-Hydrograph Methods: Pennsylvania CEOS_EXTRA STAC Catalog 1885-01-01 2001-12-31 -80.82, 39.43, -74.41, 42.56 https://cmr.earthdata.nasa.gov/search/concepts/C2231554566-CEOS_EXTRA.umm_json This study, completed by the U.S. Geological Survey (USGS) in cooperation with the Pennsylvania Department of Conservation and Natural Resources, Bureau of Topographic and Geologic Survey (T&GS), provides estimates of ground-water recharge for watersheds throughout Pennsylvania computed by use of two automated streamflow-hydrograph-analysis methods--PART and RORA. The PART computer program uses a hydrograph-separation technique to divide the streamflow hydrograph into components of direct runoff and base flow. Base flow can be a useful approximation of recharge if losses and interbasin transfers of ground water are minimal. The RORA computer program uses a recession-curve displacement technique to estimate ground-water recharge from each storm period indicated on the streamflow hydrograph. Recharge estimates were made using streamflow records collected during 1885-2001 from 197 active and inactive streamflow-gaging stations in Pennsylvania where streamflow is relatively unaffected by regulation. Estimates of mean-annual recharge in Pennsylvania computed by the use of PART ranged from 5.8 to 26.6 inches; estimates from RORA ranged from 7.7 to 29.3 inches. Estimates from the RORA program were about 2 inches greater than those derived from the PART program. Mean-monthly recharge was computed from the RORA program and was reported as a percentage of mean-annual recharge. On the basis of this analysis, the major ground-water recharge period in Pennsylvania typically is November through May; the greatest monthly recharge typically occurs in March. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1339_1.0 Gravity Studies of Cave, Dry Lake, and Delamar Valleys, East-Central Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -120.35, 34.65, -113.69, 42.34 https://cmr.earthdata.nasa.gov/search/concepts/C2231549975-CEOS_EXTRA.umm_json Analysis of gravity anomalies in Cave, Dry Lake, and Delamar valleys in east-central Nevada defines the overall shape of their basins, provides estimates of the depth to pre-Cenozoic basement rocks, and identifies buried faults beneath the sedimentary cover. In all cases, the basins are asymmetric in their cross section and in their placement beneath the valley, reflecting the extensional tectonism that initiated during Miocene time in this area. Absolute values of basin depths are estimated using a density-depth profile calibrated by deep oil and gas wells that encountered basement rocks in Cave Valley. The basin beneath southern Cave Valley extends down to -6.0 km, that of Dry Lake Valley extends to -8.2 km, and that of Delamar Valley extends to -6.4 km. The ranges surrounding Dry Lake and Delamar valleys are dominated by volcanic units that may produce lower-density basin infill, which in turn, would make the maximum depth estimates somewhat less. Dry Lake Valley is characterized by a slot-like graben in its center, whereas the deep portions of Cave and Delamar valleys are more bowl-shaped. Significant portions of the basins are shallow (<1 km deep), as are the transitions between each of these valleys. A seismic reflection image across southern Cave and Muleshoe valleys confirms the basin shapes inferred from gravity analysis. The architecture of these basins inferred from gravity will aid in interpreting the hydrogeologic framework of Cave, Dry Lake, and Delamar valleys by placing estimates on the volume and connectivity of potential unconsolidated alluvial aquifers and by identifying faults buried beneath basin deposits. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1402 Interpreted Regional Seismic Reflection Lines, National Petroleum Reserve in Alaska CEOS_EXTRA STAC Catalog 1970-01-01 -162, 67, -148, 76 https://cmr.earthdata.nasa.gov/search/concepts/C2231551534-CEOS_EXTRA.umm_json Interpretation of reprocessed data from a regional grid of 25 public-domain 2-D seismic profiles in the National Petroleum Reserve in Alaska has enabled an analysis of subsurface geologic relations throughout that region. Notable results include interpretations of the geometry of the Mississippian Umiat and Meade basins, and depositional patterns in the thick succession of younger strata that were influenced by major structural features such as the Barrow arch and the Brooks Range. Pre-Mississippian low-grade metamorphic rocks and subordinate granites of the Franklinian sequence are the basement rocks of the region. The top of the Franklinian is imaged as one of the highest amplitude, most continuous reflections. The sedimentary succession includes (1) the Mississippian to Triassic Ellesmerian sequence (consisting of the Endicott, Lisburne and Sadlerochit groups, and the Shublik Formation and Sag River Sandstone; (2) the Beaufortian sequence, comprising the Jurassic to Lower Cretaceous Kingak Shale and the overlying Lower Cretaceous pebble shale unit; and (3) the Cretaceous to Paleocene Brookian sequence, which includes the Hue Shale and the Torok, Nanushuk, Seabee, Tuluvak, Schrader Bluff, and Prince Creek formations. Stratigraphic horizons that were mapped seismically include the tops of the Franklinian basement, the Endicott, Lisburne, and Sadlerochit groups, the Shublik Formation, the Sag River Sandstone, the Lower Cretaceous unconformity (LCU), and the gamma-ray zone of the Hue Shale. Distinguishing criteria were established for the seismic-reflection characteristics for each of these horizons, and the results were used in the correlation of units across the basins and onto the bordering margins. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1405 Landslide Susceptibility Estimated From Mapping Using Light Detection and Ranging (LIDAR) Imagery and Historical Landslide Records, Seattle, Washington CEOS_EXTRA STAC Catalog 1970-01-01 -123, 46, -121, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2231550356-CEOS_EXTRA.umm_json Landforms in Seattle, Washington, that were created primarily by landsliding were mapped using LIDAR-derived imagery. These landforms include landslides (primarily landslide complexes), headscarps, and denuded slopes. Over 93 percent of about 1,300 reported historical landslides are located within the LIDAR-mapped landform boundaries. The spatial densities of reported historical landslides within the LIDAR-mapped landforms provide the relative susceptibilities of the landforms to past landslide activity. Because the landforms were primarily created by prehistoric landslides, the spatial densities also provide reasonable estimates of future landslide susceptibility. The mapped landforms and susceptibilities provide useful tools for landslide hazard reduction in Seattle. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1407 Fracture Trace Map and Single-Well Aquifer Test Results in a Carbonate Aquifer in Jefferson County, West Virginia CEOS_EXTRA STAC Catalog 1970-01-01 -82.89, 36.96, -77.48, 40.88 https://cmr.earthdata.nasa.gov/search/concepts/C2231550959-CEOS_EXTRA.umm_json These data contain information on the results of single-well aquifer tests, lineament analysis, and a bedrock geologic map compilation for Jefferson County, West Virginia. Efforts have been initiated by management agencies of Jefferson County in cooperation with the U.S. Geological Survey to further the understanding of the spatial distribution of fractures in the carbonate regions and their correlation with aquifer properties. This report presents transmissivity values from 181 single-well aquifer tests and a map of fracture-traces determined from aerial photos and field investigations. Transmissivity values were compared to geologic factors possibly affecting their magnitude. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1450 Geochemical Analyses of Geologic Materials from Areas of Critical Environmental Concern, Clark and Nye Counties, Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -117, 34, -113, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231554141-CEOS_EXTRA.umm_json An assessment by the U.S. Geological Survey (USGS), Nevada Bureau of Mines and Geology (NBMG), and University of Nevada, Las Vegas (UNLV) is in progress of known and undiscovered mineral resources of selected areas administered by the Bureau of Land Management (BLM) in Clark and Nye Counties, Nevada. The purpose of this work is to provide the BLM with information for use in their long-term planning process in southern Nevada so that they can make better-informed decisions. Existing information about the areas, including geology, geophysics, geochemistry, and mineral-deposit information is being compiled, and field examinations of selected areas and mineral occurrences have been conducted. This information will be used to determine the geologic setting, metallogenic characteristics, and mineral potential of the areas. Twenty-five Areas of Critical Environmental Concern (ACECs) have been identified by BLM as the object of this study. They range from tiny (less than one square km) to large (more than 1,000 square km). This report includes geochemical data for rock samples collected by the USGS and NBMG in these ACECs and nearby areas. Samples have been analyzed from the Big Dune, Ash Meadows, Arden, Desert Tortoise Conservation Center, Coyote Springs Valley, Mormon Mesa, Virgin Mountains, Gold Butte A and B, Whitney Pockets, Rainbow Gardens, River Mountains, and Piute-Eldorado Valley ACECs. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1032 Estimating Landslide Losses - Preliminary Results of a Seven-State Pilot Project CEOS_EXTRA STAC Catalog 1970-01-01 -124.96, 32.02, -74.41, 46.68 https://cmr.earthdata.nasa.gov/search/concepts/C2231548978-CEOS_EXTRA.umm_json In 2001, the U.S. Geological Survey Landslide Hazards Program provided funding for seven State geological surveys to report on the status of landslide investigation strategies in each of their States, and to suggest improved ways to approach the tracking of landslides, their effects, losses associated with the landslides, and hazard mitigation strategies. Each State was to provide a draft report suggesting innovative ways to track landslides, and to participate in subsequent workshops. A workshop was convened in June 2003 in Lincoln, Neb., to discuss the results and future strategies on how best to incorporate the seven pilot projects into one methodology that all of the 50 States could adopt. The seven individual reports produced by the State surveys are published here to put forth a forum for discussion of the varying methods of tracking landslides. This pilot study, conducted by seven State geological surveys, examines the feasibility of collecting accurate and reliable information on economic losses associated with landslides. Each State survey examined the availability, distribution, and inherent uncertainties of economic loss data in their study areas. Their results provide the basis for identifying the most fruitful methods of collecting landslide loss data nationally, using methods that are consistent and provide common goals. These results can enhance and establish the future directions of scientific investigation priorities by convincingly documenting landslide risks and consequences that are universal throughout the 50 States. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1038 Geologic and Mineral Resource Map of Afghanistan CEOS_EXTRA STAC Catalog 1970-01-01 59.9, 28.66, 75.65, 39.11 https://cmr.earthdata.nasa.gov/search/concepts/C2231553654-CEOS_EXTRA.umm_json This map shows the physical characteristics, geology and mineral resources for Afghanistan. proprietary +USGS_OFR_2006_1042 Gravity and Magnetic Data in the Vicinity of Virgin Valley, Southern Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -120.35, 34.65, -113.69, 42.34 https://cmr.earthdata.nasa.gov/search/concepts/C2231551992-CEOS_EXTRA.umm_json Gravity and magnetic data were collected in the vicinity of Virgin Valley to help better characterize the buried sedimentary Mesquite and Mormon basins. Detailed gravity measurements were made over the buried saddle between the Mesquite and Mormon basins, discovered by earlier gravity studies, in order to calculate the depth to pre-Cenozoic basement. The purpose of this study was to provide estimates of sedimentary fill in this area prior to drilling a water well on Mormon Mesa. The calculated depth-to-basement results in an estimate of about 1.5 km of alluvial fill in this area. Additional gravity data were collected to help better define the shape and magnitude of the anomaly associated with the Mesquite Basin. Testing of an experimental towed magnetometer was also carried out, which showed very good correlation with an existing aeromagnetic survey. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1051 Isotopic Ages of Rocks in the Northern Front Range, Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -106, 39, -105, 41 https://cmr.earthdata.nasa.gov/search/concepts/C2231550579-CEOS_EXTRA.umm_json These maps, and the tables that accompany them, are a compilation of isotopic age determinations of rocks and minerals in four 1:100,000-scale quadrangles in the northern and central Front Range, Colorado. Phanerozoic (primarily Tertiary and Cretaceous) age data are shown on one map; Proterozoic data are on the other (sheet 1). A sample location map (sheet 2) is included for ease of matching specific localities and data in the tables to the maps. Several records in the tables were not included in the maps because either there were ambiguous dates or lack of location precluded accurate plotting. To illustrate the geological setting for the samples, the plutonic rocks are shown on the maps. The boundaries of the plutons are from the Geologic Map of Colorado with a few modifications. For ease of reference, we labeled each of the larger (and some of the smaller) plutons with a generally accepted name from the literature. As a convenience in using the data, we have informally named some plutons based on geographic features on or near those plutons. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1070 Major- and Trace-Element Concentrations in Rock Samples Collected in 2004 from the Taylor Mountains 1:250,000-scale Quadrangle, Alaska CEOS_EXTRA STAC Catalog 1970-01-01 156, 60, 159, 61 https://cmr.earthdata.nasa.gov/search/concepts/C2231552409-CEOS_EXTRA.umm_json The Kuskokwim mineral belt of Bundtzen and Miller (1997) forms an important metallogenic region in southwestern Alaska that has yielded more than 3.22 million ounces of gold and 400,000 ounces of silver. Precious-metal and related deposits in this region associated with Late Cretaceous to early Tertiary igneous complexes extend into the Taylor Mountains 1:250,000-scale quadrangle. The U.S. Geological Survey is conducting geologic mapping and a mineral resource assessment of this area that will provide a better understanding of the geologic framework, regional geochemistry, and may provide targets for mineral exploration and development. During the 2004 field season 137 rock samples were collected for a variety of purposes. All samples were analyzed for a suite of 42 trace-elements to provide data for use in geochemical exploration as well as some baseline data. Selected samples were analyzed by additional methods; 104 targeted geochemical exploration samples were analyzed for gold, arsenic, and mercury; 21 of these samples were also analyzed to obtain concentrations of 10 loosely bound metals; 33 rock samples were analyzed for major element oxides to support the regional mapping program, of which 28 sedimentary rock samples were also analyzed for total carbon, and carbonate carbon. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1081 Geologic Characteristics of Benthic Habitats in Glacier Bay, Southeast Alaska CEOS_EXTRA STAC Catalog 1970-01-01 -144, 50, -130, 63 https://cmr.earthdata.nasa.gov/search/concepts/C2231552010-CEOS_EXTRA.umm_json In April 2004, more than 40 hours of georeferenced submarine digital video was collected in water depths of 15-370 m in Glacier Bay to (1) ground-truth existing geophysical data (bathymetry and acoustic reflectance), (2) examine and record geologic characteristics of the sea floor, and (3) investigate the relation between substrate types and benthic communities, and (4) construct predictive maps of sea floor geomorphology and habitat distribution. Common substrates observed include rock, boulders, cobbles, rippled sand, bioturbated mud, and extensive beds of living horse mussels and scallops. Four principal sea-floor geomorphic types are distinguished by using video observations. Their distribution in lower and central Glacier Bay is predicted using a supervised, hierarchical decision-tree statistical classification of geophysical data. [Summary provided by the USGS.] proprietary USGS_OFR_2006_1085 Coastal Circulation and Sediment Dynamics in Hanalei Bay, Kauai. PART I: Measurements of waves, currents, temperature, salinity and turbidity: June-August, 2005 CEOS_EXTRA STAC Catalog 2005-06-08 2005-08-22 -160, 22, -159, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2231550215-CEOS_EXTRA.umm_json High-resolution measurements of waves, currents, water levels, temperature, salinity and turbidity were made in Hanalei Bay, northern Kauai, Hawaii, during the summer of 2005 to better understand coastal circulation and sediment dynamics in coral reef habitats. A series of bottom-mounted instrument packages were deployed in water depths of 10 m or less to collect long-term, high-resolution measurements of waves, currents, water levels, temperature, salinity and turbidity. These data were supplemented with a series of vertical instrument casts to characterize the vertical and spatial variability in water column properties within the bay. The purpose of these measurements was to collect hydrographic data to learn how waves, currents and water column properties vary spatially and temporally in an embayment that hosts a nearshore coral reef ecosystem adjacent to a major river drainage. These measurements support the ongoing process studies being conducted as part of the U.S. Geological Survey (USGS) Coastal and Marine Geology Program's Coral Reef Project; the ultimate goal is to better understand the transport mechanisms of sediment, larvae, pollutants and other particles in coral reef settings. This report, the first part in a series, describes data acquisition, processing and analysis. [Summary provided by the USGS.] proprietary USGS_OFR_2006_1091 Concentrations of Nutrients, Pesticides, and Suspended Sediment in the Karst Terrane of the Sinking Creek Basin, Kentucky, 2004 CEOS_EXTRA STAC Catalog 2004-01-01 2004-12-31 -87, 37, -86, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231550210-CEOS_EXTRA.umm_json Water samples were collected in streams and springs in the karst terrane of the Sinking Creek Basin in 2004 as part of study in cooperation with the Kentucky Department of Agriculture. A total of 48 water samples were collected at 7 sites (4 springs, 2 streams, and 1 karst window) from April through November 2004. The karst terrane of the Sinking Creek Basin (also known as Boiling Spring Basin) encompasses about 125 square miles in Breckinridge County and portions of Meade and Hardin Counties in Kentucky. Fourteen pesticides were detected of the 52 pesticides analyzed in the stream and spring samples. Of the 14 detected pesticides, 12 were herbicides and 2 were insecticides. The most commonly detected pesticides; atrazine, simazine, metolachlor, and acetochlor-were those most heavily used on crops during the study. Atrazine was detected in 100 percent of all samples; simazine, metolachlor, and acetochlor were detected in more than 35 percent of all samples. The pesticide-transformation compound, deethylatrazine, was detected in 98 percent of the samples. Only one nonagricultural herbicide, prometon, was detected in more than 30 percent of the samples. Malathion, the most commonly detected insecticide, was found in 4 percent of the samples, which was followed by carbofuran (2 percent). Most of the pesticides were present in low concentrations; however, atrazine was found in springs exceeding the U.S. Environmental Protection Agency's (USEPA) standards for drinking water. Atrazine exceeded the USEPA's maximum contaminant level 2 times in 48 detections. Concentrations of nitrate greater than 10 milligrams per liter (mg/L) were not found in water samples from any of the sites. Concentrations of nitrite plus nitrate ranged from 0.21 to 3.9 mg/L at the seven sites. The median concentration of nitrite plus nitrate for all sites sampled was 1.5 mg/L. Concentrations of nitrite plus nitrate generally were higher in the springs than in the main stem of Sinking Creek. Forty-two percent of the concentrations of total phosphorus at all seven sites exceeded the USEPA's recommended maximum concentration of 0.1 mg/L. The median concentration of total phosphorus for all sites sampled was 0.09 mg/L. The highest median concentrations of total phosphorus were found in the springs. Median concentrations of orthophosphate followed the same pattern as concentrations of total phosphorus in the springs. Concentrations of orthophosphate ranged from 0.006 to 0.192 mg/L. Concentrations of suspended sediment generally were low throughout the basin; the median concentration of suspended sediment for all sites sampled was 23 mg/L. The highest concentration of suspended sediment (1,486 mg/L) was measured following a storm event at Sinking Creek near Lodiburg, Ky. proprietary USGS_OFR_2006_1096 Coastal Classification Atlas: Central Texas Coastal Classification Maps - Aransas Pass to Mansfield Channel CEOS_EXTRA STAC Catalog 1970-01-01 -102, 24, -96, 29 https://cmr.earthdata.nasa.gov/search/concepts/C2231551630-CEOS_EXTRA.umm_json The primary purpose of the USGS National Assessment of Coastal Change Project is to provide accurate representations of pre-storm ground conditions for areas that are designated high priority because they have dense populations or valuable resources that are at risk from storm waves. A secondary purpose of the project is to develop a geomorphic (land feature) coastal classification that, with only minor modification, can be applied to most coastal regions in the United States. A Coastal Classification Map describing local geomorphic features is the first step toward determining the hazard vulnerability of an area. The Coastal Classification Maps of the National Assessment of Coastal Change Project present ground conditions such as beach width, dune elevations, overwash potential, and density of development. In order to complete a hazard-vulnerability assessment, that information must be integrated with other information, such as prior storm impacts and beach stability. The Coastal Classification Maps provide much of the basic information for such an assessment and represent a critical component of a storm-impact forecasting capability. The map above shows the areas covered by this web site. Click on any of the location names or outlines to view the Coastal Classification Map for that area. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1110 Geophysical Studies of the Crump Geyser Known Geothermal Resource Area, Oregon, in 1975 CEOS_EXTRA STAC Catalog 1970-01-01 -130, 42, -122, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2231552525-CEOS_EXTRA.umm_json "The U.S. Geological Survey (USGS) conducted geophysical studies in support of the resource appraisal of the Crump Geyser Known Geothermal Resource Area (KGRA). This area was designated as a KGRA by the USGS, and this designation became effective on December 24, 1970. The land classification standards for a KGRA were established by the Geothermal Steam Act of 1970 (Public Law 91-581). Federal lands so classified required competitive leasing for the development of geothermal resources. The author presented an administrative report of USGS geophysical studies entitled ""Geophysical background of the Crump Geyser area, Oregon, KGRA"" to a USGS resource committee on June 17, 1975. This report, which essentially was a description of geophysical data and a preliminary interpretation without discussion of resource appraisal, is in Appendix 1. Reduction of sheets or plates in the original administrative report to page-size figures, which are listed and appended to the back of the text in Appendix 1, did not seem to significantly degrade legibility. Bold print in the text indicates where minor changes were made. A colored page-size index and tectonic map, which also show regional geology not shown in figure 2, was substituted for original figure 1. Detailed descriptions for the geologic units referenced in the text and shown on figures 1 and 2 were separately defined by Walker and Repenning (1965) and presumably were discussed in other reports to the committee. Heavy dashed lines on figures 1 and 2 indicate the approximate KGRA boundary. One of the principal results of the geophysical studies was to obtain a gravity map (Appendix 1, fig. 10; Plouff, and Conradi, 1975, pl. 9), which reflects the fault-bounded steepness of the west edge of sediments and locates the maximum thickness of valley sediments at about 10 kilometers south of Crump Geyser. Based on the indicated regional-gravity profile and density-contrast assumptions for the two-dimensional profile, the maximum sediment thickness was estimated at 820 meters. A three-dimensional gravity model would have yielded a greater thickness. Audiomagnotelluric measurements were not made as far south as the location of the gravity low, as determined in the field, due to a lack of communication at that time. A boat was borrowed to collect gravity measurements along the edge of Crump Lake, but the attempt was curtailed by harsh, snowy weather on May 21, 1975, which shortly followed days of hot temperature. Most of the geophysical data and illustrations in Appendix 1 have been published (Gregory and Martinez, 1975; Plouff, 1975; and Plouff and Conradi, 1975), and Donald Plouff (1986) discussed a gravity interpretation of Warner Valley at the Fall 1986 American Geophysical Union meeting in San Francisco. Further interpretation of possible subsurface geologic sources of geophysical anomalies was not discussed in Appendix 1. For example, how were apparent resistivity lows (Appendix 1, figs. 3-6) centered near Crump Geyser affected by a well and other manmade electrically conductive or magnetic objects? What is the geologic significance of the 15-milligal eastward decrease across Warner Valley? The explanation that the two-dimensional gravity model (Appendix 1, fig. 14) was based on an inverse iterative method suggested by Bott (1960) was not included. Inasmuch as there was no local subsurface rock density distribution information to further constrain the gravity model, the three-dimensional methodology suggested by Plouff (1976) was not attempted. Inasmuch as the associated publication by Plouff (1975), which released the gravity data, is difficult to obtain and not in digital format, that report is reproduced in Appendix 2. Two figures of the publication are appended to the back of the text. A later formula for the theoretical value of gravity for the given latitudes at sea level (International Association of Geodesy, 1971) should be used to re-compute gravity anomalies. To merge the observed-gravity values printed in that report with later measurements, an empirically determined constant gravity datum shift should be applied. [Summary provided by the USGS.]" proprietary USGS_OFR_2006_1129_WIPP_NM_1.0 Online Aquifer-Test Data for Wells H-1, H-2A, H-2B, H-2C, and H-3 at the Waste Isolation Pilot Plant, Southeastern New Mexico CEOS_EXTRA STAC Catalog 1979-02-01 1980-07-31 -103.75, 32.33, -103.5, 32.41 https://cmr.earthdata.nasa.gov/search/concepts/C2231551503-CEOS_EXTRA.umm_json The U.S.Geological Survey Open-File Report consists of the results of a series of aquifer tests (shut-in test, flow test, bailing test, slug test, swabbing test and pressure-pulse test)performed by the U.S. Geological Survey on geologic units of Permian age at the Waste Isoliation Pilot Plant site between February 1979 and July 1980 in wells H-1, H-2 complex (H2-2A, H-2B, and H-2C), and H-3. The tested geologic units included the Magenta Dolomite and Culebra Dolomite Members of the Rustler Formation, and the contact zone between the Rustler and Salado Formations. Selected information on the tested formations, test dates, pre-test static water levels, test configurations, and raw data collected during these tests are tabulated in this report. [Summary taken in large part from U.S. Geological Survey Open-File Report abstract] proprietary USGS_OFR_2006_1136 Aeromagnetic Survey of Dillingham Area in Southwest Alaska, A Website for the Preliminary Distribution of Data CEOS_EXTRA STAC Catalog 2005-09-01 2005-10-22 -159.19, 58.3, -155.45, 60.06 https://cmr.earthdata.nasa.gov/search/concepts/C2231551877-CEOS_EXTRA.umm_json This is a USGS Open-File-Report for the preliminary release of aeromagnetic data collected in the Dillingham Area of Southwest Alaska and associated contractor reports. An airborne high-resolution magnetic survey was completed over the Dillingham and Nushagak Bay and Naknek area in southwestern Alaska. The flying was undertaken by McPhar Geosurveys Ltd. on behalf of the United States Geological Survey (USGS). First tests and calibration flights were completed by August 26th, 2005 and data acquisition was initiated on September 1st, 2005. The final data acquisition flight was completed on October 22nd, 2005. A total of 8,630 line-miles of data were acquired during the survey. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1247 High-resolution chirp seismic reflection data acquired from the Cap de Creus shelf and canyon area, Gulf of Lions, Spain in 2004 CEOS_EXTRA STAC Catalog 2003-09-25 2003-10-01 3.1808, 42.1763, 3.4586, 42.4418 https://cmr.earthdata.nasa.gov/search/concepts/C2231550660-CEOS_EXTRA.umm_json This report consists of high-resolution chirp seismic reflection profiledata from the northern Gulf of Lions, Spain. These data were acquired in2004 using the Research Vessel Oceanus (USGS Cruise ID: O-1-04-MS). Thedata are available in binary and JPEG image formats. Binary data arein Society of Exploration Geologists (SEG) SEG-Y format and may bedownloaded for further processing or display. Reference maps andJPEG images of the profiles may be viewed with your Web browser. Marine seismic reflection data are used to image and mapsedimentary and structural features of the seafloor and subsurface.These data were acquired across the shelf and canyon area of the Gulfof Lions, Spain as part of a multinational effort to characterize thegeologic framework and sedimentary environment of the region.The specific objective of this seismic survey is to provide seismicreflection images of the depositional geometry of the upper 50 meters ofsubbottom stratigraphy in order to better understand the mechanisms ofsediment transport and deposition. These chirp seismic profiles providehigh-quality images with approximately 20 cm of verticalresolution and up to 80 m of subbottom penetration. Chirp seismic reflection profiles are acquired by means of anacoustic source and a hydrophone array, both contained in a single unittowed in the water behind a survey vessel. The sound source emits ashort (30 ms) swept-frequency (500 to 7200 Hz)acoustic pulse,which propagates through the water and sediment columns. The acousticenergy is reflected at density boundaries (such as the seafloor orsediment layers beneath the seafloor), and detected by the hydrophonearray, and digitally recorded by the onboard PC-based acquisition system.As the vessel moves, this process is repeated multiple times per second,producing a two-dimensional image of the shallow geologic structurebeneath the ship track. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1274 Land Area Changes in Coastal Louisiana After the 2005 Hurricanes: A Series of Three Maps CEOS_EXTRA STAC Catalog 1956-01-01 2005-12-31 -96, 30, -88, 32 https://cmr.earthdata.nasa.gov/search/concepts/C2231553246-CEOS_EXTRA.umm_json This report includes three posters with analyses of net land area changes in coastal Louisiana after the 2005 hurricanes (Katrina and Rita). The first poster presents a basic analysis of net changes from 2004 to 2005; the second presents net changes within marsh communities from 2004 to 2005; and the third presents net changes from 2004 to 2005 within the historical perspective of change in coastal Louisiana from 1956 to 2004. The purpose of this analysis was to provide preliminary information on land area changes shortly after Hurricanes Katrina and Rita and to serve as a regional baseline for monitoring wetland recovery following the 2005 hurricane season. Estimation of permanent losses cannot be made until several growing seasons have passed and the transitory impacts of the hurricanes are minimized, but this preliminary analysis indicates an approximate 217-mi2 (562.03-km2) decrease in land/increase in water across coastal Louisiana. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1280 Metallogeny of the Great Basin: Crustal Evolution, Fluid Flow, and Ore Deposits CEOS_EXTRA STAC Catalog 1970-01-01 -126, 29, -116, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2231551576-CEOS_EXTRA.umm_json The Great Basin physiographic province in the Western United States contains a diverse assortment of world-class ore deposits. It currently (2006) is the world's second leading producer of gold, contains large silver and base metal (Cu, Zn, Pb, Mo, W) deposits, a variety of other important metallic (Fe, Ni, Be, REE's, Hg, PGE) and industrial mineral (diatomite, barite, perlite, kaolinite, gallium) resources, as well as petroleum and geothermal energy resources. Ore deposits are most numerous and largest in size in linear mineral belts with complex geology. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1299_1.0 Inversion of Gravity Data to Define the Pre-Cenozoic Surface and Regional Structures Possibly Influencing Groundwater Flow in the Rainier Mesa Region, Nye County, Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -117, 36, -116, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231552439-CEOS_EXTRA.umm_json A three-dimensional inversion of gravity data from the Rainier Mesa area and surrounding regions reveals a topographically complex pre-Cenozoic basement surface. This model of the depth to pre-Cenozoic basement rocks is intended for use in a 3D hydrogeologic model being constructed for the Rainier Mesa area. Prior to this study, our knowledge of the depth to pre-Cenozoic basement rocks was based on a regional model, applicable to general studies of the greater Nevada Test Site area but inappropriate for higher resolution modeling of ground-water flow across the Rainier Mesa area. The new model incorporates several changes that lead to significant improvements over the previous regional view. First, the addition of constraining wells, encountering old volcanic rocks lying above but near pre-Cenozoic basement, prevents modeled basement from being too shallow. Second, an extensive literature and well data search has led to an increased understanding of the change of rock density with depth in the vicinity of Rainier Mesa. The third, and most important change, relates to the application of several depth-density relationships in the study area instead of a single generalized relationship, thereby improving the overall model fit. In general, the pre-Cenozoic basement surface deepens in the western part of the study area, delineating collapses within the Silent Canyon and Timber Mountain caldera complexes, and shallows in the east in the Eleana Range and Yucca Flat regions, where basement crops out. In the Rainier Mesa study area, basement is generally shallow (< 1 km). The new model identifies previously unrecognized structures within the pre-Cenozoic basement that may influence ground-water flow, such as a shallow basement ridge related to an inferred fault extending northward from Rainier Mesa into Kawich Valley. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1396_1.0 Geophysical Studies Based on Gravity and Seismic Data of Tule Desert, Meadow Valley Wash, and California Wash Basins, Southern Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -115, 36.1, -114, 37.2 https://cmr.earthdata.nasa.gov/search/concepts/C2231549521-CEOS_EXTRA.umm_json Gravity and seismic data from Tule Desert, Meadow Valley Wash, and California Wash, Nevada, provide insight into the subsurface geometry of these three basins that lie adjacent to rapidly developing areas of Clark County, Nevada. Each of the basins is the product of Tertiary extension accommodated with the general form of north-south oriented, asymmetrically-faulted half-grabens. Geophysical inversion of gravity observations indicates that Tule Desert and Meadow Valley Wash basins are segmented into subbasins by shallow, buried basement highs. In this study, basement refers to pre-Cenozoic bedrock units that underlie basins filled with Cenozoic sedimentary and volcanic units. In Tule Desert, a small, buried basement high inferred from gravity data appears to be a horst whose placement is consistent with seismic reflection and magnetotelluric observations. Meadow Valley Wash consists of three subbasins separated by basement highs at structural zones that accommodated different styles of extension of the adjacent subbasins, an interpretation consistent with geologic mapping of fault traces oblique to the predominant north-south fault orientation of Tertiary extension in this area. California Wash is a single structural basin. The three seismic reflection lines analyzed in this study image the sedimentary basin fill, and they allow identification of faults that offset basin deposits and underlying basement. The degree of faulting and folding of the basin-fill deposits increases with depth. Pre-Cenozoic units are observed in some of the seismic reflection lines, but their reflections are generally of poor quality or are absent. Factors that degrade seismic reflector quality in this area are rough land topography due to erosion, deformed sedimentary units at the land surface, rock layers that dip out of the plane of the seismic profile, and the presence of volcanic units that obscure underlying reflectors. Geophysical methods illustrate that basin geometry is more complicated than would be inferred from extrapolation of surface topography and geology, and these methods aid in defining a three-dimensional framework to understand groundwater storage and flow in southern Nevada. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1397_1.0.1 Map showing Features and Displacements of the Scenic Drive Landslide, La Honda, California, During the Period March 31, 2005 - November 5, 2006 CEOS_EXTRA STAC Catalog 2005-03-31 2006-11-05 -122.269485, 37.317654, -122.26557, 37.3204 https://cmr.earthdata.nasa.gov/search/concepts/C2231549626-CEOS_EXTRA.umm_json The Scenic Drive landslide in La Honda, San Mateo County, California began movement during the El Niño winter of 1997-98. Recurrent motion occurred during the mild El Nino winter of 2004-2005 and again during the winter of 2005-06. This report documents the changing geometry and motion of the Scenic Drive landslide in 2005-2006, and it documents changes and persistent features that we interpret to reflect underlying structural control of the landslide. We have also compared the displacement history to near-real time rainfall history at a continuously recording gauge for the period October 2004-November 2006. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1006 Mapping Phyllic and Argillic-Altered Rocks in Southeastern Afghanistan using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Data CEOS_EXTRA STAC Catalog 1970-01-01 62, 28, 69, 34 https://cmr.earthdata.nasa.gov/search/concepts/C2231554413-CEOS_EXTRA.umm_json ASTER data and logical operators were successfully used to map phyllic and argillic-altered rocks in the southeastern part of Afghanistan. Hyperion data were used to correct ASTER band 5 and ASTER data were georegistered to orthorectified Landsat TM data. Logical operator algorithms produced argillic and phyllic byte ASTER images that were converted to vector data and overlain on ASTER and Landsat TM images. Alteration and fault patterns indicated that two areas, the Argandab igneous complex, and the Katawaz basin may contain potential polymetallic vein and porphyry copper deposits. ASTER alteration mapping in the Chagai Hills indicates less extensive phyllic and argillic-altered rocks than mapped in the Argandab igneous complex and the Katawaz basin and patterns of alteration are inconclusive to predict potential deposit types. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1011_1.0 Circulation and Physical Processes within the San Gabriel River Estuary During Summer 2005 CEOS_EXTRA STAC Catalog 2005-05-01 -119, 33, -118, 34 https://cmr.earthdata.nasa.gov/search/concepts/C2232411653-CEOS_EXTRA.umm_json The Southern California Coastal Water Research Project (SCCWRP) is developing a hydrodynamic model of the SGR estuary, which is part of the comprehensive water-quality model of the SGR estuary and watershed investigated by SCCWRP and other local agencies. The hydrodynamic model will help understanding of 1) the exchange processes between the estuary and coastal ocean; 2) the circulation patterns in the estuary; 3) upstream natural runoff and the cooling discharge from PGS. Like all models, the SGR hydrodynamic model is only useful after it is fully calibrated and validated. In May 2005, SCCWRP requested the assistance of the U.S. geological Survey (USGS) Coastal and Marine Geology team (CMG) in collecting data on the hydrodynamic conditions in the estuary during the summer dry season. The summer was chosen for field data collection as this was assumed to be the season with the greatest potential for chronic degraded water quality due to low river flow and high thermal stratification within the estuary (due to both higher average air temperature and PGS output). Water quality can be degraded in winter as well, when higher river discharge events bring large volumes of water from the Los Angeles basin into the estuary. The objectives of this project were to 1) collect hydrodynamic data along the SGR estuary; 2) study exchange processes within the estuary through analysis of the hydrodynamic data; and 3) provide field data for model calibration and validation. As the data only exist for the summer season, the results herein only apply to summer conditions. proprietary +USGS_OFR_2007_1029_1.0 Landsat ETM+ False-Color Image Mosaics of Afghanistan CEOS_EXTRA STAC Catalog 1970-01-01 59.9, 28.66, 75.65, 39.11 https://cmr.earthdata.nasa.gov/search/concepts/C2231554838-CEOS_EXTRA.umm_json In 2005, the U.S. Agency for International Development and the U.S. Trade and Development Agency contracted with the U.S. Geological Survey to perform assessments of the natural resources within Afghanistan. The assessments concentrate on the resources that are related to the economic development of that country. Therefore, assessments were initiated in oil and gas, coal, mineral resources, water resources, and earthquake hazards. All of these assessments require geologic, structural, and topographic information throughout the country at a finer scale and better accuracy than that provided by the existing maps, which were published in the 1970's by the Russians and Germans. The very rugged terrain in Afghanistan, the large scale of these assessments, and the terrorist threat in Afghanistan indicated that the best approach to provide the preliminary assessments was to use remotely sensed, satellite image data, although this may also apply to subsequent phases of the assessments. Therefore, the first step in the assessment process was to produce satellite image mosaics of Afghanistan that would be useful for these assessments. This report discusses the production of the Landsat false-color image database produced for these assessments, which was produced from the calibrated Landsat ETM+ image mosaics described by Davis (2006). [Summary provided by the USGS.] proprietary USGS_OFR_2007_1054 Assessment and Management of Dead-Wood Habitat CEOS_EXTRA STAC Catalog 1970-01-01 -130, 23, -76, 51 https://cmr.earthdata.nasa.gov/search/concepts/C2231551129-CEOS_EXTRA.umm_json Dead wood has become an increasingly important conservation issue in managed forests, as awareness of its function in providing wildlife habitat and in basic ecological processes has dramatically increased over the last several decades. The Decayed Wood Advisor (DecAID) is the most comprehensive tool currently available to inform dead-wood management. This report highlights the advantages of using DecAID to evaluate and manage dead-wood resources. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1055 Geochemical Data from Produced Water Contamination Investigations: Osage-Skiatook Petroleum Environmental Research (OSPER) Sites, Osage County, Oklahoma CEOS_EXTRA STAC Catalog 2001-02-01 -97, 36, -96, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2232411629-CEOS_EXTRA.umm_json The USGS reports chemical and isotopic analyses of 345 water samples collected from the Osage-Skiatook Petroleum Environmental Research (OSPER) project. Water samples were collected as part of an ongoing multi-year USGS investigation to study the transport, fate, natural attenuation, and ecosystem impacts of inorganic salts and organic compounds present in produced water releases at two oil and gas production sites from an aging petroleum field located in Osage County, in northeast Oklahoma. The water samples were collected primarily from monitoring wells and surface waters at the two research sites, OSPER A (legacy site) and OSPER B (active site), during the period March, 2001 to February, 2005. The data include produced water samples taken from seven active oil wells, one coal-bed methane well and two domestic groundwater wells in the vicinity of the OSPER sites. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1073_1.0 Hawaiian Volcano Observatory Seismic Data, January to December 2006 CEOS_EXTRA STAC Catalog 2006-01-01 2006-12-31 -157, 18, -154, 21 https://cmr.earthdata.nasa.gov/search/concepts/C2231552064-CEOS_EXTRA.umm_json The Hawaiian Volcano Observatory (HVO) summary presents seismic data gathered during the year. The seismic summary is offered without interpretation as a source of preliminary data. It is complete in the sense that most data for events of M≥1.5 routinely gathered by the Observatory are included. The HVO summaries have been published in various forms since 1956. Summaries prior to 1974 were issued quarterly, but cost, convenience of preparation and distribution, and the large quantities of data dictated an annual publication beginning with Summary 74 for the year 1974. Summary 86 (the introduction of CUSP at HVO) includes a description of the seismic instrumentation, calibration, and processing used in recent years. Beginning with 2004, summaries are simply identified by the year, rather than Summary number. The present summary includes background information on the seismic network and processing to allow use of the data and to provide an understanding of how they were gathered. A report by Klein and Koyanagi (1980) tabulates instrumentation, calibration, and recording history of each seismic station in the network. It is designed as a reference for users of seismograms and phase data and includes and augments the information in the station table in this summary. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1084 Heavy Oil and Natural Bitumen Resources in Geological Basins of the World CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231550538-CEOS_EXTRA.umm_json Heavy oil and natural bitumen are oils set apart by their high viscosity (resistance to flow) and high density (low API gravity). These attributes reflect the invariable presence of up to 50 weight percent asphaltenes, very high molecular weight hydrocarbon molecules incorporating many heteroatoms in their lattices. Almost all heavy oil and natural bitumen are alteration products of conventional oil. Total resources of heavy oil in known accumulations are 3,396 billion barrels of original oil in place, of which 30 billion barrels are included as prospective additional oil. The total natural bitumen resource in known accumulations amounts to 5,505 billion barrels of oil originally in place, which includes 993 billion barrels as prospective additional oil. This resource is distributed in 192 basins containing heavy oil and 89 basins with natural bitumen. Of the nine basic Klemme basin types, some with subdivisions, the most prolific by far for known heavy oil and natural bitumen volumes are continental multicyclic basins, either basins on the craton margin or closed basins along convergent plate margins. The former includes 47 percent of the natural bitumen, the latter 47 percent of the heavy oil and 46 percent of the natural bitumen. Little if any heavy oil occurs in fore-arc basins, and natural bitumen does not occur in either fore-arc or delta basins. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1108 Debris Flows and Record Floods from Extreme Mesoscale Convective Thunderstorms over the Santa Catalina Mountains, Arizona CEOS_EXTRA STAC Catalog 2006-07-31 2006-07-31 -118, 32, -110, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231551472-CEOS_EXTRA.umm_json "Ample geologic evidence indicates early Holocene and Pleistocene debris flows from the south side of the Santa Catalina Mountains north of Tucson, Arizona, but few records document historical events. On July 31, 2006, an unusual set of atmospheric conditions aligned to produce record floods and an unprecedented number of debris flows in the Santa Catalinas. During the week prior to the event, an upper-level area of low pressure centered near Albuquerque, New Mexico generated widespread heavy rainfall in southern Arizona. After midnight on July 31, a strong complex of thunderstorms developed over central Arizona in a deformation zone that formed on the back side of the upper-level low. High atmospheric moisture (2.00"" of precipitable water) coupled with cooling aloft spawned a mesoscale thunderstorm complex that moved southeast into the Tucson basin. A 15-20 knot low-level southwesterly wind developed with a significant upslope component over the south face of the Santa Catalina Mountains advecting moist and unstable air into the merging storms. National Weather Service radar indicated that a swath of 3-6"" of rainfall occurred over the lower and middle elevations of the southern Santa Catalina Mountains. This intense rain falling on saturated soil triggered over 250 hill slope failures and debris flows throughout the mountain range. Sabino Canyon, a heavily used recreation area administered by the U.S. Forest Service, was the epicenter of mass wasting, where at least 18 debris flows removed structures, destroyed the roadway in multiple locations, and closed public access for months. The debris flows were followed by stream flow floods which eclipsed the record discharge in the 75-year gaging record of Sabino Creek. In five canyons adjacent to Sabino Canyon, debris flows approached or excited the mountain front, compromising flow conveyance structures and flooding some homes. [Summary provided by the USGS.]" proprietary +USGS_OFR_2007_1115_1.0 Major crustal fault zone trends and their relation to mineral belts in the north-central Great Basin, Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -120, 34, -112, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2231552969-CEOS_EXTRA.umm_json The Great Basin physiographic province covers a large part of the western United States and contains one of the world's leading gold-producing areas, the Carlin Trend. In the Great Basin, many sedimentary-rock-hosted disseminated gold deposits occur along such linear mineral-occurrence trends. The distribution and genesis of these deposits is not fully understood, but most models indicate that regional tectonic structures play an important role in their spatial distribution. Over 100 magnetotelluric (MT) soundings were acquired between 1994 and 2001 by the U.S. Geological Survey to investigate crustal structures that may underlie the linear trends in north-central Nevada. MT sounding data were used to map changes in electrical resistivity as a function of depth that are related to subsurface lithologic and structural variations. Two-dimensional (2-D) resistivity modeling of the MT data reveals primarily northerly and northeasterly trending narrow 2-D conductors (1 to 30 ohm-m) extending to mid-crustal depths (5-20 km) that are interpreted to be major crustal fault zones. There are also a few westerly and northwesterly trending 2-D conductors. However, the great majority of the inferred crustal fault zones mapped using MT are perpendicular or oblique to the generally accepted trends. The correlation of strike of three crustal fault zones with the strike of the Carlin and Getchell trends and the Alligator Ridge district suggests they may have been the root fluid flow pathways that fed faults and fracture networks at shallower levels where gold precipitated in favorable host rocks. The abundant northeasterly crustal structures that do not correlate with the major trends may be structures that are open to fluid flow at the present time. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1122 Flood of May 2006 in New Hampshire CEOS_EXTRA STAC Catalog 2006-05-13 2006-05-17 -72.68, 42.57, -70.58, 45.43 https://cmr.earthdata.nasa.gov/search/concepts/C2231551865-CEOS_EXTRA.umm_json From May 13-17, 2006, central and southern New Hampshire experienced severe flooding caused by as much as 14 inches of rainfall in the region. As a result of the flood damage, a presidential disaster declaration was made on May 25, 2006, for seven counties-Rockingham, Hillsborough, Strafford, Merrimack, Belknap, Carroll, and Grafton. Following the flooding, the U.S. Geological Survey, in a cooperative investigation with the Federal Emergency Management Agency, determined the peak stages, peak discharges, and recurrence-interval estimates of the May 2006 flood at 65 streamgages in the counties where the disaster declaration was made. Data from flood-insurance studies published by the Federal Emergency Management Agency also were compiled for each streamgage location for comparison purposes. The peak discharges during the May 2006 flood were the largest ever recorded at 14 long-term (more than 10 years of record) streamgages in New Hampshire. In addition, peak discharges equaled or exceeded a 100-year recurrence interval at 14 streamgages and equaled or exceeded a 50-year recurrence interval at 22 streamgages. The most severe flooding occurred in Rockingham, Strafford, Merrimack, and eastern and northern Hillsborough Counties. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1133 National Assessment of Shoreline Change Part 4: Historical Coastal Cliff Retreat along the California Coast CEOS_EXTRA STAC Catalog 1970-01-01 -128, 32, -118, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2231550419-CEOS_EXTRA.umm_json Coastal cliff retreat, the landward migration of the cliff face, is a chronic problem along many rocky coastlines in the United States. As coastal populations continue to grow and community infrastructures are threatened by erosion, there is increased demand for accurate information regarding trends and rates of coastal cliff retreat. There is also a need for a comprehensive analysis of cliff retreat that is consistent from one coastal region to another. To meet these national needs, the U.S. Geological Survey is conducting an analysis of historical coastal cliff retreat along open-ocean rocky coastlines of the conterminous United States and parts of Hawaii, Alaska, and the Great Lakes. One purpose of this work is to develop standard repeatable methods for mapping and analyzing coastal cliff retreat so that periodic updates of coastal erosion can be made nationally that are systematic and internally consistent. This report on the California Coast is an accompaniment to a report on long-term sandy shoreline change for California. This report summarizes the methods of analysis, interprets the results, and provides explanations regarding long-term rates of cliff retreat. Neither detailed background information on the National Assessment of Shoreline Change Project nor detailed descriptions of the geology and geomorphology of the California coastline are presented in this report. The reader is referred to the shoreline change report (Hapke et al., 2006) for this type of background information. Cliff retreat evaluations are based on comparing one historical cliff edge digitized from maps, with a recent cliff edge interpreted from lidar (Light Detection and Ranging) topographic surveys. The historical cliff edges are from a period ranging from 1920-1930, whereas the lidar cliff edges are from either 1998 or 2002. Long-term (~70-year) rates of retreat are calculated using the two cliff edges. The rates of retreat presented in this report represent conditions from the 1930s to 1998, and are not intended for predicting future cliff edge positions or rates of retreat. Due to the geomorphology of much of California's rocky coast (high-relief, steep slopes with no defined cliff edge) as well as to gaps in both the historical maps and lidar data, we were able to derive two cliff edges and therefore calculate cliff retreat rates for a total of 353 km. The average rate of coastal cliff retreat for the State of California was -0.3±0.2 m/yr, based on rates averaged from 17,653 individual transects measured throughout all areas of California's rocky coastline. The average amount of cliff retreat was 17.7 m over the 70-year time period of our analysis. Retreat rates were generally lowest in Southern California where coastal engineering projects have greatly altered the natural coastal system. California permits shoreline stabilization structures where homes, buildings or other community infrastructure are imminently threatened by erosion. While seawalls and/or riprap revetments have been constructed in all three sections of California, a larger proportion of the Southern California coast has been protected by engineering works, due, in part, to the larger population pressures in this area. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1146 Estimated Magnitudes and Recurrence Intervals of Peak Flows on the Mousam and Little Ossipee Rivers for the Flood of April 2007 in Southern Maine CEOS_EXTRA STAC Catalog 2007-04-15 2007-04-16 -71, 43.2, -70.3, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2231549127-CEOS_EXTRA.umm_json Large amounts of rain fell on southern Maine from the afternoon of April 15, 2007, to the afternoon of April 16, 2007, causing substantial damage to houses, roads, and culverts. This report provides an estimate of the peak flows on two rivers in southern Maine - the Mousam River and the Little Ossipee River because of their severe flooding. The April 2007 estimated peak flow of 9,230 ft per second at the Mousam River near West Kennebunk had a recurrence interval between 100 and 500 years; 95-percent confidence limits for this flow ranged from 25 years to greater than 500 years. The April 2007 estimated peak flow of 8,220 ft per second at the Little Ossipee River near South Limington had a recurrence interval between 100 and 500 years; 95-percent confidence limits for this flow ranged from 50 years to greater than 500 years. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1152 High-Resolution Seismic Imaging Investigations in Salt Lake and Utah Valleys for Earthquake Hazards CEOS_EXTRA STAC Catalog 2003-09-01 2005-09-30 -113, 40, -111.5, 41 https://cmr.earthdata.nasa.gov/search/concepts/C2231549137-CEOS_EXTRA.umm_json In support of earthquake hazards and ground motion studies by researchers at the Utah Geological Survey, University of Utah, Utah State University, Brigham Young University, and San Diego State University, the U.S. Geological Survey Geologic Hazards Team Intermountain West Project conducted three high-resolution seismic imaging investigations along the Wasatch Front between September 2003 and September 2005. These three investigations include: (1) a proof-of-concept P-wave minivib reflection imaging profile in south-central Salt Lake Valley, (2) a series of seven deep (as deep as 400 m) S-wave reflection/refraction soundings using an S-wave minivib in both Salt Lake and Utah Valleys, and (3) an S-wave (and P-wave) investigation to 30 m at four sites in Utah Valley and at two previously investigated S-wave (Vs) minivib sites. In addition, we present results from a previously unpublished downhole S-wave investigation conducted at four sites in Utah Valley. The locations for each of these investigations are shown in figure 1. Coordinates for the investigation sites are listed in Table 1. With the exception of the P-wave common mid-point (CMP) reflection profile, whose end points are listed, these coordinates are for the midpoint of each velocity sounding. Vs30 and Vs100, also shown in Table 1, are defined as the average shear-wave velocities to depths of 30 and 100 m, respectively, and details of their calculation can be found in Stephenson and others (2005). The information from these studies will be incorporated into components of the urban hazards maps along the Wasatch Front being developed by the U.S. Geological Survey, Utah Geological Survey, and numerous collaborating research institutions. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1159_2007-1159 Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions in a Subbasin of the Red River of the North CEOS_EXTRA STAC Catalog 1970-01-01 -106, 37, -84, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231553843-CEOS_EXTRA.umm_json Concern over flooding along rivers in the Prairie Pothole Region has stimulated interest in developing spatially distributed hydrologic models to simulate the effects of wetland water storage on peak river flows. Such models require spatial data on the storage volume and interception area of existing and restorable wetlands in the watershed of interest. In most cases, information on these model inputs is lacking because resolution of existing topographic maps is inadequate to estimate volume and areas of existing and restorable wetlands. Consequently, most studies have relied on wetland area to volume or interception area relationships to estimate wetland basin storage characteristics by using available surface area data obtained as a product from remotely sensed data (e.g., National Wetlands Inventory). Though application of areal input data to estimate volume and interception areas is widely used, a drawback is that there is little information available to provide guidance regarding the application, limitations, and biases associated with such approaches. Another limitation of previous modeling efforts is that water stored by wetlands within a watershed is treated as a simple lump storage component that is filled prior to routing overflow to a pour point or gaging station. This approach does not account for dynamic wetland processes that influence water stored in prairie wetlands. Further, most models have not considered the influence of human-induced hydrologic changes, such as land use, that greatly influence quantity of surface water inputs and, ultimately, the rate that a wetland basin fills and spills. The goals of this study were to (1) develop and improve methodologies for estimating and spatially depicting wetland storage volumes and interceptions areas and (2) develop models and approaches for estimating/simulating the water storage capacity of potentially restorable and existing wetlands under various restoration, land use, and climatic scenarios. To address these goals, we developed models and approaches to spatially represent storage volumes and interception areas of existing and potentially restorable wetlands in the upper Mustinka subbasin within Grant County, Minn. We then developed and applied a model to simulate wetland water storage increases that would result from restoring 25 and 50 percent of the farmed and drained wetlands in the upper Mustinka subbasin. The model simulations were performed during the growing season (May October) for relatively wet (1993; 0.67 m of precipitation) and dry (1987; 0.32 m of precipitation) years. Results from the simulations indicated that the 25 percent restoration scenario would increase water storage by 2732 percent and that a 50 percent scenario would increase storage by 5363 percent. Additionally, we estimated that wetlands in the subbasin have potential to store 11.5720.98 percent of the total precipitation that fell over the entire subbasin area (52,758 ha). Our simulation results indicated that there is considerable potential to enhance water storage in the subbasin; however, evaluation and calibration of the model is necessary before simulation results can be applied to management and planning decisions. In this report we present guidance for the development and application of models (e.g., surface area-volume predictive models, hydrology simulation model) to simulate wetland water storage to provide a basis from which to understand and predict the effects of natural or human-induced hydrologic alterations. In developing these approaches, we tried to use simple and widely available input data to simulate wetland hydrology and predict wetland water storage for a specific precipitation event or a series of events. Further, the hydrology simulation model accounted for land use and soil type, which influence surface water inputs to wetlands. Although information presented in this report is specific to the Mustinka subbasin, the approaches and methods developed should be applicable to other regions in the Prairie Pothole Region. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1161 Historical Changes in the Mississippi-Alabama Barrier Islands and the Roles of Extreme Storms, Sea Level, and Human Activities CEOS_EXTRA STAC Catalog 1970-01-01 -94, 30, -86, 32 https://cmr.earthdata.nasa.gov/search/concepts/C2231555148-CEOS_EXTRA.umm_json An historical analysis of images and documents shows that the Mississippi-Alabama (MS-AL) barrier islands are undergoing rapid land loss and translocation. The barrier island chain formed and grew at a time when there was a surplus of sand in the alongshore sediment transport system, a condition that no longer prevails. The islands, except Cat, display alternating wide and narrow segments. Wide segments generally were products of low rates of inlet migration and spit elongation that resulted in well-defined ridges and swales formed by wave refraction along the inlet margins. In contrast, rapid rates of inlet migration and spit elongation under conditions of surplus sand produced low, narrow, straight barrier segments. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1169 2005 Hydrographic Survey of South San Francisco Bay, California CEOS_EXTRA STAC Catalog 1970-01-01 -126, 37, -122, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231550095-CEOS_EXTRA.umm_json An acoustic hydrographic survey of South San Francisco Bay (South Bay) was conducted in 2005. Over 20 million soundings were collected within an area of approximately 250 sq km (97 sq mi) of the bay extending south of Coyote Point on the west shore, to the San Leandro marina on the east, including Coyote Creek and Ravenswood, Alviso, Artesian, and Mud Sloughs. This is the first survey of this scale that has been conducted in South Bay since the National Oceanic and Atmospheric Administration National Ocean Service (NOS) last surveyed the region in the early 1980s. Data from this survey will provide insight to changes in bay floor topography from the 1980s to 2005 and will also serve as essential baseline data for tracking changes that will occur as restoration of the South San Francisco Bay salt ponds progress. This report provides documentation on how the survey was conducted, an assessment of accuracy of the data, and distributes the sounding data with Federal Geographic Data Committee (FGDC) compliant metadata. Reports from NOS and Sea Surveyor, Inc., containing additional survey details are attached as appendices. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1190 Geophysical Data from Spring Valley to Delamar Valley, East-Central Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -115, 37, -113, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231549251-CEOS_EXTRA.umm_json Cenozoic basins in eastern Nevada and western Utah constitute major ground-water recharge areas in the eastern part of the Great Basin and these were investigated to characterize the geologic framework of the region. Prior to these investigations, regional gravity coverage was variable over the region, adequate in some areas and very sparse in others. Cooperative studies described herein have established 1,447 new gravity stations in the region, providing a detailed description of density variations in the middle to upper crust. All previously available gravity data for the study area were evaluated to determine their reliability, prior to combining with our recent results and calculating an up-to-date isostatic residual gravity map of the area. A gravity inversion method was used to calculate depths to pre-Cenozoic basement rock and estimates of maximum alluvial/volcanic fill in the major valleys of the study area. The enhanced gravity coverage and the incorporation of lithologic information from several deep oil and gas wells yields a much improved view of subsurface shapes of these basins and provides insights useful for the development of hydrogeologic models for the region. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1202 Geochemistry of Selected Coal Samples from Sumatra, Kalimantan, Sulawesi, and Papua, Indonesia CEOS_EXTRA STAC Catalog 1970-01-01 90, -20, 140, 20 https://cmr.earthdata.nasa.gov/search/concepts/C2231555267-CEOS_EXTRA.umm_json Indonesia is an archipelago of more than 17,000 islands that stretches astride the equator for about 5,200 km in southeast Asia (figure 1) and includes major Cenozoic volcano-plutonic arcs, active volcanoes, and various related onshore and offshore basins. These magmatic arcs have extensive Cu and Au mineralization that has generated much exploration and mining in the last 50 years. Although Au and Ag have been mined in Indonesia for over 1000 years (van Leeuwen, 1994), it was not until the middle of the nineteenth century that the Dutch explored and developed major Sn and minor Au, Ag, Ni, bauxite, and coal resources. The metallogeny of Indonesia includes Au-rich porphyry Cu, porphyry Mo, skarn Cu-Au, sedimentary-rock hosted Au, epithermal Au, laterite Ni, and diamond deposits. For example, the Grasberg deposit in Papua has the world's largest gold reserves and the third-largest copper reserves (Sillitoe, 1994). Coal mining in Indonesia also has had a long history beginning with the initial production in 1849 in the Mahakam coal field near Pengaron, East Kalimantan; in 1891 in the Ombilin area, Sumatra, (van Leeuwen, 1994); and in South Sumatra in 1919 at the Bukit Asam mine (Soehandojo, 1989). Total production from deposits in Sumatra and Kalimantan, from the 19thth century to World War II, amounted to 40 million metric tons (Mt). After World War II, production declined due to various factors including politics and a boom in the world-wide oil economy. Active exploration and increased mining began again in the 1980's mainly through a change in Indonesian government policy of collaboration with foreign companies and the global oil crises (Prijono, 1989). This recent coal revival (van Leeuwen, 1994) has lead Indonesia to become the largest exporter of thermal (steam) coal and the second largest combined thermal and metallurgical (coking) coal exporter in the world market (Fairhead and others, 2006). The exported coal is desirable as it is low sulfur and ash (generally <1 and < 10 wt.%, respectively). Coal mining for both local use and for export has a very strong future in Indonesia although, at present, there are concerns about the strong need for a major revision in mining laws and foreign investment policies (Wahju, 2004; United States Embassy Jakarta, 2004). The World Coal Quality Inventory (WoCQI) program of the U.S. Geological Survey (Tewalt and others, 2005) is a cooperative project with about 50 countries (out of 70 coal-producing countries world-wide). The WoCQI initiative has collected and published extensive coal quality data from the world's largest coal producers and consumers. The important aspects of the WoCQI program are; (1) samples from active mines are collected, (2) the data have a high degree of internal consistency with a broad array of coal quality parameters, and (3) the data are linked to GIS and available through the world-wide-web. The coal quality parameters include proximate and ultimate analysis, sulfur forms, major-, minor-, and trace-element concentrations and various technological tests. This report contains geochemical data from a selected group of Indonesian coal samples from a range of coal types, localities, and ages collected for the WoCQI program. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1208 Geophysical Characterization of Pre-Cenozoic Basement for Hydrocarbon Assessment, Yukon Flats, Alaska CEOS_EXTRA STAC Catalog 1970-01-01 -170, 52, -132, 79 https://cmr.earthdata.nasa.gov/search/concepts/C2231549660-CEOS_EXTRA.umm_json The Cenozoic basins of interior Alaska are poorly understood, but may host undiscovered hydrocarbon resources in sufficient quantities to serve remote villages and for possible export. Purported oil seeps and the regional occurrence of potential hydrocarbon source and reservoir rocks fuel an exploration interest in the 46,000 km2 Yukon Flats basin. Whether hydrocarbon source rocks are present in the pre-Cenozoic basement beneath Yukon Flats is difficult to determine because vegetation and surficial deposits obscure the bedrock geology, only limited seismic data are available, and no deep boreholes have been drilled. Analysis of regional potential field data (aeromagnetics and gravity) is valuable, therefore, for preliminary characterization of basement lithology and structure. We present our analysis as a red-green-blue composite spectral map consisting of: (1) reduced-to-the-pole magnetics (red), (2) magnetic potential (green), and (3) basement gravity (blue). The color and texture patterns on this composite map highlight domains with common geophysical characteristics and, by inference, lithology. The observed patterns yield the primary conclusion that much of the basin is underlain by Devonian to Jurassic oceanic rocks related to the Angayucham and Tozitna terranes (JDat). These rocks are part of a lithologically diverse assemblage of brittlely deformed, generally low-grade metamorphic rocks of oceanic affinity; such rocks probably have little or no potential for hydrocarbon generation. The JDat geophysical signature extends from the Tintina fault system northward to the Brooks Range. Along the eastern edge of the basin, JDat appears to overlie moderately dense and non-magnetic Proterozoic(?) and Paleozoic continental margin rocks. The western edge of the JDat in subsurface is difficult to distinguish due to the presence of magnetic granites similar to those exposed in the Ruby geanticline. In the southern portion of the basin, geophysical patterns indicate the possibility of overthrusting of Cenozoic sediments and underlying JDat by Paleozoic and Proterozoic rocks of the Schwatka sequence. These structural hypotheses provide the basis for an overthrust play within the Cenozoic section just south of the basin. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1217 Coastal Processes Study at Ocean Beach, San Francisco, CA: Summary of Data Collection 2004-2006 CEOS_EXTRA STAC Catalog 1970-01-01 -124, 37, -121, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231554950-CEOS_EXTRA.umm_json Ocean Beach in San Francisco, California, contains a persistent erosional section in the shadow of the San Francisco ebb tidal delta and south of Sloat Boulevard that threatens valuable public infrastructure as well as the safe recreational use of the beach. Coastal managers have been discussing potential mediation measures for over a decade, with little scientific research available to aid in decision making. The United States Geological Survey (USGS) initiated the Ocean Beach Coastal Processes Study in April 2004 to provide the scientific knowledge necessary for coastal managers to make informed management decisions. This study integrates a wide range of field data collection and numerical modeling techniques to document nearshore sediment transport processes at the mouth of San Francisco Bay, with emphasis on how these processes relate to erosion at Ocean Beach. The Ocean Beach Coastal Processes Study is the first comprehensive study of coastal processes at the mouth of San Francisco Bay. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1238 Estimates of Monthly Ground-Water Recharge to the Yakima River Basin Aquifer System, Washington, 1960-2001, for Current Land-Use and Land-Cover Conditions CEOS_EXTRA STAC Catalog 1960-01-01 2001-12-31 -128, 45, -120, 53 https://cmr.earthdata.nasa.gov/search/concepts/C2231554323-CEOS_EXTRA.umm_json Monthly values of ground-water recharge, for current land-use and land-cover conditions, to the Yakima River Basin aquifer system, Washington, during water years 1960-2001 were previously estimated. Monthly estimates are spatially related to a Geographic Information System raster dataset with a grid cell size of 500 ft on a side. These estimates of monthly recharge are provided in 42 ASCII files, 1 file for each water year. The grid with its metadata and 42 files provide potential users easy access to the information. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1248 Digital Data From the Taos West Aeromagnetic Survey in Taos County, New Mexico CEOS_EXTRA STAC Catalog 2006-10-01 2006-10-31 -107, 36, -105, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231553240-CEOS_EXTRA.umm_json This report contains digital data, image files, and text files describing data formats and survey procedures for aeromagnetic data collected during a survey covering the southwestern portion of Taos County west of the Town of Taos, New Mexico, in October, 2006. Several derivative products from these data are also presented as grids and images, including reduced-to-pole data and data continued to a reference surface. Images are presented in various formats and are intended to be used as input to geographic information systems, standard graphics software, or map plotting packages. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1264 Lava Flow Hazard Assessment, as of August 2007, for Kilauea East Rift Zone Eruptions, Hawai'i Island CEOS_EXTRA STAC Catalog 1970-01-01 -180, 14, -146, 33 https://cmr.earthdata.nasa.gov/search/concepts/C2231552308-CEOS_EXTRA.umm_json The most recent episode in the ongoing Pu OO-Kupaianaha eruption of Kilauea Volcano is currently producing lava flows north of the east rift zone. Although they pose no immediate threat to communities, changes in flow behavior could conceivably cause future flows to advance downrift and impact communities thus far unaffected. This report reviews lava flow hazards in the Puna District and discusses the potential hazards posed by the recent change in activity. Members of the public are advised to increase their general awareness of these hazards and stay up-to-date on current conditions. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1269 Modeling the Spatial and Temporal Variation of Monthly and Seasonal Precipitation on the Nevada Test Site and Vicinity, 1960-2006 CEOS_EXTRA STAC Catalog 1960-01-01 2006-12-31 -117, 36, -115, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231551716-CEOS_EXTRA.umm_json The Nevada Test Site (NTS), located in the climatic transition zone between the Mojave and Great Basin Deserts, has a network of precipitation gages that is unusually dense for this region. This network measures monthly and seasonal variation in a landscape with diverse topography. Precipitation data from 125 climate stations on or near the NTS were used to spatially interpolate precipitation for each month during the period of 1960 through 2006 at high spatial resolution (30 m). The data were collected at climate stations using manual and/or automated techniques. The spatial interpolation method, applied to monthly accumulations of precipitation, is based on a distance-weighted multivariate regression between the amount of precipitation and the station location and elevation. This report summarizes the temporal and spatial characteristics of the available precipitation records for the period 1960 to 2006, examines the temporal and spatial variability of precipitation during the period of record, and discusses some extremes in seasonal precipitation on the NTS. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1270 High-Resolution Topographic, Bathymetric, and Oceanographic Data for the Pleasure Point Area, Santa Cruz County, California: 2005-2007 CEOS_EXTRA STAC Catalog 1970-01-01 -126, 33, -120, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231552295-CEOS_EXTRA.umm_json The County of Santa Cruz Department of Public Works and the County of Santa Cruz Redevelopment Agency requested the U.S. Geological Survey (USGS) Western Coastal and Marine Geology Team (WCMG) to provide baseline geologic and oceanographic information on the coast and inner shelf at Pleasure Point, Santa Cruz County, California. The rationale for this proposed work is a need to better understand the environmental consequences of a proposed bluff stabilization project on the beach, the near shore and the surf at Pleasure Point, Santa Cruz County, California. To meet these information needs, the USGS-WCMG Team collected baseline scientific information on the morphology and waves at Pleasure Point. This study provided high-resolution topography of the coastal bluffs and bathymetry of the inner shelf off East Cliff Drive between 32nd Avenue and 41st Avenue. The spatial and temporal variation in waves and their breaking patterns at the study site were documented. Although this project did not actively investigate the impacts of the proposed bluff stabilization project, these data provide the baseline information required for future studies directed toward predicting the impacts of stabilization on the sea cliffs, beach and near shore sediment profiles, natural rock reef structures, and offshore habitats and resources. They also provide a basis for calculating potential changes to wave transformations into the shore at Pleasure Point. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1305 Bathymetry, Substrate and Circulation in Westcott Bay, San Juan Islands, Washington CEOS_EXTRA STAC Catalog 1970-01-01 -123.2, 48, -122.2, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231552121-CEOS_EXTRA.umm_json Nearshore bathymetry, substrate type, and circulation patterns in Westcott Bay, San Juan Islands, Washington, were mapped using two acoustic sonar systems, video and direct sampling of seafloor sediments. The goal of the project was to characterize nearshore habitat and conditions influencing eelgrass (Z. marina) where extensive loss has occurred since 1995. A principal hypothesis for the loss of eelgrass is a recent decrease in light availability for eelgrass growth due to increase in turbidity associated with either an increase in fine sedimentation or biological productivity within the bay. To explore sources for this fine sediment and turbidity, a dual-frequency Biosonics sonar operating at 200 and 430 kHz was used to map seafloor depth, morphology and vegetation along 69 linear kilometers of the bay. The higher frequency 430 kHz system also provided information on particulate concentrations in the water column. A boat-mounted 600 kHz RDI Acoustic Doppler Current Profiler (ADCP) was used to map current velocity and direction and water column backscatter intensity along another 29 km, with select measurements made to characterize variations in circulation with tides. An underwater video camera was deployed to ground-truth acoustic data. Seventy one sediment samples were collected to quantify sediment grain size distributions across Westcott Bay. Sediment samples were analyzed for grain size at the Western Coastal and Marine Geology Team sediment laboratory in Menlo Park, Calif. These data reveal that the seafloor near the entrance to Westcott Bay is rocky with a complex morphology and covered with dense and diverse benthic vegetation. Current velocities were also measured to be highest at the entrance and along a deep channel extending 1 km into the bay. The substrate is increasingly comprised of finer sediments with distance into Westcott Bay where current velocities are lower. This report describes the data collected and preliminary findings of USGS Cruise B-6-07-PS conducted between May 31, 2007 and June 5, 2007. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1306_1.0 Gravity Data from Newark Valley, White Pine County, Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -116, 39, -115, 41 https://cmr.earthdata.nasa.gov/search/concepts/C2231552498-CEOS_EXTRA.umm_json The Newark Valley area, eastern Nevada is one of thirteen major ground-water basins investigated by the BARCAS (Basin and Range Carbonate Aquifer Study) Project. Gravity data are being used to help characterize the geophysical framework of the region. Although gravity coverage was extensive over parts of the BARCAS study area, data were sparse for a number of the valleys, including the northern part of Newark Valley. We addressed this lack of data by establishing seventy new gravity stations in and around Newark Valley. All available gravity data were then evaluated to determine their reliability, prior to calculating an isostatic residual gravity map to be used for subsequent analyses. A gravity inversion method was used to calculate depths to pre-Cenozoic basement rock and estimates of maximum alluvial/volcanic fill. The enhanced gravity coverage and the incorporation of lithologic information from several deep oil and gas wells yields a view of subsurface shape of the basin and will provide information useful for the development of hydrogeologic models for the region. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1308 Development of an Impervious-Surface Database for the Little Blackwater River Watershed, Dorchester County, Maryland CEOS_EXTRA STAC Catalog 1970-01-01 -77, 37, -75, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231551917-CEOS_EXTRA.umm_json Many agricultural and forested areas in proximity to National Wildlife Refuges (NWR) are under increasing economic pressure for commercial or residential development. The upper portion of the Little Blackwater River watershed - a 27 square mile area within largely low-lying Dorchester County, Maryland, on the eastern shore of the Chesapeake Bay - is important to the U.S. Fish and Wildlife Service (USFWS) because it flows toward the Blackwater National Wildlife Refuge (BNWR), and developmental impacts of areas upstream from the BNWR are unknown. One of the primary concerns for the Refuge is how storm-water runoff may affect living resources downstream. The Egypt Road project (fig. 1), for which approximately 600 residential units have been approved, has the potential to markedly change the land use and land cover on the west bank of the Little Blackwater River. In an effort to limit anticipated impacts, the Maryland Department of Natural Resources (Maryland DNR) recently decided to purchase some of the lands previously slated for development. Local topography, a high water table (typically 1 foot or less below the land surface), and hydric soils present a challenge for the best management of storm-water flow from developed surfaces. A spatial data coordination group was formed by the Dorchester County Soil and Conservation District to collect data to aid decision makers in watershed management and on the possible impacts of development on this watershed. Determination of stream flow combined with land cover and impervious-surface baselines will allow linking of hydrologic and geologic factors that influence the land surface. This baseline information will help planners, refuge managers, and developers discuss issues and formulate best management practices to mitigate development impacts on the refuge. In consultation with the Eastern Region Geospatial Information Office, the dataset selected to be that baseline land cover source was the June-July 2005 National Agricultural Imagery Program (NAIP) 1-meter resolution orthoimagery of Maryland. This publicly available, statewide dataset provided imagery corresponding to the closest in time to the installation of a U.S. Geological Survey (USGS) Water Resources Discipline gaging station on the Little Blackwater River. It also captures land cover status just before major residential development occurs. This document describes the process used to create a database of impervious surfaces for the Little Blackwater watershed. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1309 Development of a Land Use Database for the Little Blackwater Watershed, Dorchester County, Maryland CEOS_EXTRA STAC Catalog 1970-01-01 -77, 37, -75, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231551781-CEOS_EXTRA.umm_json Many agricultural and forested areas in proximity to National Wildlife Refuges (NWR) are under increasing economic pressure to develop lands for commercial or residential development. The upper portion of the Little Blackwater River watershed - a 27 square mile area within largely low-lying Dorchester County, Maryland, on the eastern shore of the Chesapeake Bay - is important to the U.S. Fish and Wildlife Service (USFWS) because it flows toward the Blackwater National Wildlife Refuge (BNWR), and developmental impacts of areas upstream from the BNWR are unknown. One of the primary concerns for the refuge is how storm-water runoff may affect living resources downstream. The Egypt Road project (fig. 1), for which approximately 600 residential units have been approved, has the potential to markedly change the land use and land cover on the west bank of the Little Blackwater River. In an effort to limit anticipated impacts, the Maryland Department of Natural Resources (Maryland DNR) recently decided to purchase some of the lands previously slated for development. Local topography, a high water table (typically 1 foot or less below the land surface), and hydric soils present a challenge for the best management of storm-water flow from developed surfaces. A spatial data coordination group was formed by the Dorchester County Soil and Conservation District to collect data to aid decision makers in watershed management and on the possible impacts of development on this watershed. Determination of streamflow combined with land cover and impervious-surface baselines will allow linking of hydrologic and geologic factors that influence the land surface. This baseline information will help planners, refuge managers, and developers discuss issues and formulate best management practices to mitigate development impacts on the refuge. In consultation with the Eastern Region Geospatial Information Office, the dataset selected to be that baseline land cover source was the June-July 2005 National Agricultural Imagery Program (NAIP) 1-meter resolution orthoimagery of Maryland. This publicly available, statewide dataset provided imagery corresponding to the closest in time to the installation of a U.S. Geological Survey (USGS) Water Resources Discipline gaging station on the Little Blackwater River. It also captures land cover status just before major residential development occurs. This document describes the process used to create a land use database for the Little Blackwater watershed. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1367 Data from Theodolite Measurements of Creep Rates on San Francisco Bay Region Faults, California: 1979-2007 CEOS_EXTRA STAC Catalog 1970-01-01 -124, 36, -121, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231553883-CEOS_EXTRA.umm_json The purpose is to update with six additional years of data, our creep data archive on San Francisco Bay region active faults for use by the scientific research community. Earlier data (1979-2001) were reported in Galehouse (2002) and were analyzed and described in detail in a summary report (Galehouse and Lienkaemper, 2003). A complete analysis of our earlier results obtained on the Hayward fault was presented in Lienkaemper, Galehouse and Simpson (2001). Jon Galehouse of San Francisco State University (SFSU) and many student research assistants measured creep (aseismic slip) rates on these faults from 1979 until his retirement from the project in 2001. The creep measurement project, which was initiated by Galehouse, has continued through the Geosciences Department at SFSU from 2001-2006 under the direction of Co-P.I's Karen Grove and John Caskey (Grove and Caskey, 2005), and by Caskey since 2006. Forrest McFarland has managed most of the technical and logistical project operations as well as data processing and compilation since 2001. We plan to publish detailed analyses of these updated creep data in future publications. We maintain a project web site (http://funnel.sfsu.edu/creep/) that includes the following information: project description, project personnel, creep characteristics and measurement, map of creep measurement sites, creep measurement site information, and data plots for each measurement site. Our most current, annually updated results are therefore accessible to the scientific community and to the general public. Information about the project can currently be requested by the public by an email link (fltcreep@sfsu.edu) found on our project website [Summary provided by the USGS.] proprietary USGS_OFR_2007_1372 Changes in Streamflow, Concentrations, and Loads in Selected Nontidal Basins in the Chesapeake Bay Watershed, 1985-2006 CEOS_EXTRA STAC Catalog 1985-01-01 2006-12-31 -80, 38, -74, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2231553744-CEOS_EXTRA.umm_json Water-quality and streamflow data from 34 sites in nontidal parts of the Chesapeake Bay watershed are presented to document annual nutrient and sediment loads and trends for 1985 through 2006, as part of an annual evaluation of water-quality conditions by the U.S. EPA Chesapeake Bay Program. This study presents the results of trends analysis for streamflow, loads, and concentrations. Annual mean flow to the bay for 2006 (78,650 cubic feet per second) was approximately 1 percent above the long-term annual mean flow from 1937 to 2005. Total freshwater flow entering the bay for the summer season (July-August-September) was the only season classified as 'wet' in 2006. For the period 1985 through 2006, streamflow was significantly increasing at two of the 34 sites. Observed (bias-corrected) concentration summaries indicate higher ranges in concentrations of total nitrogen in the northern major river basins (Pennsylvania, Maryland, and northern Virginia) than in the southern basins in Virginia. Results indicate almost half of the monitoring sites in the northern basins exhibited significant downward bias-corrected concentration trends in total nitrogen over time; results were similar for total phosphorus and sediment. Generally, loads for all constituents at the nine River Input Monitoring Program (RIM) sites, which comprise 78 percent of the streamflow entering the bay, were lower in 2006 than in 2005. The loads for total nitrogen are below the long-term average loads at eight of the nine RIM sites and total phosphorus and sediment loads are also below the long-term average at seven RIM sites. Combined annual mean total nitrogen flow-weighted concentrations from the nine RIM sites indicated an upward tendency in 2006; in contrast, total phosphorus and sediment indicated a downward tendency. From 1990 to 2006 for the 9 RIM sites, the mean concentrations of total nitrogen, total phosphorus, and sediment were 3.49, 0.195, and 116 milligrams per liter, respectively. Flow-weighted concentrations for phosphorus and sediment were lowest in the Susquehanna River at Conowingo, Md., most likely because of the trapping efficiency of three large reservoirs upstream from the sampling point. For all 34 sites and all constituents, trends in concentrations (not adjusted for flow) showed 12 statistically significant upward trends and 59 statistically significant downward trends for the period 1985 through 2006. When trends in concentrations are adjusted for flow, they can be used as indicators of human activity and effectiveness of management actions. The flow-adjusted trends indicated significant downward trends at approximately 74, 68, and 32 percent of the sites for total nitrogen, total phosphorus, and sediment, respectively. This may indicate that management actions are having some effect in reducing nutrients and sediments. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1392 Long-term and Storm-related Shoreline Change Trends in the Florida Gulf Islands National Seashore CEOS_EXTRA STAC Catalog 1970-01-01 -90, 28, -84, 32 https://cmr.earthdata.nasa.gov/search/concepts/C2231549325-CEOS_EXTRA.umm_json Coastal erosion on Northern Gulf of Mexico barrier islands is an ongoing issue that was exacerbated by the storm seasons of 2004 and 2005 when several hurricanes made landfall in the Gulf of Mexico. Two units of the Gulf Islands National Seashore (GUIS), located on Santa Rosa Island, a barrier island off the Panhandle coast of Florida, were highly impacted during the hurricanes of 2004 (Ivan) and 2005 (Cindy, Dennis, Katrina and Rita). In addition to the loss of or damage to natural and cultural resources within the park, damage to park infrastructure, including park access roads and utilities, occurred in areas experiencing rapid shoreline retreat. The main park road was located as close as 50 m to the pre-storm (2001) shoreline and was still under repair from damage incurred during Hurricane Ivan when the 2005 hurricanes struck. A new General Management Plan is under development for the Gulf Islands National Seashore. This plan, like the existing General Management Plan, strives to incorporate natural barrier island processes, and will guide future efforts to provide access to units of Gulf Islands National Seashore on Santa Rosa Island. To assess changes in island geomorphology and provide data for park management, the National Park Service and the U.S. Geological Survey are currently analyzing shoreline change to better understand long-term (100+ years) shoreline change trends as well as short-term shoreline impact and recovery to severe storm events. Results show that over an ~140-year period from the late 1800s to May 2004, the average shoreline erosion rates in the Fort Pickens and Santa Rosa units of GUIS were -0.7m/yr and -0.1 m/yr, respectively. Areas of historic erosion, reaching a maximum rate of -1.3 m/yr, correspond to areas that experienced overwash and road damage during the 2004 hurricane season.. The shoreline eroded as much as ~60 m during Hurricane Ivan, and as much as ~88 m over the course of the 2005 storm season. The shoreline erosion rates in the areas where the park road was heavily damaged were as high as -70.2 m/yr over the 2004-2005 time period. Additional post-storm monitoring of these sections of the island, to assess whether erosion rates stabilize, will help to parks to determine the best long-term management strategy for the park infrastructure. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1405 Magnetotelluric Data, San Luis Valley, Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -107, 35, -104, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231555045-CEOS_EXTRA.umm_json The San Luis Valley region population is growing. Water shortfalls could have serious consequences. Future growth and land management in the region depend on accurate assessment and protection of the region's ground-water resources. An important issue in managing the ground-water resources is a better understanding of the hydrogeology of the Santa Fe Group and the nature of the sedimentary deposits that fill the Rio Grande rift, which contain the principal ground-water aquifers. The shallow unconfined aquifer and the deeper confined Santa Fe Group aquifer in the San Luis Basin are the main sources of municipal water for the region. The U.S. Geological Survey (USGS) is conducting a series of multidisciplinary studies of the San Luis Basin located in southern Colorado. Detailed geologic mapping, high-resolution airborne magnetic surveys, gravity surveys, an electromagnetic survey (called magnetotellurics, or MT), and hydrologic and lithologic data are being used to better understand the aquifers. The MT survey primary goal is to map changes in electrical resistively with depth that are related to differences in rock types. These various rock types help control the properties of aquifers. This report does not include any data interpretation. Its purpose is to release the MT data acquired at 24 stations. Two of the stations were collected near Santa Fe, New Mexico, near deep wildcat wells. Well logs from those wells will help tie future interpretations of this data with geologic units from the Santa Fe Group sediments to Precambrian basement. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1410 Climate Variation at Flagstaff, Arizona 1950 to 2007 CEOS_EXTRA STAC Catalog 1950-01-01 2007-12-31 -112.2, 34.4, -111.1, 35.4 https://cmr.earthdata.nasa.gov/search/concepts/C2231555217-CEOS_EXTRA.umm_json Flagstaff is becoming warmer and drier. Estimated average-daily temperatures of the Flagstaff area are 2.3-degrees warmer since 1970 and annual precipitation at Flagstaff has been below average for nine of 11 years since 1996. Rising temperatures in the area parallel those of global-surface temperatures, particularly the rapid rise since the early 1970s. Ongoing drought since 1996 is strongly affecting winter, spring, and fall precipitation. Winter moisture has been below average in 11 of the past 12 years, spring was below average in eight of the past 11 years, while fall was below normal in nine of the past 12 years. The precipitation decrease of the three seasons is 44 percent since 1996. In contrast, summer-monsoon related rainfall is unaffected by the ongoing drought. Although summer rainfall tends to be more abundant and dependable than the other seasons, cool season moisture is more important hydrologically. This means that aspects of Flagstaff's environment that require cool-season moisture, particularly the ponderosa pine forest, are increasingly stressed. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1435 Concentrations of Metals in Aquatic Invertebrates from the Ozark National Scenic Riverways, Missouri CEOS_EXTRA STAC Catalog 1970-01-01 -91.4, 36.4, -90.4, 37.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231548599-CEOS_EXTRA.umm_json This report summarizes the findings of a study conducted as a pilot for part of a park-wide monitoring program being developed for the Ozark National Scenic Riverways (ONSR) of southeastern Missouri. The objective was to evaluate using crayfish (Orconectes spp.) and Asian clam (Corbicula fluminea) for monitoring concentrations of metals associated with lead-zinc mining. Lead-zinc mining presently (2007) occurs near the ONSR and additional mining has been proposed. Three composite samples of each type (crayfish and Asian clam), each comprising ten animals of approximately the same size, were collected during late summer and early fall of 2005 from five sites on the Current River and Jacks Fork within the ONSR and from one site on the Eleven Point River and the Big River, which are outside the ONSR. The Big River has been contaminated by mine tailings from historical lead zinc mining. Samples were analyzed by inductively coupled plasma mass spectrometry for lead, zinc, cadmium, cobalt, and nickel concentrations. All five metals were detected in all samples; concentrations were greatest in samples of both types from the Big River, and lowest in samples from sites within the ONSR. Concentrations of zinc and cadmium typically were greater in Asian clams than in crayfish, but differences were less evident for the other metals. In addition, differences among sites were small for cobalt in Asian clams and for zinc in crayfish, indicating that these metals are internally regulated to some extent. Consequently, both sample types are recommended for monitoring. Concentrations of metals in crayfish and Asian clams were consistent with those reported by other studies and programs that sampled streams in southeast Missouri. [Summary provided by the USGS.] proprietary +USGS_OFR_2008_1005 Geomorphic Map of Worcester County, Maryland, Interpreted from a LIDAR-Based, Digital Elevation Model CEOS_EXTRA STAC Catalog 1970-01-01 -78, 38, -74, 41 https://cmr.earthdata.nasa.gov/search/concepts/C2231548668-CEOS_EXTRA.umm_json A recently compiled mosaic of a LIDAR-based digital elevation model (DEM) is presented with geomorphic analysis of new macro-topographic details. The geologic framework of the surficial and near surface late Cenozoic deposits of the central uplands, Pocomoke River valley, and the Atlantic Coast includes Cenozoic to recent sediments from fluvial, estuarine, and littoral depositional environments. Extensive Pleistocene (cold climate) sandy dune fields are deposited over much of the terraced landscape. The macro details from the LIDAR image reveal 2 meter-scale resolution of details of the shapes of individual dunes, and fields of translocated sand sheets. Most terrace surfaces are overprinted with circular to elliptical rimmed basins that represent complex histories of ephemeral ponds that were formed, drained, and overprinted by younger basins. The terrains of composite ephemeral ponds and the dune fields are inter-shingled at their margins indicating contemporaneous erosion, deposition, and re-arrangement and possible internal deformation of the surficial deposits. The aggregate of these landform details and their deposits are interpreted as the products of arid, cold climate processes that were common to the mid-Atlantic region during the Last Glacial Maximum. In the Pocomoke valley and its larger tributaries, erosional remnants of sandy flood plains with anastomosing channels indicate the dynamics of former hydrology and sediment load of the watershed that prevailed at the end of the Pleistocene. As the climate warmed and precipitation increased during the transition from late Pleistocene to Holocene, dune fields were stabilized by vegetation, and the stream discharge increased. The increased discharge and greater local relief of streams graded to lower sea levels stimulated down cutting and created the deeply incised valleys out onto the continental shelf. These incised valleys have been filling with fluvial to intertidal deposits that record the rising sea level and warmer, more humid climate in the mid-Atlantic region throughout the Holocene. Thus, the geomorphic details provided by the new LIDAR DEM actually record the response of the landscape to abrupt climate change. Holocene trends and land-use patterns from Colonial to modern times can also be interpreted from the local macro- scale details of the landscape. Beyond the obvious utility of these data for land-use planning and assessments of resources and hazards, the new map presents new details on the impact of climate changes on a mid-latitude, outer Coastal plain landscape. [Summary provided by the USGS.] proprietary +USGS_OFR_2008_1086 Ground-Water Quality in the Mohawk River Basin, New York, 2006 CEOS_EXTRA STAC Catalog 1970-01-01 -76, 42, -73, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2232411634-CEOS_EXTRA.umm_json Water samples were collected from 27 wells from August through November 2006 to characterize ground-water quality in the Mohawk River Basin. The Mohawk River Basin covers 3,500 square miles in central New York; most of the basin is underlain by sedimentary bedrock, including shale, sandstone, and carbonates. Sand and gravel form the most productive aquifers in the basin. Samples were collected from 13 sand and gravel wells and 14 bedrock wells, including production and domestic wells. The samples were collected and processed through standard U.S. Geological Survey procedures and were analyzed for 226 physical properties and constituents, including physical properties, major ions, nutrients, trace elements, radon-222, pesticides, volatile organic compounds, and bacteria. Many constituents were not detected in any sample, but concentrations of some constituents exceeded current or proposed Federal or New York State drinking-water quality standards, including color (1 sample), pH (2 samples), sodium (11 samples), chloride (2 samples), fluoride (1 sample), sulfate (1 sample), aluminum (2 samples), arsenic (2 samples), iron (10 samples), manganese (10 samples), radon-222 (12 samples), and bacteria (6 samples). Dissolved oxygen concentrations were greater in samples from sand and gravel wells (median 5.6 milligrams per liter [mg/L]) than from bedrock wells (median 0.2 mg/L). The pH was typically neutral or slightly basic (median 7.3); the median water temperature was 11°C. The ions with the highest concentrations were bicarbonate (median 276 mg/L), calcium (median 58.9 mg/L), and sodium (median 41.9 mg/L). Ground water in the basin is generally very hard (180 mg/L as CaCO3 or greater), especially in the Mohawk Valley and areas with carbonate bedrock. Nitrate-plus-nitrite concentrations were generally higher samples from sand and gravel wells (median concentration 0.28 mg/L as N) than in samples from bedrock wells (median < 0.06 mg/L as N), although no concentrations exceeded established State or Federal drinking-water standards of 10 mg/L as N for nitrate and 1 mg/L as N for nitrite. Ammonia concentrations were higher in samples from bedrock wells (median 0.349 mg/L as N) than in those from samples from sand and gravel wells (median 0.006 mg/L as N). The trace elements with the highest concentrations were strontium (median 549 micrograms per liter [¼g/L]), iron (median 143 ¼g/L), boron (median 35 ¼g/L), and manganese (median 31.1 ¼g/L). Concentrations of several trace elements, including boron, copper, iron, manganese, and strontium, were higher in samples from bedrock wells than those from sand and gravel wells. The highest radon-222 activities were in samples from bedrock wells (maximum 1,360 pCi/L); 44 percent of all samples exceeded a proposed U.S. Environmental Protection Agency drinking water standard of 300 pCi/L. Nine pesticides and pesticide degradates were detected in six samples at concentrations of 0.42 ¼g/L or less; all were herbicides or their degradates, and most were degradates of alachlor, atrazine, and metolachlor. Six volatile organic compounds were detected in four samples at concentrations of 0.8 ¼g/L or less, including four trihalomethanes, tetrachloroethene, and toluene; most detections were in sand and gravel wells and none of the concentrations exceeded drinking water standards. Coliform bacteria were detected in six samples but fecal coliform bacteria, including Escherichia coli, were not detected in any sample. [Summary provided by the USGS.] proprietary +USGS_OFR_2008_1088 Interior River Lowland Ecoregion Summary Report CEOS_EXTRA STAC Catalog 1970-01-01 -92, 37, -86, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231551934-CEOS_EXTRA.umm_json The Interior River Lowlands ecoregion encompasses 93,200 square kilometers (km2) across southern and western Illinois, southwest Indiana, east-central Missouri, and fractions of northwest Kentucky and southeast Iowa. The ecoregion includes the confluence areas of the Mississippi, Missouri, Ohio, Illinois, and Wabash Rivers, and their tributaries. This ecoregion was formed in non-resident, non-calcareous sedimentary rock (U.S. Environmental Protection Agency, 2006). The unstratified soil deposits present north of the White River in Indiana are evidence that pre-Wisconsinan ice once covered much of the Interior River Lowlands. The geomorphic characteristics of this area also include terraced valleys filled with alluvium as well as outwash, acolian, and lacustrine deposits. Historically, agricultural land use has been a vital economic resource for this region. The drained alluvial soils are farmed for feed grains and soybeans, whereas the valley uplands also are used for forage crops, pasture, woodlots, mixed farming, and livestock (USEPA, 2006). This ecoregion provides a key component of national energy resources as it contains the second largest coal reserve in the United States, and the largest reserve of bituminous coal (Varanka and Shaver, 2007). One of the primary reasons for change in the ecoregion is urbanization. [Summary provided by the USGS.] proprietary +USGS_OFR_2008_1100 Modeling Soil Moisture in the Mojave Desert CEOS_EXTRA STAC Catalog 1970-01-01 -122, 32, -112, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231551546-CEOS_EXTRA.umm_json This publication describes soil moisture modeling in the Mojave Desert. It provides a general background on the process of pedogenesis, or soil development, which is a major factor affecting soil moisture properties. Soil texture changes with pedogenesis, which, in turn, affects soil moisture. Soil moisture is vital to plant survival, and therefore to the survival of all desert organisms associated with plants. Developing soil moisture models provides valuable information that can be used in predicting the impacts of disturbance, an area's ability to recover from disturbance, and in making land management decisions. [Summary provided by the USGS.] proprietary +USGS_OFR_2008_1119 Geochemistry of Rock Samples Collected from the Iron Hill Carbonatite Complex, Gunnison County, Colorado CEOS_EXTRA STAC Catalog 1970-01-01 -108, 37, -107, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231552479-CEOS_EXTRA.umm_json A study conducted in 2006 by the U.S. Geological Survey collected 57 surface rock samples from nine types of intrusive rock in the Iron Hill carbonatite complex. This intrusive complex, located in Gunnison County of southwestern Colorado, is known for its classic carbonatite-alkaline igneous geology and petrology. The Iron Hill complex is also noteworthy for its diverse mineral resources, including enrichments in titanium, rare earth elements, thorium, niobium (columbium), and vanadium. This study was performed to reexamine the chemistry and metallic content of the major rock units of the Iron Hill complex by using modern analytical techniques, while providing a broader suite of elements than the earlier published studies. The report contains the geochemical analyses of the samples in tabular and digital spreadsheet format, providing the analytical results for 55 major and trace elements. [Summary provided by the USGS.] proprietary +USGS_OFR_2008_1121_1.0 Modified Mercalli Intensity Maps for the 1868 Hayward Earthquake Plotted in ShakeMap Format CEOS_EXTRA STAC Catalog 1970-01-01 -123, 37, -121, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231553098-CEOS_EXTRA.umm_json This data set provides Modified Mercalli Intensity maps for the Hayward earthquake of October 21, 1868. To construct the Modified Mercalli Intensity (MMI) ShakeMap for the 1868 Hayward earthquake, we started with two sets of damage descriptions and felt reports. The first set of 100 sites was compiled by A.A. Bullock in the Lawson (1908) report on the 1906 San Francisco earthquake. The second set of 45 sites was compiled by Toppozada et al. (1981) from an extensive search of newspaper archives. We supplemented these two sets of reports with new observations from 30 sites using surveys of cemetery damage, reports of damage to historic adobe structures, pioneer narratives, and reports from newspapers that Toppozada et al. (1981) did not retrieve. [Summary provided by the USGS.] proprietary USGS_OFR_2008_1130_1.0 Catalog of Mount St. Helens 2004-2007 Dome Samples with Major- and Trace-element Chemistry CEOS_EXTRA STAC Catalog 1970-01-01 -122.189, 46.2, -122.189, 46.2 https://cmr.earthdata.nasa.gov/search/concepts/C2231552700-CEOS_EXTRA.umm_json Sampling and analysis of eruptive products at Mount St. Helens is an integral part of volcano monitoring efforts conducted by the U.S. Geological Survey's Cascades Volcano Observatory (CVO). The objective of our eruption sampling program is to enable petrological assessments of pre-eruptive magmatic conditions, critical for ascertaining mechanisms for eruption triggering and forecasting potential changes in eruption behavior. This report provides a catalog of near-vent lithic debris and new dome-lava collected during 34 intra-crater sampling forays throughout the October 2004 to October 2007 (2004-7) eruptive interval at Mount St. Helens. In addition, we present comprehensive bulk-rock geochemistry for a time-series of representative (2004-7) eruption products. This data, along with that in a companion report on Mount St. Helens 2004 to 2006 tephra by Rowe and others (2008), are presented in support of the contents of the U.S. Geological Survey Professional Paper 1750 (Sherrod and others, eds., 2008). Readers are referred to appropriate chapters in USGS Professional Paper 1750 for detailed narratives of eruptive activity during this time period and for interpretations of sample characteristics and geochemical data. The suite of rock samples related to the 2004-7 eruption of Mount St. Helens and presented in this catalog are archived at the David A. Johnson Cascades Volcano Observatory, Vancouver, Wash. The Mount St. Helens 2004-7 Dome Sample Catalogue with major- and trace-element geochemistry is tabulated in 3 worksheets of the accompanying Microsoft Excel file, of2008-1130.xls. Table 1 provides location and sampling information. Table 2 presents sample descriptions. In table 3, bulk-rock major and trace-element geochemistry is listed for 44 eruption-related samples with intra-laboratory replicate analyses of 19 dacite lava samples. A brief overview of the collection methods and lithology of dome samples is given below as an aid to deciphering the dome sample catalog. This is followed by an explanation of the categories of sample information (column headers) in Tables 1 and 2. A summary of the analytical methods used to obtain the geochemical data in this report introduces the presentation of major- and trace-element geochemistry of 2004-7 Mount St. Helens dome samples in table 3. Intra-laboratory results for the USGS AGV-2 standard are presented (tables 4 and 5), which demonstrate the compatibility of chemical data from different sources. [Summary provided by the USGS.] proprietary USGS_OFR_2008_1131_1.0 Catalog of Mount St. Helens 2004 - 2005 Tephra Samples with Major- and Trace-Element Geochemistry CEOS_EXTRA STAC Catalog 1970-01-01 -122.189, 46.2, -122.189, 46.2 https://cmr.earthdata.nasa.gov/search/concepts/C2231552475-CEOS_EXTRA.umm_json This open-file report presents a catalog of information about 135 ash samples along with geochemical analyses of bulk ash, glass and individual mineral grains from tephra deposited as a result of volcanic activity at Mount St. Helens, Washington, from October 1, 2004 until August 15, 2005. This data, in conjunction with that in a companion report on 2004-2007 Mount St. Helens dome samples by Thornber and others (2008a) are presented in support of the contents of the U.S. Geological Survey Professional Paper 1750 (Sherrod and others, ed., 2008). Readers are referred to appropriate chapters in USGS Professional Paper 1750 for detailed narratives of eruptive activity during this time period and for interpretations of sample characteristics and geochemical data presented here. All ash samples reported herein are currently archived at the David A. Johnston Cascades Volcano Observatory in Vancouver, Washington. The Mount St. Helens 2004-2005 Tephra Sample Catalogue along with bulk, glass and mineral geochemistry are tabulated in 6 worksheets of the accompanying Microsoft Excel file, of2008-1131.xls. Samples in all tables are organized by collection date. Table 1 is a detailed catalog of sample information for tephra deposited downwind of Mount St. Helens between October 1, 2004 and August 18, 2005. Table 2 provides major- and trace-element analyses of 8 bulk tephra samples collected throughout that interval. Major-element compositions of 82 groundmass glass fragments, 420 feldspar grains, and 213 mafic (clinopyroxene, amphibole, hypersthene, and olivine) mineral grains from 12 ash samples collected between October 1, 2004 and March 8, 2005 are presented in tables 3 through 5. In addition, trace-element abundances of 198 feldspars from 11 ash samples (same samples as major-element analyses) are provided in table 6. Additional mineral and bulk ash analyses from 2004 and 2005 ash samples are published in chapters 30 (oxide thermometry; Pallister and others, 2008), 32 (amphibole major elements; Thornber and others, 2008b) and 37 (210Pb; 210Pb/226Pa; Reagan and others, 2008) of U.S. Geological Survey Professional Paper 1750 (Sherrod and others, 2008). A brief overview of sample collection methods is given below as an aid to deciphering the tephra sample catalog. This is followed by an explanation of the categories of sample information (column headers) in table 1. A summary of the analytical methods used to obtain the geochemical data in this report introduces the presentation of major and trace-element geochemistry of Mount St. Helens 2004-2005 tephra samples in tables 2-6. Rhyolite glass standard analyses are reported (Appendix 1) to demonstrate the accuracy and precision of similar glass analyses presented herein. [Summary provided by the USGS.] proprietary +USGS_OFR_2008_1132 Geochemical Data for Samples Collected in 2007 Near the Concealed Pebble Porphyry Cu-Au-Mo Deposit, Southwest Alaska CEOS_EXTRA STAC Catalog 1970-01-01 -157, 59, -149, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2231552099-CEOS_EXTRA.umm_json In the summer of 2007, the U.S. Geological Survey (USGS) began an exploration geochemical research study over the Pebble porphyry copper-gold-molydenum (Cu-Au-Mo) deposit in southwest Alaska. The Pebble deposit is extremely large and is almost entirely concealed by tundra, glacial deposits, and post-Cretaceous volcanic and volcaniclastic rocks. The deposit is presently being explored by Northern Dynasty Minerals, Ltd., and Anglo-American LLC. The USGS undertakes unbiased, broad-scale mineral resource assessments of government lands to provide Congress and citizens with information on national mineral endowment. Research on known deposits is also done to refine and better constrain methods and deposit models for the mineral resource assessments. The Pebble deposit was chosen for this study because it is concealed by surficial cover rocks, it is relatively undisturbed (except for exploration company drill holes), it is a large mineral system, and it is fairly well constrained at depth by the drill hole geology and geochemistry. The goals of the USGS study are (1) to determine whether the concealed deposit can be detected with surface samples, (2) to better understand the processes of metal migration from the deposit to the surface, and (3) to test and develop methods for assessing mineral resources in similar concealed terrains. [Summary provided by the USGS.] proprietary +USGS_OFR_2008_1169_1.0 Digital Elevation Models of the Pre-Eruption 2000 Crater and 2004–07 Dome-Building Eruption at Mount St. Helens, Washington, USA CEOS_EXTRA STAC Catalog 2004-01-01 2007-12-31 -122.12, 46.12, -122.12, 46.12 https://cmr.earthdata.nasa.gov/search/concepts/C2231555276-CEOS_EXTRA.umm_json Presented in this report are 27 digital elevation model (DEM) datasets for the crater area of Mount St. Helens. These datasets include pre-eruption baseline data collected in 2000, incremental model subsets collected during the 2004–07 dome building eruption, and associated shaded-relief image datasets. Each dataset was collected photogrammetrically with digital softcopy methods employing a combination of manual collection and iterative compilation of x,y,z coordinate triplets utilizing autocorrelation techniques. DEM data points collected using autocorrelation methods were rigorously edited in stereo and manually corrected to ensure conformity with the ground surface. Data were first collected as a triangulated irregular network (TIN) then interpolated to a grid format. DEM data are based on aerotriangulated photogrammetric solutions for aerial photograph strips flown at a nominal scale of 1:12,000 using a combination of surveyed ground control and photograph-identified control points. The 2000 DEM is based on aerotriangulation of four strips totaling 31 photographs. Subsequent DEMs collected during the course of the eruption are based on aerotriangulation of single aerial photograph strips consisting of between three and seven 1:12,000-scale photographs (two to six stereo pairs). Most datasets were based on three or four stereo pairs. Photogrammetric errors associated with each dataset are presented along with ground control used in the photogrammetric aerotriangulation. The temporal increase in area of deformation in the crater as a result of dome growth, deformation, and translation of glacial ice resulted in continual adoption of new ground control points and abandonment of others during the course of the eruption. Additionally, seasonal snow cover precluded the consistent use of some ground control points. [Summary provided by the USGS.] proprietary +USGS_OFR_2008_1270_1.0 Liquefaction Hazard Maps for Three Earthquake Scenarios CEOS_EXTRA STAC Catalog 2008-09-12 -122.206, 37.246723, -121.746, 37.48794 https://cmr.earthdata.nasa.gov/search/concepts/C2231550425-CEOS_EXTRA.umm_json Maps showing the probability of surface manifestations of liquefaction in the northern Santa Clara Valley were prepared with liquefaction probability curves. The area includes the communities of San Jose, Campbell, Cupertino, Los Altos, Los Gatos Milpitas, Mountain View, Palo Alto, Santa Clara, Saratoga, and Sunnyvale. The probability curves were based on complementary cumulative frequency distributions of the liquefaction potential index (LPI) for surficial geologic units in the study area. LPI values were computed with extensive cone penetration test soundings. Maps were developed for three earthquake scenarios, an M7.8 on the San Andreas Fault comparable to the 1906 event, an M6.7 on the Hayward Fault comparable to the 1868 event, and an M6.9 on the Calaveras Fault. Ground motions were estimated with the Boore and Atkinson (2008) attenuation relation. Liquefaction is predicted for all three events in young Holocene levee deposits along the major creeks. Liquefaction probabilities are highest for the M7.8 earthquake, ranging from 0.33 to 0.37 if a 1.5-m deep water table is assumed, and 0.10 to 0.14 if a 5-m deep water table is assumed. Liquefaction probabilities of the other surficial geologic units are less than 0.05. Probabilities for the scenario earthquakes are generally consistent with observations during historical earthquakes. [Summary provided by the USGS.] proprietary USGS_OFR_2008_1274_1.0 Debris Flows and Floods in Southeastern Arizona from Extreme Precipitation in July 2006-Magnitude, Frequency, and Sediment Delivery CEOS_EXTRA STAC Catalog 1970-01-01 -111, 31, -109, 33 https://cmr.earthdata.nasa.gov/search/concepts/C2231551850-CEOS_EXTRA.umm_json From July 31 to August 1, 2006, an unusual set of atmospheric conditions aligned to produce record floods and an unprecedented number of slope failures and debris flows in southeastern Arizona. During the week leading up to the event, an upper-level low-pressure system centered over New Mexico generated widespread and locally heavy rainfall in southeastern Arizona, culminating in a series of strong, mesoscale convective systems that affected the region in the early morning hours of July 31 and August 1. Rainfall from July 27 through 30 provided sufficient antecedent moisture that the storms of July 31 through August 1 resulted in record streamflow flooding in northeastern Pima County and eastern Pinal County. The rainfall caused at least 623 slope failures in four mountain ranges, including more than 30 near Bowie Mountain in the northern Chiracahua Mountains, and 113 at the southern end of the Huachuca Mountains within and adjacent to Coronado National Memorial. In the Santa Catalina Mountains north of Tucson, 435 slope failures spawned debris flows on July 31 that, together with flood runoff, damaged structures and roads, affecting infrastructure within Tucson’s urban boundary. Heavy, localized rainfall in the Galiuro Mountains on August 1, 2006, resulted in at least 45 slope failures and an unknown number of debris flows in Aravaipa Canyon. In the southern Santa Catalina Mountains, the maximum 3-day precipitation measured at a climate station for July 29-31 was 12.04 in., which has a 1,200-year recurrence interval. Other rainfall totals from late July to August 1 in southeastern Arizona also exceeded 1,000-year recurrence intervals. The storms produced floods of record along six watercourses, and these floods had recurrence intervals of 100-500 years. Repeat photography suggests that the spate of slope failures was historically unprecedented, and geologic mapping and cosmogenic dating of ancient debris-flow deposits indicate that debris flows reaching alluvial fans in the Tucson basin are extremely rare events. Although recent watershed changes—particularly the impacts of recent wildland fires—may be important locally, the record number of slope failures and debris flows were related predominantly to extreme precipitation, not other factors such as fire history. The large number of slope failures and debris flows in an area with few such occurrences historically underscores the rarity of this type of meteorological event in southeastern Arizona. Most slope failures appeared to be shallow-seated slope failures of colluvium on steep slopes that caused deep scour of chutes and substantial aggradation of channels downstream. In the southern Santa Catalina Mountains, we estimate that 1.5 million tons of sediment were released from slope failures into the channels of ten drainage basins. Thirty-six percent of this sediment (527,000 tons) is gravel-sized or smaller and is likely to be transported by streamflow out of the mountain drainages and into the drainage network of metropolitan Tucson. This sediment poses a potential flood hazard by reducing conveyance in fixed-section flood control structures along Rillito Creek and its major tributaries, although our estimates suggest that deposition may be small if it is distributed widely along the channel, which is expected. Using the stochastic debris-flow model LAHARZ, we simulated debris-flow transport from slope failures to the apices of alluvial fans flanking the southern Santa Catalina Mountains. Despite considerable uncertainty in applying coefficients developed from worldwide observations to conditions in the southern Santa Catalina Mountains, we predicted the approximate area of depositional zones for several 2006 debris flows, particularly for Soldier Canyon. Better results could be achieved in some canyons if sediment budgets could be developed to account for alternating transport and deposition zones in channels with abrupt expansions and contractions, such as Rattlesnake Canyon. [Summary provided by the USGS.] proprietary USGS_OFR_2008_1295 Coastal Circulation and Sediment Dynamics in Hanalei Bay, Kauai, Part IV, Measurements of Waves, Currents, Temperature, Salinity, and Turbidity, June–September 2006 CEOS_EXTRA STAC Catalog 1970-01-01 -160, 22, -159, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2231552966-CEOS_EXTRA.umm_json High-resolution measurements of waves, currents, water levels, temperature, salinity and turbidity were made in Hanalei Bay, northern Kaua‘i, Hawai‘i, during the summer of 2006 to better understand coastal circulation, sediment dynamics, and the potential impact of a river flood in a coral reef-lined embayment during quiescent summer conditions. A series of bottommounted instrument packages were deployed in water depths of 10 m or less to collect long-term, high-resolution measurements of waves, currents, water levels, temperature, salinity, and turbidity. These data were supplemented with a series of profiles through the water column to characterize the vertical and spatial variability in water column properties within the bay. These measurements support the ongoing process studies being conducted as part of the U.S. Geological Survey (USGS) Coastal and Marine Geology Program’s Pacific Coral Reef Project; the ultimate goal is to better understand the transport mechanisms of sediment, larvae, pollutants, and other particles in coral reef settings. Information regarding the USGS study conducted in Hanalei Bay during the 2005 summer is available in Storlazzi and others (2006), Draut and others (2006) and Carr and others (2006). This report, the last part in a series, describes data acquisition, processing, and analysis for the 2006 summer data set. [Summary provided by the U.S. Geological Survey.] proprietary +USGS_OFR_2008_1299 Gravity Data from Dry Lake and Delamar Valleys, east-central Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -116, 37, -113, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231550809-CEOS_EXTRA.umm_json Cenozoic basins in eastern Nevada and western Utah constitute major ground-water recharge areas in the eastern part of the Great Basin, and our continuing studies are intended to characterize the geologic framework of the region. Prior to these investigations, regional gravity coverage was variable over the region, adequate in some areas and very sparse in others. The current study in Nevada provides additional high-resolution gravity along transects in Dry Lake and Delamar Valleys to supplement data we established previously in Cave and Muleshoe Valleys. We combine all previously available gravity data and calculate an up-to-date isostatic residual gravity map of the study area. Major density contrasts are identified, indicating zones where Cenozoic tectonic activity could have been accommodated. A gravity inversion method is used to calculate depths to pre-Cenozoic basement rock and to estimate maximum alluvial/volcanic fill in the valleys. Average depths of basin fill in the deeper parts of Cave, Muleshoe, Dry Lake, and Delamar Valleys are approximately 4 km, 2 km, 5 km, and 3 km, respectively. [Summary provided by the USGS.] proprietary +USGS_OFR_2008_1306_1.0 Major- and Trace-Element Concentrations in Soils from Northern California: Results from the Geochemical Landscapes Project Pilot Study CEOS_EXTRA STAC Catalog 1970-01-01 -128, 33, -118, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231550587-CEOS_EXTRA.umm_json In 2004, the U.S. Geological Survey (USGS), the Geological Survey of Canada (GSC), and the Mexican Geological Survey (Servicio Geologico Mexicano, or SGM) initiated pilot studies in preparation for a soil geochemical survey of North America called the Geochemical Landscapes Project. The purpose of this project is to provide a better understanding of the variability in chemical composition of soils in North America. The data produced by this survey will be used to construct baseline geochemical maps for regions within the continent. Two initial pilot studies were conducted: (1) a continental-scale study involving a north-south and east-west transect across North America and (2) a regional-scale study. The pilot studies were intended to test and refine sample design, sampling protocols, and field logistics for the full continental soils geochemical survey. Smith and others (2005) reported the results from the continental-scale pilot study. The regional-scale California study was designed to represent more detailed, higher resolution geochemical investigations in a region of particular interest that was identified from the low-sample-density continental-scale survey. A 20,000-km area of northern California (fig. 1), representing a wide variety of topography, climate, and ecoregions, was chosen for the regional-scale pilot study. This study area also contains diverse geology and soil types and supports a wide range of land uses including agriculture in the Sacramento Valley, forested areas in portions of the Sierra Nevada, and urban/suburban centers such as Sacramento, Davis, and Stockton. Also of interest are potential effects on soil geochemistry from historical hard rock and placer gold mining in the foothills of the Sierra Nevada, historical mercury mining in the Coast Range, and mining of base-metal sulfide deposits in the Klamath Mountains to the north. This report presents the major- and trace-element concentrations from the regional-scale soil geochemical survey in northern California. [Summary provided by the U.S. Geological Survey.] proprietary USGS_OFR_2010_1172 Database of Recent Tsunami Deposits CEOS_EXTRA STAC Catalog 2010-08-01 2010-08-31 -170.79948, -39.454178, 156.967, 48.20359 https://cmr.earthdata.nasa.gov/search/concepts/C2231555355-CEOS_EXTRA.umm_json This report describes a database of sedimentary characteristics of tsunami deposits derived from published accounts of tsunami deposit investigations conducted shortly after the occurrence of a tsunami. The database contains 228 entries, each entry containing data from up to 71 categories. It includes data from 51 publications covering 15 tsunamis distributed between 16 countries. The database encompasses a wide range of depositional settings including tropical islands, beaches, coastal plains, river banks, agricultural fields, and urban environments. It includes data from both local tsunamis and teletsunamis. The data are valuable for interpreting prehistorical, historical, and modern tsunami deposits, and for the development of criteria to identify tsunami deposits in the geologic record. [Summary provided by the USGS.] proprietary +USGS_OFR_2010_1190_1.0 Floods of May 30 to June 15, 2008, in the Iowa River and Cedar River Basins, Eastern Iowa CEOS_EXTRA STAC Catalog 2008-05-30 2008-06-15 -94, 41, -92, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2231550194-CEOS_EXTRA.umm_json As a result of prolonged and intense periods of rainfall in late May and early June, 2008, along with heavier than normal snowpack the previous winter, record flooding occurred in Iowa in the Iowa River and Cedar River Basins. The storms were part of an exceptionally wet period from May 29 through June 12, when an Iowa statewide average of 9.03 inches of rain fell; the normal statewide average for the same period is 2.45 inches. From May 29 to June 13, the 16-day rainfall totals recorded at rain gages in Iowa Falls and Clutier were 14.00 and 13.83 inches, respectively. Within the Iowa River Basin, peak discharges of 51,000 cubic feet per second (flood-probability estimate of 0.2 to 1 percent) at the 05453100 Iowa River at Marengo, Iowa streamflow-gaging station (streamgage) on June 12, and of 39,900 cubic feet per second (flood-probability estimate of 0.2 to 1 percent) at the 05453520 Iowa River below Coralville Dam near Coralville, Iowa streamgage on June 15 are the largest floods on record for those sites. A peak discharge of 41,100 cubic feet per second (flood-probability estimate of 0.2 to 1 percent) on June 15 at the 05454500 Iowa River at Iowa City, Iowa streamgage is the fourth highest on record, but is the largest flood since regulation by the Coralville Dam began in 1958. Within the Cedar River Basin, the May 30 to June 15, 2008, flood is the largest on record at all six streamgages in Iowa located on the mainstem of the Cedar River and at five streamgages located on the major tributaries. Flood-probability estimates for 10 of these 11 streamgages are less than 1 percent. Peak discharges of 112,000 cubic feet per second (flood-probability estimate of 0.2 to 1 percent) at the 05464000 Cedar River at Waterloo, Iowa streamgage on June 11 and of 140,000 cubic feet per second (flood-probability estimate of less than 0.2 percent) at the 05464500 Cedar River at Cedar Rapids, Iowa streamgage on June 13 are the largest floods on record for those sites. Downstream from the confluence of the Iowa and Cedar Rivers, the peak discharge of 188,000 cubic feet per second (flood-probability estimate of less than 0.2 percent) at the 05465500 Iowa River at Wapello, Iowa streamgage on June 14, 2008, is the largest flood on record in the Iowa River and Cedar River Basins since 1903. High-water marks were measured at 88 locations along the Iowa River between State Highway 99 near Oakville and U.S. Highway 69 in Belmond, a distance of 319 river miles. High-water marks were measured at 127 locations along the Cedar River between Fredonia near the mouth (confluence with the Iowa River) and Riverview Drive north of Charles City, a distance of 236 river miles. The high-water marks were used to develop flood profiles for the Iowa and Cedar River. [Summary provided by the USGS.] proprietary +USGS_OFR_2010_1198 Land-Cover Change in the Ozark Highlands, 1973–2000 CEOS_EXTRA STAC Catalog 1973-01-01 2000-12-31 -95, 35, -90, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231554813-CEOS_EXTRA.umm_json "Led by the Geographic Analysis and Monitoring Program of the U.S. Geological Survey (USGS) in collaboration with the U.S. Environmental Protection Agency (EPA) and the National Aeronautics and Space Administration (NASA), the Land-Cover Trends Project was initiated in 1999 and aims to document the types, geographic distributions, and rates of land-cover change on a region by region basis for the conterminous United States, and to determine some of the key drivers and consequences of the change (Loveland and others, 2002). For 1973, 1980, 1986, 1992, and 2000 land-cover maps derived from the Landsat series are classified by visual interpretation, inspection of historical aerial photography and ground survey, into 11 land-cover classes. The classes are defined to capture land cover that is discernable in Landsat data. A stratified probability-based sampling methodology undertaken within the 84 Omernik Level III Ecoregions (Omernik, 1987) was used to locate the blocks, with 9 to 48 blocks per ecoregion. The sampling was designed to enable a statistically robust ""scaling up"" of the sample-classification data to estimate areal land-cover change within each ecoregion (Loveland and others, 2002; Stehman and others, 2005). At the time of writing, approximately 90 percent of the 84 conterminous United States ecoregions have been processed by the Land-Cover Trends Project. Results from these completed ecoregions illustrate that across the conterminous United States there is no single profile of land-cover/land-use change, rather, there are varying pulses affected by clusters of change agents (Loveland and others, 2002). Land-Cover Trends Project results for the conterminous United States to-date are being used for collaborative environmental change research with partners such as; the National Science Foundation, the National Oceanic and Atmospheric Administration, and the U.S. Fish and Wildlife Service. The strategy has also been adapted for use in a NASA global deforestation initiative, and elements of the project design are being used in the North American Carbon Program's assessment of forest disturbance. [Summary provided by the USGS.]" proprietary +USGS_OFR_2010_1223 Estimates for Self-Supplied Domestic Withdrawals and Population Served, for Selected Principal Aquifers, Calendar Year 2005 CEOS_EXTRA STAC Catalog 2005-01-01 2005-12-31 -130, 24, -60, 51 https://cmr.earthdata.nasa.gov/search/concepts/C2231549313-CEOS_EXTRA.umm_json The National Water-Quality Assessment Program of the U.S. Geological Survey has groundwater studies that focus on water-quality conditions in principal aquifers of the United States. The Program specifically focuses on aquifers that are important to public supply, domestic, and other major uses. Estimates for self-supplied domestic withdrawals and the population served for 20 aquifers in the United States for calendar year 2005 are provided in this report. These estimates are based on county-level data for self-supplied domestic groundwater withdrawals and the population served by those withdrawals, as compiled by the National Water Use Information Program, for areas within the extent of the 20 aquifers. In 2005, the total groundwater withdrawals for self-supplied domestic use from the 20 aquifers represented about 63 percent of the total self-supplied domestic groundwater withdrawals in the United States; the population served by the withdrawals represented about 61 percent of the total self-supplied domestic population in the United States. [Summary provided by the USGS.] proprietary +USGS_OFR_2010_1330 Geomorphology and Depositional Subenvironments of Gulf Islands National Seashore, Perdido Key and Santa Rosa Island, Florida CEOS_EXTRA STAC Catalog 2010-01-01 2010-12-31 -87.42046, 30.29735, -86.510414, 30.406206 https://cmr.earthdata.nasa.gov/search/concepts/C2231551388-CEOS_EXTRA.umm_json "The U.S. Geological Survey (USGS) is studying coastal hazards and coastal change to improve our understanding of coastal ecosystems and to develop better capabilities of predicting future coastal change. One approach to understanding the dynamics of coastal systems is to monitor changes in barrier-island subenvironments through time. This involves examining morphologic and topographic change at temporal scales ranging from millennia to years and spatial scales ranging from tens of kilometers to meters. Of particular interest are the processes that produce those changes and the determination of whether or not those processes are likely to persist into the future. In these analyses of hazards and change, both natural and anthropogenic influences are considered. Quantifying past magnitudes and rates of coastal change and knowing the principal factors that govern those changes are critical to predicting what changes are likely to occur under different scenarios, such as short-term impacts of extreme storms or long-term impacts of sea-level rise. Gulf Islands National Seashore was selected for detailed mapping of barrier-island morphology and topography because the islands offer a diversity of depositional subenvironments and because island areas and positions have changed substantially in historical time. The geomorphologic and subenvironmental maps emphasize the processes that formed the surficial features and also serve as a basis for documenting which subenvironments are relatively stable, such as the vegetated barrier core, and those which are highly dynamic, such as the beach and inactive overwash zones. The primary mapping procedures were supervised functions within a Geographic Information System (GIS) that were applied to delineate and classify depositional subenvironments and features, collectively referred to as map units. The delineated boundaries of the map units were exported to create one shapefile, and are differentiated by the field ""Type"" in the associated attribute table. Map units were delineated and classified based on differences in tonal patterns of features in contrast to adjacent features observed on orthophotography. Land elevations from recent lidar surveys served as supplementary data to assist in delineating the map unit boundaries. [Summary provided by the USGS.]" proprietary +USGS_OFR_2013_1305_1.0 Global Surface Displacement Data for Assessing Variability of Displacement at a Point on a Fault CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231549015-CEOS_EXTRA.umm_json This report presents a global dataset of site-specific surface-displacement data on faults. We have compiled estimates of successive displacements attributed to individual earthquakes, mainly paleoearthquakes, at sites where two or more events have been documented, as a basis for analyzing inter-event variability in surface displacement on continental faults. An earlier version of this composite dataset was used in a recent study relating the variability of surface displacement at a point to the magnitude-frequency distribution of earthquakes on faults, and to hazard from fault rupture (Hecker and others, 2013). The purpose of this follow-on report is to provide potential data users with an updated comprehensive dataset, largely complete through 2010 for studies in English-language publications, as well as in some unpublished reports and abstract volumes. [Summary provided by the U.S. Geological Survey.] proprietary USGS_OFR_2014-1094_SantaCatalinaBackscatter Backscatter – Outer Mainland Shelf and Slope, Gulf of Santa Catalina, southern California, U.S. Geological Survey, 2010-2011 CEOS_EXTRA STAC Catalog 2010-05-10 2011-06-12 -117.86, 33, -117.33, 33.46 https://cmr.earthdata.nasa.gov/search/concepts/C2231549738-CEOS_EXTRA.umm_json In 2010 and 2011, the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC), collected bathymetry and acoustic-backscatter data from the outer shelf and slope offshore the Oceanside region in southern California. These data were acquired as part of the USGS Marine Geohazards Program. Assessment of the hazards posed by offshore faults, submarine landslides, and tsunamis are facilitated by accurate and detailed bathymetric data. The surveys were conducted using the USGS R/V Parke Snavely outfitted with a 100 kHz Reson 7111 multibeam echosounder. While the surveys were focused on the collection of bathymetric data, the limited acoustic backscatter data are made available. These metadata describe the backscatter data provided in the report. The backscatter from the three separate surveys were not merged and these metadata describe the three separate backscatter images. proprietary USGS_OFR_2014-1094_SantaCatalinaBathy Bathymetry - Outer Mainland Shelf and Slope, Gulf of Santa Catalina, southern California, U.S. Geological Survey 2010-2011 CEOS_EXTRA STAC Catalog 2010-05-10 2011-06-12 -117.86, 33, -117.33, 33.46 https://cmr.earthdata.nasa.gov/search/concepts/C2231553251-CEOS_EXTRA.umm_json In 2010 and 2011, the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC), collected bathymetry and acoustic-backscatter data from the outer shelf and slope offshore the Oceanside region in southern California. These data were acquired as part of the USGS Marine Geohazards Program. Assessment of the hazards posed by offshore faults, submarine landslides, and tsunamis are facilitated by accurate and detailed bathymetric data. The surveys were conducted using the USGS R/V Parke Snavely outfitted with a 100 kHz Reson 7111 multibeam echosounder. These metadata describe the bathymetry data provided in the report. proprietary USGS_OFR_2014-5038_FlAquiferTimeSeries Creating a Monthly Time Series of the Potentiometric Surface in the Upper Floridan Aquifer, Northern Tampa Bay Area, Florida, January 2000 - December 2009 CEOS_EXTRA STAC Catalog 2000-01-01 2009-12-31 -82.920685, 27.897348, -82.09946, 28.500074 https://cmr.earthdata.nasa.gov/search/concepts/C2231550641-CEOS_EXTRA.umm_json In Florida’s karst terrain, where groundwater and surface waters interact, a mapping time series of the potentiometric surface in the Upper Floridan aquifer offers a versatile metric for assessing the hydrologic condition of both the aquifer and overlying streams and wetlands. Long-term groundwater monitoring data were used to generate a monthly time series of potentiometric surfaces in the Upper Floridan aquifer over a 573-square-mile area of west-central Florida between January 2000 and December 2009. Recorded groundwater elevations were collated for 260 groundwater monitoring wells in the Northern Tampa Bay area, and a continuous time series of daily observations was created for 197 of the wells by estimating missing daily values through regression relations with other monitoring wells. Kriging was used to interpolate the monthly average potentiometric-surface elevation in the Upper Floridan aquifer over a decade. The mapping time series gives spatial and temporal coherence to groundwater monitoring data collected continuously over the decade by three different organizations, but at various frequencies. Further, the mapping time series describes the potentiometric surface beneath parts of six regionally important stream watersheds and 11 municipal well fields that collectively withdraw about 90 million gallons per day from the Upper Floridan aquifer. Monthly semivariogram models were developed using monthly average groundwater levels at wells. Kriging was used to interpolate the monthly average potentiometric-surface elevations and to quantify the uncertainty in the interpolated elevations. Drawdown of the potentiometric surface within well fields was likely the cause of a characteristic decrease and then increase in the observed semivariance with increasing lag distance. This characteristic made use of the hole effect model appropriate for describing the monthly semivariograms and the interpolated surfaces. Spatial variance reflected in the monthly semivariograms decreased markedly between 2002 and 2003, timing that coincided with decreases in well-field pumping. Cross-validation results suggest that the kriging interpolation may smooth over the drawdown of the potentiometric surface near production wells. The groundwater monitoring network of 197 wells yielded an average kriging error in the potentiometric-surface elevations of 2 feet or less over approximately 70 percent of the map area. Additional data collection within the existing monitoring network of 260 wells and near selected well fields could reduce the error in individual months. Reducing the kriging error in other areas would require adding new monitoring wells. Potentiometric-surface elevations fluctuated by as much as 30 feet over the study period, and the spatially averaged elevation for the entire surface rose by about 2 feet over the decade. Monthly potentiometric-surface elevations describe the lateral groundwater flow patterns in the aquifer and are usable at a variety of spatial scales to describe vertical groundwater recharge and discharge conditions for overlying surface-water features. proprietary +USGS_OFR_94-710 Homestead Valley, California, Aftershocks Recorded on Portable Seismographs, SCEC CEOS_EXTRA STAC Catalog 1970-01-01 -121, 32, -114, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231549623-CEOS_EXTRA.umm_json "Information on USGS OFR 94-710 is available on-line via the World Wide Web: ""http://www.data.scec.org/ftp/ca.earthquakes/homestead/"" ""http://www.data.scec.org/fault_index/homeval.html"" The following information on the Homestead Valley earthquake aftershock data set was extracted from the Southern California Earthquake Center Data Center WWW site (""http://www.data.scec.org/""): On March 15, 1979, four moderate earthquakes (ML 4.9, 5.3, 4.5, 4.8) occurred in the Homestead Valley area of the Mojave Desert. Recently, phase data from portable instruments deployed by the U. S. Geological Survey on March 17 - 18, 1979 have been merged with those recorded by the Southern California Seismic Network (SCSN). The results of this study have been published in a U.S.Geological Survey-Open File Report. -homestead.hyp relocated hypocenters with portable data -homestead.phase phase data from portable instruments (hypoinverse format) -hvnetandport.dat SCSN and portable data -lnv8z0.mod velocity model used in relocations -homestead.sta portable instrument locations" proprietary +USGS_OFR_97_745_E Map of Debris-Flow Source Areas in the San Francisco Bay Region, California CEOS_EXTRA STAC Catalog 1970-01-01 -124, 29, -121, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2231551873-CEOS_EXTRA.umm_json This report is a digital database package containing both plotfiles and Geographic Information Systems (GIS) databases of maps of potential debris flow sources, as well as locations of historic debris flow sources, in the San Francisco Bay Region. The data are provided for both the entire region and each county within the region, in two formats. The data are provided as ARC/INFO (Environmental Systems Research Institute, Redlands, CA) coverages and grids for use in GIS packages, and as PostScript plotfiles of formatted maps similar to traditional U.S. Geological Survey map products. [Summary provided by the USGS.] proprietary USGS_OFR_99-11 Color Shaded Relief Map of the Conterminous United States CEOS_EXTRA STAC Catalog 1970-01-01 -128.07002, 22.67775, -65.25698, 48.26194 https://cmr.earthdata.nasa.gov/search/concepts/C2231549569-CEOS_EXTRA.umm_json "The color shaded relief map of the conterminous U.S. was created from 15 arc-second digital elevation model (DEM) data. The data set traces its origins back to the early 1960's when .01 inch scans of 1:250,000 USGS topographic sheets were produced by the Defense Mapping Agency and converted to 3 second data by the USGS National Cartographic Information Center. The 15 second grid cell data (Michael Webring, written communication) used in this report dates from the mid-1980's with occasional local and regional updates. The 3 second grid nodes were averaged with a 6x6 operator and decimated to 15 second grid cells which is about the resolution of the original .01 inch data set. The 3 second data is available as 950 separate 1x1 degree quadrangles from the USGS EROS Data Center. Additional information available at ""http://pubs.usgs.gov/of/of99-011/1readme.html"" [Summary provided by the USGS.] ." proprietary +USGS_OFR_99-422_1.0 Geographic Information Systems (GIS) Compilation of Geophysical, Geologic, and Tectonic Data for the Circum-North Pacific CEOS_EXTRA STAC Catalog 1970-01-01 -115, 40, 120, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2231550558-CEOS_EXTRA.umm_json The accompanying directory structure contains a Geographic Information Systems (GIS) compilation of geophysical, geological, and tectonic data for the Circum-North Pacific. This area includes the Russian Far East, Alaska, the Canadian Cordillera, linking continental shelves, and adjacent oceans. This GIS compilation extends from 120°E to 115°W, and from 40°N to 80°N. This area encompasses: (1) to the south, the modern Pacific plate boundary of the Japan-Kuril and Aleutian subduction zones, the Queen Charlotte transform fault, and the Cascadia subduction zone; (2) to the north, the continent-ocean transition from the Eurasian and North American continents to the Arctic Ocean; (3) to the west, the diffuse Eurasian-North American plate boundary, including the probable Okhotsk plate; and (4) to the east, the Alaskan-Canadian Cordilleran fold belt. This compilation should be useful for: (1) studying the Mesozoic and Cenozoic collisional and accretionary tectonics that assembled this continental crust of this region; (2) studying the neotectonics of active and passive plate margins in this region; and (3) constructing and interpreting geophysical, geologic, and tectonic models of the region. Geographic Information Systems (GIS) programs provide powerful tools for managing and analyzing spatial databases. Geological applications include regional tectonics, geophysics, mineral and petroleum exploration, resource management, and land-use planning. This CD-ROM contains thematic layers of spatial data-sets for geology, gravity field, magnetic field, oceanic plates, overlap assemblages, seismology (earthquakes), tectonostratigraphic terranes, topography, and volcanoes. The GIS compilation can be viewed, manipulated, and plotted with commercial software (ArcView and ArcInfo) or through a freeware program (ArcExplorer) that can be downloaded from http://www.esri.com for both Unix and Windows computers using the button below. [Summary provided by the USGS.] proprietary USGS_OFR_99-463_1.0 Digital Data Sets of Depth-Duration Frequency of Precipitation For Oklahoma CEOS_EXTRA STAC Catalog 1970-01-01 -103.24279, 33.305325, -94.16608, 37.309578 https://cmr.earthdata.nasa.gov/search/concepts/C2231553608-CEOS_EXTRA.umm_json "These geospatial data sets were produced as part of a regional precipitation frequency analysis for Oklahoma. The data sets consist of surface grids of precipitation depths for seven frequencies (expressed as recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years) and 12 durations (15-, 30-, and 60-minutes; 1-, 2-, 3-, 6-, 12-, and 24-hours; and 1-, 3-, and 7-days). Eighty-four depth-duration-frequency surfaces were produced from precipitation-station data. Precipitation-station data from which the surfaces were interpolated and contour lines derived from each surface also are included. Contour intervals vary from 0.05 to 0.5 inch. Data were used from precipitation gage stations with at least 10 years of record within Oklahoma and a zone extending about 50 kilometers into bordering states. Three different rain gage networks provided the data (15-minute, 1-hour, and 1-day). Precipitation annual maxima (depths) were determined from the station data for each duration for 110 15-minute, 141 hourly, and 413 daily stations. Statistical methods were used to estimate precipitation depths for each duration-frequency at each station. These station depth-duration-frequency estimates were interpolated to produce continuous grids with grid-cell spacing of 2,000 meters. Contour lines derived from these surfaces (grids) were used to produce the maps in the ""Depth-Duration Frequency of Precipitation for Oklahoma,"" by R.L. Tortorelli, Alan Rea, and W.H. Asquith, U.S. Geological Survey Water-Resources Investigations Report 99-4232. The geospatial data sets are presented in digital form for use with geographic information systems. These geospatial data sets may be used to determine an interpolated value of depth-duration-frequency of precipitation for any point in Oklahoma. [Summary provided by USGS.]" proprietary USGS_OFR_99-77_1.0 Digital Data Sets Describing Principal Aquifers, Surficial Geology, and Ground-Water Regions of the Conterminous United States CEOS_EXTRA STAC Catalog 1970-01-01 -127.84206, 22.877533, -65.40874, 48.19242 https://cmr.earthdata.nasa.gov/search/concepts/C2231550853-CEOS_EXTRA.umm_json "This report contains digital data sets describing principal aquifers, surficial geology, and ground-water regions of the conterminous United States. The data source for the principal aquifers and surficial geology data sets is ""The National Atlas of the United States of America"" (U.S. Geological Survey, 1970; Hunt, 1979). The data source for the ground-water regions data set is ""Ground-Water Regions of the United States"" by Heath (1984). Some of the digital lines describing coastlines were modified from U.S. Geological Survey boundary information (U.S. Geological Survey, 1990; Lanfear, 1984). Because most of the source materials do not cover Alaska and Hawaii, only the conterminous 48 states are included in these data sets. Compilation of the data sets was supported by the National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS). The objectives of the NAWQA Program are to: (1) describe current water-quality conditions for a large part of the Nation's freshwater streams, rivers, and aquifers, (2) describe how water quality is changing over time, and (3) improve the understanding of the primary natural and anthropogenic factors that affect water-quality conditions. National analysis of data, based on aggregation of comparable information obtained from across the United States, is a major component of the NAWQA Program. The data sets included in this report were created in support of NAWQA national data analysis activities. [Summary provided by the USGS.]" proprietary USGS_OFR_99-78_1.0 Digital Data Sets Describing Water Use, Toxic Chemical Releases, Metropolitan Areas, and Population Density of the Conterminous United States CEOS_EXTRA STAC Catalog 1970-01-01 -128.07002, 22.67775, -65.25698, 48.26194 https://cmr.earthdata.nasa.gov/search/concepts/C2231553320-CEOS_EXTRA.umm_json This report contains digital data sets describing water use, toxic chemical releases, metropolitan areas, and population density of the conterminous United States. The data source for the water-use data is from the U.S. Geological Survey (USGS) (U.S. Geological Survey, 1990, 1998b; Lanfear, 1984). The toxic chemical release information is from the U.S. Environmental Protection Agency (1997, 1998), and the metropolitan area and population density data sets were derived from data provided by the U.S. Bureau of the Census (1995) and the Consortium for International Earth Science Information Network (1995). Because most of the source materials do not cover Alaska and Hawaii, only the conterminous 48 states are included in these data sets. Compilation of the data sets was supported by the National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey. The objectives of the NAWQA Program are to: (1) describe current water-quality conditions for a large part of the Nation's freshwater streams, rivers, and aquifers, (2) describe how water quality is changing over time, and (3) improve the understanding of the primary natural and anthropogenic factors that affect water-quality conditions. National analysis of data, based on aggregation of comparable information obtained from across the United States, is a major component of the NAWQA Program. The data sets included in this report were created for NAWQA national data analysis activities. proprietary +USGS_OFR_99_414_1.0 Geologic Datasets for Weights-of-Evidence Analysis in Northeast Washington--3. Minerals-Related Permits on National Forests, 1967 to 1998 CEOS_EXTRA STAC Catalog 1998-01-01 1998-12-31 -121.25, 47.25, -117, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231554622-CEOS_EXTRA.umm_json This dataset was developed to provide mineral resource data for the region of northeast WA for use in future spatial analysis by a variety of users. This database is not meant to be used or displayed at any scale larger than 1:24,000. Permits to explore for and (or) develop mineral resources on Federal lands can be used to indicate locations and types of mineral-related activities on national forests. Permits for these activities require filing of a Notice of Intent to conduct mineral exploration activities and (or) a Plan of Operation, if significant land disturbance results. This compilation of notices and plans for the Colville, Kaniksu, Okanogan, and Wenatchee national forests between 1967 and 1998 is intended for use in combination with geologic and economic information to predict future mineral-related activities in the region. This dataset consists of one Excel 97 spreadsheet file (of99-414.xls) which contains information about permits on national forest lands in northeast Washington State. [Summary provided by the USGS.] proprietary USGS_OFR_99_436 Archive of Sparker Subbottom Data Collected During USGS Cruise ALPH 98013, New York, 10-22 September, 1997 CEOS_EXTRA STAC Catalog 1998-09-10 1998-09-22 -74, 40.16, -73.25, 40.58 https://cmr.earthdata.nasa.gov/search/concepts/C2231550021-CEOS_EXTRA.umm_json This project will generate reconnaissance maps of the sea floor offshore of the New York - New Jersey metropolitan area -- the most heavily populated, and one of the most impacted coastal regions of the United States. The surveys will provide an overall synthesis of the sea floor environment, including seabed texture and bed forms, Quaternary strata geometry, and anthropogenic impact (e.g., ocean dumping, trawling, channel dredging). The goal of this project is to survey the offshore area, the harbor, and the southern shore of Long Island, providing a regional synthesis to support a wide range of management decisions and a basis for further process-oriented investigations. The project is conducted cooperatively with the U.S. Army Corps of Engineer (USACE). This CD-ROM contains digital high resolution seismic reflection data collected during the USGS ALPH 98013 cruise. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. [Summary provided by the USGS.] proprietary +USGS_OFR_99_438_1.0 Digital geologic map of part of the Thompson Falls 1:100,000 quadrangle, Idaho CEOS_EXTRA STAC Catalog 1999-01-01 1999-12-31 -116, 47.5, -115, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2231554236-CEOS_EXTRA.umm_json This data set was developed to provide geologic map GIS of the Idaho portion of the Thompson Falls 1:100,000 quadrangle for use in future spatial analysis by a variety of users. This database is not meant to be used or displayed at any scale larger than 1:100,000 (e.g., 1:62,500 or 1:24,000). The geology of the Thompson Falls 1:100,000 quadrangle, Idaho was compiled by Reed S. Lewis in 1997 onto a 1:100,000-scale topographic base map for input into an Arc/Info geographic information system (GIS). The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps. This GIS consists of two major Arc/Info data sets: one line and polygon file (tf100k) containing geologic contacts and structures (lines) and geologic map rock units (polygons), and one point file (tfpnt) containing structural data. [Summary provided by the USGS.] proprietary USGS_OFR_Acid_Deposition Acid Deposition Sensitivity of the Southern Appalachian Assessment Area CEOS_EXTRA STAC Catalog 1970-01-01 -87, 31, -77, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231550930-CEOS_EXTRA.umm_json The Acid Deposition Sensitivity of the Southern Appalachian Assessment Area is a project that studies areas having various susceptibilities to acid deposition from air pollution which are designated on a three tier ranking in the region of the Southern Appalachian Assessment (SAA). The assessment is being conducted by Federal agencies that are members of the Southern Appalachian Man and Biosphere (SAMAB) Cooperative. Sensitivities to acid deposition, ranked high, medium, and low are assigned on the basis of bedrock compositions and their associated soils, and their capacities to neutralize acid precipitation. The data is available in Arc/Info export format. [Summary provided by the USGS] proprietary USGS_OFR_aqbound_1.0 Digital boundaries of the Antlers aquifer in southeastern Oklahoma CEOS_EXTRA STAC Catalog 1992-01-01 1992-12-31 -97.4976, 33.7288, -94.4684, 34.3644 https://cmr.earthdata.nasa.gov/search/concepts/C2231550862-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digitized aquifer boundaries of the Antlers aquifer in southeastern Oklahoma. The Early Cretaceous-age Antlers Sandstone is an important source of water in an area that underlies about 4,400-square miles of all or part of Atoka, Bryan, Carter, Choctaw, Johnston, Love, Marshall, McCurtain, and Pushmataha Counties. The Antlers aquifer consists of sand, clay, conglomerate, and limestone in the outcrop area. The upper part of the Antlers aquifer consists of beds of sand, poorly cemented sandstone, sandy shale, silt, and clay. The Antlers aquifer is unconfined where it outcrops in about an 1,800-square-mile area. The data set includes the outcrop area of the Antlers Sandstone in Oklahoma and areas where the Antlers is overlain by alluvial and terrace deposits and a few small thin outcrops of the Goodland Limestone. Most of the aquifer boundary lines were extracted from published digital geology data sets. Some of the lines were interpolated in areas where the Antlers aquifer is overlain by alluvial and terrace deposits near streams and rivers. The interpolated lines are very similar to the aquifer boundaries published in a ground-water modeling report for the Antlers aquifer. The maps from which this data set was derived were scanned or digitized from maps published at a scale of 1:250,000. This data set is one of four digital map data sets being published together for this aquifer. The four data sets are: aqbound - aquifer boundaries cond - hydraulic conductivity recharg - aquifer recharge wlelev - water-level elevation contours proprietary USGS_P-11_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231552077-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 11 (Central Coastal) are listed here by play number, type, and name: Number Type Name 1101 conventional Point Arena Oil 1102 conventional Point Reyes Oil 1103 conventional Pescadero Oil 1104 conventional La Honda Oil 1105 conventional Bitterwater Oil 1106 conventional Salinas Oil 1107 conventional Western Cuyama Basin 1109 conventional Cox Graben proprietary USGS_P-11_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231551956-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 11 (Central Coastal) are listed here by play number and name: Number Name 1101 Point Arena Oil 1102 Point Reyes Oil 1103 Pescadero Oil 1104 La Honda Oil 1105 Bitterwater Oil 1106 Salinas Oil 1107 Western Cuyama Basin 1109 Cox Graben proprietary USGS_P1650-a_1.0 Atlas of Relations Between Climatic Parameters and Distributions of Important Trees and Shrubs in North America CEOS_EXTRA STAC Catalog 1970-01-01 -170, 20, -80, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552968-CEOS_EXTRA.umm_json This atlas explores the continental-scale relations between the geographic ranges of woody plant species and climate in North America. A 25-km equal-area grid of modern climatic and bioclimatic parameters was constructed from instrumental weather records. The geographic distributions of selected tree and shrub species were digitized, and the presence or absence of each species was determined for each cell on the 25-km grid, thus providing a basis for comparing climatic data and species' distributions. The relations between climate and plant distributions are explored in graphical and tabular form. The results of this effort are primarily intended for use in biogeographic, paleoclimatic, and global-change research. These web pages provide access to the text, digital representations of figures, and supplemental data files from USGS Professional Paper 1650, chapters A and B. A printed set of these volumes can be ordered from the USGS at a cost of US$63.00. To order, please call or write: USGS Information Services Box 25286 Denver Federal Center Denver, CO 80225 Tel: 303-202-4700; Fax: 303-202-4693 [Summary provided by the USGS.] proprietary +USGS_PA_DIGIT_1.0 Digital drainage basin boundaries of named streams in Pennsylvania CEOS_EXTRA STAC Catalog 1970-01-01 -76.4304, 39.7151, -74.6865, 42.0007 https://cmr.earthdata.nasa.gov/search/concepts/C2231548560-CEOS_EXTRA.umm_json "In 1989, the Pennsylvania Department of Environmental Resources (PaDER), in cooperation with the U.S. Geological Survey, Water Resources Division (USGS published the Pennsylvania (PA) Gazetteer of Streams. This publication contains information related to named streams in Pennsylvania. Drainage basin boundaries are delineated on the 7.5-minute series topographic paper quadrangle maps for PA and parts of the bordering states of New York, Maryland, Ohio, West Virginia, and Delaware. These boundaries enclose catchment areas for named streams officially recognized by the Board on Geographic Names and other unofficially named streams that flow through named hollows, using the hollow name, e.g. ""Smith Hollow"". This was done in an effort to name as many of the 64,000 streams as possible. In 1991, work began by USGS to put these drainage basin boundaries into digital form for use in a geographic information system (GIS). Digitizing started with USGS in Lemoyne, PA., but expanded with assistance by PaDER and the Natural Resource Conservation Service (NRCS), formerly the U.S Department of Agriculture, Soil Conservation Service (SCS). USGS performed all editing, attributing, and edgematching. There are 878, 7.5-minute quadrangle maps in PA. This documentation applies to only those maps in the Delaware River basin (164). Parts of the Delaware River drainage originate outside the PA border. At this time, no effort is being made by USGS to include those named stream basins. [Summary provided by the USGS.]" proprietary +USGS_PONTCHARTRAIN Geologic Framework and Processes of the Lake Pontchartrain Basin CEOS_EXTRA STAC Catalog 1970-01-01 -91, 29, -89, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2231549642-CEOS_EXTRA.umm_json Lake Pontchartrain and adjacent lakes in Louisiana form one of the larger estuaries in the Gulf Coast region. The estuary drains the Pontchartrain Basin (at right), an area of over 12,000 km2 situated on the eastern side of the Mississippi River delta plain. In Louisiana, nearly one-third of the State population lives within the 14 parishes of the basin. Over the past 60 years, rapid growth and development within the basin, along with natural processes, have resulted in significant environmental degradation and loss of critical habitat in and around Lake Pontchartrain. Human activities associated with pollutant discharge and surface drainage have greatly affected the water quality in the lake. This change is evident in the bottom sediments, which record the historic health of the lake. Also, land-altering activities such as logging, dredging, and flood control in and around the lake, lead to shoreline erosion and loss of wetlands.The effects of pollution, shoreline erosion and wetland loss on the lake and surrounding areas have become a major public concern. To better understand the basin's origin and the processes driving its development and degradation requires a wide-ranging study involving many organizations and personnel. When the U.S. Geological Survey began the study of Lake Pontchartrain in 1994, information on four topics was needed: -Geologic Framework, or how the various sedimentary layers that make up the basin are put together -Sediment Characterization, that is, what are the sediments made of, where did they come from, and what kinds of pollutants do they contain -Shoreline and Wetland Change over time -what are the processes that control Water Circulation [Summary provided by the USGS.] proprietary USGS_PWRC_BioEco Biological and Ecological Characteristics of Terrestrial Vertebrate Species Residing in Estuaries CEOS_EXTRA STAC Catalog 1980-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231549587-CEOS_EXTRA.umm_json ABSTRACT: The Biomonitoring of Environmental Status and Trends (BEST) program is designed to assess and monitor the effects of environmental contaminants on biological resources, particularly those under the stewardship of the Department of the Interior. BEST examines contaminant issues at national, regional, and local scales, and uses field monitoring techniques and information assessment tools tailored to each scale. As part of this program, the threat of contaminants and other anthropogenic activities to terrestrial vertebrates residing in or near to Atlantic coast estuarine ecosystems is being evaluated by data synthesis and field activities. One of the objectives of this project is to evaluate the relative sensitivity and suitability of various wildlife species for regional contaminant monitoring of estuaries and ecological risk assessment. proprietary USGS_RIMP Chesapeake Bay River Input Monitoring Program CEOS_EXTRA STAC Catalog 1970-01-01 -77, 36, -75, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231550608-CEOS_EXTRA.umm_json "The USGS Chesapeake Bay River Input Monitoring (RIM) Program was established to quantify loads and long-term trends in concentrations of nutrients and suspended material entering the tidal part of the Chesapeake Bay Basin from its nine major tributaries. These nine rivers account for approximately 93% of the stream flow entering Chesapeake Bay from the non-tidal part of its watershed. Results of the RIM program are being used by resource managers, policy makers, and concerned citizens to help evaluate the effectiveness of strategies aimed at reducing nutrients and sediment entering Chesapeake Bay from its tributaries. Water samples are collected upstream of the transition area between the tidal and non-tidal regions of the nine rivers (Figure 1). This transition zone historically has been referred to as the ""Fall Line"" for the many sets of falls and rapids that are found at this point on the rivers. Below the Fall Line in the tidal areas of these rivers, tides can transport water, nutrients, and suspended material from downstream, making it difficult to determine the cause of any observed changes. Because water-quality samples are collected above the influence of tides, any observed changes in nutrients or suspended material can be attributed to upstream causes. [Summary provided by the USGS.]" proprietary +USGS_RITA_COASTAL_IMPACT Hurricane Rita Impact Studies CEOS_EXTRA STAC Catalog 2005-09-24 -98, 27, -84, 36 https://cmr.earthdata.nasa.gov/search/concepts/C2231552111-CEOS_EXTRA.umm_json Hurricane Rita made landfall on September 24, 2005 near the TX-LA border. The U.S. Geological Survey (USGS), NASA, the U.S. Army Corps of Engineers, the University of New Orleans, Louisiana State University and the Texas Bureau of Economic Geology are cooperating in a research project investigating coastal change that is expected as a result of Hurricane Rita. Aerial video, still photography, and laser altimetry surveys of post-storm beach conditions will be collected for comparison with earlier data as soon as weather allows. The comparisons will show the nature, magnitude, and spatial variability of coastal changes such as beach erosion, overwash deposition, and island breaching. These data will also be used to further refine predictive models of coastal impacts from severe storms. The data will be made available to local, state, and federal agencies for purposes of disaster recovery and erosion mitigation. [Summary provided by the USGS.] proprietary +USGS_Report_MF-2332_1.0 Geologic map and map database of the Palo Alto 30' X 60' quadrangle, California CEOS_EXTRA STAC Catalog 1970-01-01 -123, 36.934082, -121.99254, 37.504234 https://cmr.earthdata.nasa.gov/search/concepts/C2231554105-CEOS_EXTRA.umm_json This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:100,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be used to identify or delineate landslides in the region. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (pamf.ps, pamf.pdf, pamf.txt), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:62,500 or smaller. The attached text file mf2332.rev contains current revision numbers for all parts of this product. This report consists of a set of geologic map database files (Arc/ Info coverages) and supporting text and plotfiles. In addition, the report includes two sets of plotfiles (PostScript and PDF format) that will generate map sheets and pamphlets similar to a traditional USGS Miscellaneous Field Studies Report. These files are described in the explanatory pamphlets (pamf.ps, pamf.pdf, pamf.txt). The base map layer used in the preparation of the geologic map plotfiles was scanned from a scale-stable version of the USGS 1:100,000 topographic map at a resolution of 300 dpi as a monochrome TIFF image. The raster data was converted to a GRID in Arc/Info, and combined with geologic polygon data to produce the final map image. The base map coverages included with the database publication are vectorized versions of scans of scale-stable seperates. These coverages contain no database information other than position, and are included for reference only. In both cases the map digitized was the Palo Alto (1982 version) 1: 100,000 quadrangle, which has a 50-meter contour interval. proprietary USGS_SESC_CrayfishStatus American Fisheries Society Crayfish of the United States and Canada CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231553463-CEOS_EXTRA.umm_json About: This website presents the 2007 American Fisheries Society Endangered Species Committee list of freshwater crayfishes of United States and Canada. The committee evaluated the conservation status and determined the major threats impacting these taxa. Summary: This is the second compilation of crayfishes of the United States and Canada prepared by the American Fisheries Society's Endangered Species Committee. Since the last revision in 1996, the number of crayfish taxa in need of conservation attention changed little. This list includes 363 taxa representing 12 genera and 2 families. Approximately 48% of species or subspecies in the study area are imperiled. There are 54 vulnerable, 52 threatened, and 66 endangered extant taxa; 2 taxa are possibly extinct. For some crayfishes, limited natural range (e.g., one locality or one drainage system) precipitates recognition as Endangered or Threatened; but for many others, status assignments continue to be hampered by a paucity of recent distributional information. While progress has been made in this arena, basic ecological and current distributional information are lacking for 60% of the U.S. and Canadian crayfish fauna. Threats highlighted in Taylor et al. (1996) such as habitat loss and introduction of nonindigenous crayfishes continue to persist and are greatly magnified by the limited distribution of many species. Recognition of the potential for rapid decimation of crayfish species, especially those with limited ranges, should provide impetus for proactive efforts toward conservation. Maps: Each taxon on the list was assigned to one or more states or provinces that circumscribe its native distribution. Mapped distributions indicate where taxa naturally occur or occurred in the past. States or provinces with parentheses in text and tables are locations where taxa are known or suspected to be introduced. A variety of sources were used to obtain distributional information, most notably Taylor et al. (1996) and multiple state-specific literature and websites. proprietary +USGS_SESC_ExtinctFish Extinct North American Freshwater Fishes CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231553139-CEOS_EXTRA.umm_json Extinction is a natural process in nature and is the opposite of speciation—the evolution of new life forms. Importantly, 90%–96% of all species that became extinct over geological time disappeared during the normal give and take of speciation and extinction1. There is widespread evidence that modern rates of extinction in many plants and animals significantly exceed background rates in the fossil record. From 1900 to 2010, 57 species and subspecies of North American freshwater fishes became extinct, and since 1898, three distinct populations of valued fishes were extirpated from the continent2. Intuitively, this number of extinctions seems unnaturally high. Since the first tally of extinct North American fishes in 19893, the number of extinct fishes increased by 25%2,4. From the end of the 19th century to the present, modern extinctions varied by decade but significantly increased after 1950. The post-1950s increase in extinction rates likely corresponds to substantial economic, demographic, and land-use changes that occurred in North America after WWII2. A meaningful way to evaluate modern extinctions is to compare modern rates of extinction to background rates using data from the fossil record. The mean background extinction rate (from origination to extinction) for freshwater fish species is estimated to be one extinction/3 million years. The modern extinction rate in North American freshwater fishes is conservatively estimated to be 877 times greater than the background rate—for the interval 1900 to 2010. Calculation of modern to background extinction rate (M:BER) is similar to extinctions per million species years (E/MSY) but differs in that actual background extinction rates are used in lieu of one extinction/million years.) M:BER ratios fluctuate by year because total North American fishes increases each year due to descriptions of new species and because extinctions are intermittent (the last one occurred in 2006). The M:BER value for North American freshwater fishes in 2012=863 and will continue to decline annually until the next extinction occurs. During the 20th century, freshwater fishes had the highest extinction rate among all vertebrates worldwide. Low numbers of fish extinctions documented from other continents suggests that extinctions are under-reported in Africa, Eurasia, and South America at this time. It is estimated that future extinctions in North America will increase from 39 currently extinct fish species to between 53 and 86 species by 2050. proprietary USGS_SESC_ImperiledFish American Fisheries Society Imperiled Freshwater and Diadromous Fishes of North America CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551557-CEOS_EXTRA.umm_json About: This website presents the 2008 American Fisheries Society Endangered Species Committee list of imperiled North American freshwater and diadromous fishes. The committee considered continental fishes native to Canada, Mexico, and the United States, evaluated their conservation status and determined the major threats impacting these taxa. We use the terms taxon (singular) or taxa (plural) to include named species, named subspecies, undescribed forms, and distinct populations as characterized by unique morphological, genetic, ecological, or other attributes warranting taxonomic recognition. Undescribed taxa are included, based on the above diagnostic criteria in combination with known geographic distributions and documentation deemed of scientific merit, as evidenced from publication in peer-reviewed literature, conference abstracts, unpublished theses or dissertations, or information provided by recognized taxonomic experts. Although we did not independently evaluate the taxonomic validity of undescribed taxa, the committee adopted a conservative approach to recognize them on the basis of prevailing evidence which suggests that these forms are sufficiently distinct to warrant conservation and management actions. Summary: This is the third compilation of imperiled (i.e., endangered, threatened, vulnerable) plus extinct freshwater and diadromous fishes of North America prepared by the American Fisheries Society's Endangered Species Committee. Since the last revision in 1989, imperilment of inland fishes has increased substantially. This list includes 700 extant taxa representing 133 genera and 36 families, a 92% increase over the 364 listed in 1989. The increase reflects the addition of distinct populations, previously non-imperiled fishes, and recently described or discovered taxa. Approximately 39% of described fish species of the continent are imperiled. There are 230 vulnerable, 190 threatened, and 280 endangered extant taxa; 61 taxa are presumed extinct or extirpated from nature. Of those that were imperiled in 1989, most (89%) are the same or worse in conservation status; only 6% have improved in status, and 5% were delisted for various reasons. Habitat degradation and nonindigenous species are the main threats to at-risk fishes, many of which are restricted to small ranges. Documenting the diversity and status of rare fishes is a critical step in identifying and implementing appropriate actions necessary for their protection and management. Maps: In collaboration with the World Wildlife Fund, the committee developed a map of freshwater ecoregions that combines spatial and faunistic information derived from Maxwell and others (1995), Abell and others (2000; 2008), U.S. Geological Survey Hydrologic Unit Code maps (Watermolen 2002), Atlas of Canada (2003), and Commission for Environmental Cooperation (2007). Eighty ecoregions were identified based on physiography and faunal assemblages of the Atlantic, Arctic, and Pacific basins. Each taxon on the list was assigned to one or more ecoregions that circumscribes its native distribution. A variety of sources were used to obtain distributional information, most notably Lee and others (1980), Hocutt and Wiley (1986), Page and Burr (1991), Behnke (2002), Miller and others (2005), numerous state and provincial fish books for the United States and Canada, and the primary literature, including original taxonomic descriptions. Taxa were also associated with the states or provinces where they naturally occur or occurred in the past. proprietary +USGS_SESC_ImperiledFreshwaterOrganisms Imperiled Freshwater Organisms of North America CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231549663-CEOS_EXTRA.umm_json This website provides access to maps and lists of imperiled freshwater organisms of North America as determined by the American Fisheries Society (AFS) Endangered Species Committee (ESC). At this website, one can view lists of animals by freshwater ecoregion, by state or province boundary, and plot distributions of these same creatures by ecoregions or political boundaries. Both the AFS and U.S. Geological Survey (USGS) have a long standing commitment to the advancement of aquatic sciences and sharing that information with the public. Since 1972, the ESC has been tracking the status of imperiled fishes and aquatic invertebrates in North America. Recently, the fish (2008) and crayfish (2007) subcommittees provided revised status lists of at-risk taxa, and the mussel and snail subcommittees are in the process of completing similar revisions. Historically, the revised AFS lists of imperiled fauna have been published in Fisheries. With rapid advances in technology and information transfer, there is a growing need to provide to stakeholders immediate and dynamic data on imperiled resources. The USGS is a leader in aquatic resource research that effectively disseminates results from those studies to the public through print and internet media. A Memorandum Of Understanding formally establishes an agreement between the AFS and USGS to create this website that will serve as a conduit for information exchange about imperiled aquatic organisms of North America. proprietary USGS_SESC_SnailStatus American Fisheries Society List of Freshwater Snails from Canada and the United States CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551686-CEOS_EXTRA.umm_json About: This website presents the 2013 American Fisheries Society Endangered Species Committee list of freshwater snails (Gastropods) of United States and Canada. The committee evaluated the conservation status and determined the major threats impacting these taxa. Summary: This is the first conservation status review for freshwater snails (gastropods) of Canada and the United States by the American Fisheries Society's Endangered Species Freshwater Gastropod Subcommittee. The goals of this contribution are to provide: 1) a current and comprehensive taxonomic authority list for all native freshwater gastropods of Canada and the United States, 2) provincial and state distributions as presently understood, 3) a conservation assessment, and, 4) references on their biology, distribution and conservation. Freshwater gastropods occupy every type of aquatic habitat ranging from subterranean aquifers to brawling montane headwater creeks. Gastropods are ubiquitous invertebrates and frequently dominate aquatic invertebrate biomass. Of the 703 gastropods documented by Johnson et al. (2013), 74% are imperiled or extinct (278 endangered, 102 threatened, 73 vulnerable, and 67 are considered extinct); only 157 species are considered stable. Map queries display species distributions in provinces and states in which they are believed to occur or occurred in the past, but considerable fieldwork is required to determine exact geographic limits of species. We hope this list stimulates a surge in the study of freshwater gastropods. Supporting Literature: Supporting literature for the North American freshwater gastropods assessment are organized alphabetically by state and province, followed by national, regional, and other general references. This literature compilation is not comprehensive, but offers considerable information for individuals interested in freshwater snails. Recovery Examples: Although the gastropod fauna of Canada and the United States is beleaguered by multiple forms of habitat loss, the fauna is resilient and capable of remarkable recovery when suitable habitat is available. Three examples of recovery demonstrate the inherent reviving potential of freshwater gastropods. Images of the incredible diversity of freshwater snails are presented in plates and photo gallery. Maps: Each species on the list was assigned to one or more states or provinces that circumscribe its native distribution. Mapped distributions indicate where taxa naturally occur or occurred in the past. Resources used to obtain distributional information include state and regional publications. proprietary USGS_SESC_SturgeonBiblio_3 A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi. CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552134-CEOS_EXTRA.umm_json "This functional bibliography is meant to be a complete and comprehensive bibliography of all discoverable reports containing information on the Gulf Sturgeon (GS). This bibliography contains all known reports presenting, documenting, summarizing, listing, or interpreting information on the GS through 31 December 2013. Report citations are organized into four sections. Section I includes published scientific journal articles, books, dissertations and theses, published and unpublished technical reports, published harvest prohibitions, and online articles reporting substantive scientific information. Section II includes newspaper, newsletter, magazine, book, agency news releases, and online articles reporting on GS occurrences, mortalities, captures, jumping, boat collisions, aquaculture, historical photographs, and other largely non-scientific or anecdotal issues. Section III consists of books, theses, ecotour-guides, media articles, editorials, and blogs reporting a mix of anecdotal information, historical information, and opinion on GS conservation, habitat issues, exploitation, aquaculture, and human interaction - but presenting very limited or no substantive scientific information. Section IV includes videos, films and audio recordings documenting GS life history and behavior. Each reference includes a bibliographic citation, as well as a brief annotation of key topics in brackets, where possible. The names of journals, theses, dissertations, and books are given in bold within each citation, and relevant page numbers are noted in parentheses at the end of citations, where applicable. Newspaper and magazine article titles are placed within parentheses. Key topic annotations are inserted in bracketed italics on a separate line. If the reference reports GS information under a different common or scientific name (e.g., Atlantic Sturgeon, Common Sturgeon, Sturgeon, Sea Sturgeon, Acipenser oxyrinchus oxyrinchus, Acipenser oxyrhynchus or Acipenser sturio), a notation to that effect is given within the key words annotation line, e.g., [Reported as ""Atlantic sturgeon""]. A small number of reports could not be obtained. These include historical reports from newspapers and magazines long out of circulation. In these limited cases, titles are still provided to substantiate their existence. Other reports that are no longer readily available, but which have been obtained during preparation of this bibliography, have been archived in hardcopy and/or as scanned pdf files at USGS, SESC. Copies of such hard to obtain reports, if non-copyrighted, may be available upon request from USGS corresponding author, via email: mrandall@usgs.gov." proprietary USGS_SIR-5079_MSRiverFloodMaps Development of flood-inundation maps for the Mississippi River in Saint Paul, Minnesota CEOS_EXTRA STAC Catalog 1970-01-01 -93.15028, 44.90479, -92.999855, 44.97016 https://cmr.earthdata.nasa.gov/search/concepts/C2231549022-CEOS_EXTRA.umm_json Digital flood-inundation maps for a 6.3-mile reach of the Mississippi River in Saint Paul, Minnesota, were developed through a multi-agency effort by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers and in collaboration with the National Weather Service. The inundation maps, which can be accessed through the U.S. Geological Survey Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ and the National Weather Service Advanced Hydrologic Prediction Service site at http://water.weather.gov/ahps/inundation.php , depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the U.S. Geological Survey streamgage at the Mississippi River at Saint Paul (05331000). The National Weather Service forecasted peak-stage information at the streamgage may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the Mississippi River by means of a one-dimensional step-backwater model. The hydraulic model was calibrated using the most recent stage-discharge relation at the Robert Street location (rating curve number 38.0) of the Mississippi River at Saint Paul (streamgage 05331000), as well as an approximate water-surface elevation-discharge relation at the Mississippi River at South Saint Paul (U.S. Army Corps of Engineers streamgage SSPM5). The model also was verified against observed high-water marks from the recent 2011 flood event and the water-surface profile from existing flood insurance studies. The hydraulic model was then used to determine 25 water-surface profiles for flood stages at 1-foot intervals ranging from approximately bankfull stage to greater than the highest recorded stage at streamgage 05331000. The simulated water-surface profiles were then combined with a geographic information system digital elevation model, derived from high-resolution topography data, to delineate potential areas flooded and to determine the water depths within the inundated areas for each stage at streamgage 05331000. The availability of these maps along with information regarding current stage at the U.S. Geological Survey streamgage and forecasted stages from the National Weather Service provides enhanced flood warning and visualization of the potential effects of a forecasted flood for the city of Saint Paul and its residents. The maps also can aid in emergency management planning and response activities, such as evacuations and road closures, as well as for post-flood recovery efforts. proprietary USGS_SOFIA_75_29_flows Baseline hydrologic data collection along the I-75 - State Road 29 corridor in the Big Cypress National Preserve CEOS_EXTRA STAC Catalog 2005-11-01 2009-09-30 -81.325, 25.75, -80.75, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549536-CEOS_EXTRA.umm_json The objectives of this study are to develop and continue a program of surface water flow monitoring across I-75 and SR 29 in the I-75 corridor from Snake Road west to SR 29 and SR 29 from I-75 south to USGS site 02291000 Barron River near Everglades, Florida. Quarterly discharge measurements will be made along both reaches to assess hydrologic flow patterns and evaluate the feasibility of creating a stage-discharge/index-velocity relationship for this area. Data collected in this project will provide baseline information about a major current barrier to sheetflow, I-75. The data are expected to support the research on the existing linkages among geologic, hydrologic, chemical, climatological, and biological processes that currently shape the Everglades and will provide insight into the predrainage Everglades. The baseline flow will contribute to the Southwest Florida Feasibility Study addressing the health of upland and aquatic ecosystems in the 4,300 square mile area. proprietary +USGS_SOFIA_75_29_hydro_data Hydrologic Data Collected along I-75/SR29 corridor in Big Cypress National Preserve CEOS_EXTRA STAC Catalog 2005-11-01 2009-09-30 -81.325, 25.75, -80.75, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549847-CEOS_EXTRA.umm_json The location of each site is shown on a Google Map. Data are available as a Google Map with links to Station Information and Data for each site. Data are available for 58 sites along I-75 and for 28 sites along State Road 29 in Big Cypress National Preserve. Data collected in this project will provide baseline information about a major current barrier to sheetflow, I-75. The data are expected to support the research on the existing linkages among geologic, hydrologic, chemical, climatological, and biological processes that currently shape the Everglades and will provide insight into the predrainage Everglades. The baseline flow will contribute to the Southwest Florida Feasibility Study addressing the health of upland and aquatic ecosystems in the 4,300 square mile area. proprietary USGS_SOFIA_ACME_DB Aquatic Cycling of Mercury in the Everglades Project Database CEOS_EXTRA STAC Catalog 1995-01-01 2008-09-01 -80.1, 25, -81.6, 27.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231554301-CEOS_EXTRA.umm_json Between 1995 and 2008, the Aquatic Mercury Cycling in the Everglades (ACME) project examined in detail the biogeochemical parameters that influence methylmercury (MeHg) production in the Florida Everglades. The interdisciplinary ACME team studied Hg cycling in the Everglades through a process-based, biogeochemical lens (Hurley et al. 1998). In the Everglades, as in most other ecosystems, inorganic mercury is transformed into methylmercury primarily by the action of anaerobic bacteria in surficial sediments and soils. The ACME project has been a collaborative research effort designed to understand the biogeochemical drivers of mercury cycling in the Greater Florida Everglades. The project is led be a team of scientists from the USGS and the Smithsonian Institution, with additional collaborators from the University of Wisconsin, Texas A&M, the SFWMD and FL DEP. ACME�s main objective has been to define the key processes that control the fate and transport of Hg in the Everglades. The study has used a process-oriented, multi-disciplinary approach, focusing on a suite of intensively-studied sites across the trophic gradient of the Water Conservation Areas and Everglades National Park. Since 1995, a core set of sites has been examined in detail through time, including changes in season and in hydrology. The biogeochemical parameters examined focus on those that impact net methylmercury (MeHg) production, and include sulfur, carbon and nutrient biogeochemistry. The study examined Hg and MeHg concentrations, and associated biogeochemical parameters in surface waters, soils, periphyton, emergent plants and biota. The core study sites have been supplemented with survey data across many additional sites in the Greater Everglades Ecosystem. The field study was also supplemented with experimental studies of Hg complexation, photochemistry, and bioavailability. The ACME project has been funded by a variety of agencies including the USGS, NSF, EPA, SFWMD and FL DEP. proprietary USGS_SOFIA_ASR_04 A retrospective and critical review of aquifer and storage (ASR) sites and conceptual framework of the Upper Floridian aquifer in south Florida CEOS_EXTRA STAC Catalog 1999-10-01 2004-09-30 -82.55795, 24.441917, -79.84407, 27.586416 https://cmr.earthdata.nasa.gov/search/concepts/C2231549469-CEOS_EXTRA.umm_json The objectives of this study are to: (1) inventory and assess the strengths and weaknesses of available hydrogeologic, hydraulic, hydrochemical, well construction, and cycle test information at existing ASR sites, (2) conduct a critical review of the hydrogeology on a site-by-site basis and relate to existing regional hydrogeology frameworks, allowing for the delineation of hydrogeologic factors that may be important to recovery efficiency, (3) identify hydrogeologic, design, and management factors which locally or regionally constrain the efficient storage and recovery of fresh water within the Upper Floridan aquifer, and (4) conduct a comparative analysis of the performance of all ASR sites having adequate data. This five-year study is divided into two phases, the first of which was two years long. The first phase laid the groundwork for data inventory, review, and analysis, and the second phase will allow for collection of additional data as it becomes available, expand the hydrogeologic framework, and perform a more complete comparative analysis of ASR sites. The study is in the second phase. Aquifer storage and recovery (ASR) has been described as 'the storage of water in a suitable aquifer through a well during times when water is available, and recovery of the water from the same well during times when it is needed'. Water can be stored in aquifers with poor water quality. ASR in south Florida is proposed in the Comprehensive Everglades Restoration Plan (CERP) as a cost-effective water-supply alternative that can help meet needs of agricultural, municipal, and recreational users while providing the water critical for Everglades ecosystem restoration. In CERP, plans have been made to utilize ASR in the Floridan aquifer system on an unprecedented scale. Precedence for ASR in southern Florida has been set with wells having been constructed at over 30 sites, mostly by local municipalities or counties in coastal areas. The Upper Floridan aquifer, the aquifer used at most of these sites, is brackish to saline in south Florida, which can have a large impact on the recovery of the fresh or potable water recharged and stored. Few regional investigations of the Floridan aquifer system hydrogeology in south Florida have been conducted, and the focus of those studies was not on ASR. Lacking a regional ASR framework to aid the decision-making process, ASR well sites in south Florida have been primarily located based on factors such as land availability, source-water quality, and source-water proximity (preexisting surface-water bodies, surficial aquifer system well fields, or water treatment plants). Little effort has been made to link information collected from each site as part of a regional hydrogeologic analysis. Results of this study should help the managers of the CERP program in locating, designing, constructing, and cycle testing ASR wells. These results should help establish a standard cycle testing protocol that can be used to measure the performance of individual CERP wells or clusters of wells. proprietary USGS_SOFIA_ASR_coordination Aquifer Storage and Recovery (ASR) Coordination CEOS_EXTRA STAC Catalog 2002-01-01 2004-12-31 -82.5, 25, -80, 27.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231553754-CEOS_EXTRA.umm_json ABSTRACT: The Comprehensive Everglades Restoration Plan (CERP) relies heavily on Aquifer Storage and Recovery (ASR) technology. The CERP includes approximately 333 ASR wells in South Florida with a total capacity of over 1.6 billion gallons per day. Much of the 'new water' in the CERP is derived from storing excess water that was previously discharged to the ocean. However, this new water would not be very useful unless there is a place to store it for use during dry periods. ASR is included in the CERP as one mechanism to provide this storage. Despite construction of some ASR facilities by local utilities, there remains a considerable number of significant technical and engineering-related uncertainties. Key Findings: 1) An analysis was conducted to describe and interpret the lithology of a part of the Upper Floridan aquifer penetrated by the Regional Observation Monitoring Program (ROMP) 29A test corehole in Highlands County, Florida. Information obtained was integrated into a conceptual model that delineates likely CERP ASR storage zones and confining units in the context of sequence stratigraphy. Carbonate sequence stratigraphy correlation strategies appear to reduce risk of miscorrelation of key ground-water flow units and confining units. 2) A hierarchical arrangement of rock unit cycles can be identified; High Frequency Cycle formed of peritidal, subtidal, and deeper subtidal) form High Frequency Sequence, and those can be grouped into Cycle Sequences. There appears to be a spatial relation among wells that penetrate water-bearing rocks having relatively high and low transmissivities. 3) Assuming hydrogeologic conditions observed in the ROMP 29A well are representative of in south-central Florida, the uppermost (Lower Hawthorn-Suwannee) of two likely CERP ASR storage zones does not appear to be viable with respect to the proposed 200 CERP ASR facility planned to be sited northwest of Lake Okeechobee. Insufficient data were available to adequately characterize the lower flow zone contained within the Avon Park Formation. proprietary USGS_SOFIA_BigCypress_PineIsland_SatMap Big Cypress-Pine Island Satellite Image Map CEOS_EXTRA STAC Catalog 2000-01-27 -82.27, 25.78, -81.13, 26.7 https://cmr.earthdata.nasa.gov/search/concepts/C2231549800-CEOS_EXTRA.umm_json ABSTRACT: The map is a composite image of spectral bands 3 (630-690 nanometers, red), 4 (775-900 nanometers, near-infrared), and 5 (1,550-1750 nanometers, middle-infrared) and the new panchromatic band (520-900, green to near-infrared) acquired by the Landsat 7 enhanced thematic mapper (ETM) sensor on January 27, 2000. proprietary +USGS_SOFIA_Caloos_Franklin_Locks_flow Flow Monitoring Along the Tidal Caloosahatchee River and Tributaries West of Franklin Locks CEOS_EXTRA STAC Catalog 2007-01-01 2011-12-31 -82.04, 26.4, -81.6, 26.8 https://cmr.earthdata.nasa.gov/search/concepts/C2231552489-CEOS_EXTRA.umm_json Monitoring stations established thru this project are designed as part of a larger network needed for the Caloosahatchee River and tributaries that should remain in place long-term (~10 years). Data from monitoring stations included in this project will be evaluated during the third year of data collection in order to assess viability and need for changes . The objective of this study is to quantify freshwater flows into the tidal reach of the Caloosahatchee River, west of Franklin Locks. proprietary +USGS_SOFIA_Caloosahatchee_water_quality Near-Surface Water-Quality Surveys of the Caloosahatchee River and Downstream Estuaries, Florida, USA CEOS_EXTRA STAC Catalog 2011-09-30 2014-08-19 -82.25, 26.33, -81.76, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231554663-CEOS_EXTRA.umm_json Beginning in September 2011, water-quality surveys were conducted a minimum of six times per year in the tidal Caloosahatchee River and surrounding estuaries. Geo-referenced measurements were made at 5 second intervals during moving boat surveys in order to create high resolution water-quality maps of the study area. Water-quality characteristics measured and recorded include salinity, temperature, dissolved oxygen, pH, and turbidity proprietary USGS_SOFIA_CarbonFlux Carbon Flux and Greenhouse Gasses of Restored and Degraded Greater Everglades Wetlands: Flux Tower Measurements of Water, Energy and Carbon Cycling in the Big Cypress National Preserve - USGS_SOFIA_CarbonFlux CEOS_EXTRA STAC Catalog 2011-01-01 2013-09-30 -81.6, 25, -80.1, 27.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231550206-CEOS_EXTRA.umm_json "Greenhouse gas emissions, specifically carbon dioxide (CO2), are commonly linked with increasing global temperatures and rising sea-level. Of particular concern are rates of sea-level rise and carbon cycling including CO2 emissions or ""footprints"" of urban areas and the capacity of plant communities to absorb and release CO2. Defining and predicting ecosystem response to regional (e.g., freshwater discharge) and global (e.g., sea level rise) environmental change will require empirical baseline data on the functioning of water, energy and carbon cycles within Greater Everglades ecosystems. However, measurements of carbon and surface-energy cycling are sparse over plant communities within Department of Interior (DOI) managed lands in south Florida. Specifically, the quantity of CO2 absorbed or released annually within subtropical forests and wetlands as well as carbon and energy cycling in response to changes in hydrology, salinity, forest-fires and/or other factors are poorly known. To reduce these uncertainties, eddy-covariance flux stations were constructed by the U.S. Geological Survey and South Florida Water Management District in the Big Cypress National Preserve in 2006. Water, energy and carbon fluxes are empirically measured at these stations. The goals of the project are to (1) quantify key variables of interest to researchers and policy makers such as latent heat flux (the energy equivalent of evapotranspiration), sensible heat flux, incoming solar radiation, net radiation, changes in stored heat energy, albedos, Bowen ratios, net ecosystem production (NEP), gross ecosystem production (GEP), ecosystem respiration; (2) understand variability and linkages within water, energy and carbon-cycles imposed by both natural processes and regional / global stresses; and (3) publish project results in USGS reports and peer-reviewed journal papers. Defining and predicting ecosystem response to regional (e.g., freshwater discharge) and global (e.g., sea level rise) environmental change will require empirical baseline data on the state and functioning of water, energy and carbon cycles within DOI lands. However, measurements of carbon and surface-energy cycling are sparse over plant communities within DOI managed lands in south Florida. This project intends to measure water and surface energy fluxes within the BCNP. We propose to begin carbon cycling measurements in 2012 and 2013, as time and funding permits. Plant communities selected for study included Pine Upland, Marsh, Cypress Swamp, and Dwarf Cypress. These plant communities are spatially extensive within DOI lands and resources" proprietary +USGS_SOFIA_Ding_Darling_baseline Ding Darling National Wildlife Refuge - Greater Everglades Baseline Information and Response to CERP CEOS_EXTRA STAC Catalog 2009-10-01 2014-09-30 -82.5, 26.3, -81.6, 27 https://cmr.earthdata.nasa.gov/search/concepts/C2231549274-CEOS_EXTRA.umm_json The greater Everglades Restoration program includes a management plan for the C-43 Canal, or Caloosahatchee River. This plan affects the quantity, quality, and timing of freshwater releases at control structure S-79 at Franklin Locks. Freshwater contributions are from Lake Okeechobee, and farming runoff along the canal from Lake Okeechobee to the town of Alva. This study will provide basic information on the effects on the quality of water entering J. N. Ding Darling National Wildlife Refuge as the result of freshwater releases at control structure S-79 proprietary +USGS_SOFIA_EDEN_grid_shapefile_v02 EDEN Grid Shapefile CEOS_EXTRA STAC Catalog 1970-01-01 -81.51, 24.7, -79.9, 27.2 https://cmr.earthdata.nasa.gov/search/concepts/C2231549862-CEOS_EXTRA.umm_json This shapefile serves as a net (fishnet or grid) to be placed over the South Florida study area to allow for sampling within the 400 meter cells (grid cells or polygons). The origin and extent of the Everglades Depth Estimation Network (EDEN) grid were selected to cover not only existing Airborne Height Finder (AHF) data and current regions of interest for Everglades restoration, but to cover a rectangular area that includes all landscape units (USACE, 2004) and conservation areas in place at the time of its development. This will allow for future expansion of analyses throughout the Greater Everglades region should resources allow and scientific or management questions require it. Combined with the chosen extent, the 400m cell resolution produces a grid that is 675 rows and 375 columns.. The shapefile contains the 253125 grid cells described above. Some characteristics of this grid, such as the size of its cells, its origin, the area of Florida it is designed to represent, and individual grid cell identifiers, could not be changed once the grid database was developed. These characteristics were selected to design as robust a grid as possible and to ensure the grid’s long-term utility. proprietary +USGS_SOFIA_EDEN_proj Everglades Depth Estimation Network (EDEN) CEOS_EXTRA STAC Catalog 1999-01-01 2008-10-28 -81.3, 25, -80.16, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231550596-CEOS_EXTRA.umm_json The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring, ground elevation modeling, and water-surface modeling that provides scientists and managers with current (1999-present), on-line water-depth information for the entire freshwater portion of the Greater Everglades. Presented on a 400-square-meter grid spacing, EDEN offers a consistent and documented dataset that can be used by scientists and managers to:1) guide large-scale field operations, 2) integrate hydrologic and ecological responses, and 3) support biological and ecological assessments that measure ecosystem responses to the implementation of the comprehensive Everglades Restoration plan (CERP) from the U.S. Army Corps of Engineers in 1999. Research has shown that relatively high-resolution data are needed to explicitly represent variations in the Everglades topography and vegetation that are important for landscape analysis and modeling. The EDEN project will provide a better representation of water depths if elevation variation within each 400-meter grid cell can be taken into account. The EDEN network provides a framework to integrate data collected by other agencies in a common quality-assured database. In addition to real-time network, collaboration among agencies will provide the EDEN project with valuable historic vegetation and water-depth data. This is the first time these data have been compiled and analyzed as a collective set. proprietary USGS_SOFIA_Eco_hist_db_2008_present_2 2008 - Present Ecosystem History of South Florida's Estuaries Database version 2 CEOS_EXTRA STAC Catalog 2008-03-16 2012-09-30 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549653-CEOS_EXTRA.umm_json The 2008 - Present Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), and modern monitoring site survey information (water chemistry, floral and faunal data, etc.). Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - location information. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain basic location information. proprietary +USGS_SOFIA_Ever_hydr_FB_dynam Interrelationships of Everglades Hydrology and Florida Bay Dynamics CEOS_EXTRA STAC Catalog 1850-01-01 2004-12-31 -80.89015, 25.1004, -80.39827, 25.471722 https://cmr.earthdata.nasa.gov/search/concepts/C2231554284-CEOS_EXTRA.umm_json This interdisciplinary synthesis project is designed to identify and document the interrelation of Everglades’ hydrology and tidal dynamics of Florida Bay on ecosystem response to past environmental changes, both natural and human imposed. The project focuses on integrating historical, hydrological, and ecological findings of scientific investigations within the Southern Inland and Coastal System (SICS), which encompasses the transition zone between the wetlands of Taylor Slough and C-111 canal and nearshore embayments of Florida Bay. In the ecological component, hindcast simulations of historical flow events are being developed for ecological analyses. The Across Trophic Level System Simulation (ATLSS) ecological modeling team is collaborating with the SICS hydrologic modeling team to develop the necessary hydrologic inputs for refined indicator species models. The interconnected freshwater wetland and coastal marine ecosystems of south Florida have undergone numerous human disturbances, including the introduction of exotic species and the alteration of wetland hydroperiods, landscape characteristics, and drainage patterns through implementation of the extensive canal and road system and the expansion of agricultural activity. In this project, collaborative efforts are focused on documenting the impact of past hydrological and ecological changes along the southern Everglades interface with Florida Bay by reconstructing past hydroperiods and investigating the correlation of human-imposed and natural impacts on hydrological changes with shifts in biotic species. The primary objectives are to identify the historical effects of past management practices, to integrate refined hydrological and ecological modeling efforts at indicator species levels to identify cause-and-effect relationships, and to produce a report that documents findings that link hydrological and ecological changes to management practices, wherever evident. proprietary +USGS_SOFIA_Fbbslmap Florida Bay Bottom Salinity Maps CEOS_EXTRA STAC Catalog 1994-11-01 1996-12-31 -81.167, 24.83, -80.33, 25.33 https://cmr.earthdata.nasa.gov/search/concepts/C2231549334-CEOS_EXTRA.umm_json The maps show the bottom salinity for Florida Bay at 5ppt salinity intervals approximately every other month beginning in November 1994 through December 1996. Recent algal blooms and seagrass mortality have raised concerns about the water quality of Florida Bay, particularly its nutrient content (nitrogen and phosphorous), hypersalinity, and turbidity. Water quality is closely tied to sediment transport processes because resuspension of sediments increases turbidity, releases stored nutrients, and facilitates sediment export to the reef tract. The objective of this research is to provide a better understanding of how and when sediments within Florida Bay are resuspended and deposited, to define the spatial distribution of the potential for resuspension, to delineate patterns of potential bathymetric change, and to predict the impacts of storms or seagrass die-off on bathymetry and circulation within the bay. By combining these results with the findings of other research being conducted in Florida Bay, we hope to quantify sediment export from the bay, better define the nutrient input during resuspension events, and assist in modeling circulation and water quality. Results will enable long-term sediment deposition and erosion in various regions of the bay to be integrated with data on the anticipated sea-level rise to predict future water depths and volumes. Results from this project, together with established sediment production rates, will provide the basis for a sediment budget for Florida Bay. proprietary +USGS_SOFIA_Fbbtypes Florida Bay Bottom Types map - USGS_SOFIA_Fbbtypes CEOS_EXTRA STAC Catalog 1996-01-01 1997-01-31 -81.25, 24.75, -80.25, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231553376-CEOS_EXTRA.umm_json The map shows the bottom types for Florida Bay that resulted from site surveys and boat transects (summer 1996-January 1997) compared with aerial photographs (December 1994-January 1995) and SPOT satellite imagery (1987). The purpose of this map is to describe the bottom types found within Florida Bay for use in 1) assessing bottom friction associated with sediment and benthic communities and 2) providing a very general description for other research needs. For these purposes, two descriptors were considered particularly important: density of seagrass cover and sediment texture. Seagrass estimates are visual estimates of the amount of seagrass cover including both number of plants and leaf length. Therefore, seagrass cover may be greater in areas with long leaves than in areas with short blades, even though the number of shoots may be the same. Seagrass cover is a different measure than density (Zieman et al., 1989 or Durako et al., 1996). It is used here to more accurately reflect hydrodynamic influence than the standing crop of seagrass. The use and definitions of dense, intermediate and sparse seagrass cover are similar to those used by Scoffin (1970). This map and associated descriptions are not meant to assess ecologic communities or detail sedimentological facies. The resolution of the map has been selected in an effort to define broad regions for use in modeling efforts. For these purposes, small-scale changes in bottom type (e.g. small seagrass patches) are not delineated. proprietary +USGS_SOFIA_Fbsaldat Florida Bay Salinity Data CEOS_EXTRA STAC Catalog 1994-11-01 2001-11-30 -81.167, 24.83, -80.33, 25.33 https://cmr.earthdata.nasa.gov/search/concepts/C2231554208-CEOS_EXTRA.umm_json The raw data files contain a point ID, date of collection, salinity values in ppt, and longitude and latitude. For some dates water temperature, time of data collection, and conductivity in millisiemens were recorded. Surface salinity values for Florida Bay are available beginning in November 1994 through November 2001 and bottom salinity values from November 1994 through December 1996. The data are in comma-separated ASCII text files. Recent algal blooms and seagrass mortality have raised concerns about the water quality of Florida Bay, particularly its nutrient content (nitrogen and phosphorous), hypersalinity, and turbidity. Water quality is closely tied to sediment transport processes because resuspension of sediments increases turbidity, releases stored nutrients, and facilitates sediment export to the reef tract. The objective of this research is to provide a better understanding of how and when sediments within Florida Bay are resuspended and deposited, to define the spatial distribution of the potential for resuspension, to delineate patterns of potential bathymetric change, and to predict the impacts of storms or seagrass die-off on bathymetry and circulation within the bay. By combining these results with the findings of other research being conducted in Florida Bay, we hope to quantify sediment export from the bay, better define the nutrient input during resuspension events, and assist in modeling circulation and water quality. Results will enable long-term sediment deposition and erosion in various regions of the bay to be integrated with data on the anticipated sea-level rise to predict future water depths and volumes. Results from this project, together with established sediment production rates, will provide the basis for a sediment budget for Florida Bay. proprietary +USGS_SOFIA_FireHydroSoils Fire, Hydrology, and Soils along the Mangrove Ecotone within the Greater Everglades Ecosystem CEOS_EXTRA STAC Catalog 2010-01-01 2013-12-31 -81.6, 25, -80.1, 27.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231554228-CEOS_EXTRA.umm_json Fire in the south Florida landscape has historically been influential in shaping the ecosystem. The link between hydrology, soil formation, and fire is a critical complex component in the persistence of the biotic components of the Everglades (Smith et al 2003, Beckage 2005). As a result, Everglades National Park has been at the forefront of NPS fire policy development since the inception of the park. It was the first to allow prescribed burns and one of the first to develop a fire management plan (Taylor 1981). The occurrence of invasive exotic plants has confounded the fire regime in Everglades National Park by changing the dynamics of how the vegetation burns. This phenomenon has been observed in mangrove forests especially along ecotones with upland vegetation communities. By examining the association between fire, soil, water and vegetation we can begin to understand the ecology and dynamics of these areas. proprietary +USGS_SOFIA_HAED_WCA_Everglades High Accuracy Elevation Data - Water Conservation Areas and Greater Everglades Region CEOS_EXTRA STAC Catalog 1995-01-01 2007-12-31 -81.5, 25.125, -80.125, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231550369-CEOS_EXTRA.umm_json The High Accuracy Elevation Data Project collected elevation data (meters) on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters to define the topography in South Florida. The data are referenced to the horizontal datum North American Datum 1983 (NAD 83) and the vertical datum North American Vertical Datum 1988 (NAVD 88). In some areas, the surveying was accomplished using airboats. Because access was a logistical problem with airboats, the USGS developed a helicopter-based instrument known as the Airborne Height Finder (AHF). All subsequent data collection used the AHF. Data were collected from the Loxahatchee National Wildlife Refuge, south through the Water Conservation Areas (1A, 2A, 2B, 3A, and 3B), Big Cypress National Park, the Everglades National Park, to the Florida Bay. The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html . The work was performed for Everglades ecosystem restoration purposes. The data are from regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that are being developed for ecosystem restoration activities. Surveying services were also rendered to provide vertical reference points for numerous water level gauges. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) were collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques. proprietary +USGS_SOFIA_HAED_okee High Accuracy Elevation Data - Lake Okeechobee Littoral Zone CEOS_EXTRA STAC Catalog 2006-07-24 2006-10-12 -81.25, 26.625, -80.625, 27.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231552794-CEOS_EXTRA.umm_json The U.S. Geological Survey (USGS) coordinated the acquisition of high accuracy elevation data (meters) for the Lake Okeechobee Littoral Zone collected on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters. The elevations are referenced to the horizontal North American Datum of 1983 (NAD83) and vertical North American Vertical Datum of 1988 (NAVD88). The topographic surveys were performed using differential GPS technology and a USGS developed helicopter-based instrument known as the Airborne Height Finder (AHF). The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html This project performed regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that are being developed for ecosystem restoration activities. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists have determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) are being collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques. proprietary +USGS_SOFIA_HAED_truck High Accuracy Elevation Data - Truck CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 -80.625, 25.375, -80.25, 25.625 https://cmr.earthdata.nasa.gov/search/concepts/C2231548984-CEOS_EXTRA.umm_json The High Accuracy Elevation Data Project collected elevation data (meters) on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters to define the topography in South Florida. The data are referenced to the horizontal datum North American Datum 1983 (NAD 83) and the vertical datum North American Vertical Datum 1988 (NAVD 88). The High Accuracy Elevation Data Project began with a pilot study in FY 1995 to determine if the then state-of-the-art GPS technology could be used to perform a topographic survey that would meet the vertical accuracy requirements of the hydrologic modeling community. The initial testing platform was from a truck and met the accuracy requirements. Data were collected in areas near Homestead, Florida. The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html These data are from topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that were being developed for ecosystem restoration activities. Surveying services were also rendered to provide vertical reference points for numerous water level gauges. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists have determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) were collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques. proprietary +USGS_SOFIA_Hg_DOC_fy04 Interactions of Mercury with Dissolved Organic Carbon in the Florida Everglades CEOS_EXTRA STAC Catalog 1995-03-01 2006-12-31 -80.89114, 25.597273, -80.10298, 26.78571 https://cmr.earthdata.nasa.gov/search/concepts/C2231551732-CEOS_EXTRA.umm_json This project is designed to more clearly define the factors that control the occurrence, nature, and reactivity of dissolved organic matter (DOM) in the Florida Everglades, especially with regard to the biological transformation and accumulation of mercury (Hg). The primary objectives of our research are (1) to more clearly define the factors that control the occurrence, nature and reactivity of dissolved organic matter (DOM) in the Florida Everglades, and (2) to quantify the effects of DOM on the transport and reactivity of Hg, especially with regard to the biological transformation and accumulation of mercury (Hg) in the Everglades. To meet these objectives, we have adopted a combined field/ laboratory approach. In conjunction with other research projects our field efforts are designed (1) to characterize DOM at a variety of field locations chosen to provide information about the influences of hydrology, seasonal factors (wetting and drying events) and source materials (e.g. vegetation, periphyton, peat) on the nature and amount of DOM in the system, and (2) to elucidate the roles of DOM in controlling the reactivity and bioavailability of Hg in the Everglades. This research is relevant because of the high natural production of organic carbon in the peat soils and wetlands, the relatively high carbon content of shallow ground water systems in the region, the interactions of organic matter with other chemical species, such as trace metals, divalent cations, mercury, and anthropogenic compounds, the accumulation of organic carbon in corals and carbonate precipitates, and the potential changes in the quality and reactivity of dissolved organic carbon (DOC) resulting from land use and water management practices. Proposed attempts to return the Everglades to more natural flow conditions will result in changes to the current transport of organic matter from the Everglades Agricultural Area and the northern conservation areas to Florida Bay. In addition, the presence of dissolved organic matter is important in the production of drinking water, contributes to pollutant transport, and will influence ASR performance. Finally, interactions of mercury (Hg) with organic matter play important roles in controlling the reactivity, bioavailability and transport of Hg in the Everglades. proprietary +USGS_SOFIA_Hi_res_bathy_FB High-Resolution Bathymetry of Florida Bay CEOS_EXTRA STAC Catalog 1889-01-01 1999-12-31 -81.11667, 24.733334, -80.36667, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231552903-CEOS_EXTRA.umm_json The objective of this research was to collect new bathymetry for all of Florida Bay, digitize the historical shoreline and bathymetric data, compare previous data to modern data, and produce maps and digital grids of historical and modern bathymetry. Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay had not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. An accurate, modern bathymetric survey provides a baseline for assessing future sedimentation rates in the Bay, and a foundation for developing a sediment budget. Due to the complexity of the Bay and age of existing data, a current bathymetric grid (digitally derived from the survey) is critical for numerical models. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Application of these models is complicated due to the complex seafloor topography (basin/mudbank morphology) of the Bay. The only complete topography data set of the Bay is 100 years old. Consequently, an accurate, modern seafloor bathymetry map of the Bay is critical for numerical modeling research. A modern bathymetry data set will also permit a comparison to historical data in order to help access sedimentation rates within the Bay. proprietary +USGS_SOFIA_IMMAGE IMMAGE -Internet-based Modelling, Mapping, and Analysis for the Greater Everglades CEOS_EXTRA STAC Catalog 1996-01-01 2015-12-31 -81.65, 25, -80, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231551660-CEOS_EXTRA.umm_json IMMAGE will develop a coupled GIS-enabled web-based decision support (DS) framework to provide interactive model-based scenarios to evaluate the potential impact of sea level rise on water supply, inland flooding, storm surge, and habitat management in South Florida. The DS framework will be developed to allow scientists, local planners and resource managers to evaluate the impact of sea level rise on: 1. salt water intrusion into coastal water well fields, 2. the optimal use of canals to impede the inland movement of saline groundwater, 3. urban flooding, 4. the risk to populated areas and natural habitat from catastrophic storm surge, 5. wetland inundation periods and depths, 6. habitat suitability, 7. magnitude and distribution of future population growth, and 8. the impact of forecasted population growth on water demand and protected areas. The IMMAGE project will address the need to run the model with changing input parameters by developing a framework of online GIS-based interfaces to four selected models, thereby enhancing their usability and making them available to a broader user community. proprietary +USGS_SOFIA_L-31NSeep_Pilot L-31N Seepage Management Pilot CEOS_EXTRA STAC Catalog 2003-01-01 2004-12-31 -80.5, 25.6, -80.4, 25.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231554708-CEOS_EXTRA.umm_json The objectives of this data acquisition project were to provide the on-site geologic expertise while the four coreholes and the deepest well in each cluster are drilled and constructed, and to complete the downhole geophysical logging including video and flow meter logging of these boreholes. The USGS on-site geologist provided technical guidance to the drill crew, described the lithology of the core and unconsolidated sediments, and stored the cores and sediment samples for the duration of the project. USGS staff provided and ran gamma ray, fluid conductivity and temperature, EM-induction, 3-arm caliper, full wave form sonic tools, a heat-pulse flow meter, an OBI-40 Mark III digital optical tool, and the Laval video tool. The goal of the L-31N Seepage Management Pilot Project is to reduce levee seepage that moves from Everglades National Park to the east. As participants in this pilot project, the South Florida Water Management District, the United States Army Corps of Engineers, and the United States Geological Survey worked together to provide a hydrogeologic characterization of the Surficial aquifer underlying the L-31N Levee in Miami-Dade County, Florida. Because there is little detailed hydrogeologic data of the Surficial aquifer (to depth) in this area, the L-31N Seepage Management Project Delivery Team (PDT) installed two clusters of monitor wells and four additional coreholes along the levee to provide the necessary detailed hydrogeologic data to the PDT. The L-31N project was spilt into two seperate projects, a data acquisition project and an interpretive project. The objectives of the data acquisition project was to provide the on-site geologic expertise while the four coreholes and the deepest well in each cluster were drilled and constructed, and to complete the downhole geophysical logging including video and flow meter logging of these boreholes. The objective of the data interpretation project was to provide the PDT with detailed hydrogeologic information in order to understand the movement of water in the Surficial aquifer along the L-31N levee and delineate the lithology and hydrostratigraphy of the rocks and sediments underlying the levee. proprietary +USGS_SOFIA_L-31N_wells_data L-31N Lithological and Geophysical Data CEOS_EXTRA STAC Catalog 2003-01-01 2004-12-31 -80.5, 25.6, -80.4, 25.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231554608-CEOS_EXTRA.umm_json The objectives of this data acquisition project were to provide the on-site geologic expertise while the four coreholes and the deepest well in each cluster were drilled and constructed, and to complete the downhole geophysical logging including video and flow meter logging of these boreholes. The USGS on-site geologist provided technical guidance to the drill crew, described the lithology of the core and unconsolidated sediments, and stored the cores and sediment samples for the duration of the project. USGS staff provided and ran gamma ray, fluid conductivity and temperature, EM-induction, 3-arm caliper, full wave form sonic tools, a heat-pulse flow meter, an OBI-40 Mark III digital optical tool, and the Laval video tool. The goal of the L-31N Seepage Management Pilot Project is to reduce levee seepage that moves from Everglades National Park to the east. As participants in this pilot project, the South Florida Water Management District, the United States Army Corps of Engineers, and the United States Geological Survey worked together to provide a hydrogeologic characterization of the Surficial aquifer underlying the L-31N Levee in Miami-Dade County, Florida. Because there is little detailed hydrogeologic data of the Surficial aquifer (to depth) in this area, the L-31N Seepage Management Project Delivery Team (PDT) installed two clusters of monitor wells and four additional coreholes along the levee to provide the necessary detailed hydrogeologic data to the PDT. The L-31N project was spilt into two seperate projects, a data acquisition project and an interpretive project. The objectives of the data acquisition project was to provide the on-site geologic expertise while the four coreholes and the deepest well in each cluster were drilled and constructed, and to complete the downhole geophysical logging including video and flow meter logging of these boreholes. The objective of the data interpretation project was to provide the PDT with detailed hydrogeologic information in order to understand the movement of water in the Surficial aquifer along the L-31N levee and delineate the lithology and hydrostratigraphy of the rocks and sediments underlying the levee. proprietary +USGS_SOFIA_LOX_NWR_data Loxahatchee National Wildlife Refuge Data CEOS_EXTRA STAC Catalog 1974-06-01 2002-10-31 -81.55, 25.11, -80.125, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231552557-CEOS_EXTRA.umm_json The Loxahatchee National Wildlife Refuge (LOX) is a water-dominated ecosystem that is susceptible to water-quality impacts. A comprehensive analysis of historical water-quality and ancillary data is needed to direct the restoration of the Everglades and the adoption of water-quality standards in LOX because of its designation as Outstanding Florida Waters. Loxahatchee National Wildlife Refuge (LOX) maintains a separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout the units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data, and dependence on surface water depth and season. Collection and analysis of water-quality samples at LOX was done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. Water-quality data have been collected at 14 internal marsh sites in LOX by the U.S. Fish and Wildlife Service for over 10 years. These samples have been analyzed by SFWMD laboratory. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. The study area was extended into LOX in 2003. proprietary +USGS_SOFIA_LinkingLandAirManagement Linking Land, Air, and Water Management in the Southern Everglades and Coastal Zone to Water Quality and Ecosystem Restoration CEOS_EXTRA STAC Catalog 1994-01-01 2009-12-31 -82, 24.4, -80, 28 https://cmr.earthdata.nasa.gov/search/concepts/C2232411684-CEOS_EXTRA.umm_json "The approaches used will be extensions of previous efforts by the lead investigators, whereby we will enhance our abilities to address land management and ecosystem restoration questions. Major changes implemented in this project will include the use of environmental chambers (controlled enclosures or mesocosums) and isotopic tracers to provide a more definitive means addressing specific management questions, such as ""What reductions in toxicity (methylation and bioaccumulation) would be realized if atmospheric mercury emissions were reduced by 75%?"" or, ""Over what time scales could we expect to see improvements to the ecosystem if nutrient and sulfur loading were reduced by implementation of agricultural best management practices and the storm water treatment areas (STA)?"" Results of these geochemical investigations will provide critical elements for building ecosystem models and screening-level risk assessment for contaminants in the ecosystem" proprietary +USGS_SOFIA_LinkingLandAirManagement_Task1 Linking Land, Air, and Water Management in the Southern Everglades and Coastal Zone to Water Quality and Ecosystem restoration: Task 1, Mercury Cycling, Fate and Bioaccumulation CEOS_EXTRA STAC Catalog 2001-01-01 2007-12-31 -81.33137, 24.67165, -80.22201, 25.890877 https://cmr.earthdata.nasa.gov/search/concepts/C2231548635-CEOS_EXTRA.umm_json This proposal identifies work elements that are logical extensions, and which build off, our previous work. Our overall scientific objective is to provide a complete understanding of the external factors (such as atmospheric mercury and sulfate runoff loads) and internal factors (such as hydroperiod maintenance and water chemistry) that result in the formation and bioaccumulation of MeHg in south Florida ecosystems, and to conduct this research is such a way that it will be directly useable by land and water resource managers. More specifically, we will seek to achieve the following subobjectives (1) Extend our mesocosms studies to provide a more omprehensive examination of the newly discovered 'new versus old' mercury effect by conducting studies under differing hydrologic conditions and sub-ecosystem settings so that our experimental results will be more generally applicable to the greater south Florida ecosystem including the STA’s that have been recently constructed and are yielding very high levels of methylmercury but the cause is currently unknown; (2) Seek to further identify the mechanisms that result in extremely high levels of MeHg after natural drying and rewetting cycles in the Everglades and which have major implications for the Restoration Plan; (3) Further our studies on the production of methylmercury in south Florida estuaries and tidal marshes by conducting mass-balance studies of tidal marshes; (4) Begin to partner with wildlife toxicologists funded by the State of Florida to unravel the complexities surrounding methylmercury exposure and effects to higher order wildlife in south Florida; and , (5) Continue to participate with mercury ecosystem modelers who are funded by the State of Florida and the USEPA to evaluate the overall ecological effects of reducing mercury emissions and the risks associated with methylmercury exposure. proprietary +USGS_SOFIA_LinkingLandAirManagement_Task2 Linking Land, Air and Water Management in the Southern Everglades and Coastal Zone to Water Quality and Ecosystem Restoration: Task 2, Sulfur and Nutrient Contamination, Biogeochemical Cycling, and Effects CEOS_EXTRA STAC Catalog 2000-01-01 2007-09-30 -82, 24.4, -80.1, 28 https://cmr.earthdata.nasa.gov/search/concepts/C2231549234-CEOS_EXTRA.umm_json The scientific focus of this project is to examine the complex interactions (synergistic and antagonistic) of contaminants (externally derived nutrients, mercury, sulfur, pesticides, herbicides, polycyclic aromatic and aliphatic hydrocarbons, and other metals), ecosystem responses to variations in contaminant loading (time and space dimensions), and how imminent ecosystem restoration steps may affect existing contaminant pools. The major objectives of this project are to use an integrated biogeochemical approach to examine: (1) anthropogenic-induced changes in the water chemistry of the Everglades ecosystem, (2) biogeochemical processes within the ecosystem affecting water chemistry, and (3) the predicted impacts of restoration efforts on water chemistry. The project uses a combination of field investigations, experimental approaches (mesocosm experiments in the ecosystem, and controlled laboratory experiments), and modeling to achieve these objectives. Contaminants of concern will include nutrients, sulfur, mercury, organic compounds, and other metals. Protocols for the collection of samples and chemical analyses developed during earlier studies will be employed in these efforts. Integration of the individual tasks within the project is achieved by colocation of field sampling sites, and cooperative planning and execution of laboratory and mesocosm experiments. Data available for this project include dissolved sulfate and solid sulfur geochemistry and surface and pore water chemistry. proprietary +USGS_SOFIA_LinkingLandAirManagement_Task3 Linking Land, Air and Water Management in the Southern Everglades and Coastal Zone to Water Quality and Ecosystem Restoration: Task 3, Natural Organic Matter-Mercury Interactions CEOS_EXTRA STAC Catalog 2006-01-01 2010-12-31 -81.5, 25, -80, 27 https://cmr.earthdata.nasa.gov/search/concepts/C2231549144-CEOS_EXTRA.umm_json This Task (Task 3 of the overall study) focuses on the factors that control the occurrence, nature and reactivity of dissolved organic matter (DOM) in the Florida Everglades, especially with regard to the biological transformation and accumulation of mercury (Hg). Our goal is to provide fundamental information on the nature and reactivity of DOM in the Everglades and to elucidate the mechanisms and pathways by which the DOM influences the chemistry of Hg throughout the system. proprietary USGS_SOFIA_Mangrove_Sawfish Characterizing Past and Present Mangrove Shorelines to Aid Conservation of the Smalltooth Sawfish, Pristis pectinata, Along the Southwest Coast of Florida CEOS_EXTRA STAC Catalog 2008-01-01 2010-12-31 -82.16, 25.3, -80.8, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231550180-CEOS_EXTRA.umm_json This pilot project has several related goals concerning a specific type of habitat thought to be important for juvenile sawfish habitat: mangrove shorelines. First, we will delineate and classify historic mangrove shorelines. Second, we will map and classify current mangrove shorelines. Third, we will determine amounts of shoreline change. Lastly, we will conduct an analysis to compare sawfish sightings and / or captures with the type of shoreline where those sightings-captures occurred. This will allow us to answer the question: Are juvenile sawfish selecting for a specific type of mangrove shoreline, and if so, what type of mangrove shoreline is it? proprietary +USGS_SOFIA_MeHg_degrad_rates Methylmercury Degradation Rates CEOS_EXTRA STAC Catalog 1996-06-01 1998-06-01 -81, 25, -80, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231551413-CEOS_EXTRA.umm_json The spreadsheet contains the data for 12 sites for sediment methylmercury degradation potential rate measurements. High concentrations of methyl-mercury (CH3Hg+), a toxic substance to both animals and humans, recently have been measured in a number of top predators (including panthers and game fish) native to the Florida Everglades. The objective of this research was to provide ecosystem managers with CH3Hg+ degradation rate data from a number of study sites that represent a diversity of hydrologic and nutrient regimes common to the Everglades. The focus was on better understanding the microbial and geochemical controls regulating CH3Hg+ degradation. At the time of the study, little was known about the specific factors influencing this process in natural systems. proprietary USGS_SOFIA_SF_CIR_DOQs Color Infrared Digital Orthophoto Quadrangles for the South Florida Ecosystem Area CEOS_EXTRA STAC Catalog 1994-01-01 1999-12-31 -82.2, 24.6, -80.1, 27.8 https://cmr.earthdata.nasa.gov/search/concepts/C2231553946-CEOS_EXTRA.umm_json The digital orthophoto quadrangles (DOQ's) produced by the USGS for the South Florida Ecosystem Initiative iare color-infrared, 1-meter ground resolution quadrangle images covering 3.75 minutes of latitude by 3.75 minutes of longitude at a map scale of 12,000. Orthophotos combine the image characteristics of a photograph with the geometric qualities of a map. The primary digital orthophotoquadrangle (DOQ) is a 1-meter ground resolution, quarter-quadrangle (3.75 minutes of latitude by 3.75 minutes of longitude) image cast on the Universal Transverse Mercator projection (UTM) on the North American Datum of 1983 (NAD83). The geographic extent of the DOQ is equivalent to a quarter-quadrangle plus the overedge ranges from a minimum of 50 meters to a maximum of 300 meters beyond the extremes of the primary and secondary corner points. The overedge is included to facilitate tonal matching for mosaicking and for the placement of the NAD83 and secondary datum corner ticks. The normal orientation of data is by lines (rows) and samples (columns). Each line contains a series of pixels ordered from west to east with the order of the lines from north to south. The radiometric image brightness values are stored as 256 gray levels, ranging from 0 to 255. The standard, uncompressed gray scale DOQ format contains an ASCII header followed by a series of 8-bit image data lines. The keyword-based, ASCII header may vary in the number of data entries. The header is affixed to the beginning of the image and is composed of strings of 80 characters with an asterisk (*) as character 79 and an invisible newline character as character 80. Each keyword string contains information for either identification, display, or registration of the image. Additional strings of blanks are added to the header so that the length of a header line equals the number of bytes in a line of image data. The header line will be equal in length to the length of an image line. If the sum of the byte count of the header is less than the sample count of one DOQ image line, then the remainder of the header is padded with the requisite number of 80 character blank entries, each terminated with an asterisk and newline character. The objective of this project was to provide color infrared (CIR) digital orthophoto coverage for the entire south Florida ecosystem area. The main advantage of a digital orthophoto is that it gives a measurable image free of distortion. Therefore, the digital orthophotos for the ecosystem provide multi-use base images for identifying natural and manmade features and for determining their extent and boundaries; the images can also be used for the interpretation and classification of these areas. proprietary USGS_SOFIA_SnailKites_AppleSnails Comprehensive Monitoring Plan for Snail Kites and Apple Snails in the Greater Everglades CEOS_EXTRA STAC Catalog 2010-01-01 2015-12-31 -81.6, 25, -80.6, 27.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231554049-CEOS_EXTRA.umm_json The endangered snail kite (Rostrhamus sociabilis) is a wetland-dependent raptor feeding almost exclusively on a single species of aquatic snail, the Florida apple snail (Pomacea paludosa). The viability of the kite population is dependent on the hydrologic conditions (both short-term and long-term) that (1) maintain sufficient abundances and densities of apple snails, and (2) provide suitable conditions for snail kite foraging and nesting, which include specific vegetative community compositions. Many wetlands comprising its range are no longer sustained by the natural processes under which they evolved (USFWS 1999, RECOVER 2005), and not necessarily characteristic of the historical ecosystems that once supported the kite population (Bennetts and Kitchens 1999, Martin et al. 2008). Natural resource managers currently lack a fully integrative approach to managing hydrology and vegetative communities with respect to the apple snail and snail kite populations. At this point in time the kite population is approximately 1,218 birds (Cattau et al 2012), down from approximately 4000 birds in 1999. It is imperative to improve our understanding hydrological conditions effecting kite reproduction and recruitment. Water Conservation area 3-A, WCA3A, is one of the 'most critical' wetlands comprising the range of the kite in Florida (see Bennetts and Kitchens 1997, Mooij et al. 2002, Martin et al. 2006, 2008). Snail kite reproduction in WCA3A sharply decreased after 1998 (Martin et al. 2008), and alarmingly, no kites were fledged there in 2001, 2005, 2007, or 2008. Bowling (20098) found that juvenile movement probabilities away (emigrating) from WCA3A were significantly higher for the few kites that did fledge there in recent years (i.e. 2003, 2004, 2006) compared to those that fledged there in the 1990s. The paucity of reproduction in and the high probability of juveniles emigrating from WCA3A are likely indicative of habitat degradation (Bowling 20098, Martin et al. 2008), which may stem, at least in part, from a shift in water management regimes (Zweig and Kitchens 2008). Given the recent demographic trends in snail kite population, the need for a comprehensive conservation strategy is imperative; however, information gaps currently preclude our ability to simultaneously manage the hydrology in WCA3A with respect to vegetation, snails, and kites. While there have been significant efforts in filling critical information gaps regarding snail kite demography (e.g., Martin et al. 2008) and variation in apple snail density to water management issues (e.g., Darby et al. 2002, Karunaratne et al. 2006, Darby et al. 2008), there is surprisingly very little information relevant for management that directly links variation in apple snail density with the demography and behavior of snail kites (but see Bennetts et al. 2006). The U.S. Fish and Wildlife Service (USFWS), the U. S. Army Corps of Engineers, and the Florida Fish and Wildlife Conservation Commission (FWC) have increasingly sought information pertaining to the potential effects of specific hydrological management regimes with respect to the apple snail and snail kite populations, as well as the vegetative communities that support them. proprietary USGS_SOFIA_YY_Males Development of YY male technology to control non-native fishes in the Greater Everglades CEOS_EXTRA STAC Catalog 2009-10-01 -81, 25, -80, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231552421-CEOS_EXTRA.umm_json Dozens of non-native fish species have established throughout south Florida (including Everglades National Park, Big Cypress National Preserve, Biscayne National Park and various state and private lands). Thus far, research on these species has focused on documenting their distributions, natural history, and physiological tolerances. Research is beginning to emerge on interactions of native species with non-natives, although it is only in the early stages. Research on control of non-native fishes in South Florida is also lacking, although it is potentially the most important and useful to natural resource managers. At present, the only management techniques available to control non-native fishes are physical removal, dewatering or ichthyocides. Unfortunately, all of these methods negatively impact native fauna as well as the targeted non-native fishes and require a great deal of effort (and therefore, funding). Herein, we propose a research program focused on applying a genetic technique common in aquaculture to control of non-native fishes. This proposal focuses on developing a technique (YY supermales) to control a non-native fish in South Florida (African jewelfish Hemichromis letourneuxi). However, the concept can be applied to a wide variety of species, including other fishes (e.g., brown hoplo Hoplosternum littorale), invasive applesnails (Pomacea spp.), the Australian red claw crayfish (Cherax spp.) and the green mussel (Perna veridis). proprietary @@ -14151,8 +14672,13 @@ USGS_SOFIA_atlss_prog Across Trophic Level System Simulation (ATLSS) Program CEO USGS_SOFIA_avian_ecology_spoonbills Avian Ecology of the Greater Everglades (Roseate Spoonbill and Limpkins) CEOS_EXTRA STAC Catalog 2002-10-01 2005-09-30 -81.25, 24.875, -80.375, 25.375 https://cmr.earthdata.nasa.gov/search/concepts/C2231549705-CEOS_EXTRA.umm_json "The primary objectives of our research are to (1) quantify the changes in spatial distribution and success of nesting spoonbills relative to hydrologic patterns, (2) test hypotheses about the causal mechanisms for observed changes, (3) establish a science-based criteria for nesting distribution and success to be used as a performance measure for hydrologic restoration, and (4) estimate demographic parameters. To meet these objectives, we will use a combined field/modeling approach. Based on previous and concurrent research, hypothesized relationships between hydrology, fish populations, and spoonbill nesting distribution and success will be expressed in a simple, but spatially explicit, conceptual model. Field data will be collected and compared with predicted responses to monitor changes in spoonbill nesting as hydrologic restoration is implemented, and to test the hypothesized mechanisms for observed changes. Variation of hydrologic conditions among years and locations is a virtual certainty; thus we will treat this variation in a quasi-experimental framework where the variation in wet and dry season conditions constitutes a series of ""natural experiments"". Our project is designed to evaluate the effect of hydrologic restoration on the nesting distribution and success of Roseate Spoonbills (Ajaia ajaia) in Florida Bay and surrounding mangrove estuarine habitats. This project is further designed to test hypotheses about the causal mechanisms of observed changes. The Everglades ecosystem has suffered extensive degradation over the past century, including an 85-90% decrease in the numbers of wading birds. Previous monitoring of Roseate Spoonbills in Florida Bay over the past 50 years has shown that this species responds markedly to changes in hydrology and corresponding changes in prey abundance and availability. Shifts in nesting distribution and declines in nest success have been attributed to declines in prey populations as a direct result of water management. Consequently, the re-establishment of spoonbill colonies in northeast Florida Bay is one change predicted under a conceptual model of the mangrove estuarine transition zone of Florida Bay. Changes in nesting distribution and success will further be used as a performance measure for success of restoration efforts and will be incorporated in a model linking mangrove fish populations and spoonbills to alternative hydrologic scenarios." proprietary USGS_SOFIA_ba_geologic_data Biscayne Aquifer geologic data CEOS_EXTRA STAC Catalog 1998-01-01 2005-12-31 -80.6, 25.5, -80.3, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231550961-CEOS_EXTRA.umm_json This report from which the data is taken identifies and characterizes candidate ground-water flow zones in the upper part of the shallow, eogenetic karst limestone of the Biscayne aquifer using GPR, cyclostratigraphy, borehole geophysical logs, continuously drilled cores, and paleontology. About 60 mi of GPR profiles were acquired and are used to calculate the depth to shallow geologic contacts and hydrogeologic units, image karst features, and produce a qualitative perspective of the porosity distribution within the upper part of the karstic Biscayne aquifer in the Lake Belt area of north-central Miami-Dade County. . Descriptions of lithology, rock fabric, cyclostratigraphy, and depositional environments of 50 test coreholes were linked to geophysical data to provide a more refined hydrogeologic framework for the upper part of the Biscayne aquifer. Interpretation of depositional environments was constrained by analysis of depositional textures and molluscan and benthic foraminiferal paleontology. Digital borehole images were used to help quantify large-scale vuggy porosity. Preliminary heat-pulse flowmeter data were coupled with the digital borehole image data to identify potential ground-water flow zones. The objectives of this cooperative project were to identify and characterize candidate ground-water flow zones in the upper part of the shallow, eogenetic karst limestone of the Biscayne aquifer using ground-penetrating radar, cyclostratigraphy, borehole geophysical logs, continuously drilled cores and paleontology. In 1998, the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District (SFWMD), initiated a study to provide a regional-scale hydrogeologic framework of a shallow semiconfining unit within the Biscayne aquifer of southeastern Florida. Initially, the primary objective was to characterize and delineate a low-permeability zone in the upper part of the Biscayne aquifer that spans the base of the Miami Limestone and uppermost part of the Fort Thompson Formation. Delineation of this zone was to aid development of a conceptual hydrogeologic model to be used as input into the SFWMD Lake Belt ground-water model. The approximate area encompassed by the conceptual hydrogeologic model is shown as the study area at http://sofia.usgs.gov/exchange/cunningham/bbwelllocation.html. Subsequent analysis of the preliminary data suggested hydraulic compartmentalization occurred within the Biscayne aquifer, and that there was a need to characterize and delineate ground-water flow zones and relatively low-permeability zones within the upper part of the Biscayne aquifer. Consequently, preliminary results suggested that the historical understanding of the porosity and preferential pathways for Biscayne aquifer ground-water flow required considerable revision. This project was carried out in cooperation with the South Florida Water Management District (SFWMD). proprietary USGS_SOFIA_bbcw_geophysical Biscayne Bay Coastal Wetlands Geophysical Data CEOS_EXTRA STAC Catalog 2004-01-01 -80.4, 25.4, -80.3, 25.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231549059-CEOS_EXTRA.umm_json The objectives of this data acquisition project were to complete the downhole geophysical logging including video and flowmeter logging of two core holes (9A and 11A), which are the deepest wells at monitor well sites 0009AB and 0011AB. The goal of the Comprehensive Everglades Restoration Plan Biscayne Bay Coastal Wetlands Project (BBCWP) is to rehydrate wetlands and reduce point-source discharge to Biscayne Bay. The BBCWP will replace lost overland flow and partially compensate for the reduction in ground-water seepage by redistributing, through a spreader system, available surface water entering the area from regional canals. The proposed redistribution of freshwater flow across a broad front is expected to restore or enhance freshwater wetlands, tidal wetlands, and near shore bay habitat. A critical component of the BBCWP is the development of a realistic representation of ground-water flow within the karst Biscayne aquifer. Mapping these ground-water flow units is key to the development of models that simulate ground-water flow from the Everglades and urban areas through the coastal wetlands to Biscayne Bay. Because there is little detailed hydrogeologic data of the Surficial aquifer (to depth) in this area, the Biscayne Bay Coastal Wetlands Project Delivery Team installed two monitor-well sites and collected the necessary detailed hydrogeologic data. The L-31 North Canal Seepage Management Pilot Project is intended to curtail easterly seepage emanating from within Everglades National Park (ENP). The pilot project is examining various seepage management technologies as well as operational changes that could be implemented to reduce the water losses from ENP. This project is in close proximity to Biscayne Bay so an effort has been made to combine ongoing work efforts at the two project areas. The distribution of seepage into the L-31 North Canal and beneath it is not known with any degree of certainty today. A canal draw down experiment was conducted to provide additional field data that will be utilized to refine seepage estimates in the study area as well as determine aquifer parameters in the study area. This project was funded by the USGS Florida Integrated Science Center and the South Florida Water Management District (SFWMD). proprietary +USGS_SOFIA_bcunits_pts_point Hydrogeologic unit depth information sites in Broward County and northern Dade County, WRIR 92-4061 figure 3.4.3-1 CEOS_EXTRA STAC Catalog 1987-01-01 2002-12-31 -80.87278, 25.84111, -80.07235, 26.240347 https://cmr.earthdata.nasa.gov/search/concepts/C2231554686-CEOS_EXTRA.umm_json Hydrogeologic unit depths at 321 selected points, determined from published cross sections and contour maps, were entered into a point data layer. Generalized land-surface elevations were also entered for each point. Geographic information systems (GIS) have become an important tool in assessing and planning for the protection of natural resources. Most Federal and State natural resource agencies and many County environmental agencies in Florida are currently using GIS to assist in mathematical modeling, resource mapping, and risk assessments. The U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District (SFWMD) and the Broward County Office of Natural Resource Protection (BCONRP), developed a digital spatial data base for Broward County consisting of layers of data that can be used in water-resources investigations. These data layers include manmade features such as municipal boundaries and roads, topographic features, hydrologic features such as canals and lakes, and hydrogeologic features such as aquifer thickness. Computer programs were written for use in developing additional layers of data from existing data bases such as the Florida Department of Environmental Regulation (FDER) underground storage tank data base. This report describes the digital spatial data base that was developed and the five computer programs that can be used to create additional data layers from existing data files or to document existing layers. Most of the data layers cover Broward County east of the conservation areas. Some data layers cover all of Broward and may include parts of Miami-Dade County. proprietary USGS_SOFIA_bicy_fish_inventory Big Cypress National Preserve Fish Inventory and Monitoring Data CEOS_EXTRA STAC Catalog 2002-10-01 2004-12-31 -81.5, 25.75, -80.75, 26.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231555020-CEOS_EXTRA.umm_json The Big Cypress national Preserve Fish Inventory database contains records of the inventory of freshwater fishes of the Big Cypress National Preserve (BICY) conducted by the National Audubon Society's Tavernier Science Center as part of the National Park Service (NPS) Inventory and Monitoring Project. The database includes data from October 2002 through April 2004. The Big Cypress National Preserve Fish Monitoring and Assessment data collections for aquatic animals from BICY were begun in July 2004. The spreadsheet contains worksheets for Raccoon Point, L28, and Bear Island. Although a major ecosystem of the South Florida area, the Big Cypress National Preserve (BICY), is poorly understood in biological terms. To detect changes in natural and artificial habitats resulting from Comprehensive Everglades Restoration Plan (CERP) restoration programs, baseline data on constituent aquatic communities and their ecology are needed before and after restoration actions. Fishes and aquatic invertebrates serve as indicators of the health of these wetlands. These organisms are also important because they are major prey for many of the characteristic South Florida predatory species, especially alligators and wading birds. This project has several objectives, the foremost of which is to continue a program of aquatic study in BICY begun in 2002. Work will be performed in partnership with National Audubon Society (NAS) and the National Park Service to design and implement a spatially and temporally explicit, quantitative sampling program for aquatic animals in BICY. This program will 1) provide baseline data which may be used to track changes in hydrology as a result of CERP projects 2) document the distribution, composition, and habitat use by native and introduced aquatic animals to evaluate the effects of CERP on BICY aquatic habitats, and 3) provide ecological data for use in the ATLSS fish simulation model used to plan and evaluate restoration actions during CERP (presently, inappropriate data from the Everglades are being used in the model for cells that lie in BICY). The strategy used to accomplish these goals will be to employ techniques used by the co-principal investigators in establishing monitoring programs in the Everglades (since 1977) and the mangrove zone of Florida Bay (since 1989). proprietary USGS_SOFIA_brwd_biscayne_limit_west_arc Approximate Western and Northern Limit of the Biscayne Aquifer in Broward County, USGS WRIR 87-4034, figure 37 CEOS_EXTRA STAC Catalog 1939-01-01 1984-12-31 -80.78596, 25.978994, -80.27207, 26.335 https://cmr.earthdata.nasa.gov/search/concepts/C2231553365-CEOS_EXTRA.umm_json The approximate western and northern limits of the Biscayne aquifer are shown in this map. The limit is drawn where the thickness of very highly permeable limestone or calcareous sandstone is estimated to decrease to less than 10 feet. The sediments in the excluded area are predominantly muddy sands and shell or limestone that are generally not highly permeable. Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses. proprietary +USGS_SOFIA_brwd_config_base_biscayne_arc Generalized Configuration of the Base of the Biscayne Aquifer in Broward County, USGS WRIR 87-4034, figure 37 CEOS_EXTRA STAC Catalog 1939-01-01 1984-12-31 -80.73605, 25.953321, -80.094734, 26.334244 https://cmr.earthdata.nasa.gov/search/concepts/C2231550736-CEOS_EXTRA.umm_json The base of the Biscayne aquifer are shown in this map. The base is drawn on the bottom of highly permeable limestone or sandstone in the Tamiami Formation that is virtually contiguous with overlying rocks of very high permeability in the Fort Thompson Formation, Anastasia Formation, or Tamiami Formation. In general, the Biscayne aquifer is shallow, and the base deepens gradually in west and central Broward county. However, the aquifer thickens, and the base deepens very rapidly in the coastal area to more than 300 feet below sea level. Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses. proprietary +USGS_SOFIA_brwd_config_base_surficial_arc Generalized Configuration of the Base of the Surficial Aquifer System in Broward County, USGS WRIR 87-4034, figure 35 CEOS_EXTRA STAC Catalog 1939-01-01 1984-12-31 -80.86949, 25.952961, -80.103165, 26.336115 https://cmr.earthdata.nasa.gov/search/concepts/C2231549571-CEOS_EXTRA.umm_json This map shows the altitude of the base of the surficial aquifer system below sea level. In addition to the test holes drilled in this study, eight others from Parker and others (1955) or from the U.S. Geological Survey files were used to select the base. The contour interval is 20 feet. Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses. proprietary +USGS_SOFIA_brwd_glime_altbase_arc Generalized Configuration of the Base of the Gray Limestone Aquifer in Broward County, USGS WRIR 87-4034, figure 36 CEOS_EXTRA STAC Catalog 1939-01-01 1984-12-31 -80.89983, 25.975756, -80.35531, 26.336508 https://cmr.earthdata.nasa.gov/search/concepts/C2231555340-CEOS_EXTRA.umm_json This map contains contours of the base of the highly permeable gray limestone aquifer in the Tamiami Formation. The contour interval is 10 feet. Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses. proprietary +USGS_SOFIA_brwd_glime_alttop_arc Generalized Configuration of the Top of the Gray Limestone Aquifer in Broward County, USGS WRIR 87-4034, figure 36 CEOS_EXTRA STAC Catalog 1939-01-01 1984-12-31 -80.883705, 25.956867, -80.351875, 26.336231 https://cmr.earthdata.nasa.gov/search/concepts/C2231548959-CEOS_EXTRA.umm_json This is map contains contours of the top of the highly permeable gray limestone aquifer in the Tamiami Formation. The contour interval is 10 feet. Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses. proprietary USGS_SOFIA_ccsoil Collier County FL soil map CEOS_EXTRA STAC Catalog 1947-01-01 -81.85, 25.79, -80.85, 26.54 https://cmr.earthdata.nasa.gov/search/concepts/C2231550398-CEOS_EXTRA.umm_json The data sets consist of two files, an ARC/INFO shape file with associated files and an ARC/INFO export file, of a composite of soil maps for Collier County, Florida issued by the Soil Conservation Service in March, 1954. The data is at 1:40,000 scale. Getting geographic information into a form that can be analyzed in a Geographic Information System (GIS) has always been a labor-intensive process. Graphic information was historically captured using variations of manual digitizing techniques. Users either digitized directly from printed materials on digitizing tablets or tables or by a variation of heads-up digitizing from scanned graphics displayed on computer monitors. Data collection involves considerable interaction between the user and a computer to capture and manipulate graphical data into a GIS layers. By using inexpensive image processing software to process and manipulate scanned images before processing these images in the GIS, features can be semi-automatically extracted from the scanned graphics, virtually eliminating the process of manual delineation. Common photo editing techniques combined with GIS expertise can dramatically decrease the time required to collect GIS data layers. The mentioning of specific software brands or registered trademarks does not constitute a commercial endorsement; their mention is done for clarity only. Mention of software products in the description of graphic processing techniques should be viewed as a use of available tools and not a recommendation for a software product. proprietary USGS_SOFIA_ch1999cont_arc Altitude of the base of the surficial aquifer system in eastern Palm Beach, Martin, and St. Lucie counties, Florida, USGS WRIR 99-4214, fig. 1 CEOS_EXTRA STAC Catalog 1997-01-01 1998-12-31 -80.51576, 26.29299, -80.03573, 27.559755 https://cmr.earthdata.nasa.gov/search/concepts/C2231552925-CEOS_EXTRA.umm_json The surficial aquifer system underlies Palm Beach, Martin, and St. Lucie Counties and primarily consists of sand, clay, silt, shell, and limestone of Holocene, Pleistocene, and Pliocene age. Its thickness is variable (decreasing westward and northward) and is estimated to be as much as 400 feet in Palm Beach County (Miller, 1987; Shine and others, 1989), 210 feet in Martin County (Miller, 1980), and 180 feet in St. Lucie County (Miller, 1980).The surficial aquifer system is composed of several stratigraphic units. The maps shows the altitude of the base of the surficial aquifer system in Palm Beach, Martin, and St. Lucie counties in 1997-1998. The contour interval is 40 feet. Urban development in Palm Beach, Martin, and St. Lucie Counties, Florida, has expanded rapidly in recent decades, resulting in a need for additional freshwater withdrawals from the surficial aquifer system - the primary source of drinking water for this tri-county area. Potable-water demand for urban users is projected to increase 115 percent in Palm Beach County and 89 percent each in Martin and St. Lucie Counties from 1990 to 2010 (South Florida Water Management District, 1998).The increased demand on the coastal well fields, which draw water from the surficial aquifer system, may contribute to saltwater intrusion. There are limited data as to the location or movement of the saltwater interface in the tri-county area, with the exception of previously collected data in the immediate vicinity of the existing coastal well fields. It is possible that the combination of pumpage from the well fields and drainage caused by rivers and canals has a regional effect on the saltwater interface. In October 1996, the U.S.Geological Survey (USGS) entered into a cooperative study with the South Florida Water Management District to determine the present location of the interface between freshwater and oceanic saltwater in the surficial aquifer system along the coast of southeastern Florida. This map report documents the position of the saltwater interface in the surficial aquifer system in 1997- 98 through the evaluation of chloride and geophysical data. This map was developed to delineate the base of the surficial aquifer system in the study area. proprietary USGS_SOFIA_chron_isotope_geochem_FL_Keys Chronology and Isotope Geochemistry of Ground Waters in the Florida Keys and Offshore Areas CEOS_EXTRA STAC Catalog 1996-01-01 1998-12-31 -82.2, 24.27, -79.82, 25.69 https://cmr.earthdata.nasa.gov/search/concepts/C2231550525-CEOS_EXTRA.umm_json This project involves sampling surface waters and ground waters from Florida Bay, the Keys, and offshore to the barrier reef. Analyses will be done on a variety of isotopic and chemical species that have been used elsewhere to determine ground-water ages, contaminant sources, and geo- chemical reactions. Water Research Discipline researchers will coordinate ground water sampling and analytical work; Geologic Discipline researchers will provide access to wells and back- ground data, handle field logistics, etc. A significant issue of concern in South Florida is the potential effect of anthropogenic pollutants from the Florida Keys or elsewhere on the water quality and health of offshore marine ecosystems. It has been suggested that certain contaminants (e.g., bacteria, excess nutrients) found in some offshore ground waters may be transported 'in the subsurface to discharge sites beneath Florida Bay or the reef tract, where they may be contributing to declining ecosystem health. But not much is known about the origins of the ground waters underlying the region, how the subsurface flow systems operate, and what is the fate of contaminants emplaced in ground water in the Keys. Ground waters are potential sources, sinks, and carriers of nutrients and other contaminants beneath the Florida Keys and offshore regions to the north and south. This project is designed to provide new data on the sources, flow directions, exchange rates, and chemical characteristics of ground waters underlying the region of Florida Bay, the Keys, and offshore reefs. The results, to be derived in part from analyses of environmental tracers and isotopes, will provide general empirical information about subsurface transport processes and their potential impact on surface water chemistry. proprietary @@ -14165,51 +14691,217 @@ USGS_SOFIA_dade_config_base_biscayne_arc Configuration of the Base of the Biscay USGS_SOFIA_dade_config_base_glime_arc Configuration of the Base of the Gray Limestone Aquifer in Dade County, Fl, USGS WRIR 90-4108, figure 15 CEOS_EXTRA STAC Catalog 1939-01-01 1985-12-31 -80.85567, 25.2942, -80.331, 25.994343 https://cmr.earthdata.nasa.gov/search/concepts/C2231554187-CEOS_EXTRA.umm_json Contours of the altitude below sea level of the base of the highly permeable gray limestone aquifer in the Tamiami Formation are shown in this map. The aquifer, as mapped, includes all intervals of the gray limestone that are at least 10 ft. thick and have an estimated hydraulic conductivity of at least 100ft/d. The contour interval is 10 feet. Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system. proprietary USGS_SOFIA_dade_config_base_surficial_arc Configuration of the Base of the Surficial Aquifer System in Dade County, USGS WRIR 90-4108, figure 13 CEOS_EXTRA STAC Catalog 1939-01-01 1987-12-31 -80.87687, 25.315292, -80.12477, 25.998829 https://cmr.earthdata.nasa.gov/search/concepts/C2231550577-CEOS_EXTRA.umm_json Contours of the base of the surficial aquifer system are shown on this map. The base of the aquifer system occurs at a relatively uniform elevation of 180 to 220 ft. below sea level over most of Dade County. The contour interval is 20 feet. Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system. proprietary USGS_SOFIA_dade_config_top_glime_arc Configuration of the Top of the Gray Limestone Aquifer in Dade County, USGS WRIR 90-4108, figure 14 CEOS_EXTRA STAC Catalog 1939-01-01 1987-12-31 -80.873474, 25.250011, -80.334076, 25.987766 https://cmr.earthdata.nasa.gov/search/concepts/C2231550162-CEOS_EXTRA.umm_json Contours of the elevation of the top of the highly permeable gray limestone aquifer in the Tamiami Formation are shown in this map. The aquifer, as mapped, includes all intervals of the gray limestone that are at least 10 ft. thick and have an estimated hydraulic conductivity of at least 100ft/d. Also included are highly permeable beds of coarse, shelly sands (sometimes with sandstone) that are contiguous with limestone above or below or are likely to connect laterally with the limestone. The contour interval is 10 feet. Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system. proprietary +USGS_SOFIA_dawmet Ecosystem History: Terrestrial and Fresh-Water Ecosystems of southern Florida CEOS_EXTRA STAC Catalog 1994-01-01 2007-12-31 -81.83, 25, -80.3, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231553647-CEOS_EXTRA.umm_json Analysis of 209 pollen assemblages from surface samples in ten vegetation types in the Florida Everglades form the basis to identify wetland sub-environments from the pollen record. This calibration dataset makes it possible to infer past trends in hydrology and disturbance regime based on pollen assemblages preserved in sediment cores. Pollen assemblages from sediment cores collected in different vegetation types throughout the Everglades provide evidence on wetland response to natural fluctuations in climate as well as impacts of human alteration of Everglades hydrology. Sediment cores were located primarily in sawgrass marshes, cattail marshes, tree islands, sawgrass ridges, sloughs, marl prairies, and mangroves. The datasets contain raw data on pollen abundance as well as pollen concentration (pollen grains per gram dry sediment). This project is designed to document the terrestrial ecosystem history of south Florida and is collaborating with other projects at the USGS and other agencies on Florida Bay, Biscayne Bay, and the Buttonwood Embankment. The specific goals of the project are 1) document the patterns of floral and faunal change at sites throughout southern Florida over the last 150 years; 2) determine whether changes occurred throughout the entire region or whether they were localized; 3) examine the floral and faunal history of the region over the last few millennia; 4) determine the baseline level of variability in the communities prior to significant human activity in the region; and 5) determine whether the fire frequency, extent, and influence can be quantified, and if so, document the fire history for sites in the region. Data generated from this project will be integrated with data from other projects to provide biotic reconstructions for the area at selected time slices and will be useful in testing ecological models designed to predict floral and faunal response to changes in environmental parameters. proprietary +USGS_SOFIA_discharge_tamiami_canal Discharge Data (Tamiami Canal) CEOS_EXTRA STAC Catalog 1986-01-01 2001-12-31 -81.5, 25.75, -80.5, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231553115-CEOS_EXTRA.umm_json The data are from the following four stations: Station 02288800 - Tamiami Canal Outlets, Monroe to Carnestown; Station 02288900 - Tamiami Canal Outlets, 40-Mile Bend to Monroe, near Miami, FL; Station 02289040 - Tamiami Canal Outlets, Levee 67A to 40-Mile Bend, near Miami, FL; Station 02289060 - Tamiami Canal Outlets, Levee 30 to Levee 67A, near Miami, FL. The data were compiled from records from 1986 to 1999 in the USGS Ft. Lauderdale, FL office of the Water Resources Discipline in 2000. Each station has numerous individual flow measurements at gages that were used in the calculation of the mean flow for each station. The data were collected by USGS personnel and the gages are maintained and operated by USGS Ft. Lauderdale office personnel. Canals are a major water-delivery component of the south Florida ecosystem. They interact with surrounding flow systems and waterbodies, either directly through structure discharges and levee overflows or indirectly through levee seepage and leakage, and thereby quantitatively affect wetland hydroperiods as well as estuarine salinities. Knowledge of this flow interaction, as well as timing, extent, and duration of inundation that it contributes to, is needed to identify and eliminate any potential adverse effects of altered flow conditions and transported constituents on vegetation and biota. Comprehensive analytical tools and methods are needed to assess the effects of nutrient and contaminant loads from agricultural and urban run-off entering canals and thereby conveyed into connected wetlands and other adjoining coastal ecosystems. These data from the individual gages were transferred to electronic form to provide a better understanding of the distribution of flow from north to south under the Tamiami Trail to aid in decisions about future changes to flow along the Trail. proprietary +USGS_SOFIA_dk_merc_cycl_bio Mercury Cycling and Bioaccumulation CEOS_EXTRA STAC Catalog 2000-10-01 2006-12-31 -81.33137, 24.67165, -80.22201, 25.890877 https://cmr.earthdata.nasa.gov/search/concepts/C2231550667-CEOS_EXTRA.umm_json This proposal identifies work elements that are logical extensions, and which build off, our previous work. Our overall scientific objective is to provide a complete understanding of the external factors (such as atmospheric mercury and sulfate runoff loads) and internal factors (such as hydroperiod maintenance and water chemistry) that result in the formation and bioaccumulation of MeHg in south Florida ecosystems, and to conduct this research is such a way that it will be directly useable by land and water resource managers. More specifically, we will seek to achieve the following subobjectives (1) Extend our mesocosms studies to provide a more omprehensive examination of the newly discovered 'new versus old' mercury effect by conducting studies under differing hydrologic conditions and sub-ecosystem settings so that our experimental results will be more generally applicable to the greater south Florida ecosystem including the STA’s that have been recently constructed and are yielding very high levels of methylmercury but the cause is currently unknown; (2) Seek to further identify the mechanisms that result in extremely high levels of MeHg after natural drying and rewetting cycles in the Everglades and which have major implications for the Restoration Plan; (3) Further our studies on the production of methylmercury in south Florida estuaries and tidal marshes by conducting mass-balance studies of tidal marshes; (4) Begin to partner with wildlife toxicologists funded by the State of Florida to unravel the complexities surrounding methylmercury exposure and effects to higher order wildlife in south Florida; and , (5) Continue to participate with mercury ecosystem modelers who are funded by the State of Florida and the USEPA to evaluate the overall ecological effects of reducing mercury emissions and the risks associated with methylmercury exposure. Although ecological impacts from phosphorous contamination have become synonymous with water quality in south Florida, especially for Everglades restoration, there are several other contaminants presently entering the Everglades that may be of equal or greater impact, including: pesticides, herbicides, polycyclic aromatic hydrocarbons, and trace metals. This project focuses on mercury, a sparingly soluble trace metal that is principally derived from atmospheric sources and affects the entire south Florida ecosystem. Mercury interacts with another south Florida contaminant, sulfur, that is derived from agricultural runoff, and results in a problem with potentially serious toxicological impacts for all the aquatic food webs (marine and freshwater) in the south Florida ecosystem. The scientific focus of this project is to examine the complex interactions of these contaminants (synergistic and antagonistic), ecosystem responses to variations in contaminant loading (time and space dimensions), and how imminent ecosystem restoration steps may affect existing contaminant pools. The Everglades restoration program is prescribing ecosystem-wide changes to some of the physical, hydrological and chemical components of this ecosystem. However, it remains uncertain what overall effects will occur as these components react to the perturbations (especially the biological and chemical components) and toward what type of 'new ecosystem' the Everglades will evolve. The approaches used by this study have been purposefully chosen to yield results that should be directly useable by land management and restoration decision makers. Presently, we are addressing several major questions surrounding the mercury research field, and the Everglades Restoration program: (l) What, if any, ecological benefit to the Everglades would be realized if mercury emissions reductions would be enacted, and over what time scales (years or tens of years) would improvements be realized? (2) What is the role of old mercury (previously deposited and residing in soils and sediment) versus new mercury (recent deposition) in fueling the mercury problem? (3) In the present condition, is controlling sulfur or mercury inputs more important for reducing the mercury problem in the Everglades? (4) Does sulfur loading have any additional ecological impacts that have not been realized previously (e.g., toxicity to plant and animals) that may be contributing to an overall decreased ecological health? (5) Commercial fisheries in the Florida Bay are contaminated with mercury, is this mercury derived from Everglades runoff or atmospheric runoff? (6) What is the precise role of carbon (the third member of the 'methylmercury axis of evil', along with sulfur and mercury), and do we have to be concerned with high levels of natural carbon mobilization from agricultural runoff as well? (7) Hundreds of millions of dollars are being, or have been spent, on STA construction to reduce phosphorus loading to the Everglades, however, recently constructed STAs have yielded the highest known concentration of toxic methylmercury; can STA operations be altered to reduce methylmercury production and maintain a high level of phosphorus retention over extended periods of time? The centerpiece of our research continues to be the use of environmental chambers (enclosures or mesocosms), inside which we conduct dosing experiments using sulfate, dissolved organic carbon and mercury isotopic tracers. The goal of the mesocosm experiments is to quantify the in situ ecological response to our chemical dosing, and to also determine the ecosystem recovery time to the doses. proprietary +USGS_SOFIA_eco_assess_risk_toxics Ecological Risk Assessment of Toxic Substances in the Greater Everglades Ecosystem: Wildlife Effects and Exposure Assessment CEOS_EXTRA STAC Catalog 2000-10-01 2004-09-30 -81.125, 25.125, -80.125, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231551985-CEOS_EXTRA.umm_json This project will be carried out in several locations throughout those areas critical to the South Florida Restoration Initiative. These areas include: 1) Water Conservation Areas 1, 2, and 3 of the Central Everglades, 2) Everglades National Park, 3) Loxahatchee National Wildlife Refuge, 4) Big Cypress National Preserve, 5) multiple Miami Metropolitan area canals and drainages, and 6) restoration related STA’s (STA’s 1-6) adjacent to the Everglades. Specific site selections will be based upon consideration of USACE restoration plans and upon discussions with other place-based and CESI approved projects. The overall objectives are characterize the exposure of wildlife to contaminants within the aquatic ecosystems of South Florida, through a multi-stage process: a) screening of biota to identify hazards/contaminants posing risk, and b) evaluation of the potential effects of those contaminants on appropriate animal/wildlife receptors. This project will focus upon each of these stages/needs, with an emphasis on understanding the effects of contaminants on alligators, fishes, birds, amphibians and macroinvertebrates. Historically, little consideration has been given to environmental chemical stressors/contaminants within the ecosystem restoration efforts for the Greater Everglades Ecosystem. The restoration is primarily guided by determining and restoring the historical relationships between ecosystem function and hydrology. The restoration plan was formulated to restore the natural hydrology and therefore, the resultant landscape patterns, bio-diversity and wildlife abundance. However, additional efforts need to consider the role that chemical contaminants such as pesticides and other inorganic/organic contaminants play in the structure and function of the resultant South Florida ecosystems. Indeed, the current level of agriculture and expanding urbanization and development necessitate that more emphasis be placed on chemical contaminants within this sensitive ecosystem and the current restoration efforts. The primary goal of the proposed project, therefore, is to develop an improved understanding of the exposure/fate (i.e. degradation, metabolism, dissipation, accumulation and transport) and potential ecological effects produced as a result of chemical stressors and their interactions in South Florida freshwater and wetland ecosystems. The overall objectives are to evaluate the risk posed by contaminants to biota within the aquatic ecosystems of South Florida, through a multi-stage process: a) screening of biota to identify hazards/contaminants posing risk and b) evaluation of the potential effects of those contaminants on appropriate animal/wildlife receptors. This project will focus upon each of these stages/needs, with an emphasis on understanding the effects of contaminants on alligators, fishes, birds, amphibians and macroinvertebrates. The specific objectives of this project are to: 1. Assess current exposure and potential adverse effects for appropriate receptors/species within the South Florida ecosystems with some emphasis on DOI trust species. These efforts will determine whether natural populations are significantly exposed to a variety of chemical stressors/contaminants, such as mercury, chlorinated hydrocarbon pesticides, historic and/or current use agricultural chemicals, and/or mixtures, as well as document lethal and non-lethal adverse effects in multiple health, physiologic and/or endocrine endpoints. 2. Assess exposure and potential adverse effects for appropriate species within South Florida as a function of restoration implementation. proprietary USGS_SOFIA_eco_hist_db1995-2007_version 7 1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7 CEOS_EXTRA STAC Catalog 1994-09-27 2007-04-03 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231554288-CEOS_EXTRA.umm_json The 1995 - 2007 Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), modern monitoring site survey information (water chemistry, floral and faunal data, etc.), and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain either percent abundance data or actual counts of the distribution of mollusks, ostracodes, forams, and pollen within the cores collected in the estuaries. For some cores dinocyst or diatom data may be available. proprietary +USGS_SOFIA_eco_hist_db_version 3 Ecosystem History of South Florida Estuaries Data CEOS_EXTRA STAC Catalog 1994-02-24 2008-03-20 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552167-CEOS_EXTRA.umm_json The Ecosystem History Access Database contains listings of all sites (modern and core), modern monitoring site survey information, and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Scientists over the past few decades have noticed that the South Florida ecosystem has become increasingly stressed. The purposes of the ecosystem history projects (started in 1995) are to determine what south Florida's estuaries have looked like over time, how they have changed, and what is the rate and frequency of change. To accomplish this, shallow sediment cores are collected within the bays, and the faunal and floral remains, sediment geochemistry, and shell biochemistry are analyzed. Modern field data are collected from the same region as the cores and serve as proxies to allow accurate interpretation of past depositional environments. The USGS South Florida Ecosystem History Project is designed to integrate studies from a number of researchers compiling data from terrestrial, marine, and freshwater ecosystems within south Florida. The project is divided into 3 regions: Biscayne Bay and the Southeast coast, Florida Bay and the Southwest coast, and Terrestrial and Freshwater Ecosystems of Southern Florida. The purpose of the projects is to provide information about the ecosystem's recent history based on analyses of paleontology, geochemistry, hydrology, and sedimentology of cores taken from the south Florida region. Data generated from the South Florida Ecosystem History project will be integrated to provide biotic reconstructions for the area at selected time slices and will be useful in testing ecological models designed to predict floral and faunal response to changes in environmental parameters. Biscayne Bay is of interest to scientists because of the rapid urbanization that has occurred in the Miami area and includes Biscayne National Park. Dredging, propeller scars, and changes in freshwater input have altered parts of Biscayne Bay. Currently, the main freshwater input to Biscayne Bay is through the canal system, but many scientists believe subsurface springs used to introduce fresh groundwater into the Bay ecosystem. Study of the modern environment and core sediments from Biscayne Bay will provide important information on past salinity and seagrass coverage which will be useful for predicting future change within the Bay. Plant and animal communities in the South Florida ecosystem have undergone striking changes over the past few decades. In particular, Florida Bay has been plagued by seagrass die-offs, algal blooms, and declining sponge and shellfish populations. These alterations in the ecosystem have traditionally been attributed to human activities and development in the region. Scientists at the U.S. Geological Survey (USGS) are studying the paleoecological changes taking place in Florida Bay in hopes of understanding the physical environment to aid in the restoration process. As in Biscayne Bay, scientists must first determine which changes are part of the natural variation in Florida Bay and which resulted from human activities. To answer this question, scientists are studying both modern samples and piston cores that reveal changes over the past 150-600 years. These two types of data complement each other by providing information about the current state of the Bay, changes that occurred over time, and patterns of change. Terrestrial ecosystems of South Florida have undergone numerous human disturbances, ranging from alteration of the hydroperiod, fire history, and drainage patterns through implementation of the canal system to expansion of the agricultural activity to the introduction of exotic species such as Melalueca, Australian pine, and the Pepper Tree. Over historical time, dramatic changes in the ecosystem have been documented and these changes attributed to various human activities. However, cause-and-effect relationships between specific biotic and environmental changes have not been established scientifically. One part of the South Florida Ecosystem History group of project is designed to document changes in the terrestrial ecosystem quantitatively, to date any changes and determine whether they resulted from documented human activities, and to establish the baseline level of variability in the South Florida ecosystem to estimate whether the observed changes are greater than what would occur naturally. Specific goals of this part of the project are to 1) document the patterns of floral and faunal changes at sites throughout southern Florida over the last 150 years, 2) determine whether the changes occurred throughout the region or whether they were localized, 3) examine the floral and faunal history of the region over the last few millennia, 4) determine the baseline level of variability in the communities prior to significant human activity in the region, and 5) determine whether the fire frequency, extent, and influence can be quantified, and if so, document the fire history for sites in the region. proprietary +USGS_SOFIA_eco_hist_swcoast_srs_04 Ecosystem History of the Southwest Coast-Shark River Slough Outflow Area CEOS_EXTRA STAC Catalog 2003-10-01 2008-09-30 -81.75, 25, -80.83, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231554376-CEOS_EXTRA.umm_json The objectives of this project are to document impacts of changes in salinity, water quality, coastal plant and animal communities and other critical ecosystem parameters on a subdecadal-centennial scale in the southwest coastal region (from Whitewater Bay, north to the 10,000 Islands), and to correlate these changes with natural events and resource management practices. Emphasis will be placed on 1) determining the amount, timing and sources of freshwater influx (groundwater vs. runoff) into the coastal ecosystem prior to and since significant anthropogenic alteration of flow; and 2) determining whether the rate of mangrove and brackish marsh migration inland has increased since 20th century water diversion and what role sealevel rise might play in the migration. First, the environmental preferences and distributions of modern fauna and flora are established through analyses of modern samples in south Florida estuaries and coastal systems. Much of these data have already been obtained through project work conducted in Florida Bay and the terrestrial Everglades starting in 1995. These modern data are used as proxies for interpreting the historical data from Pb-210 and C-14 dated sediment cores based on assemblage analysis. On the basis of USGS data obtained from cores in Florida Bay and Biscayne Bay, the temporal span of the cores should be at a minimum the last 150 years; this is in agreement with University of Miami data showing sedimentation rates in Whitewater Bay to be approximately 1cm/year. For the estuarine/coastal ecosystems, a multidisciplinary, multiproxy approach will be utilized on cores from a transect from Whitewater Bay north to 10,000 Islands. Biochemical analyses of shells and chemical analyses of sediments will be used to refine data on salinity and nutrient supply, and isotopic analyses of shells will determine sources of water influx into the system. Nutrient analyses will be conducted to determine historical patterns of nutrient influx. To examine the inland migration of the mangrove/coastal marsh ecotone, transects from the mouth of the Shark and Harney Rivers inland into Shark River slough will be taken. These cores will be evaluated for floral remains, nutrients, charcoal, and if present, faunal remains. This project will provide 1) baseline data for restoration managers and hydrologic modelers on the amount and sources of freshwater influx into the southwest coastal zone and the quality of the water, 2) the relative position of the coastal marsh-mangrove ecotone at different periods in the past, and 3) data to test probabilities of system response to restoration changes. One of the primary goals of the Central Everglades Restoration Plan (CERP) is to restore the natural flow of water through the terrestrial Everglades and into the coastal zones. Historically, Shark River Slough, which flows through the central portion of the Everglades southwestward, was the primary flow path through the Everglades Ecosystem. However, this flow has been dramatically reduced over the last century as construction of canals, water conservation areas and the Tamiami Trail either retained or diverted flow from Shark River Slough. The reduction in flow and changes in water quality through Shark River have had a profound effect on the freshwater marshes and the associated coastal ecosystems. Additionally, the flow reduction may have shifted the balance of fresh to salt-water inflow along coastal zones, resulting in an acceleration of the rate of inland migration of mangroves into the freshwater marshes. For successful restoration to occur, it is critical to understand how CERP and the natural patterns of freshwater flow, precipitation, and sea level rise will affect the future maintenance of the mangrove-freshwater marsh ecotone and the coastal environment. proprietary +USGS_SOFIA_eden_dem_cm_nov07_nc Everglades Depth Estimation Network (EDEN) November 2007 Digital Elevation Model for use with EDENapps CEOS_EXTRA STAC Catalog 1995-01-01 2007-12-31 -81.36353, 25.229605, -80.22176, 26.683613 https://cmr.earthdata.nasa.gov/search/concepts/C2231551925-CEOS_EXTRA.umm_json This is the 1st release of the third version of an Everglades Depth Estimation Network (EDEN) digital elevation model (DEM) generated from certified airborne height finder (AHF) and airboat collected ground surface elevations for the Greater Everglades Region. This version includes all data collected and certified by the USGS prior to the conclusion of the AHF collection process. It differs from the previous elevation model (EDEN_EM_JAN07) in that the modeled area of WCA3N (all the WCA3A area north of I-75) is increased while the modeled area of the Big Cypress National Preserve (BNCP) has been both refined and reduced to the region where standard error of cross-validation points falls below 0.16 meters. EDEN offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to Comprehensive Everglades Restoration Plan. To produce historic and near-real time maps of water depths, the EDEN requires a system-wide DEM of the ground surface. This file is a modification of the eden dem released in October of 2007 (i.e., eden_em_oct07) in which the elevation values have been converted from meters (m) to centimeters(cm) for use by EDEN applications software. This file is intended specifically for use in the EDEN applications software. Aside from this difference in horizontal units, the following documentation applies. These data were specifically created for the development of water depth information using interpolated water surfaces from the EDEN stage data network. proprietary +USGS_SOFIA_eden_em_oct07_400m Everglades Depth Estimation Network (EDEN) October 2007 Digital Elevation Model CEOS_EXTRA STAC Catalog 1995-01-01 2007-12-31 -81.36353, 25.229605, -80.22176, 26.683613 https://cmr.earthdata.nasa.gov/search/concepts/C2231548584-CEOS_EXTRA.umm_json This is the 1st release of the third version of an Everglades Depth Estimation Network (EDEN) digital elevation model (DEM) generated from certified airborne height finder (AHF) and airboat collected ground surface elevations for the Greater Everglades Region. This version includes all data collected and certified by the USGS prior to the conclusion of the AHF collection process. It differs from the previous elevation model (EDEN_EM_JAN07) in that the modeled area of WCA3N (all the WCA3A area north of I-75) is increased while the modeled area of the Big Cypress National Preserve (BNCP) has been both refined and reduced to the region where standard error of cross-validation points falls below 0.16 meters. EDEN offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to Comprehensive Everglades Restoration Plan. To produce historic and near-real time maps of water depths, the EDEN requires a system-wide DEM of the ground surface. These data were specifically created for the development of water depth information using interpolated water surfaces from the EDEN stage data network. proprietary +USGS_SOFIA_eden_water_surfs Everglades Depth Estimation Network (EDEN) Water Surfaces Data CEOS_EXTRA STAC Catalog 2000-01-01 -81.3, 25, -80.16, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231549934-CEOS_EXTRA.umm_json Spatially continuous interpolation of water surface across the greater Everglades is generated for daily mean values of the water level gages for the EDEN network beginning January 1, 2000. Surfaces are recorded as elevations in centimeters relative to the North American Vertical Datum of 1988 (NAVD 88). These surfaces are served on the web as GIS data layers. Spatially explicit hydrologic information can be critical in understanding and predicting changes in biotic communities in wetland ecosystems. Repeated field measurements, the traditional method of collecting water surface information, is labor intensive and doesn't produce spatially continuous data across large areas. For this reason the EDEN project was started to collect data from over 200 real time stage monitoring gages that automatically record and radio-transmit data. The project integrates existing and new telemetered water level gages into a single network. Combined with a high resolution ground elevation model it generates a daily continuous water surface and water depth for the freshwater greater Everglades. proprietary +USGS_SOFIA_estero_bay_ap_data Estero Bay Aquatic Preserve hydrological data CEOS_EXTRA STAC Catalog 2001-10-01 2005-09-30 -81.96, 26.33, -81.83, 26.47 https://cmr.earthdata.nasa.gov/search/concepts/C2231552405-CEOS_EXTRA.umm_json The data for each of the collection sites are available for fiscal years 2002-2005. The files are available in several formats. Salinity and temperature were collected for all stations. Stage, discharge, and wind speed and direction were also collected at some of the stations. Estero Bay is a shallow estuary, across which salinity gradients from freshwater to saltwater occur over short land-sea distances. Such gradient compressions can result in a highly variable salinity environment and affect a diverse range of estuarine flora and fauna when even a small change in watershed runoff occurs. Rapid development within the bay's watershed has a changing effect on the amount, timing, and quality of runoff into the bay. Currently there is no information available to assess the effect that these alterations of runoff may have on the bay and its biota, nor to define watershed runoff and loading limits that provide desirable ranges in salinity and water quality at historical, current, and potential locations for seagrass, oysters, and other species of concern. To manage and preserve the Estero Bay ecosystem, it is necessary to: (1) understand the salinity patterns of the bay in relation to freshwater inflow and water exchange with the Gulf of Mexico; (2) describe the mixing and freshwater residence times within the bay; and (3) study the effects on light penetration from increased Total Suspended-Solids (TSS) load and re-suspension. Results from this study will facilitate management decisions geared toward defining flow and sediment loading limits that provide desirable ranges in salinity and water quality by providing necessary hydrological information. To carry out the objectives of the study, a network of monitoring stations will be established and will include: (1) the monitoring of flow, water level, salinity, temperature, Acoustic Backscatter Strength (ABS), and turbidity near the mouth of three of four tributaries flowing into Estero Bay; (2) monitoring of water level, salinity, temperature, turbidity, wind speed and direction, and barometric pressure at one location inside the bay; (3) monitoring of water level, flow, salinity, temperature, and ABS at three of four tidal exchange points with the Gulf of Mexico along the barrier islands; (4) monitoring of water level (depth), salinity and temperature at one open-water location in the Gulf of Mexico. proprietary +USGS_SOFIA_ever_hydro_wq_data Everglades Hydrology and Water Quality Data CEOS_EXTRA STAC Catalog 1995-06-01 1998-12-21 -80.67, 25.9, -80.33, 26.7 https://cmr.earthdata.nasa.gov/search/concepts/C2231550405-CEOS_EXTRA.umm_json At present there are few reliable estimates of hydrologic fluxes between groundwater and surface water in the Everglades. This gap in hydrological investigations not only leaves the water budget of the Everglades uncertain, it also hampers progress in understanding the processes that determine mobility and transformation of contaminants, such as mercury, sulfate and nutrients. The objective of this project is to quantify hydrologic exchange fluxes between groundwater and surface water and its effects on transport of contaminants in the Everglades. The research furthermore relates surface water and ground water interactions to past, present, and proposed management of surface-water levels and flows in the Everglades. The principal research sites are the Everglades Nutrient Removal Project (ENR), Water Conservation Area 2A (WCA-2A), and the freshwater wetlands of Everglades National Park. Results are being used to quantify ground-water exchange with surface flow, and to quantify the enhancement of chemical transformations of contaminants during transport across the interface between surface water and ground water. Two hydrology and water quality datasets are available for this project. The Northern Everglades Research Site and Sample Information dataset contains a summary of the site locations, data types, and measurement periods in ENR, WCA2A, and WCA2B. The Seepage Meters Site and Sample Information dataset contains vertical fluxes across wetland peat surface measured by seepage meters at research sites in ENR, WCA2A, WCA2B, and WCA3A. Additional data can be found in the appendices of the Open-File Reports 00-168 and 00-483. For restoration of the Everglades to succeed there must be comprehensive knowledge about physical, chemical, and biological processes throughout the system. A key measure of success in the Everglades is the improvement or protection of water quality under changing hydrologic conditions. Although there is a basic understanding of how interactions between groundwater and surface water will affect water budgets under restoration, there is only a rudimentary understanding of how interactions between groundwater and surface water will affect water quality. Only field-oriented research and modeling can determine whether interactions between groundwater and surface water are currently storing pollutants in groundwater, how long those pollutants are likely to be stored in the aquifer, and under what changing management conditions associated with restoration will those pollutants be returned into the surface water system. proprietary +USGS_SOFIA_ever_isotope_data Everglades Isotope Data CEOS_EXTRA STAC Catalog 1995-03-01 1999-10-31 -81.0202, 25.2475, -80.3069, 26.6712 https://cmr.earthdata.nasa.gov/search/concepts/C2231548535-CEOS_EXTRA.umm_json "Data are available for the following sites: Cell 4, ENR-OUT, L7, Cell 3, LOX, North Holeyland, E0, F1, U3/Glory Hole, L35B, 2BS, L67, 3A-15, 3A-TH, Lostmans Creek, North Prong Creek, TS-7, and TS-9 for the plants and animals found at each site. A first step of the Everglades restoration efforts is ""getting the water right"". However, the underlying goal is actually to re-establish, as much as possible, the ""pre-development"" spatial and temporal distribution of ecosystems throughout the Everglades. Stable isotope compositions of dissolved nutrients, biota, and sediments provide critical information about current and historic ecosystem conditions in the Everglades, including temporal and spatial variations in contaminant sources, biogeochemical reactions in the water column and shallow subsurface, and trophic relations. Hence, the scientific focus of this project is to use stable isotope techniques to examine ecosystem responses (especially variations in foodweb base and trophic structure) to temporal and spatial variations in hydroperiod and contaminant loading for the entire freshwater Everglades. The major ""long-term"" objectives of this project have been to: (1) determine the stable C, N, and S isotopic compositions of Everglades biota, (2) use bulk and compound-specific isotopic ratios to determine relative trophic positions for major organisms, (3) examine the spatial and temporal changes in foodweb structures across the ecosystem, especially with respect to the effect of anthropogenically derived nutrients and contaminants from agricultural land uses on foodwebs, (4) evaluate the effectiveness of isotopic techniques vs. gut content analysis for determining trophic relations in the Everglades, (5) evaluate the role of algae vs. detritus/microbial materials in foodwebs for the entire freshwater marsh part of the Everglades, and (6) work with modelers to correctly incorporate food web and MeHg bioaccumulation information into predictive models. We have generally completed the sample analysis parts of objectives #1-5, and are writing interpretative reports on topics #1-5. More recent and specific objectives include: (1) link our data on seasonal and temporal differences in foodweb bases and trophic levels with SFWMD, FGFFC, and USGS Hg datasets (first for large fish and, more recently, for lower trophic levels), (2) investigate the effects of seasonal/spatial changes in nutrients, water levels, and reactions on the isotopic compositions at the base of the foodweb (that affect our interpretation of relative trophic positions of organisms), and (3) continue our efforts to link our foodweb isotope data from samples collected at USGS-ACME and EPA-REMAP sites with the spatial environmental patterns observed by the REMAP program. This work started as part of the Aquatic Cycling of Mercury in the E verglades (ACME) project in 1996 and was made a separate project in 2000." proprietary USGS_SOFIA_exist_core Analysis of Existing Core in the Floridan Aquifer CEOS_EXTRA STAC Catalog 2000-12-01 2001-04-30 -81.45, 27.3, -81.4, 27.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231549266-CEOS_EXTRA.umm_json ABSTRACT: The proposed work was divided into several phases: (1) collection of existing core samples and slab preparation of core samples, (2) lithologic examination, and (3) report preparation proprietary USGS_SOFIA_fb-fk_grndwtr_flow Determination of Groundwater-Flow Direction and Rate Beneath Florida Bay, the Florida Keys and Reef Tract CEOS_EXTRA STAC Catalog 1995-07-01 2000-12-31 -80.6, 25, -80.3, 25.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231554732-CEOS_EXTRA.umm_json The strategy of this study was to use artificial tracers to determine rate and direction of flow. Tracers were injected into well clusters, existing sewage treatment facilities, and sewage disposal wells. In addition to tracer studies groundwaters were collected for contamination analysis so as to provide a baseline against which the effects of population increase and success of future wastewater treatment facilities can be evaluated. Treated sewage is injected into the limestone under the Florida Keys via on-site disposal systems (OSDs). There are 25,000 septic tank systems, approximately 5,000 cesspools, and approximately 1000 class 5 injection wells. Depths of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases and both marine grass and sponge mortality is perceived by the local population, NOAA, and EPA to be caused by sewage nutrients leaking from groundwater on both sides of the Florida Keys. Determining the rate and direction of saline groundwater movement beneath the Key, and the Florida Bay was considered critical to understanding the fate and effects of subsurface waste water disposal n the Florida Keys. The objective of this research was to determine the rate, direction of flow, and contamination levels of saline groundwater in the Florida Keys and Florida Bay. Contamination studies are necessary to determine if nutrient and other contaminant levels are rising and to provide a baseline of data for future decision making. proprietary +USGS_SOFIA_fb_1890-1990_data_version 1 Florida Bay 1890 and 1990 data CEOS_EXTRA STAC Catalog 1889-01-01 1999-12-31 -81.11667, 24.733334, -80.36667, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231554102-CEOS_EXTRA.umm_json The maps of the tracklines are linked to the corresponding data sets which contain values for X (easting), Y (northing), Z (elevation), and the Root Mean Square (RMS) error. Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay has not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Digitizing the historical shoreline and bathymetric data for comparison with modern data provides information on sedimentation rates within the Bay. proprietary +USGS_SOFIA_fb_bb_pollen_data Florida Bay and Biscayne Bay pollen data CEOS_EXTRA STAC Catalog 1994-02-26 1996-11-01 -80.62, 25, -80, 25.26 https://cmr.earthdata.nasa.gov/search/concepts/C2231550479-CEOS_EXTRA.umm_json This project developed, refined, and utilized a variety of proxies to provide estimates of seasonal, interannual, and decadal salinity history of Florida Bay and Biscayne Bay based on strategically placed sediment cores that aided in the validation and sensitivity testing of hydrologic models and decision making in water management. The datasets contain the pollen information at various depths in the cores. Terrestrial ecosystems of south Florida have undergone numerous human disturbances, ranging from alteration of hydroperiod, fire history, and drainage patterns from the introduction of the canal system to expansion of agricultural activity to the introduction of exotic species, Over historical time, dramatic changes in the ecosystem have been documented and these changes have been attributed to various human activities. However, the natural variability of the ecosystem was unknown and needed to be determined to assess the true impact of human activity on the modern ecosystem. The project was designed to document historical changes in the terrestrial ecosystem quantitatively, to date any changes and determine whether they resulted form documented human activities, and to establish the baseline level of variability on the south Florida ecosystem to estimate whether the observed changes are greater than would occur naturally. proprietary +USGS_SOFIA_field_data_bicy Field measurement, major ion, and nutrient data for water from the Big Cypress National Preserve CEOS_EXTRA STAC Catalog 1988-01-01 2000-12-31 -81.4, 25.2, -80.5, 26.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231554990-CEOS_EXTRA.umm_json "The data catagories include site name, date, time, station ID, record #, agency analyzing sample, agency collecting sample, discharge (daily mean), gage height, lab spec condition, field spec condition, total dissolved solids, water temp, pH (lab and field), and amounts of oxygen, nitrogen and nitrogen compounds, phosphorus, magnesium, sodium, potassium, chloride, sulfate, calcium, and silica. Big Cypress National Preserve (BICY) and Everglades National Park (EVER) maintain separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout their units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data (bi-monthly for BICY and monthly for EVER), and dependence on surface water depth and season. Collection and analysis of water-quality samples at BICY and EVER are done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. EVER has been sampling water quality on a monthly basis at 9 ""internal marsh"" stations since 1984 as part of this program. BICY has been sampling water quality on a monthly basis at 10 ""internal"" stations since 1995 as part of this agreement, with water quality data at these sites extending as far back to 1988 (but not as part of the agreement). Water-quality data collected at the BICY and EVER stations has been archived and reported for short-time intervals (yearly and bi-yearly), but an analysis that covers all sampled parameters, extends over the full period of record, and provides comparisons between the two units has yet to be performed. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. One purpose is to look for long-term trends and possibly relate the trends to human or natural influences on water quality such as agriculture, drought, hurricanes, changes in water management, etc. Another purpose is to interpret data from the most remote and unimpacted areas to discern, if possible, what the natural background concentrations are for water-quality constituents that have sufficient data. An attempt will be made to find correlations between available water-quality, physical, and meteorological parameters. Such analyses of water-quality and ancillary data may assist in establishing water-quality standards appropriate for the designation as Outstanding Florida Waters in both the Everglades National Park and the Big Cypress National Preserve. Ancillary data such as precipitation, water-level, water flow, dates of major storms, and beginning and ending dates of water-control effects will be studied to relate their timing to any noticeable changes in water quality. CURRENTNESS REFERENCE: ground condition SPATIAL DATA ORGANIZATION INFORMATION Indirect Spatial Reference: Big Cypress National Preserve Direct Spatial Reference: Point SDTS Point and Vector Object Type: Point Point and Vector Object Count: 5 SPATIAL REFERENCE INFORMATION - GEODETIC MODEL Horizontal Datum Name: North American Datum of 1983 Ellipsoid Name: Geodetic Reference System 80 Semi-major Axis: 6378137 Denominator of Flattening Ratio: 298.257 NATIVE: Data are provided as Excel spreadsheets." proprietary +USGS_SOFIA_field_data_br105 Field measurements, major ions, nutrient, and carbon data for Bridge-105 and the 40-Mile Bend to Monroe reach in the Big Cypress National Preserve CEOS_EXTRA STAC Catalog 1967-01-01 1999-12-31 -81.4, 25.2, -80.5, 26.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231551160-CEOS_EXTRA.umm_json "The data catagories include site name, date, time, station ID, medium, record #, agency analyzing sample, agency collecting sample, discharge (daily mean and instantaneous), gage height, lab spec condition, field spec condition, total dissolved solids, air and water temp, pH (lab and field), and amounts of oxygen, nitrogen and nitrogen compounds, phosphorus, carbonates, magnesium, sodium, potassium, chloride, sulfate, calcium, silica, and carbon. Big Cypress National Preserve (BICY) and Everglades National Park (EVER) maintain separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout their units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data (bi-monthly for BICY and monthly for EVER), and dependence on surface water depth and season. Collection and analysis of water-quality samples at BICY and EVER are done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. EVER has been sampling water quality on a monthly basis at 9 ""internal marsh"" stations since 1984 as part of this program. BICY has been sampling water quality on a monthly basis at 10 ""internal"" stations since 1995 as part of this agreement, with water quality data at these sites extending as far back to 1988 (but not as part of the agreement). Water-quality data collected at the BICY and EVER stations has been archived and reported for short-time intervals (yearly and bi-yearly), but an analysis that covers all sampled parameters, extends over the full period of record, and provides comparisons between the two units has yet to be performed. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. One purpose is to look for long-term trends and possibly relate the trends to human or natural influences on water quality such as agriculture, drought, hurricanes, changes in water management, etc. Another purpose is to interpret data from the most remote and unimpacted areas to discern, if possible, what the natural background concentrations are for water-quality constituents that have sufficient data. An attempt will be made to find correlations between available water-quality, physical, and meteorological parameters. Such analyses of water-quality and ancillary data may assist in establishing water-quality standards appropriate for the designation as Outstanding Florida Waters in both the Everglades National Park and the Big Cypress National Preserve. Ancillary data such as precipitation, water-level, water flow, dates of major storms, and beginning and ending dates of water-control effects will be studied to relate their timing to any noticeable changes in water quality." proprietary +USGS_SOFIA_field_data_interEver Field measurements, major ions, nutrient, and carbon data for sites in the interior of the Everglades National Park CEOS_EXTRA STAC Catalog 1959-12-16 2000-12-31 -80.9, 25.2, -80.5, 25.7 https://cmr.earthdata.nasa.gov/search/concepts/C2231552386-CEOS_EXTRA.umm_json "The data catagories include site name, date, time, station ID, medium, record #, agency analyzing sample, agency collecting sample, discharge (daily mean and instantaneous), gage height, lab spec condition, field spec condition, total dissolved solids, air and water temp, pH (lab and field), and amounts of oxygen, nitrogen and nitrogen compounds, phosphorus, carbonates, magnesium, sodium, potassium, chloride, sulfate, calcium, floride, silica, and carbon. Big Cypress National Preserve (BICY) and Everglades National Park (EVER) maintain separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout their units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data (bi-monthly for BICY and monthly for EVER), and dependence on surface water depth and season. Collection and analysis of water-quality samples at BICY and EVER are done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. EVER has been sampling water quality on a monthly basis at 9 ""internal marsh"" stations since 1984 as part of this program. BICY has been sampling water quality on a monthly basis at 10 ""internal"" stations since 1995 as part of this agreement, with water quality data at these sites extending as far back to 1988 (but not as part of the agreement). Water-quality data collected at the BICY and EVER stations has been archived and reported for short-time intervals (yearly and bi-yearly), but an analysis that covers all sampled parameters, extends over the full period of record, and provides comparisons between the two units has yet to be performed. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. One purpose is to look for long-term trends and possibly relate the trends to human or natural influences on water quality such as agriculture, drought, hurricanes, changes in water management, etc. Another purpose is to interpret data from the most remote and unimpacted areas to discern, if possible, what the natural background concentrations are for water-quality constituents that have sufficient data. An attempt will be made to find correlations between available water-quality, physical, and meteorological parameters. Such analyses of water-quality and ancillary data may assist in establishing water-quality standards appropriate for the designation as Outstanding Florida Waters in both the Everglades National Park and the Big Cypress National Preserve. Ancillary data such as precipitation, water-level, water flow, dates of major storms, and beginning and ending dates of water-control effects will be studied to relate their timing to any noticeable changes in water quality." proprietary +USGS_SOFIA_fire_ecol_sfl_04 Fire Ecology of South Florida Wetlands CEOS_EXTRA STAC Catalog 1996-01-01 2004-09-30 -81.5, 24.65, -80.75, 26.48 https://cmr.earthdata.nasa.gov/search/concepts/C2231552149-CEOS_EXTRA.umm_json The project objective is to determine the importance that season of burning has on the response of vegetation to fire. We have addressed this through the use of experimental prescribed fires at different times of the year. In Big Cypress National Preserve we have established a long-term study of season and frequency of burning in the unlogged hydric pinelands of the Raccoon Point area. This study includes three seasonal treatments: winter (dry season), spring (early wet season) and summer (mid wet season). A shorter study comparing the response to winter and summer burns was carried out in the pine rocklands on Big Pine Key. We are also studying the effect of season of burning on muhly grass (Muhlenbergia filipes), a component of hydric pinelands and often a dominant in short-hydroperiod wetlands known as muhly or marl prairies. We are conducting field and nursery studies to determine how the season of burning effects the rate of recovery of muhly and its ability to tolerate flooding. Prescribed fire constitutes one of the most pervasive management actions influencing the restoration and maintenance of the Greater Everglades Ecosystem. It is generally assumed that lightning-ignited fires were common at the beginning of the rainy season, but there have probably been human-caused fires at other times for the last several thousand years. Since lighting-ignited fire cannot be allowed to operate naturally in South Florida, prescribed (or management-ignited) fire must be used to maintain these habitats. The seasonal occurrence of fire can have an important influence on ecological responses. We have conducted a set of experimental studies to determine the response of vegetation to different seasons of burning. The results of this work will influence the fire management of the publicly owned lands in the Greater Everglades ecosystem. proprietary USGS_SOFIA_fish_sample Development of Integrated Sampling of Fishes in Forested Wetlands in South Florida with Emphasis on Food Web Structure CEOS_EXTRA STAC Catalog 2005-01-01 2007-12-31 -81.75, 25, -80.75, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231554222-CEOS_EXTRA.umm_json This study seeks to refine sampling methodology in the forested wetlands, to collect baseline data for aquatic animals to enable comparisons between Comprehensive Everglades Restoration Plan (CERP) and non-CERP impacted wetlands, and to begin studies of food-web structure in cypress and mangrove wetlands. Forested wetlands, mainly comprised by mangrove and cypress swamps in south Florida, and contiguous marshes formerly functioned as critical feeding and nesting sites for wading birds, populations of which have declined precipitously in coincidence with changes to the hydrology of the region. Human-induced changes have affected the natural variability of environmental conditions through the construction of canals and levees that can either act to drain or flood the wetlands. These changes are hypothesized to have negatively affected the production and availability of fish prey for the birds. A major target of restoration is the reestablishment of the natural hydrological conditions in the wetlands. Another alteration to these systems has been the introduction of more than 10 species of non-native fishes. The Big Cypress Swamp and mangrove ecosystems have been affected by these anthropogenic activities, yet the effects are unclear because of the lack of study. In both ecosystems, there is little quantitative information on the community composition, size-structure, and biomass of fishes and macro-invertebrates because few studies have been carried out there, This is especially true in the forested habitats of those ecosystems. Reasons for lack of study include logistical problems such as access to study areas and difficulties in devising appropriate sampling methods and feasible designs. However, because of the scope of anthropogenic changes in the drainage basins, there can be little doubt that the standing stocks of aquatic animals and habitat use have been affected negatively. proprietary +USGS_SOFIA_fk_gw_seep Groundwater Seepage data (Florida Keys) CEOS_EXTRA STAC Catalog 1995-07-07 1996-08-20 -80.9, 24.8, -80.2, 25.7 https://cmr.earthdata.nasa.gov/search/concepts/C2231549211-CEOS_EXTRA.umm_json The dataset contains information and data collected during the seepage meter (groundwater seepage) experiments along the Florida Keys on both the Florida Bay and Atlantic Ocean sides. Treated sewage is injected into the limestone under the Florida Keys via on-site disposal systems (OSDs). There are 25,000 septic tank systems, approximately 5,000 cesspools, and approximately 1000 class 5 injection wells. Depths of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases and both marine grass and sponge mortality is perceived by the local population, NOAA, and EPA to be caused by sewage nutrients leaking from groundwater on both sides of the Florida Keys. Determining the rate and direction of saline groundwater movement beneath the Key, and the Florida Bay was considered critical to understanding the fate and effects of subsurface waste water disposal n the Florida Keys. The objective of this research was to determine the rate, direction of flow, and contamination levels of saline groundwater in the Florida Keys and Florida Bay. Contamination studies are necessary to determine if nutrient and other contaminant levels are rising and to provide a baseline of data for future decision making. proprietary +USGS_SOFIA_fl_coop_map Florida Cooperative Geologic Mapping Project CEOS_EXTRA STAC Catalog 1996-10-01 1999-09-30 -82, 24.5, -80.28, 25.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549816-CEOS_EXTRA.umm_json This project was designed to provide the framework for understanding (1) ecosystem variability and change prior to and during human development of South Florida (i.e., the detailed ecosystem history over the last 200 years, differentiating natural variability from man-made change) and (2) the resource distribution (primarily water and phosphate) in the subsurface of Florida (i.e., the detailed geology of constraining and resource units). The overall strategy is is to: 1. Sample modern environments throughout the Greater Everglades Ecosystem to understand the present ecosystem and locate undisturbed shallow sediment cores to analyze ecosystem variability and change over the last few hundred years. 2. Analyze deep cores for sedimentology, diagenesis, biostratigraphy, paleoecology, and chemostratigraphy in transects across the southern Florida Peninsula to better understand the factors controlling ground water movement and to define aquifer characteristics. In order to understand the role of facies relationships and genetic depositional units in determining groundwater flow, the distribution and abundance of micro mollusks, foraminifers, dinocysts, ostracodes, pollen and spores, and charcoal will be analyzed, and strontium isotopes will be used for geochronology. A multitude of water-related societal issues face southern Florida in the 1990's. These issues include the increasing demands for water for agriculture; business, and the rapidly growing population in the Naples and Miami area (Miami showing the fourth fastest growth rate in the U.S. in the 1980's), the recently mandated restoration of natural sheet flow through the Everglades ecosystem, the effects of runoff from agricultural and urban areas, and the vitality of the important fisheries of Florida Bay and Biscayne Bay. This project provides baselines for ecosystem variability and tracks the change in ecosystems through the last several hundred years to provide critical information for reasonable restoration targets to land planners and managers in southern Florida. In addition, it provides the geologic framework for the aquifers that supply water to the area. proprietary +USGS_SOFIA_flow_murray_solis Flow Data (Murray/Solis) CEOS_EXTRA STAC Catalog 1997-01-01 2004-12-31 -80.96, 25.13, -80.81, 26.26 https://cmr.earthdata.nasa.gov/search/concepts/C2231554681-CEOS_EXTRA.umm_json Proposed modified water deliveries to Indian Tribal Lands, Big Cypress National Preserve, and Water Conservation Area 3A require that flow and nutrient loads at critical points in the interior surface water network be measured. Defining the foundation for water levels, flows, and nutrient loads has become an important baseline for Storm Treatment Area 5 and 6 development, recent C-139 Basin flow re-diversions, and future L-28 Interceptor Canal de-compartmentalization including flow rerouting into the Big Cypress Preserve. Flow monitoring for the two primary flow routes for both L-28 Interceptor Canal and L-28 is key to developing this network. Data are available for L-28 Interceptor Canal below S-190, L-28 Canal above S-140, and L-28 Interceptor South. The accurate determination of flow through the interior canal networks south of Lake Okeechobee and the C-139 basin remains critical for water budgets and regional model calibrations as defined by the Everglades Forever Act of 1994 and due to the Comprehensive Everglades Restoration Plan (CERP) initiative to reroute Big Cypress Preserve flows. The implementation of strategically located stream flow gaging points and associated data collection for nutrients has helped define future surface-water flow requirements and has provided valuable baseline flow data prior to the establishment of the recently constructed northern Storm Treatment Areas (STA’s 5 and 6) and the Rotenberger Wildlife Management Area. Generating continuous flow data at selected impact points for interior basins has complemented the existing eastern coastal canal discharge network, and has allowed for more accurately timed surface-water releases while providing flow and nutrient monitoring after recent STA implementation. A unique multi-agency experiment was conducted with much success with the focus on cooperation and development of new instrumentation and acoustic flow-weight auto-sampler protocols. The original data collection and processing was provided by three separate entities at each site with responsibilities originally allocated between the U.S. Geological Survey (USGS), the Seminole Tribe of Florida, and SFWMD. USGS provides calibration, analysis and processing of acoustic velocity meters (AVM’s) and side-looking Doppler systems and stage shaft encoders, SFWMD provides data loggers with real-time flow-weighted algorithms, and radio frequency (RF) telemetry instrumentation. The Seminole Tribe provides auto-sampler service and funds nutrient load analysis through the USGS Ocala Lab. This project was initiated by Mitch Murray in October 1995. proprietary +USGS_SOFIA_flow_velocity Flow Velocity and Water Level Transects CEOS_EXTRA STAC Catalog 1997-07-01 -80.4, 25.5, -80.2, 25.7 https://cmr.earthdata.nasa.gov/search/concepts/C2231553114-CEOS_EXTRA.umm_json The sheet flow over the Buttonwood Embankment during periods of high flow is an unknown element of the water budget for the Everglades. An ongoing project to estimate the flows over the embankment through modeling will require water-level and water velocity data measured at the embankment to accurately estimate simulated flows over this physical land feature. The actual measurement of water velocities and depths at the embankment would greatly improve the model calibration. Although it is virtually impossible to conventionally measure flow over the entire embankment, water depths and velocities at known points along the embankment, combined with the detailed topography of the embankment being developed in another ongoing project, should allow a much better estimate of the total flow than presently available. The objective of the project is to make the best possible estimate of the flow velocities and water depths across Buttonwood Embankment during high flow for use as model input for an ongoing project being conducted in the USGS-WRD office in Miami. Estimates will be used to provide management alternatives and will create salinity concentrations suitable for ecologic integrity. This project has been integrated into the TIME project. The project was started by Marvin Franklin. proprietary +USGS_SOFIA_flow_velocity_data Flow and Velocity Data - USGS_SOFIA_flow_velocity_data CEOS_EXTRA STAC Catalog 1997-07-01 1998-07-31 -80.9, 25.25, -80.5, 25.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231551525-CEOS_EXTRA.umm_json Velocity data are being provided in an Access database and Excel spreadsheets. The database summarizes the velocity data, site location and description, vegetative characteristics, and water quality parameters. The spreadsheet filters and averages the complete raw velocity data set per measurement. These data were collected at two minute intervals for 10 minutes. The file naming convention is as follows: (example) E07T31197.xls Column 1: E or W East or west from Taylor Slough Airboat Trail Column 2-3: Sequential numbers from the center of the transect Column 4-5: Sequential transect number Column 6-9: Month and Year data was collected. The objective of the project is to make the best possible estimate of the flow velocities and water depths across Buttonwood Embankment during high flow for use as model input for an ongoing project being conducted in the USGS-WRD office in Miami. Estimates will be used to provide management alternatives and will create salinity concentrations suitable for ecologic integrity. proprietary +USGS_SOFIA_freshwater_east_coast Freshwater Flows to the East Coast CEOS_EXTRA STAC Catalog 1994-11-01 1996-09-30 -80.45, 25.28, -80.05, 26.65 https://cmr.earthdata.nasa.gov/search/concepts/C2231552319-CEOS_EXTRA.umm_json Discharges through 10 selected coastal control structures in Broward and Palm Beach Counties and the 16 coastal structures in Miami-Dade County, Fla., Florida, are presently computed using the theoretical discharge-coefficient ratings developed from scale modeling, theoretical discharge coefficients, and some field calibrations whose accuracies for specific sites are unknown. To achieve more accurate discharge-coefficient ratings for the coastal control structures, field discharge measurements were taken with an Acoustic Doppler Current Profiler at each coastal control structure under a variety of flow conditions. These measurements were used to determine computed discharge-coefficient ratings for the coastal control structures under different flow regimes: submerged orifice flow, submerged weir flow, free orifice flow, and free weir flow. Theoretical and computed discharge-coefficient ratings for submerged orifice and weir flows were determined at the coastal control structures, and discharge ratings for free orifice and weir flows were determined at three coastal control structures. The difference between the theoretical and computed discharge-coefficient ratings varied from structure to structure. A system of canals and levees has been constructed over the last century for the purpose of drainage, flood control, and aquifer discharge. Strategically placed control structures allow the water management officials to move water from inland areas during high-rainfall periods and retain water in dry periods. Freshwater discharged to tide through coastal structures not only affects the amount of water available for water supply in the lower east coast and the Everglades, but it also affects the biota in the Intracoastal Waterway and Biscayne Bay. Therefore, it is imperative that there be accurate ratings for these structures to predict the effects of various water restoration alternatives. Although these coastal structures are a pivotal part of the man-made system, the discharge through most of them is computed only from theoretical ratings. Actual field measurements are needed in order to determine if the theoretical ratings are adequate, and to develop more accurate ratings. Stage measurements are made by the South Florida Water Management District (SFWMD) or the USGS at the east coast structures. The flows through the coastal structures in Miami-Dade, Broward, and Palm Beach counties can be computed by developing stage-discharge ratings from field measurements of flow, stage, and structure operations. Although theoretical ratings exist for the structures, no check as to the accuracy of these ratings has been made. In order to develop ratings from field measurements, discharge measurements must be made at the structure simultaneously with water level and structure operation measurements. Difficulties in making accurate discharge measurements arise from the slow flows and non-standard velocity profiles in south Florida canals. The Acoustic Doppler Current Profiler (ADCP), which uses the Doppler shift in acoustic signals to determine water velocity and compute discharge, is ideal for measurements in slow and spatially varying velocity fields. Statistical techniques were used to determine the best-fit ratings for the structures and error analysis of the ratings. The objective of this study was to determine discharge ratings for 10 coastal hydraulic control structures (7 in eastern Broward and 3 in southeastern Palm Beach counties as well as for 16 coastal hydraulic control structures in eastern Miami-Dade county. proprietary +USGS_SOFIA_freshwtr_flow Freshwater Flows to Northeastern Florida Bay CEOS_EXTRA STAC Catalog 1994-10-01 -80.8, 24.9, -80.3, 25.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231548619-CEOS_EXTRA.umm_json In 1995, the U.S. Geological Survey (USGS) began a study to gage several major creeks that discharge freshwater into northeastern Florida Bay. This study provides flow, salinity, and water-level data for model development and calibration and also provides baseline data for other physical, biological and chemical studies being conducted in the area. The monitoring network provides coastal discharge data for the majority of estuarine creeks in northeastern Florida Bay. The timing and distribution of freshwater deliveries to northeastern Florida Bay have been documented since 1996. In 2003 the USGS coastal and estuarine unit also began calculating nutrient loads at selected sites in northeastern Florida Bay and along the southwestern Everglades coast. The larger network has provided discharge information to researchers to develop nutrient budgets and loading (Rudnick, 1999; Sutula and others, 2003; Davis, 2004; and Levesque, 2004). In South Florida, changes in water-management practices to accommodate a large and rapidly growing urban population along the Atlantic coast, as well as intensive agricultural activities, have resulted in a highly managed hydrologic system. This managed system altered the natural hydrology of the Everglades ecosystem, including Florida Bay. During the last few decades, Florida Bay has experienced seagrass die-offs and algal blooms. Both are signals of ecological deterioration that has been attributed to increases in salinity and nutrient content of bay waters. With plans to restore water levels in the Everglades to more natural conditions, changes also are expected in the amount and timing of freshwater discharge through the major creeks into Florida Bay. Flow through the estuarine creeks through the Buttonwood Embankment and into Florida Bay is naturally controlled by the water level in the Everglades; regional wind patterns; and to a lesser extent, tides. Florida Bay restoration requires an understanding of the linkage between the amount of freshwater flowing into the bay and the salinity and quality of the bay environment. Historically, there has been no accurate quantification of the amount of freshwater being discharged into Florida Bay from the mainland due to the difficulties of accurately gaging flows in shallow, bi-directional, and vertically stratified streams. The project objectives are to determine the quantity, timing and distribution of freshwater flow into Florida Bay and adjacent estuaries, determine baseline hydrologic conditions and provide information on hydrologic change during the restoration process. This project helps determine how freshwater flow affects the health of Florida Bay, a critical component of the CERP, and how changes in water-management practices upstream (Taylor Slough and C-111 basins) directly influence flow and salinity conditions in the estuary. The project managers for this study include Eduardo Patino (1995-2000), Clinton Hittle (2001-2003), and Mark Zucker (2003 -present). proprietary +USGS_SOFIA_frnkflow Flow and Velocity Data CEOS_EXTRA STAC Catalog 1997-07-01 1998-07-31 -80.9, 25.25, -80.5, 25.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552937-CEOS_EXTRA.umm_json Velocity data are being provided in an Access database and Excel spreadsheets. The database summarizes the velocity data, site location and description, vegetative characteristics, and water quality parameters. The spreadsheet filters and averages the complete raw velocity data set per measurement. This data was collected at two minute intervals for 10 minutes. The file naming convention is as follows: (example) E07T31197.xls Column 1: E or W East or west from Taylor Slough Airboat Trail Column 2-3: Sequential numbers from the center of the transect Column 4-5: Sequential transect number Column 6-9: Month and Year data was collected. The objective of the project is to make the best possible estimate of the flow velocities and water depths across Buttonwood Embankment during high flow for use as model input for an ongoing project being conducted in the USGS-WRD office in Miami. Estimates will be used to provide management alternatives and will create salinity concentrations suitable for ecologic integrity. proprietary +USGS_SOFIA_gachemca Everglades Water chemistry - Cations and Anions CEOS_EXTRA STAC Catalog 1995-03-01 1995-03-31 -80.9, 25.59, -80.1, 26.79 https://cmr.earthdata.nasa.gov/search/concepts/C2231554630-CEOS_EXTRA.umm_json This data set contains the following parameters: Lab ID, site ID, lab pH, lab alkalinity, Cl, SO4, Ca, Mg, Na, K, and ion balance for 27 samples collected from 10 sites. It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylation and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiagency research project. proprietary +USGS_SOFIA_gachmdoc Everglades Water chemistry - DOC and other parameters CEOS_EXTRA STAC Catalog 1995-03-01 1995-03-31 -80.9, 25.59, -80.1, 26.79 https://cmr.earthdata.nasa.gov/search/concepts/C2231549042-CEOS_EXTRA.umm_json This data set contains the following parameters: Lab ID, site ID, DOC, specific UV, B, Ba, Fe, H4SiO4, Li, Mn, Sr, and Zn for 27 samples collected from 10 sites. It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylation and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiagency research project. proprietary USGS_SOFIA_gaines_04 Computer Simulation Modeling of Intermediate Trophic Levels for ATLSS of the Everglades/Big Cypress Region CEOS_EXTRA STAC Catalog 2002-02-01 2007-12-31 -81.39776, 24.686419, -80.277306, 26.264269 https://cmr.earthdata.nasa.gov/search/concepts/C2231555165-CEOS_EXTRA.umm_json This project includes models for primary food bases; the functional group of small fishes, upon which many of the wading birds depend, and the main reptile and amphibian functional groups, which constitute much of the diet of the American alligator. In addition, population models for several important species have been developed. These include a model for the snail kite population of Florida, models for the key wading bird species, and a model of the American crocodile population, all focusing on the effects of hydrology. This project has the goal of developing models for key components of the Everglades landscape as part of the overall Across Trophic Level System Simulation (ATLSS) program. The proposed work has four major objectives: 1. Provide rapid support for CERP by producing output and interpretation of requested runs of ATLSS models. 2. Complete an ATLSS model for the American crocodile that is in the final stage of work. 3. Validate models of the snail kite and the Cape Sable seaside sparrow. 4. Providing field work and habitat quality indices for effects of hydrology on selected small mammal and amphibian species. proprietary +USGS_SOFIA_gaqwfp Everglades Water Quality -Field Parameters CEOS_EXTRA STAC Catalog 1995-03-01 1995-03-31 -80.9, 25.59, -80.1, 26.79 https://cmr.earthdata.nasa.gov/search/concepts/C2231553178-CEOS_EXTRA.umm_json This data set contains the following parameters: Lab ID, site ID, collection date and time, field pH, field specific conductivity, and water temperature at 10 locations. It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylation and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiagency research project. proprietary +USGS_SOFIA_gaqwssi Everglades Water Quality - Site and Sample Information CEOS_EXTRA STAC Catalog 1995-03-01 1995-03-31 -80.9, 25.59, -80.1, 26.79 https://cmr.earthdata.nasa.gov/search/concepts/C2231555346-CEOS_EXTRA.umm_json This data set contains the following parameters: Lab ID, site ID, site name, latitude/longitude, sampling depth, sample type, subsample type, and method for 27 samples from 10 locations. It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylation and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiagency research project. proprietary +USGS_SOFIA_gawlik_wading_birds Effects of Hydrology on Wading Bird Foraging Parameters CEOS_EXTRA STAC Catalog 1998-12-01 2003-08-02 -80.625, 26.3, -80.125, 26.875 https://cmr.earthdata.nasa.gov/search/concepts/C2231551167-CEOS_EXTRA.umm_json The conceptual model for this study is based on the idea that hydroperiod is a long-term process that primarily influences the abundance, body size, and species composition of the prey community whereas water depth has immediate effects on individual birds by influencing their ability to capture prey. This study seeks to determine through field experiments, the proximate effects of water depth, prey density, prey size, and prey species on wading bird foraging parameters. The species of wading birds examined in this study are those in the ATLSS wading bird model: the Wood Stork, White Ibis, Great Egret, and Great Blue Heron. The recovery of wading bird populations has been identified as a key component of successful Everglades restoration. Proposed causes for the decline in wading bird numbers have in common the notion that current hydropatterns have altered the availability of prey. Indeed, food availability may be the single most important factor limiting populations of wading birds in the Everglades. In the face of conflicting management scenarios, knowing the relative importance of each component of food availability is a precursor to understanding the effects of specific water management regimes on wading birds. Ongoing modeling efforts in south Florida such as the ATLSS program, integrate such information and provide predictive power for future management decisions. Currently, the biggest information gap limiting the wading bird model of ATLSS is foraging success as a function of prey availability. The South Florida Water Management District (SFWMD) is currently conducting a series of experiments aimed at determining the effects of water management on the use of foraging sites by wading birds. Site-use data are available immediately after each experiment and thus allow for quick analyses and write-up. However, also as part of those experiments, we recorded on film, foraging behavior of wading birds at feeding sites with known prey availabilities. proprietary +USGS_SOFIA_geochem_asr_lo Geochemical Parameters to Evaluate Aquifer Storage and Recovery Reactions with Native Water and Aquifer Materials CEOS_EXTRA STAC Catalog 1999-08-01 2000-05-31 -81.08, 26.35, -80.28, 27.2 https://cmr.earthdata.nasa.gov/search/concepts/C2231550988-CEOS_EXTRA.umm_json The objective of this project was to determine geochemically significant water-quality characteristics of possible aquifer storage and recovery (ASR) source and receiving waters north of Lake Okeechobee and at a site along the Hillsboro Canal. The data from this study will be combined with similar information on the detailed composition of aquifer materials in ASR receiving zones to develop geochemical models. Such models are needed to evaluate the possible chemical reactions that may change the physical properties of the aquifer matrix and/or the quality of injected water prior to recovery. To meet water-supply needs of natural systems as well as existing and future urban and agricultural water demands in South Florida, the U.S. Army Corps of Engineers (Corps) has identified ASR near Lake Okeechobee and in other areas as a critical component needed to provide adequate water storage functions for successful Everglades restoration. Several ASR pilot studies have demonstrated the feasibility of storing and recovering potable water from the brackish Floridan aquifer system on a local scale in south Florida (Muniz and Ziegler, 1995; Pyne, 1995). However, to demonstrate the viability of ASR on a greatly expanded regional scale, as proposed by the Corps, considerably more water-quality information is needed to provide assurance that recovered water is suitable for intended uses. At present, little or no information exists to address the following questions: 1. Will interactions between injected water, aquifer material, and native ground water result in elevated levels of radionuclides or trace elements that would be of concern to human or environmental health? 2. What is the fate of nutrients (C, N, P) from injected surface water that could be stored in the aquifer for prolonged time periods? 3.Would chemically aggressive waters injected into target aquifers cause chemical reactions that would result in clogging, biological fouling, or extensive dissolution of aquifer material? 4. If disinfection of surface water is needed prior to injection, what is the fate of resultant disinfection byproducts in water stored in the aquifer? Geochemical models are used to answer these questions and to evaluate other geochemical processes that may affect water quality during ASR operations. These models require knowledge of the chemical composition of the injected (source) water, the native aquifer (receiving) water, and the aquifer materials. This study will provide the characterization of potential source and receiving water in areas of proposed ASR development that are needed for geochemical modeling. Characterization of aquifer materials will be done as part of a Federally funded study following exploratory drilling and recovery of core material from target zones in the Floridan aquifer system. The results of this study will also determine if seasonal changes in water chemistry will require the removal of undesirable constituents prior to injection. proprietary +USGS_SOFIA_geochem_mon_restore_fy04 Geochemical Monitoring of Restoration Progress CEOS_EXTRA STAC Catalog 1999-10-01 2004-09-30 -81.21, 24.72, -80.3, 25.27 https://cmr.earthdata.nasa.gov/search/concepts/C2231549034-CEOS_EXTRA.umm_json Continued geochemical monitoring efforts will provide a measure of the progress and effects of restoration on environmental health and water quality, and complement biological monitoring of indicator species. This information is essential for identifying when successful restoration has been accomplished. Additionally, this geochemical monitoring program will serve as a model for developing similar programs for monitoring other coastal and lacustrine environments targeted in future projects. Products include a productivity database for Florida Bay and bimonthly salinity, dissolved oxygen, pH, carbon speciation, and air:sea CO2 gas flux maps of Florida Bay. The flow of fresh water from the Everglades to Florida Bay and the interaction of Bay water with the Gulf of Mexico and Atlantic Ocean are critical processes that have defined the Florida Bay Ecosystem. Reconstruction of historical changes in the Florida Bay Ecosystem using paleoecological and geochemical data from cores and historical databases indicates that significant changes in water quality and circulation (McIvor et al., 1994; Rudnick et al., 1999; Boyer et al., 1999; Halley and Roulier, 1999; Swart et al., 1999), and biological species composition and ecology (Brewster-Wingard and Ishman, 1999; Fourqurean and Robblee, 1999; Hall et al., 1999; Zieman et al., 1999) have been coincident with alteration of drainage patterns in the Everglades and construction of bridges linking the Keys. Paleoecological data from cores also indicates that changes in the abundance of seagrass and algae in the Bay have been coincident with salinity changes and that significant loss of seagrass on mud banks and basins has occurred over the last several years. Stable isotope data from sediment cores indicate decreased circulation in the Bay coincident with railroad building and early drainage in South Florida. Water management practices in South Florida are already being altered in an effort to restore the Everglades and Florida Bay. Resulting changes in water chemistry will first affect biogeochemical processes, and may, subsequently, result in changes in species distributions (such as seagrass, algae, etc.) in the Bay. An extensive water quality monitoring program for Florida Bay has been in operation for several years. Primary participants include ENP - fixed water quality monitoring stations, NOAA -salinity, chlorophyll, and transmittance bimonthly surveys, SFWMD - northeast Bay and north coast monitoring, and Florida International University (FIU) - nutrient monitoring. These programs have provided detailed information on concentrations of water quality parameters in the Bay. However, in situ monitoring of key biogeochemical processes resulting directly from biological activity has not been undertaken. Monitoring changes in biogeochemical processes is critical to early identification of ecological response to restoration and predicting changes in species distribution within the Bay. Additionally, these processes may directly impact water quality. Calcification, photosynthesis, and respiration directly affect dissolved oxygen, pH, dissolved inorganic carbon and a number of other chemical characteristics of the water column. This information will enable managers to evaluate the progress and success of South Florida restoration efforts. proprietary +USGS_SOFIA_geophys_mon_fy04 Geophysical Monitoring of the Southwest Florida Coast CEOS_EXTRA STAC Catalog 1994-12-01 -81.23795, 25.051413, -80.30868, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231552919-CEOS_EXTRA.umm_json Water management decisions that impact Everglades restoration efforts require high quality data and reliable hydrologic models. Traditionally these data for hydrologic models have been obtained through observation wells. In the Everglades, this approach is limited by the difficult access due to water which covers most of the area and to the limited number of roads. Airborne geophysical techniques provide a means of accessing large parcels of land and developing three-dimensional resistivity models of the area. The overall objective of this project is the collection of geophysical data that can be used to develop ground-water flow models of the area capable of modeling saltwater intrusion. This objective includes mapping of subsurface electrical properties of the aquifer and correlation of lateral variation in these properties to aspects of aquifer geometry and water quality that are pertinent to hydrologic model development. Completion of combined ground and airborne geophysical surveys in Everglades National Park and Big Cypress National Preserve has shown the utility of these methods to map saltwater intrusion and provide geological information needed to develop ground-water flow models. The strategy that has been used is to interpret the HEM data as layered-earth resistivity models that slowly vary from place to place. Surface geophysical measurements (time-domain electromagnetic soundings) have been used to assist in this interpretation and provide an independent check on the HEM data. Borehole data in the form of formation resistivities and water quality sampling have allowed us to develop relationships for converting the interpreted resistivity-depth models into estimated water quality given as specific conductance (SC) or chloride concentration. This information is of great value to hydrologic modelers. These data will be used to develop a ground-water flow model which is bounded on the north by the Tamiami Trail, on the south by Florida Bay, on the east by the Atlantic coastal ridge, and on the west by the Gulf of Mexico. Completion of a combined ground and airborne geophysical study in the southern portion of Everglades National Park has shown the utility of these methods to map the extent of saltwater intrusion and provide geological information needed to develop ground-water flow models. The same approach should prove equally useful in the development of hydrologic models in the region to the west where little subsurface information exists. The approach requires three components: ground-based, airborne, and borehole electrical geophysical measurements. In combination these measurements can provide detailed information on the location of geologic and hydrologic boundaries essential for ground-water model development. The mapping of saltwater intrusion in coastal aquifers has traditionally relied upon observation wells and collection of water samples. This approach may miss important hydrologic features related to saltwater intrusion in areas where access is difficult and wells are widely spaced, such as the Everglades. To map saltwater intrusion in Everglades National Park, a different approach has been used. We have relied heavily on helicopter electromagnetic (HEM) measurements to map lateral variations of electrical resistivity, which are directly related to water quality. The HEM data are inverted to provide a three-dimensional resistivity model of the subsurface. Borehole geophysical and water quality measurements made in a selected set of observations wells are used to determine the relation between formation resistivity and specific conductance of pore water. Applying this relation to the 3-D HEM resistivity model produces an estimated water-quality model. This model provides constraints for variable density, ground-water models of the area. Time-domain electromagnetic (TEM) soundings have also be used to map saltwater intrusion. Because of the high density of HEM sampling (a measurement point every 10 meters along flight lines) models with a cell size of 100 meters on a side are possible, revealing features which could not be recognized from either the TEM or the observation wells alone. The very detailed resistivity maps show the extent of saltwater intrusion and the effect of former and present canals and roadbeds. The TEM survey provides a means of quickly obtaining a synoptic picture of saltwater intrusion, which also serves as a baseline for monitoring the effects of Everglades restoration activities. proprietary +USGS_SOFIA_german_et_04 Evapotranspiration Measuring & Modeling in the Everglades CEOS_EXTRA STAC Catalog 1994-10-01 2003-09-30 -80.83, 25.29, -80.26, 26.76 https://cmr.earthdata.nasa.gov/search/concepts/C2231554463-CEOS_EXTRA.umm_json The overall objective is to develop a process-oriented appraisal of evapotranspiration within the Everglades drainage unit, excluding agricultural and brackish environments. Specific objectives include: 1) Field measurement of evapotranspiration at a variety of sites encompassing a regionally representative range of environmental factors.; 2) Integration of evapotranspiration estimates into a process-oriented model; 3) Verification and refinement of model using ET measurements at additional sites. Everglades restoration efforts will rely heavily upon development of hydrologic flow models that will be used to help guide restoration and management decisions. Any hydrologic model requires an assessment of the water budget, including the amount of water removed from the system by evapotranspiration (ET). ET is a major part of the water budget in the Everglades, being similar in magnitude to rainfall. The Everglades ET project provides the necessary ET data, and methods of estimating ET throughout the Everglades system, that are required by all flow models. proprietary +USGS_SOFIA_german_et_data Evapotranspiration Data CEOS_EXTRA STAC Catalog 1996-01-01 2003-12-31 -81.02, 25.33, -80.33, 26.65 https://cmr.earthdata.nasa.gov/search/concepts/C2231552655-CEOS_EXTRA.umm_json "A regional evaluation of evapotranspiration (Et) in the Florida Everglades began in 1996 with operation of 9 sites at locations selected to represent the sawgrass or cattail marshes, wet prairie, and open-water areas that constitute most of the natural Everglades system. The Bowen-ratio energy-budget method was used to measure Et at 30-minute intervals. Site models were developed to determine Et for intervals when a Bowen ratio could not be accurately determined. Regional models were then developed for determining 30-minute Et at any location as a function of solar intensity and water depth using data from the 9 sites for 1996-97. Five of the original 9 sites continued in operation after 1997 for various periods. Two of these sites were operated continuously until September 2003. Three new sites were installed in the western part of Shark Valley in November 2001 for the purpose of testing regional model transferability. Additionally, an evaporation pan was installed at one site in April 2001 for comparing actual Et determined by the Bowen-ratio site with potential pan evaporation. All data collection ended in September 2003. The dataset contains the meteorological and evapotranspiration data. Additionally, tables listing model coefficients and goodness-of-fit statistics for site models for the period 1998-2003 are included, and tables listing a comparison for measured Et and Et estimated from the regional models. Data is available by year for each of the collection sites. The a_read_me file in the Data summary and data files for Everglades Et sites, 1996-2003 describes the format of data files of meteorological and evapotranspiration data. Additionally, tables listing model coefficients and goodness-of-fit statistics for site models for the period 1998-2003 are included, and tables listing a comparison for measured Et and Et estimated from the regional models. This latest data release is different in format from the original release for all data from 1998 on. No changes were made in the 1996-97 data. One change made in reporting format is that Et data from 1998 on are not smoothed by averaging over one or more measurement intervals. With this release data are provided at the measurement interval so that users may use whatever smoothing technique that is appropriate for the intended use. Another change in format for data from 1998 on is that Et sums are provided for ""raw"" and ""edited"" 30-minute periods. The ""raw"" data refer to Et sums that have not been edited from computed results, although the Et sum may be an actual measurement that has passed all input-data screening tests (see WRI 00-4217), or may be a ""gap-filled"" value computed from the Priestley-Taylor site mode that was developed using only data that passed all screening tests. Data in the ""edited"" column have been edited graphically by comparing each value to the pattern of Et defined by the entire set of data during part of a day. The final change in format for data from 1998 on is that a flag indicator is provided to show which 30-minute Et data are measured and which are model derived because the input data did not pass screening criteria. Everglades restoration efforts will rely heavily upon development of hydrologic flow models that will be used to help guide restoration and management decisions. Any hydrologic model requires an assessment of the water budget, including the amount of water removed from the system by evapotranspiration (ET). ET is a major part of the water budget in the Everglades, being similar in magnitude to rainfall. The overall objective was to develop a process-oriented appraisal of evapotranspiration within the Everglades drainage unit, excluding agricultural and brackish environments. Specific objectives included: 1) Field measurement of evapotranspiration at a variety of sites encompassing a regionally representative range of environmental factors and 2) Verification and refinement of model using ET measurements at additional sites." proprietary +USGS_SOFIA_gfr_bay Groundwater Flow Rates at the Bayside Well Cluster study site CEOS_EXTRA STAC Catalog 1996-08-20 1997-04-17 -80.469, 25.071, -80.469, 25.071 https://cmr.earthdata.nasa.gov/search/concepts/C2231550408-CEOS_EXTRA.umm_json The dataset contains the values for the dyes from the tracer study on the bayside of Key Largo. Treated sewage is injected into the limestone under the Florida Keys via on-site disposal systems (OSDs). There are 25,000 septic tank systems, approximately 5,000 cesspools, and approximately 1000 class 5 injection wells. Depths of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases and both marine grass and sponge mortality is perceived by the local population, NOAA, and EPA to be caused by sewage nutrients leaking from groundwater on both sides of the Florida Keys. Determining the rate and direction of saline groundwater movement beneath the Key, and the Florida Bay was considered critical to understanding the fate and effects of subsurface waste water disposal n the Florida Keys. The objective of this research was to determine the rate, direction of flow, and contamination levels of saline groundwater in the Florida Keys and Florida Bay. Contamination studies are necessary to determine if nutrient and other contaminant levels are rising and to provide a baseline of data for future decision making. proprietary +USGS_SOFIA_gfr_ocean Groundwater Flow Rates at the Oceanside Well Cluster study site CEOS_EXTRA STAC Catalog 1996-06-18 1997-04-17 -80.466, 25.067, -80.466, 25.067 https://cmr.earthdata.nasa.gov/search/concepts/C2231550135-CEOS_EXTRA.umm_json The dataset contains the values for the dyes from the tracer study on the oceanside of Key Largo. Treated sewage is injected into the limestone under the Florida Keys via on-site disposal systems (OSDs). There are 25,000 septic tank systems, approximately 5,000 cesspools, and approximately 1000 class 5 injection wells. Depths of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases and both marine grass and sponge mortality is perceived by the local population, NOAA, and EPA to be caused by sewage nutrients leaking from groundwater on both sides of the Florida Keys. Determining the rate and direction of saline groundwater movement beneath the Key, and the Florida Bay was considered critical to understanding the fate and effects of subsurface waste water disposal n the Florida Keys. The objective of this research was to determine the rate, direction of flow, and contamination levels of saline groundwater in the Florida Keys and Florida Bay. Contamination studies are necessary to determine if nutrient and other contaminant levels are rising and to provide a baseline of data for future decision making. proprietary USGS_SOFIA_gis_tool A GIS-based decision-support tool to evaluate land management policies in south Florida CEOS_EXTRA STAC Catalog 2005-01-01 2009-12-31 -81.7, 25.25, -80.25, 25.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231551931-CEOS_EXTRA.umm_json The primary objective of the project is to develop an integrated ecological and socioeconomic land use evaluation model (the Ecosystem Portfolio Model, EPM) for Department of the Interior (DOI) resource managers to use to reconcile the need to maintain the ecological health of South Florida parks and refuges with increasing pressures for higher density development in the agricultural lands outside of the Urban Development Boundary in Miami-Dade County. The EPM has three major components: (1) an ecological value model based on ecological criteria relevant to National Park Service and US Fish & Wildlife Service resource management and species protection mandates; (2) a real estate market-based land value model sensitive to relevant land use/cover attributes indicative of conservation and development decisions; and (3) a set of socioeconomic indicators sensitive to land use/cover changes relevant to regional environmental and ecological planning. The current version is implemented for Miami-Dade County, with the protection of ecological values in the lands between the Everglades and Biscayne National Parks as the focus. The first two components have been implemented in the GIS web-enabled prototype interface and the third component is being developed in draft form in FY08 in consultation with the Florida Atlantic University Dept of Urban and Regional Planning. South Florida’s national parks and wildlife refuges are threatened by accelerated growth of the surrounding built environment which alters the natural hydrology and ecology, and introduces harmful levels of sediment, nutrients and toxins. Department of the Interior (DOI) scientists and land managers are faced with major informational and financial challenges and conflicting stakeholder interests in their efforts to manage and protect resources to fulfill their stewardship responsibilities. The web-based EPM will contribute to improved public understanding and awareness of the importance of protecting South Florida habitats and ecosystem functions, as well as the possible externalities associated with upcoming land use decisions. proprietary USGS_SOFIA_glime_alt_ucu_arc Altitude of the top of the upper confining unit of the gray limestone aquifer, southern Florida, USGS WRIR 99-4213 figure 21 CEOS_EXTRA STAC Catalog 1995-10-01 1999-09-30 -81.375694, 25.364763, -80.25413, 26.54825 https://cmr.earthdata.nasa.gov/search/concepts/C2231552039-CEOS_EXTRA.umm_json The map shows the altitude of the top of the confining unit which ranges from 10 ft above sea level to 50 ft below sea level in much of the study area, and slopes downward to the east and southeast. The contour interval is 25 feet. The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer. proprietary USGS_SOFIA_glime_altbase_arc Altitude of the base of the gray limestone aquifer, southern Florida, USGS WRIR 99-4213 figure 15 CEOS_EXTRA STAC Catalog 1995-10-01 1999-09-30 -81.375725, 25.364763, -80.25426, 26.54827 https://cmr.earthdata.nasa.gov/search/concepts/C2231551718-CEOS_EXTRA.umm_json The base of the gray limestone aquifer is shallowest in Collier and Hendry Counties and slopes to the southeast and east. The altitude of the base of the aquifer generally ranges from 50 to 160 ft below sea level, but the basal surface can be comparatively irregular in some areas. The map shows the altitude of the base of the gray limestone aquifer in feet below sea level. The contour interval is 50 feet. The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer. proprietary USGS_SOFIA_glime_alttop_arc Altitude of the top of the gray limestone aquifer, southern Florida, USGS WRIR 99-4213 figure 14 CEOS_EXTRA STAC Catalog 1995-10-01 1999-09-30 -81.37565, 25.364769, -80.37922, 26.54827 https://cmr.earthdata.nasa.gov/search/concepts/C2231549573-CEOS_EXTRA.umm_json The top of the gray limestone aquifer is shallowest in Collier and Hendry Counties and slopes to the southeast and east. The altitude of the top of the gray limestone aquifer generally ranges between sea level and 100 ft below sea level in the study area. The map shows the altitude of the top of the gray limestone aquifer in feet bwelow sea level. The contour interval is 50 feet. The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer. proprietary +USGS_SOFIA_glime_ext_aq_polygon Extent of gray limestone aquifer (interpreted to be unconfined), southwestern Florida, USGS WRIR 99-4213, figure 29 CEOS_EXTRA STAC Catalog 1964-01-01 1999-03-31 -81.374565, 25.59151, -80.85008, 26.547632 https://cmr.earthdata.nasa.gov/search/concepts/C2231549505-CEOS_EXTRA.umm_json Leakance, which is the vertical hydraulic conductivity of the confining unit divided by its thickness, can be used to provide an indication of the degree of confinement of the aquifer. For purposes of this discussion, an aquifer is considered to be well confined, or have 'good confinement', if leakance was less than 1.0 x 10-3 1/d. Sites where leakance was determined by aquifer testing to be less than 1.0 x 10-3 1/d or the behavior of the aquifer was described as confined or well confined (tables 8 and 9) are shown in figure 29. These sites are located in southern Hendry County, western Broward County, and central Miami-Dade County and are in areas where the thickness of the confining unit approaches or is more than 50 ft. However, confining bed thickness did not necessarily prove to be a determinant of confinement. The map shows the extent of the gray limestone aquifer in southwestern Florida. Results from 35 new test coreholes and aquifer-test, water-level, and water quality data were combined with existing hydrogeologic data to define the extent, thickness, hydraulic properties, and degree of confinement of the gray limestone aquifer in Southern Florida. The western boundary is not mapped and is set to the western boundary of the study area. The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer. proprietary +USGS_SOFIA_glime_lim_ucu_arc Limit of the Upper Confining Unit of the Gray Limestone Aquifer, Southwestern Florida, USGS WRIR 99-4213, fig. 21 CEOS_EXTRA STAC Catalog 1995-10-01 1999-03-31 -81.37569, 25.567564, -80.854866, 26.342693 https://cmr.earthdata.nasa.gov/search/concepts/C2231551436-CEOS_EXTRA.umm_json The upper confining unit of the gray limestone aqifer in southwestern Florida ranges from 20 to 60 ft in thickness in most of the study area, but is absent to the west and southwest in much of Collier County and most of Monroe County. The upper confining unit exists east of the line in the data set, with two small circular areas depicting areas where the unit is absent (to the north) and present (to the south). The unit is also present to the southwest of the short line in the southwest part of the area. The map shows the limit of the upper confining unit of the gray limestone aquifer in Collier, Hendry, Miami-Dade, and Monroe counties. The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer. proprietary +USGS_SOFIA_glime_limit_arc Limit of Gray Limestone Aquifer, Southeastern Florida, USGS WRIR 99-4213 CEOS_EXTRA STAC Catalog 1995-10-01 1999-09-30 -81.37501, 25.364754, -80.25426, 26.54827 https://cmr.earthdata.nasa.gov/search/concepts/C2231549111-CEOS_EXTRA.umm_json The maps shows the limit of the gray limestone aquifer in southern Florida. The lower Tamiami aquifer is mapped as being present in most of western and northeastern Hendry County, which are outside of the study area. However, the limestones of the Tamiami Formation, which are included in the lower Tamiami aquifer, thin to the north, and sand and sandstone layers make up most of the thickness of the formation in central Hendry County. The easternmost extent of the gray limestone aquifer corresponds closely to the limits previously delineated by Fish (1988) and Fish and Stewart (1991). In northeastern Broward County, the eastern edge of the aquifer occurs at the transition from highly permeable limestone or contiguous shell sand to a significantly less permeable facies composed of sandy, clayey limestone and quartz sand and sandstone. In northeastern Miami-Dade County, the eastern limit of the aquifer is mapped where the aquifer merges with the Biscayne aquifer and the intervening semiconfining unit wedges out. South of the Tamiami Trail, the eastern boundary occurs at a transition to less-permeable siliciclastic sediments. The northern and western extents of the gray limestone aquifer were not defined in this study. The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer. proprietary USGS_SOFIA_glime_limit_brwd_east_arc Approximate Eastern Limit of the Gray Limestone Aquifer in Broward County, USGS WRIR 87-4034, figure 36 CEOS_EXTRA STAC Catalog 1981-01-01 1984-12-31 -80.46253, 25.95879, -80.24904, 26.360125 https://cmr.earthdata.nasa.gov/search/concepts/C2231553449-CEOS_EXTRA.umm_json An investigation of the surficial aquifer system in Broward County, begun in 1981, is part of a regional study of the aquifer system in southeast Florida. Test drilling for lithologic samples, flow measurements taken during drilling, aquifer testing, and analyses of previously available data permitted delineation of the permeability framework (on geologic sections), the aquifers in the system and the generalized transmissivity distribution, and interpretation of the ground-water flow system. In addition to the Biscayne aquifer, a previously undefined aquifer, composed of gray (in places, greenish-gray or tan) limestone of the lower part and locally the middle part of the Tamiami Formation, was found at depth in west Broward County. Although it is less permeable than the Biscayne aquifer, the gray limestone is nevertheless a significant aquifer and a potential source of water. The aquifer is informally and locally named as the gray limestone aquifer. It is defined as that part of the limestone beds (usually gray) and contiguous coarse clastic beds of the lower to middle part of the Tamiami Formation that are highly permeable (having a hydraulic conductivity of about 100 ft/d or more) and at least 10 feet thick. Above and below the gray limestone aquifer in west Broward County and separating it from the Biscayne aquifer and the base of the surficial aquifer system are sediments having relatively low permeability, such as mixtures of sand, clay, silt, shell, and lime mud, and some sediments of moderate to low permeability, such as limestone, sandstone, and claystone. Subsequent drilling has traced the gray limestone aquifer into southwest Palm Beach County where the water contains high dissolved solids and into northwest Dade County where the water generally has low dissolved solids. The aquifer probably extend westward into Collier County, and it likely is the source of water for irrigation and drinking on the Seminole Indian Reservation and sugar cane fields of southeast Hendry County. The map shows the approximate eastern limit of the gray limestone aquifer in Broward County. Most previous work in southeast Florida had been concentrated in the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or overlying zones. Hence, information concerning the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system were insufficient for present needs. Because of persistent increases in demand from the surficial aquifer system in the highly populated and growing coastal area of southeast Florida and because of attendant concerns for the protection and management of the water supply, the U.S. Geological Survey, in cooperation with the South Florida Water management District, began an investigation to define the extent of the surficial aquifer system and its characteristics on a regional scale. The overall objectives of the regional study are to determine the hydrogeologic framework, the extent and thickness of the surficial aquifer system and the aquifers within it, the areal and vertical water-quality distribution and factors that affect the water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system. proprietary USGS_SOFIA_glime_limit_dade_east_arc Approximate Eastern Limit of the Gray Limestone Aquifer in Dade County, USGS WRIR 90-4108, figure 14 CEOS_EXTRA STAC Catalog 1939-01-01 1989-12-31 -80.6464, 25.273907, -80.33076, 25.974077 https://cmr.earthdata.nasa.gov/search/concepts/C2231554562-CEOS_EXTRA.umm_json An aquifer identified by Fish (1988) in Broward County, composed of predominantly gray (in places, greenish-gray or tan) limestone of the lower part and locally the middle part of the Tamiami Formation, was identified at depths of about 70 to 160 ft below land surface in western Dade County. Although it is less permeable than the Biscayne aquifer, the gray limestone aquifer is still significant and is a potential source of water, particularly west of the western limit of the Biscayne aquifer. It is defined as that part of the limestone beds (usually gray) and contiguous, very coarse, elastic beds of the lower to middle part of the Tamiami Formation that are highly permeable (having a hydraulic conductivity of about 100 ft/d or greater) and at least 10 ft thick. Above and below the gray limestone aquifer in western Dade County, and separating it from the Biscayne aquifer and the base of the surficial aquifer system, are sediments having relatively low permeability, such as mixtures of sand, clay, silt, shell, and lime mud, as well as some sediments having moderate to low permeability, such as limestone, sandstone, and claystone. Drilling has identified the gray limestone aquifer in western Broward County and in southwestern Palm Beach County; in these areas, water in the aquifer contains high concentrations of dissolved solids. The aquifer may extend westward into Collier County, and it may be the source of water for irrigation of sugarcane fields in southeastern Hendry County and domestic use on the Seminole Indian Reservation. The map shows the approximate eastern limit of the gray limestone aquifer in Miami-Dade County. Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system. proprietary +USGS_SOFIA_grndwtr_seepage Groundwater Seepage in the Florida Keys CEOS_EXTRA STAC Catalog 1995-07-17 1996-08-20 -80.9, 24.8, -80.3, 25.2 https://cmr.earthdata.nasa.gov/search/concepts/C2231549417-CEOS_EXTRA.umm_json This project installed seepage meters to measure the volume of groundwater seepage into the overlying marine environment. The water will be analyzed for major nutrient levels. The data from this project include the site and values of seepage flux. The Florida Keys contain 25,000 septic tank systems, approximately 5000 cesspools, and 1000 class 5 injection wells. Depth of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases, and both marine grass and sponge lortality is perceived to be caused by sewage nutrients leaking from roundwater on both sides of the Florida Keys. Determining the volume and composition of groundwaters seeping into the marine environment from teh sea floor is vital to management decisions on the area. The objective of this study was to determine the volume and composition of groundwaters seeping upward through the rock water interface into Florida Bay and the coral reef tract. Submarine groundwater input into Florida Bay has been ignored by modelers and results show current models are likely to be erroneous. An additional major product will be an improved seepage meter design. proprietary +USGS_SOFIA_gw-sw_wq_everglades Groundwater-Surface Water Interactions and Relation to Water Quality in the Everglades CEOS_EXTRA STAC Catalog 1995-01-01 1998-12-31 -80.87469, 25.094265, -80.03598, 26.577616 https://cmr.earthdata.nasa.gov/search/concepts/C2231550617-CEOS_EXTRA.umm_json At present there are few reliable estimates of hydrologic fluxes between groundwater and surface water in the Everglades. This gap in hydrological investigations not only leaves the water budget of the Everglades uncertain, it also hampers progress in understanding the processes that determine mobility and transformation of contaminants, such as mercury, sulfate and nutrients. The objective of this project is to quantify hydrologic exchange fluxes between groundwater and surface water and its effects on transport of contaminants in the Everglades. The research furthermore relates surface water and ground water interactions to past, present, and proposed management of surface-water levels and flows in the Everglades. The principal research sites are the Everglades Nutrient Removal Project (ENR), Water Conservation Area 2A (WCA-2A), and the freshwater wetlands of Everglades National Park. Results are being used to quantify ground-water exchange with surface flow, and to quantify the enhancement of chemical transformations of contaminants during transport across the interface between surface water and ground water. Two data sets are available for this project. The Northern Everglades Research Site and Sample Information data set contains a summary of the site locations, data types, and measurement periods in ENR, WCA2A, and WCA2B. The Seepage Meters Site and Sample Information data set contains vertical fluxes across wetland peat surface measured by seepage meters at research sites in ENR, WCA2A, WCA2B, and WCA3A. Additional data can be found in the appendices of the Open-File Reports 00-168 and 00-483. For restoration of the Everglades to succeed there must be comprehensive knowledge about physical, chemical, and biological processes throughout the system. A key measure of success in the Everglades is the improvement or protection of water quality under changing hydrologic conditions. Although there is a basic understanding of how interactions between groundwater and surface water will affect water budgets under restoration, there is only a rudimentary understanding of how interactions between groundwater and surface water will affect water quality. Only field-oriented research and modeling can determine whether interactions between groundwater and surface water are currently storing pollutants in groundwater, how long those pollutants are likely to be stored in the aquifer, and under what changing management conditions associated with restoration will those pollutants be returned into the surface water system. proprietary +USGS_SOFIA_gw_flow_trans_TIME Ground Water Flow and Transport for the SICS and TIME Models CEOS_EXTRA STAC Catalog 2000-10-01 2006-09-30 -81.55504, 25.026571, -80.30481, 25.976713 https://cmr.earthdata.nasa.gov/search/concepts/C2231552234-CEOS_EXTRA.umm_json "The objective of this project is to develop a numerical groundwater flow model that can be used with the TIME surface water model to quantify and predict flows and salinities in the coastal wetlands of the southern Everglades. Field data will be collected to help formulate the hydrogeologic conceptual model and for calibration of the model to flows, water levels, and salinities. Data collection will consist of monitoring well installation, seepage measurements, spatial characterization of peat thickness, and continuous monitoring of water levels and salinities at selected locations. The SICS model encompasses Taylor Slough and uses a 300-m grid resolution. The larger TIME model encompasses Shark and Taylor Sloughs and uses a 500-m grid resolution. A groundwater model has already been developed and linked with the SICS surface water model. This integrated SICS model simulates flows, stages, and salinities for the 5-year period from 1995 to 2000. Plans for the SICS model are to extend the simulation period through 2002 and complete a linkage to the South Florida Water Management District’s model, called the ""2x2"" model. The SICS model will then be capable of performing detailed restoration scenarios for the Taylor Slough area. A preliminary groundwater model has also been developed for the TIME area, but this groundwater model has not yet been linked with a surface water model. Ray Schaffranek is currently finalizing a 3-month simulation with the TIME surface water model. As part of this project, the groundwater model will be linked with the TIME surface water model, and the simulation period will be extended to cover 2 years. A related CERP (Comprehensive Everglades Restoration Plan) project will extend this simulation period to 7 years and link with the 2x2 to perform Everglade restoration scenarios. This project also involves quantifying surface water and groundwater interactions by using nested monitoring wells and seepage meters. Data from the field studies are used to calibrate and verify the SICS and TIME models. The interaction between surface water and groundwater can be a potentially significant component of the hydrologic water budget in the Everglades. Recent research has shown that surface water and groundwater interactions also can affect salinities in coastal wetlands. As Everglades restoration is largely dependent upon ""getting the water right"", the U.S. Geological Survey is developing the TIME (Tides and Inflows in the Mangroves of the Everglades) and SICS (Southern Inland and Coastal Systems) models, hydrodynamic surface water models of the southern Everglades. The purpose of the TIME and SICS models is to accurately simulate flows and salinities in the coastal wetlands of the southern Everglades. Once calibrated, these models will be used to evaluate proposed restoration scenarios by feeding hydrologic information into the ATLSS biological models. These biological models are highly sensitive to hydrologic inputs such as flows, stages, and salinities; thus, the TIME and SICS models are expected to play an important role in linking the hydrologic component of the Everglades to the biologic component. In recent years, this project focused on developing a groundwater component for the SICS model, an integrated model of Taylor Slough and northern Florida Bay. The SICS model is now calibrated, operational, and providing important insight into the flow and salinity patterns of the southern coastal Everglades. Hydrologic output from the SICS model is being used in development of ATLSS fish models. The next step with this groundwater project is to extend the methodologies developed as part of the SICS modeling effort to the much larger TIME model. This project is now part of the SICS and TIME model linkages and development in support of Everglades Restoration project." proprietary +USGS_SOFIA_hansen_1890_trackline_map_version 1 Florida Bay 1890 trackline map CEOS_EXTRA STAC Catalog 1889-01-01 1890-12-31 -81.11667, 24.733334, -80.36667, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549703-CEOS_EXTRA.umm_json The map shows the tracklines for historical bathymetric data for Florida Bay. The areas on the map are linked to the corresponding data sets which contain values for X (easting), Y (northing), Z (elevation), and the RMS set to 0.0. Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay has not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Digitizing the historical shoreline and bathymetric data for comparison with modern data provides information on sedimentation rates within the Bay. proprietary +USGS_SOFIA_hansen_1990_trackline_map_version 1 Florida Bay 1990 trackline map CEOS_EXTRA STAC Catalog 1995-01-01 1999-12-31 -81.11667, 24.733334, -80.36667, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549559-CEOS_EXTRA.umm_json The map shows the tracklines for bathymetric data collected between 1995 and 1999 for Florida Bay. The areas on the map are linked to the corresponding data sets which contain values for X (easting), Y (northing), Z (elevation), and the RMS computed from Ashtech PNAV software. The data set is labeled 1990 for easy comparison. The project duration was a decade. Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay has not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. An accurate, modern bathymetric survey provides a baseline for assessing future sedimentation rates in the Bay, and a foundation for developing a sediment budget. Due to the complexity of the Bay and age of existing data, a current bathymetric grid (digitally derived from the survey) is critical for numerical models. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Application of these models is complicated due to the complex seafloor topography (basin/mudbank morphology) of the Bay. The only complete topography data set of the Bay is 100 years old. Consequently, an accurate, modern seafloor bathymetry map of the Bay is critical for numerical modeling research. A modern bathymetry data set will also permit a comparison to historical data in order to help access sedimentation rates within the Bay. The objective of this research was to collect new bathymetry for all of Florida Bay, digitize the historical shoreline and bathymetric data, compare previous data to modern data, and produce maps and digital grids of historical and modern bathymetry. proprietary +USGS_SOFIA_hardness_swp_lnwr Effects of water hardness on slough-wet prairie plant communities of the A. R. M. Loxahatchee National Wildlife Refuge CEOS_EXTRA STAC Catalog 2006-01-01 -80.5, 26.3, -80.25, 26.7 https://cmr.earthdata.nasa.gov/search/concepts/C2232411613-CEOS_EXTRA.umm_json Alterations to ground-water and surface-water hydrology and water chemistry in South Florida have contributed to increased flows of mineral-rich (hard water) canal water into historically rain-fall driven (soft water) areas of the Everglades. The interior of the A. R. M. Loxahatchee National Wildlife Refuge largely has retained its historic low conductivity or soft water condition due to its relative isolation from canal flows. However, recent sampling by USGS and the Refuge has shown that canal influences on water quality extend several kilometers into the Refuge in some areas, and Refuge managers and scientists are concerned that these influences may increase depending on future changes in water management operations. A survey across existing mineral gradients will be performed to document patterns of vegetation change and their relationship to changes in water hardness and other environmental factors. Laboratory and field experiments will test these correlative relationships to determine the relative importance of increasing water hardness as a cause of observed vegetation changes across canal gradients. Intrusion of canal waters into the Refuge increases the availability of Phosphorus (P), the primary limiting plant nutrient in the Everglades, as well as concentrations of major mineral ions such as Ca 2+, Mg 2+ and SO4 2-. While the ecological effects of P enrichment on the Everglades is fairly well understood, potential impacts caused by increased mineral concentrations in this soft-water wetland are largely unknown. Understanding the types and magnitude of these impacts is particularly important given that the area of the Refuge exposed to mineral enrichment is much greater than that exposed to P enrichment. The objective of this project is to determine the effects of increased flows of mineral-rich water on the aquatic plant community of the Refuge interior. Slough-wet prairie (SWP) habitats area a major landscape feature in the Refuge and several SWP plant species may be adapted to the soft-water conditions in the Refuge interior. Increased mineral loads to the Refuge may result in a shift towards a more species-poor and spatially homogeneous community, In addition, there is a small amount of evidence to suggest that mineral enrichment may favor the growth and expansion of sawgrass and a consequent decline in the coverage of the SWP habitats. proprietary +USGS_SOFIA_helio_mag_data Helicopter Magnetic Data CEOS_EXTRA STAC Catalog 1994-12-09 2994-12-14 -81.125, 25.125, -80.375, 25.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231553766-CEOS_EXTRA.umm_json These helicopter electromagnetic data were flown over a portion of Everglades National Park and surrounding areas in south Florida 9-14 December 1994. Two versions of the data are provided: the original dataset and the corrected dataset. This project addressed the question of determining the location of the fresh-water/salt-water interface (FWSWI) in the coastal regions of southern Miami-Dade and Monroe counties, synoptic monitoring of changes in water quality associated with changes in water management practices, and looking for geophysical evidence of subsurface discharges to Florida Bay. This project provided basic information needed to create ground-water models and test various restoration strategies and their impact on ground-water quality. proprietary +USGS_SOFIA_hi_accuracy_elev_collection_04 High Accuracy Elevation Data Collection Project CEOS_EXTRA STAC Catalog 1995-01-01 2007-12-31 -81.625, 25, -80.125, 27.33 https://cmr.earthdata.nasa.gov/search/concepts/C2231551560-CEOS_EXTRA.umm_json The High Accuracy Elevation Data Project collected elevation data (meters) on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters to define the topography in South Florida. The data are referenced to the horizontal datum North American Datum 1983 (NAD 83) and the vertical datum North American Vertical Datum 1988 (NAVD 88). The High Accuracy Elevation Data Project began with a pilot study in FY 1995 to determine if the then state-of-the-art GPS technology could be used to perform a topographic survey that would meet the vertical accuracy requirements of the hydrologic modeling community. The initial testing platform was from a truck and met the accuracy requirements. In some areas, the surveying was accomplished using airboats. Because access was a logistical problem with airboats, the USGS developed a helicopter-based instrument known as the Airborne Height Finder (AHF). All subsequent data collection used the AHF. Data were collected from the Loxahatchee National Wildlife Refuge, south through the Water Conservation Areas (1A, 2A, 2B, 3A, and 3B), Big Cypress National Park, the Everglades National Park, to the Florida Bay. Data were also collected in the Lake Okeechobee littoral zone. The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html. The work was performed for Everglades ecosystem restoration purposes. The project started in 1995 and concluded in 2007. This project performed regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that were being developed for ecosystem restoration activities. Surveying services were also rendered to provide vertical reference points for numerous water level gauges. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) were collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques. proprietary +USGS_SOFIA_high_acc_elev_data High Accuracy Elevation Data CEOS_EXTRA STAC Catalog 1995-01-01 -81.375, 25.125, -80.125, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231549649-CEOS_EXTRA.umm_json The U.S. Geological Survey (USGS) is coordinating the aquisition of high accuracy elevation data. Three formats of the data are available for each data set: .cor files which contain complete lists of Global Positioning System point files, .asc files which are the same as the .cor files but have been reformatted to process into ARC/INFO coverages, and .e00 files which are the ARC/INFO coverages. The files are available in the same 7.5- by 7.5-minute coverages as USGS quadrangles. The elevation data is collected on a 400 by 400 meter grid. The elevations are referenced to the horizontal North American Datum of 1983 (NAD83) and vertical North American Vertical Datum of 1988 (NAVD88). This project is performing regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that are being developed for ecosystem restoration activities. Surveying services are also being rendered to provide vertical reference points for numerous water level gauges. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists have determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) are being collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques. proprietary +USGS_SOFIA_highres_bathy_sfl_est-coast_sys High Resolution Bathymetric Mapping of South Florida Estuarine and Coastal Systems CEOS_EXTRA STAC Catalog 2002-10-01 2005-09-30 -82.125, 25.75, -81.625, 26.875 https://cmr.earthdata.nasa.gov/search/concepts/C2231554886-CEOS_EXTRA.umm_json The plan to acquire bathymetric data for the Caloosahatchee Estuary and Estero Bay areas is to employ two methods which have been developed by the U. S. Geological Survey (USGS) and National Aeronautical and Space Administration (NASA). The USGS method is an acoustic based system named System for Accurate Nearshore Depth Surveys (SANDS), and the NASA method is an airborne LIDAR system named Experimental Advanced Airborne Research Lidar (EAARL). The plan is to use the EAARL system to map shallow (less than 1.5 secchi depth) and non-turbid areas in Estero Bay and nearshore areas. The SANDS system will be used in deeper areas and those which are turbid which includes the Caloosahatchee River. High resolution, GPS based bathymetric surveying is a proven method to map river, lake, and ocean floor elevations. Of primary interest to the South Florida Water Management District (SFWMD) is the quantification of the present day bathymetry of Caloosahatchee Estuary and Estero Bay regions. This information can be used by water management decision-makers to develop of Minimum Flows and Levels (MFL) and better preserve fragile habitats. The areas in and around the Caloosahatchee Estuary and Estero Bay Watershed have undergone dramatic increases in the rate of residential and commercial development as well as population growth during the past 15 years. As a result, a series of initiatives have been proposed to balance development and environmental interests in the region. Several recent initiatives including the development MFL and the Southwest Florida Feasibility Study (SWFFS) necessitate the development of hydrodynamic models of coastal waters in the Caloosahatchee Estuary and Estero Bay areas. One of the important data requirements for these models is the bathymetry. The information available at this time is dated (the last complete bathymetric survey is over 100 years old) and needs to be upgraded with a new survey. In addition, recommendations of the Estero Bay and Watershed Assessment completed in November of 1999 recommended the development of a Bay hydrodynamic and water quality model. Updated river, bay, and coastal bathymetry is required for these efforts. The area for bathymetry collection and interpretation includes Estero Bay, Charlotte Harbor, Pine Island Sound, offshore regions of Sanibel and Captive Islands, and the Caloosahatchee, Loxahatchee and St. Lucie Rivers. In addition, a need for an Estero Bay and Charlotte Harbor estuarine mixing model has been identified by the Southwest Florida Regional Restoration Coordination Team and the Southwest Florida Feasibility Study. In order to create an accurate numerical model, current bathymetric data must be obtained. Bathymetry data is also needed for the creation of a seagrass vision maps (an NEP effort) and to populate the species response models being created as assessment tools for several restoration programs. proprietary +USGS_SOFIA_hist_salinity_wq_veg_bb_04 Historical Changes in Salinity, Water Quality, and Vegetation in Biscayne Bay CEOS_EXTRA STAC Catalog 1994-02-24 2007-04-03 -80.42, 25.17, -80.08, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231553154-CEOS_EXTRA.umm_json The objectives of this project are to examine in broad context the historical changes in the Biscayne Bay ecosystem at selected sites on a decadal-centennial scale, and to correlate these changes with natural events and anthropogenic alterations in the South Florida region. Specific emphasis will be placed on historical changes to 1) amount, timing, and sources of freshwater influx and the resulting effects on salinity and water quality; 2) shoreline and sub-aquatic vegetation; and 3) the relationship between sea-level change, onshore vegetation, and salinity. In addition, a detailed examination of historical seasonal salinity patterns will be derived from biochemical analyses of molluscs, ostracodes, foraminifera and corals. The corals will allow us to compare marine and estuarine trends, examine the linkage between the two systems, and will provide precise chronological control. Land management agencies (principally SFWMD, ACOE and Biscayne NP) can use the data derived from this project to establish performance criteria for restoring natural flow, and to understand the consequences of altered flow. These data can also be used to forecast potential problems as upstream changes in water delivery are made during restoration. During the last century, Biscayne Bay has been greatly affected by anthropogenic alteration of the environment through urbanization of the Miami/Dade County area, and alteration of natural flow. The sources, timing, delivery, and quality of freshwater flow into the Bay, and the shoreline and sub-aquatic vegetation have changed. Current restoration goals are attempting to restore natural flow of fresh water into Biscayne and Florida Bays and to restore the natural vegetation, but first we must address of what the was environment prior to significant human alteration in order to establish targets for restoration. This project is designed to examine the natural patterns of temporal change in salinity, water quality, vegetation, and benthic fauna in Biscayne Bay over the last 100-300 years and to examine the causes of change. proprietary +USGS_SOFIA_hlms_physical_data Florida Bay Physical Data CEOS_EXTRA STAC Catalog 1994-02-24 1997-06-13 -81.75, 24.75, -80.1, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549037-CEOS_EXTRA.umm_json "The dataset contains the core number, depth (cm), wet bulk density, dry bulk density, accumulated dry bulk density, dry bulk fines, total % H2O content, % insoluble residue, % loss on ignition, coarse (% dry wt.) > 0.062 mm, fines (% dry wt.) < 0.062 mm, and total Pb-210 activity (dpm/g and error). The use of radioactive isotopes is founded on the known physical property of radioactive material, the half-life. A half-life of an isotope is the amount of time it takes for half of a given number of atoms to ""decay"" to another element. The age of objects that contain radioactive isotopes with known half-lives can be calculated by determining the percent of the remaining radioactive material. To use this method successfully certain other prerequisites must be met. These are: 1. the chemistry of the nuclide (element) is known; 2. once the nuclide is incorporated into the substrate the only change is radioactive decay, and 3. in order to be useful, it is relatively easy to measure." proprietary +USGS_SOFIA_hlms_radchem_data Florida Bay Radiochemical data CEOS_EXTRA STAC Catalog 1994-02-07 1997-06-13 -81.75, 24.75, -80.1, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231548761-CEOS_EXTRA.umm_json "The datasets contains the core number, depth (cm), and Ra-226 activity(dpm/g) and Ra-226 error (dpm/g). The use of radioactive isotopes is founded on the known physical property of radioactive material, the half-life. A half-life of an isotope is the amount of time it takes for half of a given number of atoms to ""decay"" to another element. The age of objects that contain radioactive isotopes with known half-lives can be calculated by determining the percent of the remaining radioactive material. To use this method successfully certain other prerequisites must be met. These are: 1. the chemistry of the nuclide (element) is known; 2. once the nuclide is incorporated into the substrate the only change is radioactive decay, and 3. in order to be useful, it is relatively easy to measure." proprietary +USGS_SOFIA_hlmsclog Florida Bay core logs disposition and analysis parameters CEOS_EXTRA STAC Catalog 1994-02-07 1997-06-13 -81.096, 24.9843, -80.4082, 25.2091 https://cmr.earthdata.nasa.gov/search/concepts/C2231554350-CEOS_EXTRA.umm_json "The data set contains the core number, location, latitude/longitude, date collected, storage location, core surface description, and analyses for cores taken from Rabbitt Key, Cluett Key, Whipray Basin, Bob Allen Key, Rankin Bight, Lake Ingraham, Russell Bank, Johnson Key, Porjoe Key, Trout Creek, Little Madeira Bay, Crocodile Point, Pass Key, and Park Key. The use of radioactive isotopes is founded on the known physical property of radioactive material, the half-life. A half-life of an isotope is the amount of time it takes for half of a given number of atoms to ""decay"" to another element. The age of objects that contain radioactive isotopes with known half-lives can be calculated by determining the percent of the remaining radioactive material. To use this method successfully certain other prerequisites must be met. These are: 1. the chemistry of the nuclide (element) is known; 2. once the nuclide is incorporated into the substrate the only change is radioactive decay, and 3. in order to be useful, it is relatively easy to measure." proprietary +USGS_SOFIA_hydro_flow_TT Modeling hydrologic flow and vegetation response across the Tamiami Trail and coastal watershed of Ten Thousand Islands NWR CEOS_EXTRA STAC Catalog 2006-01-01 2010-09-30 -81.7, 25.6, -81.4, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231553905-CEOS_EXTRA.umm_json The proposed study capitalizes on field expertise and existing decision support tools to assess the benefits and/or consequences of CERP hydrologic goals and projects on mangrove/marsh habitat for park and refuge lands of the Greater Everglades system. The primary goal of this study is to monitor and model surface water, groundwater, and evapotranspiration fluxes across a major hydrological barrier in south Florida (U.S. Hwy. 41, Tamiami Trail), and across the oligohaline-estuarine gradient of Ten Thousand Islands National Wildlife Refuge (TTINWR). This research will record the rate and stage of water flow under varying climatic conditions (e.g., wet and dry season) across the coastal margin of TTINWR prior to and following implementation of hydrologic restoration outlined for the Picayune Strand Restoration Project (and Southern Golden Gate Estates Hydrologic Restoration). Overall project tasks and objectives include: gaging hydrologic conditions, surveying ground and water elevations, correlating hydroperiod and plant associations, monitoring intra-annual growth response to climate and hydrology, and modeling hydrologic coupling and vegetative succession. Major restoration projects have been proposed to restore freshwater flow across the Tamiami Trail (U.S. 41) into coastal marshes and estuaries of the northern Everglades including Big Cypress National Preserve and Ten Thousand Islands National Wildlife Refuge (TTINWR) with little or no understanding of the hydrologic coupling and potential impact to vegetation communities. Monitoring activities and models are needed to assess the hydrologic exchange across the Tamiami Trail and at the estuarine interface within the coastal watersheds of TTINWR. Under the proposed Picayune Strand Restoration Project, plugs and culverts will be installed to shunt more freshwater across the Tamiami Trail north-to-south akin to historic flows which will alter the stage, discharge, timing, and distribution of flow across the marsh/mangrove coastal margin. There is a critical need for current hydrologic and vegetation data to understand current processes and relations controlling hydroperiod, salinity, and plant succession under pre-project conditions and climate in order to build models and to predict how increasing freshwater flow and sea-level rise will impact future habitat quality and distribution. This study will establish a stratified network of gaging stations to monitor continuous water levels and salinity conditions associated with vegetation type and growth response and to produce a hydrodynamic model to predict changes in hydroperiod and salinity under different rates of freshwater inflow, pre- and post-project. Gaging stations will be surveyed to vertical datum to create a digital elevation model of both land and water surface that can be used to calibrate hydroperiod and salinity relations that control vegetation growth and succession. Model applications will be extended to predict vegetation migration and succession under changing freshwater delivery regimes and changing sea-level under projected climate change. For additional information about this project, please contact : Ken Krauss 700 Cajundome Blvd. Lafayette, LA, 70506 voice: 337 266-8882 fax: 337 266-8592 email: kkrauss@usgs.gov proprietary +USGS_SOFIA_hydro_mon_joe_bay Hydrologic Monitoring in Joe Bay CEOS_EXTRA STAC Catalog 1999-05-01 2006-09-30 -80.59, 25.223, -80.524, 25.232 https://cmr.earthdata.nasa.gov/search/concepts/C2231550553-CEOS_EXTRA.umm_json The datasets contain values collected at 15 minute and hourly intervals for stage (water level), discharge (flow), salinity, and temperature between 1999 and 2006. The stage is measured in feet relative to NAVD 88, the discharge in cubic feet per second (cfs), temperature in degrees Celsius, and salinity in parts per thousand (ppt). The data are referenced to date and time in hours and minutes. Joe Bay is the primary hydrologic connection between the freshwater Everglades and northeastern Florida Bay. Flow and salinity monitoring by the U.S. Geological Survey (USGS) has determined that Trout Creek is the largest contributor of freshwater flow to northeastern Florida Bay and is connected to Joe Bay (Hittle and others 2001). Sources of freshwater to Joe Bay include Taylor Slough and the C-111 Canal. Hydrologic parameters such as water level, discharge, and salinity observations in conjunction with water quality sampling have been useful in determining contributions of freshwater flow from Taylor Slough and C-111 Canal to Joe Bay (Zucker 2003). Hourly salinity data has been collected at four locations in Joe Bay since May 1999. In 2001, three index velocity stations were installed at Joe Bay 2E, Joe Bay 5C, and Joe Bay 8W. The current monitoring network in Joe Bay can assist with determining the effect upstream restoration efforts have on the timing and distribution of freshwater flows into northeastern Florida Bay. proprietary +USGS_SOFIA_hydro_mon_net Hydrologic Monitoring Network: Data Mining and Modeling to Separate Human and Natural Hydrologic Dynamics CEOS_EXTRA STAC Catalog 2004-10-01 2008-09-30 -81, 25.75, -80.25, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231550030-CEOS_EXTRA.umm_json The objectives of the study include: (1) integration of hydrologic analysis and synthesis with biological studies; (2) separation of water level, stream flow, and salinity time series into the natural (tidal, climate) and anthropogenic components; and, (3) identification of additional areas where application of data mining techniques can address the DOI science needs in South Florida. New technologies in environmental monitoring have made it cost effective to acquire tremendous amounts of hydrologic and water-quality data. Although these data are a valuable resource for understanding environmental systems, often there is seldom a thorough analysis of the data. The monitoring network(s) supported by the Comprehensive Everglades Restoration Plan (CERP) records tremendous amounts of data each day and the data base incorporates millions of data points describing the environmental response of the system to changing conditions. To enhance the evaluation of the CERP data base, there is an immediate need to apply new methodologies to systematically analyze the data set to answer critical questions such as relative impacts of controlled freshwater releases, tidal dynamics, and meteorological forcing on streamflow, water level, and salinity. There also is a need to integrate longer-term hydrologic data with shorter-term hydrologic data collected for biological resource studies. This study will be undertaken as a series of pilot studies to demonstrate the efficacy of data mining techniques to evaluate CERP data and address hydrologic issues important to DOI's efforts in South Florida. In addition, preliminary assessment of the complete set of hydrologic data networks for further integration and analysis using data mining techniques will be conducted. proprietary +USGS_SOFIA_hydro_restoration_impacts_SW_FL Impacts of Hydrological Restoration on Three Estuarine Communities of the Southwest Florida Coast and on Associated Animal Inhabitants CEOS_EXTRA STAC Catalog 1999-10-01 2004-09-30 -81.375, 24.75, -80.25, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231553398-CEOS_EXTRA.umm_json This project sought to characterize habitat relationships between selected faunal groups and their mangrove environment on the Southwest Florida coast. We described how mangrove associated fish species are distributed in fringing forest habitat along a salinity gradient in the tidal portions of the Shark River; the ecology and population dynamics of diamondback terrapins in the Big Sable Creek complex; experimentally determined the preferred habitat of the specialist fish Rivulus marmoratus via field and laboratory experiments; and how the conversion of mangrove forests to intertidal mud flats in the Big Sable Creek complex has affected fish composition and use of those habitats. The overall strategy was to collect robust empirical field data on forage fish distribution and abundance that can serve multiple purposes: as performance measures in restoration assessment; as the beginning of a long-term dataset analogous to three very powerful datasets from other locales in the Greater Everglades Ecosystem: 15-20 yr from freshwater marshes, 10 yr from the mangrove ecotone of Taylor Slough and adjacent tidal creeks, and 10-12 yr from Florida Bay; and contribute to the basic ecological understanding of mangrove-associated fishes. proprietary +USGS_SOFIA_hydro_wq_ofr_00-168 Hydrologic measurements and water quality in ENR, WCA2 and WCA3 (OFR 00-168 appendixes) CEOS_EXTRA STAC Catalog 1995-06-01 1998-12-21 -80.45, 26.59, -80.37, 26.7 https://cmr.earthdata.nasa.gov/search/concepts/C2232411631-CEOS_EXTRA.umm_json At present there are few reliable estimates of hydrologic fluxes between groundwater and surface water in the Everglades. This gap in hydrological investigations not only leaves the water budget of the Everglades uncertain, it also hampers progress in understanding the processes that determine mobility and transformation of contaminants, such as mercury, sulfate and nutrients. The objective of this project is to quantify hydrologic exchange fluxes between groundwater and surface water and its effects on transport of contaminants in the Everglades. The research furthermore relates surface water and ground water interactions to past, present, and proposed management of surface-water levels and flows in the Everglades. The principal research sites are the Everglades Nutrient Removal Project (ENR), Water Conservation Area 2A (WCA-2A), and the freshwater wetlands of Everglades National Park. Results are being used to quantify ground-water exchange with surface flow, and to quantify the enhancement of chemical transformations of contaminants during transport across the interface between surface water and ground water. The datasets available in the appendixes of the OFR provide information on site locations and measurements in the Everglades Nutrient Removal (ENR) area and Water Conservation Area (WCA) 2A. For restoration of the Everglades to succeed there must be comprehensive knowledge about physical, chemical, and biological processes throughout the system. A key measure of success in the Everglades is the improvement or protection of water quality under changing hydrologic conditions. Although there is a basic understanding of how interactions between groundwater and surface water will affect water budgets under restoration, there is only a rudimentary understanding of how interactions between groundwater and surface water will affect water quality. Only field-oriented research and modeling can determine whether interactions between groundwater and surface water are currently storing pollutants in groundwater, how long those pollutants are likely to be stored in the aquifer, and under what changing management conditions associated with restoration will those pollutants be returned into the surface water system. proprietary +USGS_SOFIA_hydro_wq_ofr_00-483 Hydrologic Measurements and Water-Quality Data for Taylor Slough and vicinity (OFR 00-483 appendixes) CEOS_EXTRA STAC Catalog 1997-09-22 1999-10-28 -80.69, 25.25, -80.52, 25.38 https://cmr.earthdata.nasa.gov/search/concepts/C2231551903-CEOS_EXTRA.umm_json The data in the appendixes of the report are products of an investigation that quantified interactions between ground water and surface water in Taylor Slough in Everglades National Park. In order to define basic hydrologic characteristics of the wetland, depth of wetland peat was mapped and hydraulic conductivity and vertical hydraulic gradients in peat were determined. During specific time periods representing both wet and dry conditions in the area, the distribution of major ions, nutrients, and water stable isotopes throughout the slough were determined. The purpose of the chemical measurements was to identify an environmental tracer that could be used to quantify ground-water discharge. Data available in the appendixes include site locations and hydrologic characteristics of peat and individual tables for data collected on September 22-October 2, 1997; November 10, 1997; November 19-20, 1997; December 11-17, 1997; June 3-6, 1998; July 20-23, 1998; September 20-October 5, 1999; and October 25-28, 1999. For restoration of the Everglades to succeed there must be comprehensive knowledge about physical, chemical, and biological processes throughout the system. A key measure of success in the Everglades is the improvement or protection of water quality under changing hydrologic conditions. Although there is a basic understanding of how interactions between groundwater and surface water will affect water budgets under restoration, there is only a rudimentary understanding of how interactions between groundwater and surface water will affect water quality. Only field-oriented research and modeling can determine whether interactions between groundwater and surface water are currently storing pollutants in groundwater, how long those pollutants are likely to be stored in the aquifer, and under what changing management conditions associated with restoration will those pollutants be returned into the surface water system. proprietary +USGS_SOFIA_hydrology_data_zwp Hydrology Data CEOS_EXTRA STAC Catalog 1996-01-01 2007-12-31 -81.39, 24.96, -80.35, 25.87 https://cmr.earthdata.nasa.gov/search/concepts/C2231554877-CEOS_EXTRA.umm_json The data were produced by four separate projects: Coastal Gradients of Flow, Salintiy, and Nutrients; Freshwater Flows to Northeastern Florida Bay; Hydrologic Monitoring in Joe Bay; and Southwest Florida Coastal and Wetland Systems Monitoring. Data are available for 43 separate sites. The Water Resources Development Act (WRDA) of 2000 authorized the Comprehensive Everglades Restoration Plan (CERP) as a framework for modifications and operational changes to the Central and Southern Florida Project needed to restore the south Florida ecosystem. Provisions within WRDA 2000 provide for specific authorization for an adaptive assessment and monitoring program. A Monitoring and Assessment Plan (MAP) has been developed as the primary tool to assess the system-wide performance of the CERP by the REstoration, COordination and VERification (RECOVER) program. The MAP presents the monitoring and supporting enhancement of scientific information and technology needed to measure the responses of the South Florida ecosystem. Hydrologic information throughout the Everglades ecosystem is key to the development of restoration strategies and for future evaluation of restoration results. There are significant hydrologic information gaps throughout the Everglades wetlands and estuaries that need to be addressed, particularly along Florida’s southwest coast. Among these gaps are flow, water level, and salinity data. proprietary +USGS_SOFIA_impacts_20thcent Impact of 20th Century Water-Management and Land-Use Practices on the Coastal Hydrology of Southeast Florida CEOS_EXTRA STAC Catalog 1850-01-01 2000-12-31 -81, 25, -80, 27 https://cmr.earthdata.nasa.gov/search/concepts/C2231555185-CEOS_EXTRA.umm_json The data are available as Arc/Info coverages from USGS Circular 1275. The Landuse coverages are in Florida State Plane Cordinate System, east zone, units feet, zone 3601, datum NAD27. All other coverages are in UTM Coordinate System, unit meters, zone 17, datum NAD27. Saltwater intrusion into the surficial aquifer is a direct consequence of water-management practices, concurrent agricultural and urban development, and natural drought conditions. An important part of this synthesis is to link water-management practices (canal-discharge), consumptive water use, water levels within the surficial aquifer system, chloride concentrations, ground-water discharge, and Holocene paleohistory of the Florida Bay and Biscayne Bay. For example, a series of water table maps for specific selected 5-year increments have been developed to spatially identify the areal extent where long-term water levels within the surficial aquifer have declined and to compare these changes with movement of the interface. Such changes are also being compared with changes in coastal outflows from major canals to distinquish between long-term declines caused by regional drainage and a large number of municipal pumping centers. Paleontologic data are being used to prepare maps illustrate temporal changes in salinity within the Biscayne Bay over the last 150 years. Salinity changes within the bay are largely attributed to a decrease in ground-water and surface water discharge. This is a completed project. The GIS data layers have been updated as of 4/26/2006. The previous layers available from SOFIA have been replaced with the updated layers. proprietary +USGS_SOFIA_int_surf_water_flows_04 Internal Surface-water Flows CEOS_EXTRA STAC Catalog 1997-01-01 2004-12-31 -80.96, 25.13, -80.81, 26.26 https://cmr.earthdata.nasa.gov/search/concepts/C2231554603-CEOS_EXTRA.umm_json Proposed modified water deliveries to Indian Tribal Lands, Big Cypress National Preserve, and Water Conservation Area 3A require that flow and nutrient loads at critical points in the interior surface water network be measured. Defining the foundation for water levels, flows, and nutrient loads has become an important baseline for Storm Treatment Area 5 and 6 development, recent C-139 Basin flow re-diversions, and future L-28 Interceptor Canal de-compartmentaliztion including flow rerouting into the Big Cypress Preserve. Flow monitoring for the two primary flow routes for both L-28 Interceptor Canal and L-28 is key to developing this network. Data are available for L-28 Interceptor Canal below S-190, L-28 Canal above S-140, and L-28 Interceptor South . The accurate determination of flow through the interior canal networks south of Lake Okeechobee and the C-139 basin remains critical for water budgets and regional model calibrations as defined by the Everglades Forever Act of 1994 and due to the Comprehensive Everglades Restoration Plan (CERP) initiative to reroute Big Cypress Preserve flows. The implementation of strategically located stream flow gaging points and associated data collection for nutrients has helped define future surface-water flow requirements and has provided valuable baseline flow data prior to the establishment of the recently constructed northern Storm Treatment Areas (STA’s 5 and 6) and the Rotenberger Wildlife Management Area. Generating continuous flow data at selected impact points for interior basins has complemented the existing eastern coastal canal discharge network, and has allowed for more accurately timed surface-water releases while providing flow and nutrient monitoring after recent STA implementation. A unique multi-agency experiment was conducted with much success with the focus on cooperation and development of new instrumentation and acoustic flow-weight auto-sampler protocols. The original data collection and processing was provided by three separate entities at each site with responsibilities originally allocated between the U.S. Geological Survey (USGS), the Seminole Tribe of Florida, and SFWMD. USGS provides calibration, analysis and processing of acoustic velocity meters (AVM’s) and side-looking Doppler systems and stage shaft encoders, SFWMD provides data loggers with real-time flow-weighted algorithms, and radio frequency (RF) telemetry instrumentation. The Seminole Tribe provides auto-sampler service and funds nutrient load analysis through the USGS Ocala Lab. proprietary +USGS_SOFIA_integrating_manatee Effects of hydrological restoration on manatees: integrating data and models for the Ten Thousand Islands and Everglades CEOS_EXTRA STAC Catalog 2005-12-01 2008-09-30 -81.85, 25, -80.5, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231552686-CEOS_EXTRA.umm_json This project will extend previous studies into ENP, where manatees have not been intensively studied. To ascertain how restoration may affect the distribution and abundance of manatees in the region, an individual-based model has been under development, but completion of that model requires a hydrologic model for the rivers and estuaries affected by the accelerated Picayune Strand restoration. This study will provide integrated regional hydrologic models covering nearly the entire southwest coast below Naples, including portions of Picayune Strand and Big Cypress, providing much needed hydrologic modeling capabilities for evaluating restoration effects on coastal, estuarine, and freshwater ecosystems. This effort will enable us to model manatee response to restoration, and more adequately address science and management needs. Three tasks will be undertaken to develop the necessary components for this regional model: (1) Link the TIME hydrology model and the ATLSS manatee model to assess restoration effects in the Everglades and Picayune Strand, (2) Model changes to manatee thermal refugia due to hydrological restoration, and (3) Design and implement a regional manatee monitoring program using aerial surveys and use robust statistical analysis techniques to estimate manatee distribution and abundance before restoration. A significant population of the endangered West Indian manatee occurs in southwest Florida, throughout extensive estuarine and coastal areas within the Ten Thousand Islands (TTI; managed primarily by FWS) and Everglades National Park (ENP; managed by NPS). Planned restoration activities for the Everglades and Picayune Strand (an Acceler-8 project which discharges into TTI) may impact manatees by changing availability of freshwater for drinking, the quality and availability of seagrass forage, and the quality and availability of passive thermal basins used for refuge from lethal winter cold fronts. Changes in freshwater availability and forage are expected to result in a shift in manatee distribution, which could necessitate new management actions to reduce human-manatee interactions. Restoration also could negatively impact important passive thermal refugia by increasing cold sheet flow during winter or disrupting haloclines that maintain warm bottom layers of salty water. Recent telemetry and aerial survey studies of manatees in TTI have revealed much about their use of this area: this project will extend the study into ENP. proprietary +USGS_SOFIA_karst_model Linking a conceptual karst hydrogeologic model of the Biscayne aquifer to ground-water flow simulations from Everglades National Park to Biscayne National Park - Phase 1 CEOS_EXTRA STAC Catalog 2005-01-01 2009-12-31 -81.5, 25, -80, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231550454-CEOS_EXTRA.umm_json This project in being undertaken to develop a high-resolution 3-dimensional karst hydrogeologic framework of the Biscayne aquifer between Everglades National Park (ENP) and Biscayne National Park (BNP) using test coreholes, borehole geophysical logging, cyclostratigraphy, hydrostratigraphy, and hydrologic modeling. The development of an expanded conceptual karst hydrogeologic framework in this project will be used to assist development of procedures for numeric simulations to improve the monitoring and assessment of the response of the ground-water system to hydrologic changes caused by CERP-related changes in stage within the Everglades wetlands, including seepage-management pilot project implementation. Specifically, the development of procedures for ground-water modeling of the karst Biscayne aquifer in the area of Northern Shark Slough will help determine the appropriate hydrologic response to rainfall and translate that information into appropriate performance targets for input into the design and operating rules to manage water levels and flow volumes for the two Seepage Management Areas. Mapping of the karstic stratiform ground-water flow passageways in the Biscayne aquifer is recent and limited to a small area of Miami-Dade County adjacent to the Everglades wetlands. Extension of this karst framework between the Everglades wetlands and coastal Biscayne Bay will aid in the simulation of coupled ground-water and surface-water flows to Biscayne Bay. The development of procedures for modeling in the karst Biscayne aquifer will useful to the establishment of minimum flows and levels to the Biscayne Bay and seasonal flow patterns. Also, these improved procedures for simulations will assist in ecologic modeling efforts of Biscayne Bay coastal estuaries. Research is needed to determine how planned Comprehensive Everglades Restoration Plan (CERP) seepage control actions within the triple-porosity karstic Biscayne aquifer in the general area of Northeast Shark Slough will affect ground-water flows and recharge between the Everglades wetlands and Biscayne Bay. A fundamental problem in the simulation of karst ground-water flow and solute transport is how best to represent aquifer heterogeneity as defined by the spatial distribution of porosity, permeability, and storage. The triple porosity of the Biscayne aquifer is principally: (1) matrix of interparticle and separate-vug porosity, providing much of the storage and, under dynamic conditions, diffuse-carbonate flow; (2) touching-vug porosity creating stratiform ground-water flow passageways; and (3) less common conduit porosity composed mainly of bedding plane vugs, thin solution pipes, and cavernous vugs. The objectives of this project are to: (1) build on the Lake Belt area hydrogeologic framework (recently completed by the principal investigator), mainly using cyclostratigraphy and digital optical borehole images to map porosity types and develop the triple-porosity karst framework between the Everglades wetlands and Biscayne Bay; and (2) develop procedures for numerical simulation of ground-water flow within the Biscayne aquifer multi-porosity system. proprietary USGS_SOFIA_kendall_stable_isotopes Application of Stable Isotope Techniques to Identifying Foodweb Structure, Contaminant Sources, and Biogeochemical Reactions in the Everglades CEOS_EXTRA STAC Catalog 1995-03-01 1999-10-31 -81.0202, 25.2475, -80.3069, 26.6712 https://cmr.earthdata.nasa.gov/search/concepts/C2231553952-CEOS_EXTRA.umm_json "This is the largest isotope foodweb study ever attempted in a marsh ecosystem, and combines detailed, long-term, trophic and biogeochemical studies at selected well-monitored USGS/SFWMD/FGFFC sites with limited synoptic foodweb data from over 300 sites sampled during 1996 and 1999 by a collaboration with the EPA-REMAP program. The preliminary synthesis of the biota isotopes at USGS and 1996 REMAP sites provides a mechanism for extrapolating the detailed foodwebs developed at the intensive USGS sites to the entire marsh system sampled by REMAP. Furthermore, this unique study strongly suggests that biota isotopes provide a simple means for monitoring how future ecosystem changes affect the role of periphyton (vs. macrophyte-dominated detritus) in local foodchains, and for predictive models for foodweb structure and MeHg bioaccumulation under different proposed land-management changes. Data are available for the following sites: Cell 4, ENR-OUT, L7, Cell 3, LOX, North Holeyland, E0, F1, U3/Glory Hole, L35B, 2BS, L67, 3A-15, 3A-TH, Lostmans Creek, North Prong Creek, TS-7, and TS-9 for the plants and animals found at each site. A first step of the Everglades restoration efforts is ""getting the water right"". However, the underlying goal is actually to re-establish, as much as possible, the ""pre-development"" spatial and temporal distribution of ecosystems throughout the Everglades. Stable isotope compositions of dissolved nutrients, biota, and sediments provide critical information about current and historic ecosystem conditions in the Everglades, including temporal and spatial variations in contaminant sources, biogeochemical reactions in the water column and shallow subsurface, and trophic relations. Hence, the scientific focus of this project is to use stable isotope techniques to examine ecosystem responses (especially variations in foodweb base and trophic structure) to temporal and spatial variations in hydroperiod and contaminant loading for the entire freshwater Everglades. The major ""long-term"" objectives of this project have been to: (1) determine the stable C, N, and S isotopic compositions of Everglades biota, (2) use bulk and compound-specific isotopic ratios to determine relative trophic positions for major organisms, (3) examine the spatial and temporal changes in foodweb structures across the ecosystem, especially with respect to the effect of anthropogenically derived nutrients and contaminants from agricultural land uses on foodwebs, (4) evaluate the effectiveness of isotopic techniques vs. gut content analysis for determining trophic relations in the Everglades, (5) evaluate the role of algae vs. detritus/microbial materials in foodwebs for the entire freshwater marsh part of the Everglades, and (6) work with modelers to correctly incorporate food web and MeHg bioaccumulation information into predictive models. More recent and specific objectives include: (1) link our data on seasonal and temporal differences in foodweb bases and trophic levels with SFWMD, FGFFC, and USGS Hg datasets (first for large fish and, more recently, for lower trophic levels), (2) investigate the effects of seasonal/spatial changes in nutrients, water levels, and reactions on the isotopic compositions at the base of the foodweb (that affect our interpretation of relative trophic positions of organisms), and (3) continue our efforts to link our foodweb isotope data from samples collected at USGS-ACME and EPA-REMAP sites with the spatial environmental patterns observed by the REMAP program. This work started as part of the Aquatic Cycling of Mercury in the Everglades (ACME) project in 1996 and was made a separate project in 2000." proprietary +USGS_SOFIA_kitchens_snail_kite Estimation of Critical Parameters in Conjunction with Monitoring the Florida Snail Kite Population CEOS_EXTRA STAC Catalog 2000-10-01 2003-09-30 -83.32674, 24.229189, -79.897285, 29.138569 https://cmr.earthdata.nasa.gov/search/concepts/C2231550848-CEOS_EXTRA.umm_json Life history traits and the population dynamics of the snail kite may vary considerably across space and over time. Understanding the influence of environmental (spatial and temporal) variation on demographic parameters is essential to understanding the population dynamics of a given species. Recognition of information needs for management decisions and conservation strategies has resulted in an increased emphasis on correlations to spatial and temporal environmental variation in relation to demographic studies. The purpose if this study is to provide valid estimates of the demographic parameters of the snail kite, including temporal and spatial variability due to environmental factors. These parameters will be used in a predictive model of the snail kite already developed under the ATLSS Program (Mooij et al. 2002). The snail kite (Rostrhamus sociabilis) is an endangered species that resides in the highly fluctuating ecosystem in the central and southern Florida wetlands. Many demographic traits, such as stage-dependent survival, reproduction, and movement of the snail kite vary both temporally and spatially. How these demographic parameters vary as a function of environmental conditions, hydrology in particular, is crucial for understanding how the snail kite will respond to proposed changes in water regulation in South and Central Florida. In particular, these data are needed for testing and improving the existing spatially-explicit, individual-based ATLSS snail kite model, developed by Mooij and Bennetts, which has recently been delivered to Department of Interior and other agencies (Mooij et al. 2002). From these data and the model, projections can be made on snail kite response to any hydrologic scenario. Also, continued estimates will be made of the rate of population growth. Assessing the demographic parameters is critical for identifying and evaluating the effectiveness of management actions and conservation strategies. In addition, new modeling techniques, such as structural modeling are being explored to better understand the effects of hydrology on the snail kite. The objectives of this project are the following: 1. To monitor the status of the snail kite population trends in central and southern Florida. 2. To provide estimates of demographic parameters for the spatially explicit individual-based model in ATLSS. 3. To collaborate with Dr. Wolf Mooij of the Netherlands Institute of Ecology to use snail kite data to validate the snail kite model. proprietary USGS_SOFIA_la_florida "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from ""Down-Scaled"" AOGCM Climate Scenarios in Combination with Ecological Modeling" CEOS_EXTRA STAC Catalog 1970-01-01 2000-12-31 -92, 23, -75, 38.24 https://cmr.earthdata.nasa.gov/search/concepts/C2231554072-CEOS_EXTRA.umm_json The objectives of this project are to develop the knowledge necessary to make accurate predictions of the response of species and their ecosystems to climate change. We propose to down-scale predictions from a suite of coupled Atmosphere-Ocean General Circulation Models (AOGCMs) to make regional scale predictions for the southeastern United States. For the time being the hydrologic and biologic models are confined to Florida. Climate outputs will then be used as inputs to a suite of species / habitat / ecosystem models that are currently being used in two key areas: the Greater Everglades and Suwannee River-Big Bend as a proof of concept that down-scaled climate results can work in ecological forecast models. We will run three scenarios of Land Use/Land Cover (LULC): past (circa 1900), present, and future (2041-2070). Additional climate model runs will address the contribution of green house gasses to climate variability and change over the Florida peninsula. Model perturbation experiments will be performed to address sources of variability and their contribution to the output regional climate change scenarios. We will develop scenarios that specifically address potential changes in temperature (land and near sea surface) and rainfall fields over the peninsula. We will then provide these scenarios and modeling results to resource management groups (NGOs, state and federal) via workshops in which the scenarios will be used to predict responses of additional selected species, habitats and ecosystems. Our approach is to develop regional climate predictions and subsequent ecological predictions for two 30-year long time periods as well as for the present. The first 30-year period is the recent past, spanning the period from 1971-2000. This will be used as a control, with copious observations of both climate variables (e.g. rainfall, ET) and species (e.g. densities, ranges) to verify both climate and ecology model outputs and to serve as a baseline to systematically judge the impacts of an altered climate. The second 30-year time period will begin 30 years in the future and extend for the thirty years from 2041-2070. This is a time horizon that is immediately relevant to habitat management. proprietary +USGS_SOFIA_lake_okee_bathy_data Lake Okeechobee Bathymetry data CEOS_EXTRA STAC Catalog 2001-09-01 -81.125, 26.625, -80.5, 27.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231550957-CEOS_EXTRA.umm_json The data from the bathymetric mapping of Lake Okeechobee are provided in two forms: as raw data files and as elevation contour maps. High resolution acoustic bathymetric surveying is a proven method to map sea and lake floor elevations. Of primary interest to the South Florida Water Management District (SFWMD) is the quantification of the present day lakebed in Lake Okeechobee. This information can be used by water-management decision-makers to better assess the water capacity of the lake at various levels. proprietary +USGS_SOFIA_land_margin_ecosystems Dynamics of Land Margin Ecosystems: Historical Change, Hydrology, Vegetation, Sediment, and Climate CEOS_EXTRA STAC Catalog 2002-10-01 2009-12-31 -81.75, 25, -80.25, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231552313-CEOS_EXTRA.umm_json This project has three objectives (tasks): 1) operate and maintain the Mangrove Hydrology sampling network; 2) study the dynamics of coastal vegetation (mangroves, marshes) in relation to sea-level, fire, disturbance and restoration; and, 3) measure rates of sediment surface elevation change and soil accretion or loss in coastal mangrove forests and brackish marshes of the Everglades and determine how sediment elevation varies in relation to hydrology (i.e. the restoration). proprietary +USGS_SOFIA_lbwfbay Ecosystem History: Florida Bay and Southwest Coast CEOS_EXTRA STAC Catalog 1995-02-01 2003-02-06 -80.75, 24.75, -80.33, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231553226-CEOS_EXTRA.umm_json "Recent negative trends in the Florida Bay ecosystem have been attributed to human activities, however, neither the natural patterns of change, nor the pre-human baseline for the environment have been determined. The major objectives of this project are 1) to determine patterns of faunal and floral change over the last 150-200 years, and 2) to explore associations between biotic changes and anthropogenically-induced changes and/or natural changes in the physical environment. Environmental managers and policy makers responsible for restoring the Everglades ecosystem to a ""natural state"" can use these data to make economical and realistic decisions about restoration goals and to determine interim steps to ameliorate further damage to the ecosystem. The history of the ecosystem during the last 150-200 years is studied by analysis of faunal and floral assemblages from a series of shallow cores taken in Florida Bay. Cores are located at strategic sites in Florida Bay, with initial emphasis on the northeast and northern portions of the Bay where the most significant changes are thought to have occurred. These cores are submitted for Pb 210 analysis to determine the age and degree of disruption of the sediments. Cores that present a good stratigraphic record are sampled at closely spaced intervals for all macro-and micro-fauna and flora present. Quantitative down-core assemblage diagrams are drawn up and the various faunal and floral data are compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment are made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora, and data from Florida Bay will supplement and be correlated to onshore data and to Biscayne Bay (Ecosystems History: Terrestrial and Fresh Water Ecosystems of Southern Florida Project and Ecosystems History: Biscayne Bay and the southeast coast Project). The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment. This project is one component of an interdisciplinary study of the ecosystem history in Florida Bay. A number of USGS and other agencies scientist's are examining a series of shallow cores (~1-2 m) collected from Florida Bay. By studying the patterns of change that have occurred in the ecosystem over the last two centuries, we gain insight into the natural processes, including the natural range of variability that exists within any ecosystem. We can then determine the degree to which anthropogenic-induced change has effected the system. This understanding is critical to the restoration effort; otherwise we will be attempting to restore the system to a targeted snapshot in time, without understanding how realistic or obtainable those goals are. The ecosystem history component of the initiative will save time and money by providing realistic, economical, obtainable goals. Our component of this study is to analyze the down-core faunal and floral assemblages, over the last 150-200 years. Cores are located at strategic sites in Florida Bay, with initial emphasis on the northeast and northern portions of the Bay where the most significant changes are thought to have occurred. These cores are submitted for Pb 210 analysis to determine the age and degree of disruption of the sediments. Cores that present a good stratigraphic record are sampled at closely spaced intervals for all macro- and micro-fauna and flora present. Quantitative down-core assemblage diagrams are drawn up and the various faunal and floral data are compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment are made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora, and data from Florida Bay will supplement and be correlated to onshore data and to Biscayne Bay. The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment." proprietary +USGS_SOFIA_levesque_field_params Field Parameters Data (Levesque) CEOS_EXTRA STAC Catalog 1997-01-01 1998-12-31 -81.25, 25.33, -81, 25.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549719-CEOS_EXTRA.umm_json "In 1996, the U.S. Geological Survey began a 4-year study of the flow and nutrient characteristics of three major streams that drain parts of the Everglades National Park. An upward looking acoustic Doppler current profiler, a water-level sensor, and two specific conductance sensors were installed at each site. Monthly discharge measurements are made with an acoustic Doppler current profiler to develop discharge ratings. Data collected at the Broad River, Harney River, and Shark River stations include water level, water velocity, specific conductance and temperature, total and dissolved phosphorus species, pH, and dissolved oxygen. These three stations were established in 1997. The southwest coast of Florida is part of a wilderness area with unique hydraulic characteristics that has historically been described as the ""River of Grass"". Flat terrain and lack of controlled topographic information has made it difficult to define drainage divides. Low gradients, coupled with tidal effects, create complex conditions under which to measure river flow. It has been almost thirty years since any effort has been made to monitor flow characteristics continuously in the area. Significant technological advancements have occurred during this time and this new technology can be applied to help obtain the information needed to make informed decisions about the future of this unique coastal area. Flow, nutrient concentrations, and nutrient load data will provide part of the basic information needed to understand the hydrologic and water-quality characteristics for a part of the southwest coast of Florida. The analysis of these measurements will help characterize the current conditions for the three sites and explain the relation between upgradient water levels and southwest coastal stream flows, and the possible interaction between southwest coastal waters and the waters of Florida Bay. The data can also be used as input to hydrodynamic and water-quality models. This project is now part of the Tides and Inflows in the Mangrove Ecotone (TIME) Model Development project." proprietary +USGS_SOFIA_levesque_flow Flow Data (Levesque) CEOS_EXTRA STAC Catalog 1996-10-01 1998-12-31 -81.25, 25.33, -81, 25.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231553587-CEOS_EXTRA.umm_json "In 1996, the U.S. Geological Survey began a 4-year study of the flow and nutrient characteristics of three major streams that drain parts of the Everglades National Park. An upward looking acoustic Doppler current profiler, a water-level sensor, and two specific conductance sensors were installed at each site. Monthly discharge measurements are made with an acoustic Doppler current profiler to develop discharge ratings. Nutrient data are collected monthly at each site. Data collected at the Broad River, Harney River, and Shark River stations include water level, water velocity, specific conductance and temperature, total and dissolved phosphorus species, pH, and dissolved oxygen. These three stations were established in 1997. The southwest coast of Florida is part of a wilderness area with unique hydraulic characteristics that has historically been described as the ""River of Grass"". Flat terrain and lack of controlled topographic information has made it difficult to define drainage divides. Low gradients, coupled with tidal effects, create complex conditions under which to measure river flow. It has been almost thirty years since any effort has been made to monitor flow characteristics continuously in the area. Significant technological advancements have occurred during this time and this new technology can be applied to help obtain the information needed to make informed decisions about the future of this unique coastal area. Flow, nutrient concentrations, and nutrient load data will provide part of the basic information needed to understand the hydrologic and water-quality characteristics for a part of the southwest coast of Florida. The analysis of these measurements will help characterize the current conditions for the three sites and explain the relation between upgradient water levels and southwest coastal stream flows, and the possible interaction between southwest coastal waters and the waters of Florida Bay. The data can also be used as input to hydrodynamic and water-quality models. SUPPLEMENTAL INFORMATION: This project is now part of the Tides and Inflows in the Mangrove Ecotone (TIME) Model Development project." proprietary +USGS_SOFIA_mangrove_modeling_04 Mangrove Modeling of Landscape, Stand-Level and Soil-Nutrient Processes for the ATLSS Program and Everglades Restoration Project CEOS_EXTRA STAC Catalog 2000-12-15 2005-12-30 -81.30333, 25.125, -80.26212, 25.847113 https://cmr.earthdata.nasa.gov/search/concepts/C2231552486-CEOS_EXTRA.umm_json This project provides an integrated suite of vegetation and nutrient resource models of the land-margin ecosystem compatible with and undergirding other restoration models of hydrology and higher trophic levels identified as critical. This modeling project fills the gaps and needs of existing restoration models, ELM and ATLSS, for a vegetation and nutrient dynamics component and complements continuing empirical studies within the land-margin ecosystem of the Everglades restoration program. The proposed work has eight major objectives: 1. Re-measurement and analysis of mangrove permanent plots 10 years after the passage of Hurricane Andrew to verify forest structure models (SELVA-MANGRO) and to re-calibrate output accordingly. 2. Map historic marsh-mangrove ecotone boundaries in selected southwest Florida regions. 3. Survey land/water datums across the intertidal and develop tidal ebb/flow synoptic functions for incorporation into SELVA-MANGRO. 4. Site quality characterization across the mangrove landscape using ground surveys and research studies, aerial photography, and aerial videography. 5. Develop external SELVA-MANGRO model linkages and WEB-based access to SELVA-MANGRO for Everglades restoration evaluations. 6. Verify HYMAN (hydrology), NUMAN (nutrient/organic matter decomposition), and FORMAN (forest structure/primary productivity) unit ecological simulation models with application to Everglades restoration evaluations. 7. Link SALSA (Hydrology BOX model) to HYMAN and FORMAN models to develop a better link between vegetation response and hydrological fluxes to the Everglades system. 8. Conduct field and greenhouse studies on nutrient biogeochemistry and determine the effects of nutrients and hydroperiod on forest biomass allocation and soil formation. Land-margin ecosystems (mangrove forests, brackish marshes, and coastal lakes) comprise some 40% of Everglades National Park. They support the important detrital foodwebs, fisheries, and wading bird colonies of the coastal zone. These systems are at the receiving end for the water management decisions made upstream which will impact the spatial distribution, timing, and quantity of freshwater flow. Additional factors which are important include disturbance history related to hurricanes and potential effects of projected sea-level rise. This project integrates the suite of spatial simulation models necessary to evaluate the response of land-margin ecosystems to upstream water management. Included are algorithms and databases of critical processes and spatio-temporal relations operating at the landscape, stand-level, and soil interface. These process and modeling studies are critical to the extended applications of the ATLSS and ELM modeling programs into the land-margin ecosystems of the Everglades. proprietary +USGS_SOFIA_mcivor_hydroimpact Impacts of Hydrological Restoration on Three Estuarine Communities of the SW Florida Coast CEOS_EXTRA STAC Catalog 1999-10-01 2004-09-30 -81.375, 24.75, -80.25, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231548456-CEOS_EXTRA.umm_json This project seeks to characterize habitat relationships between selected faunal groups and their mangrove environment on the Southwest Florida coast. We are describing how mangrove associated fish species are distributed in fringing forest habitat along a salinity gradient in the tidal portions of the Shark River; the ecology and population dynamics of diamondback terrapins in the Big Sable Creek complex; experimentally determining the preferred habitat of the specialist fish Rivulus marmoratus via field and laboratory experiments; and how the conversion of mangrove forests to intertidal mud flats in the Big Sable Creek complex has affected fish composition and use of those habitats. The overall strategy is to collect robust empirical field data on forage fish distribution and abundance that can serve multiple purposes: as performance measures in restoration assessment; as the beginning of a long-term dataset analogous to three very powerful datasets from other locales in the Greater Everglades Ecosystem: 15-20 yr from freshwater marshes, 10 yr from the mangrove ecotone of Taylor Slough and adjacent tidal creeks, and 10-12 yr from Florida Bay; and contribute to the basic ecological understanding of mangrove-associated fishes. A primary goal of Everglades restoration is the recreation of water flows and water quality more closely approximating pre-drainage conditions in both freshwater and estuarine ecosystems within Everglades National Park. These estuarine systems include submerged aquatic vegetation (SAV), mangroves (tidal forests), and brackish marshes. Four primary groups of animals are closely associated with, and often dependent upon, one or more of these ecosystems: fish and decapod crustaceans (shrimp, crabs), diamondback terrapins, manatees, and wading birds. This research focuses on fish and decapod crustaceans and diamondback terrapins in mangrove tidal forests and associated creeks. Concern about the fate of mangrove ecosystems derives from their known use as habitat for a wide range of aquatic animal species, especially fishes and decapod crustaceans of forage as well as of commercial and recreational importance. Additionally, in South Florida, mangroves on Cape Sable support a seemingly healthy population of diamondback terrapins, a species at risk in many salt marsh environments on the Gulf and Atlantic coasts. This project is being undertaken to: (1) determine what fish species make routine use of flooded fringing mangrove forests along the tidal portion of the major drainage of the historical Everglades, i.e., Shark River, and to develop empirical relationships that link species composition, density and biomass to environmental variables at those sites; (2) describe the population structure of a species of special concern, the diamondback terrapin, in mangrove tidal creek habitat within the complex of creeks that make up Big Sable Creek on Cape Sable, and secondarily to determine how this population is related to other populations on the Atlantic and Gulf coasts via DNA analysis; (3) experimentally determine via field and lab experiments the preferred habitat of a species of special concern but a common fish along the Shark River salinity gradient, mangrove rivulus; (4) determine the fisheries impact of the hurricane-induced conversion of mangrove forests to intertidal mudflats in the Big Sable Creek complex. proprietary +USGS_SOFIA_mdcsoil Miami-Dade County FL soil map CEOS_EXTRA STAC Catalog 1947-01-01 -81, 25, -80, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231548633-CEOS_EXTRA.umm_json The data sets consist of two files, an ARC/INFO shape file with associated files and an export file, of a composite of soil maps for Miami-Dade County, Florida issued by the Soil Conservation Service in April, 1958. The data is at 1:40,000 scale. Getting geographic information into a form that can be analyzed in a Geographic Information System (GIS) has always been a labor-intensive process. Graphic information was historically captured using variations of manual digitizing techniques. Users either digitized directly from printed materials on digitizing tablets or tables or by a variation of heads-up digitizing from scanned graphics displayed on computer monitors. Data collection involves considerable interaction between the user and a computer to capture and manipulate graphical data into a GIS layers. By using inexpensive image processing software to process and manipulate scanned images before processing these images in the GIS, features can be semi-automatically extracted from the scanned graphics, virtually eliminating the process of manual delineation. Common photo editing techniques combined with GIS expertise can dramatically decrease the time required to collect GIS data layers. The mentioning of specific software brands or registered trademarks does not constitute a commercial endorsement; their mention is done for clarity only. Mention of software products in the description of graphic processing techniques should be viewed as a use of available tools and not a recommendation for a software product. proprietary +USGS_SOFIA_metholms Geochronology in the South Florida Ecosystem and Associated Ecosystem Programs CEOS_EXTRA STAC Catalog 1994-02-01 1999-09-30 -81.75, 24.75, -80.1, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231550723-CEOS_EXTRA.umm_json "In order to manage an ecosystem, it is imperative to define the rate at which ecologic, physical and chemical changes have occurred. The lack of historical records documenting ecological changes dictates that other methods are used to measure the rate of change. A common method of ""dating"" change is to measure the decay of naturally occurring radioactive nuclides. The use of radioactive isotopes is founded on the known physical property of radioactive material, the half-life. A half-life of an isotope is the amount of time it takes for half of a given number of atoms to ""decay"" to another element. The age of objects that contain radioactive isotopes with known half-lives can be calculated by determining the percent of the remaining radioactive material. To use this method successfully certain other prerequisites must be met. These are: 1. the chemistry of the nuclide (element) is known; 2. Once the nuclide is incorporated into the substrate the only change is radioactive decay, and 3. in order to be useful, it is relatively easy to measure." proprietary +USGS_SOFIA_metish Ecosystem History of Biscayne Bay and the Southeast Coast CEOS_EXTRA STAC Catalog 1996-03-01 2000-01-31 -80.3, 25.1, -80, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231549217-CEOS_EXTRA.umm_json Historical changes in South Florida related to rapid population growth in the early to mid-1900's have led to significant alteration of the natural hydrocycles and water quality of Florida and Biscayne Bays. The Biscayne Bay ecosystem shows increasing signs of distress; declines in fisheries, increased pollution, and dramatic changes in nearshore vegetation. Northern and central Biscayne Bay are strongly affected by the urban development associated with the growth of Miami. Southern Biscayne Bay is influenced by drainage from the Everglades, which has been altered by canals and agricultural activities. Restoration and preservation of Biscayne Bay and Biscayne National Park are dependent on a comprehensive understanding of the linkages between the hydrologic system and the bay ecosystem, and of the natural versus human-induced variability of the ecosystem. In this project modern surface samples were collected from 26 sites in Biscayne Bay. The primary biota analyzed were 1) benthic foraminifera, 2) ostracodes, 3) mollusks, 4) dinoflagellate cysts, 5) pollen and macro-plant material. The distribution of the biota was quantified to determine relationships with environmental conditions. These results were used to interpret historical faunal and floral changes recorded in shallow sediment cores. Water samples, ostracode and foraminiferal shells collected from the modern sediment samples are being analyzed for trace element geochemistry to derive a calibration equation to calculate absolute salinity in down-core samples. Shallow cores (1-2 meters) were collected along a north-south transect within Biscayne Bay for analysis of the downcore faunal and floral assemblages over the last 150 years. Quantitative down-core assemblage diagrams will be drawn up and the various faunal and floral data will be compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment will be made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora; data from Biscayne Bay will supplement and be correlated to onshore data and to data from Florida Bay. The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment. Living assemblages will be collected twice a year to provide data on habitat distribution, preferred substrates and seasonality of the living biota for interpretation of the down-core assemblages. Recent negative trends have been observed in the ecosystem of Florida Bay, including algal blooms, seagrass die-offs, and declining numbers or shellfish, adversely affecting the fishing and tourist industries. Many theories of cause and effect exist to explain the adverse trends, but these theories have not been scientifically tested. Prior to finalizing plans for ecosystem restoration, the relative roles of human activities versus natural ecosystem variations need to be established. This project addresses this need by focusing on two primary goals. First, to determine the characteristics of the ecosystem prior to significant human alteration, including the natural range of variation in the system; this establishes the baseline for restoration. Second, to establish the extent, range, and timing of changes to the ecosystem over approximately the last 150 years and to determine if these changes correlate to human alteration, meteorological patterns, or a combination of factors. In addition, data on recovery times of certain components of the ecosystem will be obtained allowing biologists to estimate responses to proposed restoration efforts. This project is planned as a five year study, to be completed in 2000. This project is one segment in a group of coordinated USGS projects examining the biota, geochronology, geochemistry, sedimentology, and hydrology of southern Florida, Florida Bay and the surrounding areas. Data are being compiled from terrestrial, marine, and freshwater environments in onshore and offshore sites in order to reconstruct the ecosystem history for the entire region over the last 150 years. proprietary +USGS_SOFIA_metjen Effect of Wind on Surface-Water Flows CEOS_EXTRA STAC Catalog 1997-04-01 1999-09-30 -81.25, 24.75, -80.3, 25.8 https://cmr.earthdata.nasa.gov/search/concepts/C2231552339-CEOS_EXTRA.umm_json Flows in and through the Everglades wetlands and bordering subtidal embayments are often characterized by very low velocities that are driven or controlled at various scales by wind, gravity, pressure, and vegetative resistance. Little is known about the effect of wind on water movement in these environments, and no focused efforts are currently underway to assess its importance. This project will examine the effect of wind on surface-water flows. With insight into the functional relationships and into the scales at which wind forcing data must be collected for model input gained from the field efforts, the treatment of wind forcing in models can be improved. This, in turn, can lead to enhanced understanding of the significance of wind effects on flow, transport, and horizontal mixing in the SICS (Southern inland coastal systems of Dade County) study area. This project has been integrated into the TIME project (http://time.er.usgs.gov/) proprietary +USGS_SOFIA_metkotra Geochemical Processes in Organic-rich Sediments of South Florida - Mercury and Metals CEOS_EXTRA STAC Catalog 1996-10-01 1998-10-01 -81.25, 24.75, -80.3, 26.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231550057-CEOS_EXTRA.umm_json Human activities have led to the deterioration of the productivity, biodiversity, and stability of the south Florida ecosystem. The fate of anthropogenic contaminants incorporated into the organic-rich sediments is not fully understood. Physical, chemical, and biological processes may remobilize some of the contaminants and reintroduce them into water, atmosphere, and the biological community. Other contaminants may be transformed during diagenesis and remain in surficial materials until the system is disturbed. This project examined the occurrence and cycling of mercury and metals in organic-rich sediments, pore fluids, and plants at selected sites in south Florida. An understanding of the relationship between diagenesis, concentration, speciation, and historical variation of elements of environmental significance is essential for planners in developing long-term remediation and management strategies for wetlands of south Florida. A better understanding of the controls on the cycling of these elements is critical for making informed decisions regarding the regulation of water levels and anticipating the effect of water regulation. proprietary +USGS_SOFIA_metlang Ground-Water Discharge to Biscayne Bay CEOS_EXTRA STAC Catalog 1996-06-01 1998-12-31 -80.63, 25.12, -80.12, 25.9 https://cmr.earthdata.nasa.gov/search/concepts/C2231554984-CEOS_EXTRA.umm_json The purpose of this project was to quantify the rates of ground water discharge to Biscayne Bay. This was achieved through the collection of field data and the development of two- and three-dimensional numerical models to simulate variable-density ground water flow. As part of this project, the SEAWAT code, which represents variable-density ground water flow, was developed to simulate ground water discharge. Monitoring wells were installed offshore and inland along three transects perpendicular to the shore of Biscayne Bay. Several surveys during the late 19th and early 20th centuries describe the occurrence of large quantities of ground-water flow to Biscayne Bay by way of underground channels or conduits. The construction of the drainage and flood-control network in southeastern Florida began during the early 20th century for the purpose of managing the water resources of the area. This drainage canal network affected the hydrologic pattern in southeastern Florida by replacing sheetflow with canal flow, thereby significantly reducing the altitude of the water table and diminishing ground-water flow to Biscayne Bay. This led to the inland movement of the saltwater interface. In 1960, there was still ground water discharging to the bottom of Biscayne Bay, but no quantification of the amount of ground-water discharges to the bay was made at the time. In 1967, discharges to the bay in the Cutler Ridge area were estimated by assuming Darcian flow and considering the tidal cycle. It was estimated that 210 cubic feet per square foot of flow section area was discharged during a 12.5-hour tidal cycle. The U.S. Army Corps of Engineers (COE) is planning to construct gated spillways and culverts to allow for the restoration of natural sheetflow conditions to Everglades National Park (ENP). These proposed changes may further affect the hydrologic conditions of ENP and other parts of the ecosystem, thus leading to the following questions: (1) Is ground water flowing to Biscayne Bay a significant component of the water budget in south Florida? (2) Would the quantity of ground water flowing to Biscayne Bay be greatly affected by changes in the operation of gates and control structures in canals? (3) How much change in ground-water discharges to Biscayne Bay has occurred due to modifications to the hydrologic system? Quantification of ground water flowing to Biscayne Bay is needed as input to two interagency projects: the South Florida Ecosystem Restoration Program and the Biscayne Bay Feasibility Study. The principal objective of the Biscayne Bay Feasibility Study is to investigate ongoing construction/dredging projects and propose solutions to alleviate adverse factors that affect the bay and to aid in the development of guidelines for future management of the natural resources of Biscayne Bay. The Biscayne Bay Feasibility Study includes the implementation of a surface-water circulation model which will be developed by the Waterway Experimental Station of the COE. Quantification of ground-water discharges to Biscayne Bay is needed as input to the bay water circulation model. proprietary USGS_SOFIA_metlietz Determination of Nutrient Loads to East Coast Canals CEOS_EXTRA STAC Catalog 1996-05-01 1997-10-31 -80.39, 25.35, -80.15, 25.94 https://cmr.earthdata.nasa.gov/search/concepts/C2231552186-CEOS_EXTRA.umm_json The objectives of this project were threefold: 1) to determine if historical water-quality data collected as grab samples at 0.5 and c1.0 m below the surface near the centroid of flow adequately represent stream cross-sectional chemistry, 2) to develop reliable estimates of nitrogen and phosphorus loads for east coast canals based on statistical models developed from utilizing the techniques of ordinary least squares regression, and 3) to summarize water-quality data and determine temporal trends for water-quality constituents at two sites that are strategic to Biscayne Bay and the south Florida ecosystem. During phase 1 of the project an intensive field sampling and data collection effort was undertaken. Depth-integrated samples were collected by the equal-width-increment method as well as grab samples at each canal. During Phase 2 data analysis was done. Nutrient data were collected upstream of 15 coastal control structures in Miami-Dade County. Samples were collected over a typical hydrologic period during various flow conditions. Sampling began at 5 sites in May 1996 and at 10 sites in October 1996. Constituents collected included ammonia, nitrite, nitrate, orthophosphate, and total phosphorus. Of major concern in many coastal areas around the Nation is the ecological health of bays and estuaries. A common problem in many of these areas is increased nutrient loads as a result of agricultural, commercial, industrial, and urban processes. Biscayne Bay is a shallow subtropical estuary along the southern coast of Florida. The Biscayne Bay ecosystem provides an aquatic environment that is a habitat to a diverse array of plant and animal communities. Nutrients are essential compounds for the growth and maintenance of all organisms and especially for the productivity of aquatic environments. Nitrogen and phosphorus compounds are especially important to seagrass, macroalgae, and phytoplankton. However, heavy nutrient loads to bays and estuaries can result in conditions conducive to eutrophication and the attendant problems of algal blooms and high phytoplankton productivity. Additionally, reduced light penetration in the water column because of phytoplankton blooms can adversely affect seagrasses, which many commercial and sport fish rely on for their habitat. Providing reliable estimates of nonpoint source nutrient loads to Biscayne Bay is important to the development of nutrient budgets as well as input to eutrophication models. Understanding the effects of these nutrient loads is a necessary initial step in planning restoration of the ecological health of Biscayne Bay. Nutrient data have been collected from the east coast canals for many years by various government agencies. Much of the data collected have been from grab samples at 0.5 or 1.0 meter below the stream surface near the centroid of flow. The degree to which these samples adequately represent nitrogen and phosphorus concentrations within the water column of the canals of south Florida is presently unknown and limits confidence in loading estimates. Furthermore, the relation between discharge and nutrient concentration that occurs in natural uncontrolled streams in other parts of the Nation may not apply to the artificially controlled canals of south Florida. Both of these issues need to be addressed to develop nutrient budgets and to plan effective restoration strategy now and in the future. proprietary +USGS_SOFIA_metorem Geochemistry of Wetland Sediments from South Florida CEOS_EXTRA STAC Catalog 1994-01-01 1999-12-31 -81.3, 24.4, -80.1, 27 https://cmr.earthdata.nasa.gov/search/concepts/C2231549943-CEOS_EXTRA.umm_json "This project is examining (1) sources of nutrients (nitrogen and phosphorus), sulfur, and carbon to wetlands of south Florida, (2) the important role of chemical and biological processes in the wetland sediments (biogeochemical processes) in the cycling of these elements, and (3) the ultimate fate (i.e. sinks) of these elements in the ecosystem. The focus on nutrients and carbon reflects the problem of eutrophication in the northern Everglades, where excess phosphorus from agricultural runoff has dramatically altered the biology of the ecosystem. Major project objectives are as follows - (1) use isotope and other tracer methods to examine the major sources of nutrients, carbon, and sulfur to the south Florida ecosystem, (2) use geochemical methods to examine the major forms of nutrients, carbon, and sulfur in the sediments, the stabilities of the observed chemical species, and sinks of these elements in the sediments, (3) examine the biogeochemical processes controlling the cycling of nutrients, carbon, and sulfur in the ecosystem, and use geochemical modeling of porewater and sediment chemical data to determine the rates of these recycling processes, (4) develop geochemical sediment budgets for nutrients, carbon, and sulfur on a regional scale, including accumulation rates of these elements in the sediments, fluxes out of the sediments, and sequestration rates, (5) collaborate with mercury projects (USGS ACME team and others) to examine the role of sulfur and sulfate reduction in the production of methyl mercury in wetlands of south Florida, and the bioaccumulation of mercury in fish and other wildlife, (6) develop a geochemical history of the south Florida ecosystem from an examination of changes downcore in the concentration, speciation, and isotopic composition of nutrients, carbon and sulfur; use organic marker compounds and stable isotopes to develop a model of seagrass history in Florida Bay, (7) incorporate information from nutrient studies in overall ecosystem nutrient model, and results from sulfur studies in ecosystem mercury model. This project addresses three major areas of interest to land and water managers in south Florida: (1) nutrients and eutrophication of the Everglades, (2) the role of sulfur in the methylation of mercury and its bioaccumulation, and (3) the geochemical history of the south Florida ecosystem. Our nutrient studies are focused on using isotope methods to examine the sources of nutrients to the ecosystem, and on using sediment and porewater geochemical studies to determine the rates of nutrient recycling and nutrient sinks within the sediments. A nutrient sediment budget will be developed for incorporation in the nutrient model for the ecosystem. Results will assist managers in determining the fate of excess nutrients (especially phosphorus) stored in contaminated sediments (e.g. will the excess nutrients be buried, or recycled for movement further south into protected areas). The sediment studies will also provide managers with information relevant to the effectiveness of planned remediation methods. Studies of sulfur within the ecosystem relate directly to the issue of methyl mercury production and bioaccumulation, a serious threat to both wildlife and to the human population. Microbial sulfate reduction in wetlands (an anaerobic process) is the primary driver of mercury methylation. Understanding the source of sulfate to the wetlands of south Florida may be a key to understanding why mercury methylation rates are so high, and on how remediation efforts in the Everglades may impact mercury methylation rates. We are also examining the sulfur geochemistry of sediments on a regional scale, with emphasis on areas that are methyl mercury ""hotspots"". We are emphasizing co-sampling with USGS mercury researchers (ACME team). The geochemical history component of this project will provide information on historical changes in the chemical conditions existing in south Florida wetlands. This will provide wetland managers with baseline information on the water quality goals needed to achieve ""restoration"" of the ecosystem. It will also provide land managers with an estimate of the range of water quality and environmental conditions that have affected the south Florida ecosystem in the past. Geochemical history data in combination with information from paleontologic studies of the USGS paleoecology group and others will also provide insights on how organisms in the south Florida ecosystem have responded to environmental change in the past, and predict how these organisms will likely respond to changes in the ecosystem resulting from restoration efforts." proprietary +USGS_SOFIA_metroys Evaluation of Methods to Determine Ground-water Seepage Below Levee 30, Miami-Dade County Florida CEOS_EXTRA STAC Catalog 1996-02-01 1996-12-17 -80.3, 25.85, -80.29, 25.85 https://cmr.earthdata.nasa.gov/search/concepts/C2231552379-CEOS_EXTRA.umm_json Ground-water flow models were developed to calculate a water budget, including seepage losses, for a transect perpendicular to Levee 30. Data required for input to and calibration of the models were obtained from: (1) previous studies conducted in the area, (2) analysis of a geologic core and geophysical logs from a new monitor well drilled along the transect, (3) ground-water-level data from monitor wells along the transect, (4) surface-water-stage data in Water Conservation Area 3B and in the Levee 30 canal, (5) discharge measurement made at several locations under varying conditions in the Levee 30 canal, and (6) vertical seepage fluxes between surfacewater and groundwater in Water Conservation Area 3B obtained from seepage meters. Determining the volume of water seeping from the water-conservation areas to the underlying aquifers is important in managing water levels in the conservation areas and freshwater deliveries to Everglades National Park. An accurate water budget to meet the competing natural and anthropogenic needs cannot be determined without this information. From Water Conservation Area 3B, water seeps into the Biscayne aquifer, which is about 80 feet thick directly beneath Levee 30 and thickens to the east, and flows relatively fast (due to high permeability of the aquifer) toward the urban and agricultural areas to the east. Water is also discharged to the canal along the eastern part of Levee 30. The rate of discharge is controlled by structures at the northern and southern ends of the canal. This seepage to the aquifer and canal discharge of water are critical for water-supply wells to the east and for preventing the inland movement of saltwater from the coast. However, lowering of ground-water levels to the east has resulted in higher ground-water seepage and canal discharge, reducing flows to the south in the water-conservation area. As a result, Levees 67A and 67C were constructed to direct water southward toward the central region of Everglades National Park. This water-management scheme has been effective in delivering water to the southwest; however, it reduced the flow to the southeast (northeastern part of Everglades National Park). The altering of historical flow directions and water-level durations has caused significant adverse effects to parts of the Everglades ecosystem. Water managers want to restore predevelopment flow conditions for the Everglades to survive, while also taking into consideration the urban and agricultural needs. The objective of this project was to evaluate approaches for quantifying ground-water seepage beneath Levee 30. The accounting of all significant hydrologic inflows and outflows to the Everglades ecosystem of the south Florida mainland is a key element of the South Florida Ecosystem Program. proprietary USGS_SOFIA_metschaf Canal and Wetland Flow/Transport Interaction CEOS_EXTRA STAC Catalog 1997-09-23 -80.6, 25.25, -80.4, 25.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231554720-CEOS_EXTRA.umm_json "Significant canal and wetland flow exchanges can potentially occur along the southwest overbank area of canal C-111 between hydraulic control structures S-18C and S-197. This coupled flow system is of particular concern to restoration efforts in that it provides a pathway for fresh water to nearshore embayments in Florida Bay. New construction modifications and operational strategies proposed for C-111 under the Central and Southern Florida ""Restudy"" Project are intended to enhance sheet flow to these subtidal embayments. The objectives of the canal and wetland flow/transport interaction project were to (1) develop numerical techniques and algorithms to facilitate the coupling of existing generic models for improved simulation of canal and wetland interactions, (2) translate recent findings of ongoing process studies within the South Florida Ecosystem Program (SFEP) into new mathematical formulations, empirical expressions, and numerical approximations to enhance generic simulation model capabilities for the south Florida ecosystem, (3) investigate new instrument capabilities and field deployment approaches to collect the refined data needed to identify and quantify the important flow-controlling forces and landscape features for model implementation, (4) integrate process-study findings and the results of physiographic mapping and remote sensing efforts specific to the C-111 basin into a numerical simulation model of the interconnected canal and wetland flow system, and (5) use the resultant model and data to study, evaluate, and demonstrate the significance of driving forces relative to controlling flow exchanges between canal C-111 and its bordering wetlands. Discharge data for Tamiami Canal are also available for water years 1986-1999, 2000, and 2001. A complex network of canals, levees, and control structures, designed to control flooding and provide a continuous supply of fresh water for household and agricultural use, has altered naturally occurring flow patterns through the Everglades and into Florida Bay. Quantification of dynamic flow conditions within the south Florida ecosystem is vital to assessing implications of the residence time of water, potentially nutrient-enriched (with nitrates or phosphates) or contaminant-laden (with metals or pesticides), that can alter plant life and affect biological communities. Improved numerical techniques are needed not only to more accurately evaluate discrete forces governing flow in the canals and wetlands but also to analyze their complex interaction in order to facilitate coupled representation of transport processes. Flow and transport processes are integrally linked meaning that precise quantification of the fluid dynamics is required to accurately evaluate the transport of waterborne constituents. Robust models that employ highly accurate numerical methods to invoke coupled solution of the most appropriately formulated and representative equations governing flow and transport processes are needed. Through strategic use of a model, cause-and-effect relations between discharge sources, flow magnitudes, transport processes, and changes in vegetation and biota can be systematically investigated. The effects of driving forces on nutrient cycling and contaminant transport can then be quantified, evaluated, and more effectively factored into the development of remedial management plans. A well-developed model can be used to evaluate newly devised plans to improve freshwater deliveries to Florida Bay prior to implementation. This project ended in 1999. Related work can be found at http://time.er.usgs.gov/. For additional information about this project contact either: Eric Swain, edswain@usgs.gov, 954 377-5925 or Chris Langevin, langevin@usgs.gov, 954 377-5917" proprietary +USGS_SOFIA_metweed Hydrogeology of the Surficial Aquifer System in Southwest Florida CEOS_EXTRA STAC Catalog 1996-02-01 1999-12-31 -81.7, 25.73, -80.86, 26.18 https://cmr.earthdata.nasa.gov/search/concepts/C2231548516-CEOS_EXTRA.umm_json The objective of this project is to provide to hydrologic modelers a three-dimensional database of the geologic and hydrologic properties of the sediments and rocks of the surficial aquifer system in southwest Florida, in Collier and Monroe Counties. Emphasis will be placed on the geologic framework of the aquifer. Two independent methods are used in this study to estimate the age of the aquifer rocks and sediments. Samples from cores will be examined for fossil dinoflagellate cysts, pollen, mollusks, foraminifers, and ostracodes, and their age determined by correlation to other distant sites that have been dated isotopically. Age also will be estimated by the isotopic composition of strontium in unaltered shells. The ratio of the stable isotopes of strontium in the oceans has varied over geologic time such that, in the last 40 million years, there has been a unique relation between age and isotopic composition. Marine invertebrates incorporate the strontium isotopic ratio of the ocean into their shells as they grow, thereby preserving evidence of their age. Geophysical logs provide a continuous downhole record of the properties of the rocks that form the aquifer. They are especially valuable in providing physical and chemical properties of the corehole where particular intervals of core recovery are poor. Also, they allow extension of hydrologic test data from discrete samples to the rest of the core. Geophysical logs, combined with aquifer water properties and flow measurements, will be used to relate large-scale ground-water circulation to the distribution of hydrologic properties of the aquifer. For example, flowmeter logs can confirm that the most permeable intervals, as inferred from core measurements, coincide with the intervals that conduct the most flow in the vicinity of test wells. Geophysical logs also will indicate which confining units act to separate the aquifer system into discrete aquifers having different water quality and hydraulic head. Restoration and management of the south Florida ecosystem will be guided by hydrologic models that simulate water flowing through the wetlands and shallow subsurface aquifers beneath them. The restoration of the ecosystem is, essentially, the restoration of the natural hydrologic system. As surface water is re-diverted from manmade canals to its more natural sstate and overland flow, several changes are predicted to occurr. First, because water flowing over land moves more slowly than in canals, overland flow should remain in the wetlnad ecosystem for a longer period each year. Second, as flowing water spreads out over the wetlands, recharge to the shallow aquifers should increase as more of that water infiltrates into the ground. The U.S. Corps of Engineers (USACE) and the South Florida Water Management District (SFWMD) will use hydrologic models to anticipate the consequences of these proposed restoration plans. This reseaerch project is designed to provide essential subsurface data to improve hydrologic models for land and water managers in southwest Florida where subsurface information is lacking. Obtaining hydrogeological data involves core drilling, corehole testing, and rock and sediment analysis. Understanding the geologic history of the sediments and rocks of the aquifer system is necessary to place the hydrologic properties of that system into a geologic framework. proprietary USGS_SOFIA_mmarvin Bacterial demethylation of methylmercury in the South Florida Ecosystem CEOS_EXTRA STAC Catalog 1996-06-01 -81.25, 24.8, -80.3, 25.8 https://cmr.earthdata.nasa.gov/search/concepts/C2231550592-CEOS_EXTRA.umm_json Methylmercury (MeHg) degradation was investigated along an eutrophication gradient in the Florida Everglades by quantifying 14CH4 and 14CO2 production after incubation of anaerobic sediments with 14C-MeHg. Degradation rate constants (k) were consistently <=0.1 per day, and decreased with sediment depth. Higher k values were observed when shorter incubation times and lower MeHg amendment levels were used, and k increased two-fold as in-situ MeHg concentrations were approached. The average floc layer k was 0.046 +/- 0.023/ d (n=17) for 1-2 day incubations. In-situ degradation rates were estimated to be 0.02 to 0.5 ng MeHg/g dry sed/d, increasing from eutrophied to pristine areas. Nitrate-respiring bacteria did not demethylate MeHg, and NO3- addition partially inhibited degradation in some cases. MeHg degradation rates were not affected by PO4-3 addition. 14CO2 production in all samples indicated that oxidative demethylation (OD) was an important degradation mechanism. OD occurred over five orders of magnitude of applied MeHg concentration, with lowest limits (1-18 ng MeHg/g dry sediment) in the range of in-situ MeHg levels. Sulfate reducers and methanogens were the primary agents of anaerobic OD, although it is suggested that methanogens dominate degradation at in-situ MeHg concentrations. Specific pathways of OD by these two microbial groups are proposed. The objective of this research is to provide ecosystem managers with MeHg degradation rate data from a number of study sites that represent a diversity of hydrologic and nutrient regimes common to the Everglades, and to forge a better understanding of the microbial and geochemical controls regulating MeHg degradation in this system. proprietary +USGS_SOFIA_monitor_sav_rs_fb_04 Monitoring Sub-aquatic Vegetation Through Remote Sensing: a Pilot Study in Florida Bay CEOS_EXTRA STAC Catalog 2002-10-01 2004-09-30 -80.87, 24.9, -80.75, 25 https://cmr.earthdata.nasa.gov/search/concepts/C2231553656-CEOS_EXTRA.umm_json This pilot study will focus on Florida Bay, a region that suffered the loss of 40,000 ha of turtle grass in a die-off event that began in 1987, and a small, localized die-off in 1999. These events were well documented and provide a baseline for testing methods of monitoring grass beds remotely. Remote sensing data, including aerial photos and satellite imagery data, and data extracted from sediment cores will be used to examine the long-term sequences of events leading up to seagrass die-off events. The objectives of this pilot study are to develop a methodology for monitoring spatial and temporal changes in sub-aquatic vegetation using remote sensing, satellite imagery, and aerial photography, and to analyze potential causes of seagrass die-off using geographic, geologic and biologic tools. The ultimate goal is to develop a method for forecasting potential sea-grass die-offs and to determine if remediation efforts would be cost-effective. Florida Bay is selected for the pilot study because the thorough documentation of the 1987-1988 die-off event provides a baseline for examining data preceding and succeeding the event. In addition, a small well studied die-off occurred in 1999-2000 at Barnes Key in Florida Bay. A 10-15 km2 portion of Florida Bay that encompasses areas affected by the 1987 and 1999 die-offs will be analyzed for this pilot study. Current remotely sensed data, aerial photos and satellite images from this area will be used to test different platforms, determine detection limits, and to attempt to isolate distinct signals for different types of vegetation. When ground-truthing is completed, archived remotely sensed data and/or aerial photographs can then be used to examine the sequences of events leading up to the die-offs. The remotely sensed data can be compared and compiled with the data collected by seagrass biologists in 1987 and 1999, and to sediment core data collected at the sites of seagrass die-off. Sediment cores provide a long-term perspective on changes in nutrient geochemistry, substrate, water chemistry (salinity, temperature, oxygen), and changes in the biota. The geologic, biologic and remotely sensed data will be integrated and analyzed to determine the patterns of change and sequences of events that occur in healthy seagrass beds and in beds undergoing a die-off. Several remote sensor types will be compared in this study to determine the ideal sensor bands and spatial resolution necessary to detect and monitor the health of seagrass beds. The sensors to be tested include Landsat 7 (30m multi-spectral spatial resolution), ASTER (15 and 30m multi-spectral), Quickbird (2.5m multi-spectral and <1m panchromatic), and large-scale aerial photography (anticipated spatial resolution .25m with visible and near-infrared bands). Imagery with bands in the blue wavelength may help to penetrate water and infrared or near-infrared bands are predicted to perform better for resolving vegetation. It is theorized that through a combination of blue, and infrared bands and higher spatial resolution it will be possible to map the extent of seagrass beds. Although Landsat ETM+ 7 has several bands in desirable wavelengths, this sensor is predicted to be too course of a dataset to resolve individual seagrass beds. Landsat ETM+ may be used to develop an index of chlorophyll values that may be translated into a measure of seagrass health. ASTER’s multiple infrared bands and increased spatial resolution may be successful in distinguishing between the types of vegetation, but these bands are not designed for water penetration. Higher spatial resolution platforms are predicted to have better mapping capabilities. The Quickbird sensor can provide 2.5m spatial resolution with multi-spectral capability. The multi-spectral bands include a blue band for water penetration and a near-infrared band for vegetation detection. Finally, aerial photography flown at low altitude represents the highest spatial resolution (.25m) and can be collected in visible and near-infrared to allow processing of blue and infrared bands. A combination of sensor types to maximize both spatial resolution and spectral signatures may provide the best solution for mapping and monitoring seagrass beds. Seagrass beds are essential components of any marine ecosystem because they provide feeding grounds, nurseries, and habitats for many forms of marine life, including commercially valuable species; they are important foraging grounds for migratory birds; and they anchor sediments and impede resuspension and coastal erosion during storms. This valuable natural resource has been suffering die-offs around the world in recent years, yet the causes of these die-offs are undetermined. The purpose of this project is to use a number of tools - geographic, geologic, and biologic - to investigate the causes of seagrass die-offs and to develop methods that can be used to monitor the health of seagrass meadows. If we understand the causes of the die-offs and can easily monitor the health of seagrass beds, then resource managers have a tool for forecasting areas of potential die-offs. By integrating remotely sensed data, biological data and core data the long-term (decadalscale) sequences of events leading up to die-off events can be examined. These data can be contrasted to normal seasonal changes that occur in healthy grass beds to establish criteria for identifying areas that may be on the threshold of experiencing a decline. This provides a very powerful predictive tool for resource managers. By examining the causes of die-off and the natural patterns of change in seagrass meadows over biologically significant periods of time we can determine the components of change that may be related to anthropogenic activities versus natural cycles of change. This information would allow resource managers to make informed decisions about the cost-effectiveness of and mechanisms for remediation, if an area of decline was identified via the predictive tool. Once the predictive tools and potential remediation tools have been developed in this pilot study, in well-studied seagrass meadows, the tools can be applied to threatened coastal ecosystems around the country and worldwide. proprietary USGS_SOFIA_nssmet Cycling and Speciation of Mercury in the Food Chain of South Florida CEOS_EXTRA STAC Catalog 1995-01-01 1997-12-31 -81.5, 25.75, -80.5, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231549513-CEOS_EXTRA.umm_json Methylmercury, a neurotoxin, is found in the game fish of south Florida. Samples of periphyton, the assemblage of microalgae that live in shallow submerged substrates which is home to, and food for, creatures that are the foundation of the food chain, have concentrations of methylmercury that range from non-detectable to tenths of a part per million on a dry weight basis. The report produced from this project presents data for samples of periphyton and water collected in 1995 and 1996 from Water Conservation Areas, the Big Cypress National Preserve, and the Everglades National Park in south Florida. Periphyton samples were analyzed for concentrations of total mercury, methylmercury, nitrogen, phosphorus, organic carbon, and inorganic carbon. Water-column samples collected on the same dates as the periphyton samples were analyzed for concentrations of major ions. The goal of this project is to answer the question - How does mercury produced in the aquatic environment enter the food chain and become part of the body burden of animals such as game fish in south Florida? proprietary +USGS_SOFIA_nuts_S_orgmat_04 Integrated Biogeochemical Studies of Contaminants in the Everglades: Task 1 -Nutrients, Sulfur, and Organic Matter CEOS_EXTRA STAC Catalog 2000-10-01 2005-09-30 -82, 24.4, -80.1, 28 https://cmr.earthdata.nasa.gov/search/concepts/C2231553342-CEOS_EXTRA.umm_json "The scientific focus of this project is to examine the complex interactions (synergistic and antagonistic) of contaminants (externally derived nutrients, mercury, sulfur, pesticides, herbicides, polycyclic aromatic and aliphatic hydrocarbons, and other metals), ecosystem responses to variations in contaminant loading (time and space dimensions), and how imminent ecosystem restoration steps may affect existing contaminant pools. The major objectives of this project are to use an integrated biogeochemical approach to examine: (1) anthropogenic-induced changes in the water chemistry of the Everglades ecosystem, (2) biogeochemical processes within the ecosystem affecting water chemistry, and (3) the predicted impacts of restoration efforts on water chemistry. The project uses a combination of field investigations, experimental approaches (mesocosm experiments in the ecosystem, and controlled laboratory experiments), and modeling to achieve these objectives. Contaminants of concern will include nutrients, sulfur, mercury, organic compounds, and other metals. Protocols for the collection of samples and chemical analyses developed during earlier studies will be employed in these efforts. Integration of the individual tasks within the project is achieved by colocation of field sampling sites, and cooperative planning and execution of laboratory and mesocosm experiments. Results from all tasks within the project are archived within a single database for use in Decision Management GIS systems and ecosystem models. This project is an integration of a number of individual but interrelated tasks that address environmental impacts in the south Florida ecosystem using geochemical approaches. The Everglades restoration program is prescribing ecosystem-wide changes to some of the physical, hydrological, and chemical components of this ecosystem. However, it reamins uncertain what overall effects will occur as these components react to the perturbations especially of the biological and chemical components and toward what type of ""new ecosystem"" the Everglades will evolve. Results of these geochemical investigations will provide the critical elements for building ecosystem models and screening-level risk assessment for contaminants in the ecosystem." proprietary +USGS_SOFIA_orem_fb_sed_geochem Florida Bay sediment geochemical data CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -80.75, 25, -80.5, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231552868-CEOS_EXTRA.umm_json The data set contains the sample ID, depth (cm), sediment size, fine sediment fraction (<60m), total C %, organic C %, total N %, total P %, C/N, C/P, and N/P. This project is examining (1) sources of nutrients, sulfur, and carbon to wetlands of south Florida, (2) the important role of chemical and biological processes to the wetland sediments (biogeochemical processes) in the cycling of these elements, and (3) the ultimate fate (i.e. sinks) of these elements in the ecosystem. The focus on nutrients and carbon reflects the problem of eutrophication in the northern Everglades, where excess phosphorus from agricultural runoff has dramatically altered the biology of the ecosystem. Results will be used by land and water managers to predict the fate of nutrients (especially phosphorus) in contaminated areas of the Everglades, and to evaluate the long-term effectiveness of buffer wetlands being constructed as nutrient removal areas. Studies of sulfur in the ecosystem are important for understanding the processes involved in mercury methylation in the Everglades. Methyl mercury (a potent neurotoxin) poses a severe health risk to organisms in the south Florida ecosystem and to humans. Sediment studies conducted by this project will also be used to construct a geochemical history of the ecosystem. An understanding of past changes in the geochemical environment of south Florida provides land and water managers with baseline information on what water quality goals for the ecosystem should be, and on how the ecosystem has responded to past environmental change and will likely respond to the changes that will accompany restoration. proprietary +USGS_SOFIA_panther_refuge_hydro Hydrologic monitoring and synthesis of existing hydrologic data in the Florida Panther National Wildlife Refuge and surrounding areas CEOS_EXTRA STAC Catalog 2005-10-01 2007-09-30 -81.5, 26.15, -81.3, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549236-CEOS_EXTRA.umm_json The objectives of this project are to 1. Inventory existing hydrologic data available in the vicinity of the Florida Panther National Wildlife Refuge (FPNWR) including all data that can be used for determining past and current conditions. 2. Design and install a hydrologic monitoring network for the FPNWR. The network will include continuous and intermittently monitored ground-water level and surface water stations. The network will be used to monitor hydrologic conditions within the FPNWR and to evaluate the relationship between ground water and surface water. 3. Collect other hydrologic data as needed to assist in determining the hydrologic conditions in the area. Examples of other types of data include stable isotopes, which can be used to determine sources of water in a sample, evapotranspiration data, surface and borehole geophysical data, seepage measurements. 4. Evaluate historical and current data to determine trends and baseline conditions at and in the vicinity of the FPNWR. The biologic communities of the Florida Panther National Wildlife Refuge (FPNWR) and surrounding areas have been historically impacted by the changes in hydrology associated with past highway and canal construction and will be impacted by future plans for hydrologic restoration. Currently, little hydrologic data is collected in the vicinity of the FPNWR. Two continuous recording stations located up gradient in Big Cypress National Park (stations A1 and A2) are the nearest wetland stations to the FPNWR. Additional stations are located in the canals near the FPNWR. Information on current hydrologic conditions and a monitoring network are needed in order to determine the impact of the planned Picayune Strand Hydrologic Restoration on the hydrology of the area. These hydrologic changes will have effects on the threatened and endangered species as well as other biologic communities in the FPNWR. There are two components to the hydrology of the area that have an impact on the ecology, surface water, and shallow ground water. The surface water consists of wetlands within and canals bordering the FPNWR. Canals bordering the refuge have a major impact on the hydrology in the area. The FPNWR currently maintains a hydrologic monitoring program of 8 stations (Larry Richardson, verbal communication). These hydrologic monitoring stations have not been surveyed to a vertical datum, which is required to adequately evaluate the data being collected. The survey information is required to determine the relationship between ground water and surface water in the area. Additional information needed to evaluate the hydrology of the area include stage and flow rates in the canals bordering the FPNWR. proprietary USGS_SOFIA_rice_alligators_04 American Alligator Ecology and Monitoring for CERP CEOS_EXTRA STAC Catalog 2002-10-01 2006-12-31 -81.5, 25, -80.25, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231549519-CEOS_EXTRA.umm_json This project will accomplish several tasks with a combination of field data collection, GIS mapping, and computer simulation. Our main objectives are designed to answer questions critical to restoration success and to provide the tools necessary for evaluation: 1. Develop monitoring methods necessary for evaluation of restoration success in alligator populations. 2. Understand the effects of decompartmentalization and other CERP (Comprehensive Everglades Restoration Plan) projects on restoration of alligator populations. 3. Identify and quantify the extent of aquatic refugia maintained by alligators throughout the system and develop relationships necessary to predict restoration of refugia. 4. Validate and update ecological models for use in prediction of the effects of restoration. Many important questions concerning the effects of Everglades restoration on alligator populations remain unanswered such as the impacts of decompartmentalization, the role of alligator holes as aquatic refugia, and the effects of hydrology on population growth and condition. Further, the methods for monitoring and evaluating restoration success are not clear or have not been adapted for use during CERP. Also, we need to continue to update and validate restoration tools such as population models for use in alternative selection, performance measure development, and prediction. This project will directly address the questions outlined above, develop monitoring methods, and validate restoration tools for use in CERP. All project tasks have been requested by management agencies in South Florida (NPS, USFWS), listed as critical CERP priority research needs (see USGS Ecological Modeling Workshop at http://sofia.usgs.gov/publications/infosheets/ecoworkshop/ ), and/or highlighted as science objectives for CESI proprietary +USGS_SOFIA_robblee_fb_shrimp_04 Empirical studies in Support of Florida Bay and Adjacent Marine Ecosystems Restoration CEOS_EXTRA STAC Catalog 2002-10-01 2003-09-30 -81.25, 24.75, -80.375, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231551802-CEOS_EXTRA.umm_json The objectives of these activities are broadly: 1) to develop and implement (with other agency members) a program of research to support the restoration of Florida Bay; 2) with other PDT members to develop and evaluate restoration alternatives for Florida Bay and 3) with other committee members to develop performance measures and assess restoration alternatives affecting Florida Bay, Biscayne Bay, Barnes Sound and Manatee Bay and the lower southwest coast mangrove estuaries. Florida Bay lies downstream of the Everglades ecosystem. Perceived deterioration of the Everglades over the last century - and Florida Bay since the mid-1980’s - is generally viewed as linked to changes in freshwater flow and water quality associated with water management in South Florida. The pink shrimp is a species of special interest in each of the above studies because it has been chosen as an indicator species for use in restoration of south Florida estuaries. Empirical and experimental data developed in these studies will be used to support the development of a pink shrimp landscape simulation model and restoration performance measures. proprietary +USGS_SOFIA_robblee_shrimp Empirical Studies in Support of a Pink Shrimp, Farfantepenaeus duorarum, Simulation Model for Florida Bay CEOS_EXTRA STAC Catalog 1999-10-01 2004-09-30 -81.25, 24.75, -80.375, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549090-CEOS_EXTRA.umm_json A Tortugas/Florida Bay pink shrimp simulation model has been identified as a priority need in CERP by the South Florida Water Management District, NOAA, NPS and USGS. This model has been under development through the collaboration of a team of NMFS, USGS and University of Miami (UM) researchers since 1997. To date this project has been funded by NOAA's Coastal Oceans Program, DOI's Critical Ecosystem Studies Initiative and by USGS base funds. The purpose of the model is to assist in designing and refining restoration alternatives by predicting their impact on production of pink shrimp in Florida Bay and on shrimp recruitment from Florida Bay to the Tortugas fishery. A series of monitoring or empirical studies either have been completed or are ongoing. NMFS continues to monitor Tortugas pink shrimp harvest and develop the simulation model and has completed pink shrimp salinity/temperature tolerance experiments. USGS is continuing to monitor pink shrimp distribution and abundance in relation to environmental conditions and habitat in Florida Bay and to measure water flow in order to estimate postlarval transport within the Bay. With UM a critical collaborative study to identify and quantify the seasonality and magnitude of pathways of postlarval immigration to Florida Bay is continuing. Statistical studies of these and other data are ongoing relating pink shrimp to salinity, temperature and habitat in Florida Bay. Florida Bay lies downstream of the Everglades ecosystem. Perceived deterioration of the Everglades over the last century - and Florida Bay since the mid-1980's - is generally viewed as linked to changes in freshwater flow and water quality associated with water management in South Florida. A pink shrimp simulation model is being developed to assist in designing and refining restoration alternatives by predicting their impact on production of pink shrimp in Florida Bay and on shrimp recruitment from Florida Bay to the Tortugas fishery. The pink shrimp is a good indicator of the health and productivity of the Bay. The effect of salinity and temperature on pink shrimp growth and survivorship and of habitat on juvenile density provide a basis for predicting the abundance of pink shrimp juveniles in Florida Bay and thus the magnitude of recruitment to the Tortugas fishery. A landscape model is needed to express pink shrimp performance measures as functions of spatially complex factors acting across the Bay. Florida Bay is a complex shallow water ecosystem with distinct zones of different physical and biological characteristics (Fourqurean and Robblee 1999) that differ in their potential to support pink shrimp. The influence of upstream water management on pink shrimp recruitment from Florida Bay is expected to express itself principally through changes in salinity and seagrass habitat associated with changes in freshwater inflow. Predictions of the effect of these changes on the Bay's productive capacity require consideration not only of the resulting salinity and seagrass changes but also the resulting change in the area of overlap of these factors favorable to the pink shrimp (Browder and Moore 1981; Browder 1991). Critical long-term databases exist for pink shrimp that are suitable for developing empirical relationships and baselines. proprietary +USGS_SOFIA_rsl30dv Levee 30 Water Level Daily Values CEOS_EXTRA STAC Catalog 1996-02-01 1996-12-31 -80.49, 25.86, -80.48, 25.86 https://cmr.earthdata.nasa.gov/search/concepts/C2231553711-CEOS_EXTRA.umm_json This data set contains daily maximum water level elevation in feet below mean sea level(feet msl) for 21 groundwater wells and daily mean stage in feet msl for 2 surface water stations for 1996 along a transect, approximately 1,000 feet long that is perpendicular to and bisected by Levee 30. Determining the volume of water seeping from the water-conservation areas to the underlying aquifers is important in managing water levels in the conservation areas and freshwater deliveries to Everglades National Park. An accurate water budget to meet the competing natural and anthropogenic needs cannot be determined without this information. From Water Conservation Area 3B, water seeps into the Biscayne aquifer, which is about 80 feet thick directly beneath Levee 30 and thickens to the east, and flows relatively fast (due to high permeability of the aquifer) toward the urban and agricultural areas to the east. Water is also discharged to the canal along the eastern part of Levee 30. The rate of discharge is controlled by structures at the northern and southern ends of the canal. This seepage to the aquifer and canal discharge of water are critical for water-supply wells to the east and for preventing the inland movement of saltwater from the coast. However, lowering of ground-water levels to the east has resulted in higher ground-water seepage and canal discharge, reducing flows to the south in the water-conservation area. As a result, Levees 67A and 67C were constructed to direct water southward toward the central region of Everglades National Park. This water-management scheme has been effective in delivering water to the southwest; however, it reduced the flow to the southeast (northeastern part of Everglades National Park). The altering of historical flow directions and water-level durations has caused significant adverse effects to parts of the Everglades ecosystem. Water managers want to restore predevelopment flow conditions for the Everglades to survive, while also taking into consideration the urban and agricultural needs. The objective of this project was to evaluate approaches for quantifying ground-water seepage beneath Levee 30. The accounting of all significant hydrologic inflows and outflows to the Everglades ecosystem of the south Florida mainland is a key element of the South Florida Ecosystem Program. proprietary +USGS_SOFIA_rsl30uv Levee 30 Water Level Unit Values CEOS_EXTRA STAC Catalog 1996-02-01 1996-12-31 -80.3, 25.8, -80.29, 25.85 https://cmr.earthdata.nasa.gov/search/concepts/C2231550828-CEOS_EXTRA.umm_json This data set contains hourly readings for water level elevation in feet below mean sea level(feet msl) for 21 groundwater wells and stage in feet msl for 2 surface water stations for 1996 along a transect, approximately 1,000 feet long that is perpendicular to and bisected by Levee 30. Determining the volume of water seeping from the water-conservation to the underlying aquifers is important in managing water levels in the conservation areas and freshwater deliveries to Everglades National Park. An accurate water budget to meet the competing natural and anthropogenic needs cannot be determined without this information. From Water Conservation Area 3B, water seeps into the Biscayne aquifer, which is about 80 feet thick directly beneath Levee 30 and thickens to the east, and flows relatively fast (due to high permeability of the aquifer) toward the urban and agricultural areas to the east. Water is also discharged to the canal along the eastern part of Levee 30. The rate of discharge is controlled by structures at the northern and southern ends of the canal. This seepage to the aquifer and canal discharge of water are critical for water-supply wells to the east and for preventing the inland movement of saltwater from the coast. However, lowering of ground-water levels to the east has resulted in higher ground-water seepage and canal discharge, reducing flows to the south in the water-conservation area. As a result, Levees 67A and 67C were constructed to direct water southward toward the central region of Everglades National Park. This water-management scheme has been effective in delivering water to the southwest; however, it reduced the flow to the southeast (northeastern part of Everglades National Park). The altering of historical flow directions and water-level durations has caused significant adverse effects to parts of the Everglades ecosystem. Water managers want to restore predevelopment flow conditions for the Everglades to survive, while also taking into consideration the urban and agricultural needs. The objective of this project was to evaluate approaches for quantifying ground-water seepage beneath Levee 30. The accounting of all significant hydrologic inflows and outflows to the Everglades ecosystem of the south Florida mainland is a key element of the South Florida Ecosystem Program. proprietary USGS_SOFIA_rtt_sfwmd CERP/RECOVER restoration technology transfer: USGS-CERP liaison with SFWMD CEOS_EXTRA STAC Catalog 2005-10-01 -82.5, 24.5, -80, 28.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231551954-CEOS_EXTRA.umm_json This management and coordination effort supports several of the initiatives listed in the DOI science plan. The USGS representative will participate in CERP and RECOVER meetings and share information with USGS Priority Ecosystem Science (PES) staff, represent USGS in the SFWMD Biscayne Bay work group meetings, and assist DOI partners with obtaining and using USGS technical data and information on the Greater Everglades area. This project includes support of Comprehensive Everglades Restoration Plan/REstoration COordination and VERification (CERP/RECOVER), assistance with Greater Everglades Priority Ecosystem Science coordination, and USGS liaison with the South Florida Water Management District. proprietary USGS_SOFIA_rtt_usace CERP/RECOVER restoration technology transfer, USGS-CERP liaison with USACE CEOS_EXTRA STAC Catalog 2005-10-01 2010-12-31 -82.5, 24.5, -80, 28.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231550148-CEOS_EXTRA.umm_json The objectives of this project are: 1. support of Comprehensive Everglades Restoration Plan/REstoration COordination and VERification (CERP/RECOVER) by USGS participation in CERP PDT and RECOVER meetings and working with federal, State, and other restoration partners to ensure technology transfer and science synthesis 2. Assistance with Greater Everglades Priority Ecosystem Science (GE PES) coordination 3. USGS liaison with US Army Corps of Engineers (USACE). This management and coordination effort supports several of the initiatives listed in the Department of the Interior science plan. proprietary USGS_SOFIA_smith_hist_photo_archive Creation of a Digital Archive of Historical Aerial Photographs for Everglades NP and the Greater Everglades Ecosystem CEOS_EXTRA STAC Catalog 1920-02-14 1920-08-21 -81.991234, 24.363766, -80.04646, 26.100582 https://cmr.earthdata.nasa.gov/search/concepts/C2231548623-CEOS_EXTRA.umm_json The major products are planned as a series of USGS Open-File Reports, one for each complete, or near complete, set of photos. A photoset is defined as a collection of aerial photos that were taken during a discrete time, generally 30-60 days, with the same scale, film type, and camera. All OFRs will be distributed on CD-ROM and several on DVD. Each report will encompass a photoset with descriptive text sections such as Introduction, Metadata & Procedures, Study Area, and Acknowledgements. All scanned images will be in a downloadable format. A foundation for Everglades restoration must include a clear understanding of the pre-drainage south Florida landscape. Knowledge of the spatial organization and structure of pre-drainage landscape communities such as mangrove forests, marshes, sloughs, wet prairies, and pinelands, is essential to provide potential endpoints, restoration goals and performance measures to gauge restoration success Information contained in historical aerial photographs of the Everglades can aid in this endeavor. The earliest known aerial photographs, from the mid to late 1920s, and resulted in the production of T-Sheets (Topographic Sheets) for the coasts and shorelines of south Florida. The T-Sheets are remarkably detailed, delineating features such as shorelines, ponds, and waterways, in addition to the position of the boundary between differing vegetation communities. If followed through time changes in the position of these ecotones could potentially be used to judge effects of changes in the landscape of the Everglades ecosystem, providing a standard by which restoration success can be ascertained. The overall objective is to create a digital archive of historical aerial photographs of Everglades national park and surrounding area of the greater Everglades and south Florida. The archive will be in readily available Geographic Information System formats for ease of accessibility. Each set of photos will be broadly disseminated to client agencies, academic institutions and the general public via Open-File Reports and through the Internet. proprietary +USGS_SOFIA_solomet Evaluation of Methods to Determine Ground-water Seepage Below Levee 31N, Dade County Florida CEOS_EXTRA STAC Catalog 1997-07-01 1999-09-30 -80.75, 25.5, -80.33, 25.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231549232-CEOS_EXTRA.umm_json The primary objective of this investigation is to quantify seepage below Levee L31N. The amount of water lost to the L-31N Canal versus the fraction that flows below the canal will be estimated. A conceptual model is currently being developed for the site based upon results from an on-going stable isotope (oxygen -18 and deuterium) study. Quantification of seepage rates will be based upon a computer model, MODBRANCH, which couples both groundwater and surface water flows. Particular attention will be devoted to model performance under transient conditions caused by fluctuations in the stage of the L-31N Canal and pumping operations of the West Wellfield. In addition, an alternative leakage relationship based on reach transmissivity will be incorporated into MODBRANCH; this relationship is believed to be more suitable for transient conditions. The reach transmissivity relationship will be evaluated in comparison to MODBRANCH's existing leakage relationship, which is based on Darcian flow through the bed of the surface water channel. Modeling results will be used to develop an algorithm for real time estimation of seepage beneath Levee L31N. It is expected that this algorithm will estimate seepage using head differences at monitoring stations in the vicinity of the levee. Plans to restore historical hydrologic conditions in the northeast section of Everglades National Park (ENP) include the raising of water levels in ENP and water conservation area 3B, which overlie the Biscayne aquifer, an extremely permeable aquifer. The increase in water levels is likely to cause an increase in seepage losses to the east. Quantifying this seepage loss is necessary for water management purposes as well as for models of the Everglades and coastal systems. Levee L-31N has been identified as a critical area for potential water losses. The L-31N study site includes a wetland area within ENP on the west; the L-31N Canal flows from north to south through the longitudinal center of the site, and the eastern portion of the region is a suburban area of Miami which includes a major municipal wellfield, the West Wellfield, and rock mining activities. This project was completed in 1999. proprietary +USGS_SOFIA_sus_parts Effect of Water Flow on Transport of Solutes, Suspended Particles, and Particle-Associated Nutrients in the Everglades Ridge and Slough Landscape CEOS_EXTRA STAC Catalog 2004-01-01 2006-12-31 -81, 25, -80.25, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231554693-CEOS_EXTRA.umm_json "The objectives of the study are: To quantify through detailed field experiments previously unstudied processes in the Everglades, such as rates of fine-particle movement and filtration by vegetation as well as advective solute exchange between surface water and zones of solute storage in relatively stagnant waters (in areas of thick vegetation and in peat pore water). Our study focuses on determining the effects of these processes on chemical reactions of the contaminants as well as overall effects on downstream transport. At least initially, the emphasis will be on improved understanding of factors influencing transport of dissolved and fine particle forms of phosphorus. To apply the new knowledge gained from field measurements first in our own transport models (which are necessarily limited in time and space) and then to encourage application in more widely used water-quality models (e.g. DMSTA, ELM), and water quality models currently in development (e.g. extension of USGS SICS model in Taylor Slough). The goal is more accurate simulation of the effects of restoration on Everglades water quality, thus allowing more reliable use of water-quality models for prediction of the effects of restoration. To guide the use of improved water-quality models to estimate potential rates of transport, storage, and remobilization of phosphorus (and other contaminants) in WCA-2A, Shark and Taylor Sloughs in Everglades National Park, and Loxahatchee Wildlife Refuge, with a goal to predict potential rates of downstream movement of phosphorus in these systems under ""restored"" flows. A key measure of success in the Everglades restoration is protecting water quality while increasing the quantity of water flowing through the Everglades. The restoration's goal of increasing surface-water flow through the wetlands could have the unintended consequence of transporting contaminants farther into the Everglades than ever before. Thus, the need to augment water delivery will at times inevitably result in using water with higher than desirable total dissolved solids, particulate organic matter, sulfate, nutrients, and mercury. In addition, greater water flows may increase transport of those contaminants farther into the wetlands than ever before. Our investigation seeks a better understanding of the fundamental processes that affect the rates at which contaminants are transported in wetlands, focusing especially on critical unknowns - 1) rates of contaminant transport in association with fine suspended particles, and 2) rates of solute exchange between surface water and storage areas reservoirs in relatively stagnant surface waters (in thick vegetation and subsurface pore water in peat). Our studies are planned to be the definitive experimental investigations of solute and particle transport in the Everglades." proprietary +USGS_SOFIA_sw-pore_water_DOC_SUVA Everglades Water Chemistry - Surface water DOC, pore water DOC and SUVA data CEOS_EXTRA STAC Catalog 1995-03-01 1998-06-30 -80.9, 25.59, -80.1, 26.79 https://cmr.earthdata.nasa.gov/search/concepts/C2231554408-CEOS_EXTRA.umm_json The data are for dissolved organic carbon (DOC) and specific ultraviolet absorbance (SUVA) for surface water and pore water in the South Florida Water Management District (SFWMD) water conservation areas. It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylamine and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiage research project. proprietary USGS_SOFIA_target_sal_vals Determining Target Salinity Values for South Florida's Estuaries: The Combined Effects of Climate, Sea Level, and Water Management Practices CEOS_EXTRA STAC Catalog 2006-10-01 2010-09-30 -81.6, 24.5, -80, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231550968-CEOS_EXTRA.umm_json "The primary objective of this project is to provide information to Comprehensive Everglades Restoration Plan (CERP) managers that can be used to establish target salinity values and performance measures for the estuaries and coastal ecosystems. The information provided will consider the contribution of climate, sea level rise, and anthropogenic alteration of salinity values in the estuaries and coastal ecosystems. The four areas of focus for the project are: 1. Refine existing modern analog dataset by completing analyses of modern samples collected between 1996 and 2004 and applying the data to core data compiled in the Synthesis Task 2. Collect new cores (if necessary) within the southern estuaries to fill in information gaps identified by the land management agencies (Everglades National Park (ENP), and Biscayne National Park (BNP) and the Southern Estuaries Subteam of the Regional Evaluation Team (RET) of Restoration Coordination and Verification (RECOVER) 3. Select a few sites in the transition zones to collect cores in a transect moving perpendicular to shore to analyze the rate of sea level rise in the region 4. Work with collaborators to input all of the combined paleoecology data into linear regression models that can hindcast salinity for different parts of the system. The importance and application of ecosystem history research to restoration goals has been previously identified. The Department of the Interior (DOI) Science Plan lists as one of three primary restoration activities the need to ""ensure that hydrologic performance targets accurately reflect the natural predrainage hydrology and ecology"". The primary goal of this project is to determine the predrainage and ecology of critical regions within the estuaries and coastal ecosystems of south Florida identified by the groups charged with setting performance measures and targets for these coastal zones." proprietary +USGS_SOFIA_terrapin_mark-recap_data Mangrove Terrapin Mark Recapture Study data CEOS_EXTRA STAC Catalog 2001-11-01 2003-10-31 -81.16, 25.26, -81.14, 25.28 https://cmr.earthdata.nasa.gov/search/concepts/C2231550522-CEOS_EXTRA.umm_json In 2001 a mark-recapture study on mangrove terrapins (Malaclemys terrapin) in the Big Sable Creek (BSC) complex within Everglades National Park was initiated. The summary data for terrapins in BSC were collected over 5 sampling trips in a two-year period (November 2001 - October 2003) and from analysis of individual terrapin capture histories. Study objectives were to estimate adult survival probablility, capture probablilty, and abundance of terrapins at this study site. This allowed the establishment of the first baseline assessment for mangrove terrapins in the coastal Everglades. proprietary USGS_SOFIA_willard_tree_islands_04 Development and Stability of Everglades Tree Islands, Ridge & Slough, & Marl Prairies CEOS_EXTRA STAC Catalog 1962-01-01 2007-12-31 -81.25, 25, -80.2, 27 https://cmr.earthdata.nasa.gov/search/concepts/C2231548862-CEOS_EXTRA.umm_json Analysis of 209 pollen assemblages from surface samples in ten vegetation types in the Florida Everglades form the basis to identify wetland sub-environments from the pollen record. This calibration dataset makes it possible to infer past trends in hydrology and disturbance regime based on pollen assemblages preserved in sediment cores. Pollen assemblages from sediment cores collected in different vegetation types throughout the Everglades provide evidence on wetland response to natural fluctuations in climate as well as impacts of human alteration of Everglades hydrology. Sediment cores were located primarily in sawgrass marshes, cattail marshes, tree islands, sawgrass ridges, sloughs, marl prairies, and mangroves. The datasets contain raw data on pollen abundance as well as pollen concentration (pollen grains per gram dry sediment). Everglades restoration planning requires an understanding of the impact of natural and human-induced environmental change on wetland stability, and this project focuses specifically on three wetland types: tree islands, the sawgrass ridge and slough system, and marl prairies. Tree islands are considered key indicators of the health of the Everglades ecosystem because of their sensitivity to both flooding and drought conditions. Tree islands also act as a sink for nutrients in the ecosystem and may play an important role in regulating nutrient dynamics. Although management strategies to restore and even create tree islands are being formulated, the published data on their age, developmental history, geochemistry, and response to hydrologic alterations is limited. To address these issues, this project integrates floral and geochemical data with geologic and vegetational mapping activities to establish the timing of tree-island formation and impacts of both flooding and droughts on tree islands throughout the Everglades. proprietary +USGS_SOFIF_Fbbtypes Florida Bay Bottom Types map CEOS_EXTRA STAC Catalog 1996-01-01 1997-01-31 -81.25, 24.75, -80.25, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231553334-CEOS_EXTRA.umm_json The map shows the bottom types for Florida Bay that resulted from site surveys and boat transects (summer 1996-January 1997) compared with aerial photographs (December 1994-January 1995) and SPOT satellite imagery (1987). The purpose of this map is to describe the bottom types found within Florida Bay for use in 1) assessing bottom friction associated with sediment and benthic communities and 2) providing a very general description for other research needs. For these purposes, two descriptors were considered particularly important: density of seagrass cover and sediment texture. Seagrass estimates are visual estimates of the amount of seagrass cover including both number of plants and leaf length. Therefore, seagrass cover may be greater in areas with long leaves than in areas with short blades, even though the number of shoots may be the same. Seagrass cover is a different measure than density (Zieman et al., 1989 or Durako et al., 1996). It is used here to more accurately reflect hydrodynamic influence than the standing crop of seagrass. The use and definitions of dense, intermediate and sparse seagrass cover are similar to those used by Scoffin (1970). This map and associated descriptions are not meant to assess ecologic communities or detail sedimentological facies. The resolution of the map has been selected in an effort to define broad regions for use in modeling efforts. For these purposes, small-scale changes in bottom type (e.g. small seagrass patches) are not delineated. proprietary USGS_SOIL_CHEMISTRY Chemical Analyses of Soils and Other Surficial Materials of the Conterminous United States CEOS_EXTRA STAC Catalog 1958-01-01 1976-01-01 -125, 25, -67, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231548724-CEOS_EXTRA.umm_json "The following abstract was taken from the the Chemical Analyses of Soils and Other Surficial Materials of the Conterminous United States Metadata, written by David B. Smith, Research Geologist, U.S. Geological Survey, Denver, Colorado. This metadata may be viewed in HTML at ""http://minerals.usgs.gov/"". ABSTRACT This data set contains geochemical data from soils and other regoliths collected and analyzed by Hans Shacklette and colleagues beginning in 1958 and continuing until about 1976. The samples were collected at a depth of about 20 cm from sites that, insofar as possible, had surficial materials that were very little altered from their natural condition and that supported native plants. The sample material at most sites could be termed ""soil"" because it was a mixture of disintegrated rock an organic matter. Some of the sampled deposits, however, were not soils as defined above, but were other regolith types. These included desert sands, sand dunes, some loess deposits, and beach and alluvial deposits that contained little or no visible organic material. The samples were chemically analyzed by a variety of techniques in the U.S. Geological Survey laboratories in Denver, CO. DATA The data set contains 1,323 samples for a sampling density of approximately one sample per 6,000 square kilometers. The data set is currently the only national geochemical data set collected and analyzed according to standardized protocols. The data are most appropriately used to provide information on background concentrations of elements in soil. ANALYSIS METHODS The data was acquired using various chemical analysis methods. In summary the methods used were: 1)Emission spectrography for Al, Ba, Be, B, Ca, Ce, Cr, Co, Cu, Ga, Fe, La, Pb, Mg, Mn, Mo, Nd, Ni, Nb, P, K, Sc, Na, Sr, Ti, V, Yb, Y, Zn, and Zr; 2)EDTA titration for Ca; 3)Colorimetric methods for P and Zn, 4)Flame photometry for K; 5)Flame atomic absorption for Hg, Li, Mg, Na, Rb, and Zn; 6)Flameless atomic absorption for Hg; 7)X-ray fluorescence spectrometry for Ca, Ge, Fe, K, Se, Ag, S, and Ti; 8)Combustion for total carbon; and 9)Neutron activation analysis for U and Th. THE DATA The data file is an ArcVie Shapefile and has been compressed using the WinZip program. The usere will need to uncompress the file with WinZip or compatible software before attempting to import the file into ArcView or ArcInfo. Shapefiles can only be de-compressed with programs that recognize multi-file archives. The shapefiles are designed for use with Arc/Info and ArcView, which are GIS/Mapping software marketed by ESRI. By visiting ""http://www.esri.com/software/arcexplorer/index.html"" you may download ESRI's free Arc Explorer software for viewing shape files on Windows 95, 98, or NT." proprietary +USGS_Sherman_QUAD_1.0 Digital Geologic Map of Sherman Quadrangle, North-Central Texas CEOS_EXTRA STAC Catalog 1967-01-01 1991-01-01 -98.02595, 32.982605, -95.999886, 34.01737 https://cmr.earthdata.nasa.gov/search/concepts/C2231554058-CEOS_EXTRA.umm_json "This data set was created for use in a regional ground-water model of the Lake Texoma watershed for a project by the U.S. Environmental Protection Agency, National Risk Management Research Laboratory located in Ada, Oklahoma, titled ""Development of protocols and decision support tools for assessing watershed system assimilative capacity, in support of risked-based ecosystem management and restoration practices."" Although this data set was created for use in a specific project, it may be used to make geologic maps, and determine approximate areas and locations of various geologic units. This digital data set contains geologic formations for the 1:250,000-scale Sherman quadrangle, Texas and Oklahoma. The original data are from the Bureau of Economic Geology publication, ""Geologic Atlas of Texas, Sherman sheet"", by J.H. McGowen, T.F. Hentz, D.E. Owen, M.K. Pieper, C.A. Shelby, and V.E. Barnes, 1967, revised 1991. Additional geology data sets are available for Oklahoma at URL ""http://ok.water.usgs.gov/gis/geology/index.html"". The source maps for three counties in the Oklahoma panhandle are at a scale of 1:125,000. The source maps for the rest of Oklahoma are at a scale of 1:250,000. The original geology source map was published in the Transverse Mercator Projection, Zone 14. This data set was projected to an Albers Equal Area projection (Synder, 1987), cast on the North American Datum of 1983. This electronic report was subjected to the same review standard that applies to all U.S. Geological Survey reports. Reviewers were asked to check the topological consistency, tolerances, attribute frequencies and statistics, projection, and geographic extent. Reviewers were given digital data sets and paper plots for checking against the source maps to verify the linework and attributes. The reviewers were asked to check the metadata and accompanying files for completeness and accuracy." proprietary +USGS_TamiamiFlowMonitoring_2007-2010 Flow monitoring along the western Tamiami Trail between County Road 92 and State Road 29 in support of the Comprehensive Everglades Restoration Plan, 2007-20101 CEOS_EXTRA STAC Catalog 2006-03-01 2010-09-30 -81.90686, 25.831032, -81.449066, 26.413366 https://cmr.earthdata.nasa.gov/search/concepts/C2231550120-CEOS_EXTRA.umm_json he construction of U.S. Highway 41 (Tamiami Trail), the Southern Golden Gate Estates development, and the Barron River Canal has altered the flow of freshwater to the Ten Thousand Islands estuary of Southwest Florida. Two restoration projects, the Picayune Strand Restoration Project and the Tamiami Trail Culverts Project, both associated with the Comprehensive Everglades Restoration Plan, were initiated to address this issue. Quantifying the flow of freshwater to the estuary is essential to assessing the effectiveness of these projects. The U.S. Geological Survey conducted a study between March 2006 and September 2010 to quantify the freshwater flowing under theTamiami Trail between County Road 92 and State Road 29 in southwest Florida, excluding the Faka Union Canal (which is monitored by South Florida Water Management District). The study period was after the completion of the Tamiami Trail Culverts Project and prior to most of the construction related to the Picayune Restoration Project. The section of the Tamiami Trail that was studied contains too many structures (35 bridges and 16 culverts) to cost-effectively measure each structure on a continuous basis, so the area was divided into seven subbasins. One bridge within each of the subbasins was instrumented with an acoustic Doppler velocity meter. The index velocity method was used to compute discharge at the seven instrumented bridges. Periodic discharge measurements were made at all structures, using acoustic Doppler current profilers at bridges and acoustic Doppler velocity meters at culverts. Continuous daily mean values of discharge for the uninstrumented structures were calculated on the basis of relations between the measured discharge at the uninstrumented stations and the discharge and stage at the instrumented bridge. Estimates of daily mean discharge are available beginning in 2006 or 2007 through September 2010 for all structures. Subbasin comparison is limited to water years 2008?2010. The Faka Union Canal contributed more than half (on average 60 percent) of the flow under the Tamiami Trail between State Road 29 and County Road 92 during water years 2008?2010. During water years 2008?2010, an average 9 percent of the flow through the study area came from west of the Faka Union Canal and an average 31 percent came from east of the Faka Union Canal. Flow data provided by this study serve as baseline information about the seasonal and spatial distribution of freshwater flow under the Tamiami Trail between County Road 92 and State Road 29, and study results provide data to evaluate restoration efforts. proprietary +USGS_VOLCANO Global Volcanoes CEOS_EXTRA STAC Catalog 1970-01-01 -179.2176, 17.67469, -64.56616, 72.40623 https://cmr.earthdata.nasa.gov/search/concepts/C2231552074-CEOS_EXTRA.umm_json This data shows the location of all known volcanoes in the world. [Summary provided by the USGS.] proprietary +USGS_WHFC_SUPERDIF3 Massachusetts Coastal Marine Time Series Data Held by the USGS CEOS_EXTRA STAC Catalog 1970-01-01 -73.68, 41.06, -69.75, 43.07 https://cmr.earthdata.nasa.gov/search/concepts/C2231551788-CEOS_EXTRA.umm_json "Time-series oceanographic data for the coast of Massachusetts collected by the USGS or used in conjunction with USGS projects. These data are stored as NetCDF files using conventions developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with their EPIC system. The hourly data is available online through the USGS Coastal Marine Time Series Browser ""http://stellwagen.er.usgs.gov/"". Variables include current, temperature, pressure, conductivity, light transmission (beam attenuation) and others. Available data sets for the coast of Massachusetts: * Buzzards Bay (Jul 1982 - Oct 1985) * Cape Cod Bay (Feb 1986 - Apr 1986) * Cape Cod Misc (Jul-Aug 1980) * Long Term Observations (MWRA) (Jan 1990 - Present) * Massachusetts Bay Circulation Experiment (Sep 1990 - Jun 1991) * Massachusetts Bay Internal Wave Experiment (Aug-Sep 1998) * Stellwagen Bank (Feb 1994 - Apr 1995) * Western Massachusetts Bay (Jan-May 1987)" proprietary +USGS_WHFC_SUPERDIF4 New Jersey Outer Continental Shelf (Middle Atlantic Bight) Marine Time Series Data Held by the USGS CEOS_EXTRA STAC Catalog 1970-01-01 -75.69, 38.8, -73.78, 41.47 https://cmr.earthdata.nasa.gov/search/concepts/C2231550932-CEOS_EXTRA.umm_json "Time-series oceanographic data for the New Jersey outer continental shelf (Middle Atlantic Bight) collected by the USGS or used in conjunction with USGS projects. These data are stored as NetCDF files using conventions developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with their EPIC system. The hourly data is available online through the USGS Coastal Marine Time Series Browser ""http://stellwagen.er.usgs.gov/"". Variables include current, temperature, pressure, conductivity, light transmission (beam attenuation) and others. Available data sets for the Middle Atlantic Bight: * Deep Water Dump Site 106 (Sep 1989 - Jul 1990) * Hudson Shelf Valley (Dec 1999 - Apr 2000) * Middle Atlantic Bight (Dec 1975 - Oct 1980) * New England Continental Slope (Nov 1982 - Nov 1984)" proprietary +USGS_WHFC_SUPERDIF6 Gulf of Mexico (Alabama coast) Coastal Marine Time Series Data Held by the USGS CEOS_EXTRA STAC Catalog 1970-01-01 -98, 25, -82, 34 https://cmr.earthdata.nasa.gov/search/concepts/C2231550393-CEOS_EXTRA.umm_json "Time-series oceanographic data for the Gulf of Mexico (Alabama coast) collected by the USGS or used in conjunction with USGS projects. These data are stored as NetCDF files using conventions developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with their EPIC system. The hourly data is available online through the USGS Coastal Marine Time Series Browser ""http://stellwagen.er.usgs.gov/"". Variables include current, temperature, pressure, conductivity, light transmission (beam attenuation) and others. Available data sets for the Gulf of Mexico: Chandeleur Islands (Jul - Nov 2010) * Deep Reef (May 2001) * Lake Ponchartrain (Mar-Jul 1995) * Mobile Bay (Apr-Jul 1990; May 1991 - May 1992)" proprietary USGS_WHFC_SUPERDIF7 California Coastal Marine Time Series Data Held by the USGS CEOS_EXTRA STAC Catalog 1970-01-01 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2231550439-CEOS_EXTRA.umm_json "Time-series oceanographic data for the coast of California collected by the USGS or used in conjunction with USGS projects. These data are stored as NetCDF files using conventions developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with their EPIC system. The hourly data is available online through the USGS Coastal Marine Time Series Browser ""http://stellwagen.er.usgs.gov/"". Variables include current, temperature, pressure, conductivity, light transmission (beam attenuation) and others. Available data sets for California: * California Area Monitoring Program (CAMP) (May-Jun 1987, Dec 1988 - Feb 1989) * Farallones (May 1989 - Aug 1990; Nov 1997 -Nov 1998) * Monterey Bay National Marine Sanctuary (May 1985 - Aug 1998) * Monterey Canyon (Aug 1993 - May 1995) * Orange County, CA (Jun 2001 - Jan 2003) * Palos Verdes Shelf (May 1992 - Mar 1993) * Southern California (Nov 1997 - Mar 2000) * Sediment Transport on Shelves and Slopes (STRESS) (Dec 1988 - May 1989; Nov 1990 - Mar 1991)" proprietary +USGS_WHFC_SUPERDIF8 Johnston Atoll of Hawaii Pacific Marine Time Series Data Held by the USGS CEOS_EXTRA STAC Catalog 1970-01-01 -170, 16, -168, 18 https://cmr.earthdata.nasa.gov/search/concepts/C2231553024-CEOS_EXTRA.umm_json "Time-series oceanographic data for the Pacific Ocean in the vicinity of Johnston Atoll, collected by the USGS or used in conjunction with USGS projects. These data are stored as NetCDF files using conventions developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with their EPIC system. The hourly data is available online through the USGS Coastal Marine Time Series Browser ""http://stellwagen.er.usgs.gov/"". Variables include current, temperature, pressure, conductivity, light transmission (beam attenuation) and others. Available data sets for Hawaii: * Mamala Bay (Jun 1996 - Aug 1997) * Molokai (Jan-Apr 2001; Nov 2001 - Feb 2002)" proprietary +USGS_WHSC_MassBay_89-06_3.0 Long-Term Oceanographic Observations in Massachusetts Bay, 1989-2006 - USGS_WHSC_MassBay_89-06 CEOS_EXTRA STAC Catalog 1989-12-01 2006-02-28 -71, 42, -70.5, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231550427-CEOS_EXTRA.umm_json This data report presents long-term oceanographic observations made in western Massachusetts Bay at long-term site LT-A (42� 22.6' N., 70&� 47.0' W.; nominal water depth 32 meters) from December 1989 through February 2006 and long-term site B LT-B (42� 9.8' N., 70� 38.4' W.; nominal water depth 22 meters) from October 1997 through February 2004. The observations were collected as part of a U.S. Geological Survey (USGS) study designed to understand the transport and long-term fate of sediments and associated contaminants in Massachusetts Bay. The observations include time-series measurements of current, temperature, salinity, light transmission, pressure, oxygen, fluorescence, and sediment-trapping rate. About 160 separate mooring or tripod deployments were made on about 90 research cruises to collect these long-term observations. This report presents a description of the 16-year field program and the instrumentation used to make the measurements, an overview of the data set, more than 2,500 pages of statistics and plots that summarize the data, and the digital data in Network Common Data Form (NetCDF) format. This research was conducted by the USGS in cooperation with the Massachusetts Water Resources Authority and the U.S. Coast Guard. proprietary +USGS_WILMA_COASTAL_IMPACT Hurricane Wilma Impact Studies CEOS_EXTRA STAC Catalog 1970-01-01 -86, 22, -74, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231550249-CEOS_EXTRA.umm_json Hurricane Wilma made landfall as a category 2 storm south of Fort Meyers, Florida on October 24, 2005. The U.S. Geological Survey (USGS), NASA, and the U.S. Army Corps of Engineers are cooperating in a research project investigating coastal change that might result from Hurricane Wilma. Pre-landfall vulnerability estimates for west Florida's barrier islands falling within the cone of uncertainty for Wilma's path are available. These maps highlight the extreme vulnerability of the West-Florida coastline to a direct hit from a storm of Wilma's predicted magnitude. Aerial video, still photography, and laser altimetry surveys of post-storm beach conditions will be collected for comparison with earlier data as soon as weather allows. The comparisons will show the nature, magnitude, and spatial variability of coastal changes such as beach erosion, overwash deposition, and island breaching. These data will also be used to further refine predictive models of coastal impacts from severe storms. The data will be made available to local, state, and federal agencies for purposes of disaster recovery and erosion mitigation. [Summary provided by the USGS.] proprietary +USGS_WRD_NWIS-W National Water Information System (NWISWeb) CEOS_EXTRA STAC Catalog 1970-01-01 -125, 25, -66, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2232314067-CEOS_EXTRA.umm_json "The National Water Information System database (NWIS)provide access to water-resources data collected at approximately 1.5 million sites in all 50 States, the District of Columbia, and Puerto Rico. Online access to this data is organized around these categories: - Surface Water -Ground Water -Water Quality The USGS investigates the occurrence, quantity, quality, distribution, and movement of surface and underground waters and disseminates the data to the public, State and local governments, public and private utilities, and other Federal agencies involved with managing our water resources. [Summary adapted from: ""http://waterdata.usgs.gov/usa/nwis/""]" proprietary +USGS_YosemiteRockFalls Historical rock falls in Yosemite National Park, California (1857-2011) CEOS_EXTRA STAC Catalog 1970-01-01 -119.8863, 37.4948, -119.1995, 38.1863 https://cmr.earthdata.nasa.gov/search/concepts/C2231554256-CEOS_EXTRA.umm_json Inventories of rock falls and other types of landslides are valuable tools for improving understanding of these events. For example, detailed information on rock falls is critical for identifying mechanisms that trigger rock falls, for quantifying the susceptibility of different cliffs to rock falls, and for developing magnitude-frequency relations. Further, inventories can assist in quantifying the relative hazard and risk posed by these events over both short and long time scales. This report describes and presents the accompanying rock fall inventory database for Yosemite National Park, California. The inventory database documents 925 events spanning the period 1857–2011. Rock falls, rock slides, and other forms of slope movement represent a serious natural hazard in Yosemite National Park. Rock-fall hazard and risk are particularly relevant in Yosemite Valley, where glacially steepened granitic cliffs approach 1 km in height and where the majority of the approximately 4 million yearly visitors to the park congregate. In addition to damaging roads, trails, and other facilities, rock falls and other slope movement events have killed 15 people and injured at least 85 people in the park since the first documented rock fall in 1857. The accompanying report describes each of the organizational categories in the database, including event location, type of slope movement, date, volume, relative size, probable trigger, impact to humans, narrative description, references, and environmental conditions. The inventory database itself is contained in a Microsoft Excel spreadsheet (Yosemite_rock_fall_database_1857-2011.xlsx). Narrative descriptions of events are contained in the database, but are also provided in a more readable Adobe portable document format (pdf) file (Yosemite_rock_fall_database_narratives_1857-2011.pdf) available for download separate from the database. proprietary +USGS_ag_chem_1.0 Estimates of agricultural-chemical use in counties in the conterminous United States as reported in the 1987 Census of Agriculture CEOS_EXTRA STAC Catalog 1970-01-01 -128.07002, 22.67775, -65.25698, 48.26194 https://cmr.earthdata.nasa.gov/search/concepts/C2231548606-CEOS_EXTRA.umm_json This coverage contains estimates of agricultural-chemical use in counties in the conterminous United States as reported in the 1987 Census of Agriculture (U.S. Department of Commerce, 1989a). Agricultural-chemical use data are reported as either acres on which used, tons, or as a percentage of county area. Agricultural-chemical use estimates were generated from surveys of all farms where $1,000 or more of agricultural products were sold, or normally would have been sold, during the census year. Most of the attributes summarized represent 1987 data, but some information from the 1982 Census of Agriculture also was included. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). proprietary +USGS_ag_stock_1.0 Estimates of livestock holdings in counties in the conterminous United States as reported in the 1987 Census of Agriculture CEOS_EXTRA STAC Catalog 1970-01-01 -128.07002, 22.67775, -65.25698, 48.26194 https://cmr.earthdata.nasa.gov/search/concepts/C2231552987-CEOS_EXTRA.umm_json "The livestock holdings estimates in this coverage are intend for use in estimating regional livestock holdings, and in producing visual displays and mapping relative amounts of agricultural livestock holdings across broad regions of the United States. This coverage contains estimates of livestock holdings in counties in the conterminous United States as reported in the 1987 Census of Agriculture (U.S. Department of Commerce, 1989a). Livestock holdings data are reported as either a number (for example, number of milk cows), number of farms, or in thousands of dollars. Livestock holdings estimates were generated from surveys of all farms where $1,000 or more of agricultural products were sold, or normally would have been sold, during the census year. Most of the attributes summarized represent 1987 data, but some information for the 1982 Census of Agriculture also was included. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). Livestock Census of Agriculture Counties United States Procedures_Used: CENSUS DATA An automated procedure was developed for processing the raw census data into ARC/INFO coverage attributes. The procedure is summarized below: 1) copy county2m coverage to coverage representing type of census data (i.e. ag_expn or ag_land), 2) run agadd.aml for each item added to the coverage, giving coverage name and attribute field number as arguments. The agadd.aml program runs a fortran program to extract field data from the raw census data files, and then processes that raw data finally adding it as a column of attribute data to the county coverage. Other programs were developed to calculate summary statistics of the census attribute data, and to make graphics representing attribute values across the United States. COUNTY BOUNDARIES This series of maps was published as part of the National Atlas of the United States (U.S.Geological Survey, 1970). The maps for the conterminou United States were digitized in 15 sheets and published in the Digital Lin Graph (DLG) format as described by Domeratz and others (1983). Each sheet was prepared by reading the DLG files of the political and water bodies layers, converting them to ARC/INFO, extracting the county boundaries and the coastline, respectively, and joining the two layers. FIPS codes were assigned to all polygons by using available sources and were checked manually. Boundaries with adjacent sheets of the 15-sheet set were edgematched manually, arbitrarily choosing one of the sheets as the ""correct"" border. Edgematching operations adjusted the linework as far as was necessary so that the coverages would fit to a tolerance of 100 meters. The coverage (referred to herein as Version 1.0) was stored as 49 separate coverages (48 States and the District of Columbia) because the ARC/INFO software in use at the time could not process the entire coverage. Individual States could be joined by specifying a tolerance of 100 meters. From time to time, adjustments were made to the State coverages to reflect changes in U.S. counties. It is believed the accuracy of these adjustments is comparable to the original linework. For Version 2.0, all State coverages were rejoined and manually edited to produce a perfect edgematch between all States. For States on the original map sheet boundaries, this adjustment averaged less than 20 meters and in no case was more than 100 meters. The whole coverage was CLEANed to a tolerance of 20 meters, which resulted in few, if any, effect on small offshore islands. The coverage also was checked to ensure that it represented current U.S. counties or county equivalents. The coverage in Version 1.0 stopped at the coastline. There was no attempt to depict offshore areas. This created some problems when the coverage was used to assign county codes to sampling stations located near the coast. To help in this matter, Version 2.0 includes offshore extensions of the county polygons. The (water) boundaries of many of these polygons are arbitrary. The Canadian Great Lakes features are another new addition to Version 2. They were added to improve the utility of the coverage for visual displays Although the Canadian Great Lakes are logically represented by a single polygon, practical considerations -- the inability of some software to plo polygons with a large number of vertices -- made it necessary to separate them into four polygons. The dividing lines are located in narrow channel to minimize interference with plotting patterns. Canadian islands within the Great Lakes also were included. All ticks were relocated to places that are easily visible on maps of the United States, to help in registering maps that may not otherwise have adequate registration information. To expedite accessing parts of the coverage, certain items have been indexed with the procedure, INDEX_COUNTY.AML. See Section 3 above. A spatial index also was created. When using this coverage to clip or intersect other coverages, a tolerance as low as 2 meters can be used. The processing used to derive this coverage moved boundaries from their positions on the original maps. In cases of conflicting lines, preference was given to forming the correct topology. Strictly speaking, this coverage is not identical to the source materials. These changes were unavoidable in producing a continuous coverage of the conterminous United States. Revisions: COUNTY POLYGON DATA Revision 1.0, 12/17/90. This revision represents numerous corrections and minor modifications made to this set of coverages from its construction in 1985 through the revision date. Revision 2.0, 3/18/91. Major reworking of the coverage, combining all State coverages. Reviews_Applied_to_Data: The Census of Agriculture data processing procedure and attribute data have been peer reviewed in 1993 by Leonard Orzol and Barbara Ruddy, both hydrologist with the USGS. The county boundaries in this coverage have received no formal review. They have, however, been used in numerous applications where serious error would have been obvious. Some State coverages were corrected following such use. The offshore polygon extensions and the Canadian Great Lakes polygons have had no review. Related_Spatial_and_Tabular_Data_Sets: This coverage is part of series of 1:2,000,000-scale base maps covering the United States. Layers in this set include: COUNTY -- County boundaries. STATE -- State boundaries (formed from COUNTY). WATERBOD -- Water Bodies. STREAM -- Streams. HUC -- Hydrologic cataloging units (basins)." proprietary USGS_arapbase_Version 1.0, July 22, 1998 COVERAGE ARAPBASE -- Structure contours of base of upper Arapahoe aquifer CEOS_EXTRA STAC Catalog 1998-01-01 1998-01-01 -105.22675, 39.831512, -105.14134, 39.918385 https://cmr.earthdata.nasa.gov/search/concepts/C2231549245-CEOS_EXTRA.umm_json "This data set was created to display the altitude of the base of the upper Arapahoe aquifer as depicted in Robson and others (1998). This digital geospatial data set consists of structure contours on the base of the upper member of the Arapahoe aquifer. The U.S. Geological Survey developed this data set as part of a project described in the report, ""Structure, Outcrop, and Subcrop of the Bedrock Aquifers Along the Western Margin of the Denver Basin, Colorado"" (Robson and others, 1998)." proprietary +USGS_benchmark_1.0 Locations of NASQAN benchmark stations CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -127.042595, 27.19216, -69.387886, 48.367382 https://cmr.earthdata.nasa.gov/search/concepts/C2231550268-CEOS_EXTRA.umm_json This coverage was created for the 1990-91 National Water Summary. The coverage shows locations of NASQAN benchmark stations. Procedures_Used: The point coverage was created from data taken from U.S. Geological Survey computer files. proprietary USGS_cir89_Version 1.0 Color-infrared composite of Landsat data for the Sarcobatus Flat area of the Death Valley regional flow system, Nevada and California, 1989 CEOS_EXTRA STAC Catalog 1989-06-21 1989-06-21 -117.216324, 36.997658, -116.66944, 37.40421 https://cmr.earthdata.nasa.gov/search/concepts/C2231554772-CEOS_EXTRA.umm_json "The data set was created to determine phreatophyte boundaries used in the report, ""Ground-water discharge determined from estimates of evapotranspiration, Death Valley regional flow system, Nevada and California"". The raster-based, color-infrared composite was derived from Landsat Thematic Mapper imagery data acquired during June 1989 for the Sarcobatus Flat area of the Death Valley regional flow system. The image is a single-channel, parallelepiped classification that when displayed using a 256-color color table shows a simulation of a color-infrared composite. The data set was used in determining phreatophyte boundaries for a ground-water evapotranspiration study. The raster-based, color-infrared composite (CIR) was derived from Landsat Thematic Mapper (TM) imagery data acquired during June 1989 for the Death Valley regional flow system, Nevada and California. The image is a single-channel, parallelepiped classification that when displayed using a 256-color color table shows a simulation of a color-infrared composite (Beverley and Penton, 1989). TM channels 2, 3, and 4 are used in the classification process. The wavelengths of these channels correspond to those used for a CIR composite. The data range of each channel is divided into eight divisions. The 512 possible combinations are then reduced to 256. A color table of red, green, and blue values is created for display of the image. Sixteen possible color values exist for each color. These values are scaled between 0 and 255. The image is reduced from more than 16 million colors to 256 colors. Reviews The CIR image for 1989 was checked for consistency and accuracy during the data processing. Two external reviews were done. The reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers also were asked to check the topological consistency, tolerances, projections, and geographic extent. The Landsat Entity-identification number is LT5040034008917210." proprietary USGS_cira92_Version 1.0 Color-infrared composite of Landsat data for the Death Valley regional flow system, Nevada and California, 1992 CEOS_EXTRA STAC Catalog 1992-06-01 1992-06-13 -117.550385, 35.378323, -115.251015, 37.653557 https://cmr.earthdata.nasa.gov/search/concepts/C2231551442-CEOS_EXTRA.umm_json "This data set was created to determine phreatophyte boundaries for use in the report, ""Ground-water discharge determined from estimates of evapotranspiration, Death Valley regional flow system, Nevada and California"". The raster-based, color-infrared composite was derived from Landsat Thematic Mapper imagery data acquired during June 1992 for the Death Valley regional flow system. The image is a single-channel, parallelepiped classification that when displayed using a 256-color color table shows a simulation of a color-infrared composite. The data set was used in determining phreatophyte boundaries for a ground-water evapotranspiration study. The raster-based, color-infrared composite (CIR) was derived from Landsat Thematic Mapper (TM) imagery data acquired during June 1992 for the Death Valley ground-water flow system, Nevada and California. The image is a single-channel, parallelepiped classification that when displayed using a 256-color color table shows a simulation of a color-infrared composite (Beverley and Penton, 1989). TM channels 2, 3, and 4 are used in the classification process. The wavelengths of these channels correspond to those used for a CIR composite. The data range of each channel is divided into eight divisions. The 512 possible combinations are then reduced to 256. A color table of red, green, and blue values is created for display of the image. Sixteen possible color values exist for each color. These values are scaled between 0 and 255. The image is reduced from more than 16 million colors to 256 colors." proprietary USGS_cont1992 1992 Water-Table Contours of the Mojave River Ground-Water Basin, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.652695, 34.364513, -116.55357, 35.081955 https://cmr.earthdata.nasa.gov/search/concepts/C2231553864-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River Basin. The U.S. Geological Survey, in cooperation with the Mojave Water Agency, constructed a water-table map of the Mojave River ground-water basin for ground-water levels measured in November 1992. Water-level data were collected from approximately 300 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,200 to 1,600 feet above sea level. [Summary provided by the USGS.] proprietary USGS_cont1994 1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.07194, 34.095333, -115.98976, 34.64026 https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Morongo Basin. The U.S. Geological Survey constructed a water-table map of the Morongo ground-water basin for ground-water levels measured during the period January-October 1994. Water-level data were collected from 248 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,400 to 1,500 feet above sea level. [Summary provided by the USGS.] proprietary USGS_cont1996 1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.63461, 34.109745, -115.98707, 35.31552 https://cmr.earthdata.nasa.gov/search/concepts/C2231555091-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins. The U.S. Geological Survey constructed a water-table map of the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins for ground-water levels measured during the period January-September 1996. Water-level data were collected from 632 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:175,512. The contour interval ranges from 3,400 to 1,550 feet above sea level. [Summary provided by the USGS.] proprietary +USGS_erf1_Version 1.2, August 01, 1999 ERF1 -- Enhanced River Reach File 1.2 CEOS_EXTRA STAC Catalog 1999-01-07 1999-01-07 -127.8169, 23.247017, -65.55541, 48.19323 https://cmr.earthdata.nasa.gov/search/concepts/C2231552175-CEOS_EXTRA.umm_json ERF1 was designed to be a digital data base of river reaches capable of supporting regional and national water-quality and river-flow modeling and transport investigations in the water-resources community. ERF1 has been recently used at the U.S. Geological Survey to support interpretations of stream water-quality monitoring network data (see Alexander and others, 1996; Smith and others, 1995). In these analyses, the reach network has been used to determine flow pathways between the sources of point and nonpoint pollutants (e.g., fertilizer use, municipal wastewater discharges) and downstream water-quality monitoring locations in support of predictive water-quality models of stream nutrient transport. The digital data set ERF1 includes enhancements to the U.S. Environmental Protection Agency's River Reach File 1 (RF1)to ensure the hydrologic integrity of the digital reach traces and to quantify the time of travel of river reaches and reservoirs [see U.S.EPA (1996) for a description of the original RF1]. Any use of trade, product, or firm names is for descriptive proprietary +USGS_erfi-2_2.0, November 19, 2001 ERF1-2 -- Enhanced River Reach File 2.0 CEOS_EXTRA STAC Catalog 1999-01-07 1999-01-07 -127.85945, 23.243486, -65.37739, 48.194405 https://cmr.earthdata.nasa.gov/search/concepts/C2231551816-CEOS_EXTRA.umm_json "This report describes the process of enhancements to the stream reach network, ERF1, which is an enhanced version of EPA's RF1. The U.S. Environmental Protection Agency's reach file (RF1) is a database of interconnected stream segments or ""reaches"" that comprise the surface water drainage system for the United States. A variety of attributes have been assigned to each reach in support of spatial analysis and mapping applications. ERF1-2 was designed to be a digital database of river reaches capable of supporting regional and national water-quality and river-flow modeling by the water-resources community. ERF1, on which ERF1-2 is based, is used at the U.S. Geological Survey to support national-level water-quality monitoring modeling with the SPARROW model (see Alexander and others, 2000; Smith and others, 1997). In the current and earlier analyses, the reach network is used to determine flow pathways between the sources of point and nonpoint pollutants (e.g., fertilizer use, municipal wastewater discharges) and downstream water-quality monitoring locations in support of predictive water- quality models of stream nutrient transport. Acknowledgements The authors would like to thank Richard Smith, a co-developer of the SPARROW approach, Kristine Verdin, and Stephen Char, all of the U.S. Geological Survey, for providing technical assistance. The reviewers of this report, Dave Stewart, and Mike Wieczorek, are also acknowledged for their significant contributions. The digital segmented network based on watershed boundaries, ERF1-2, includes enhancements to the U.S. Environmental Protection Agency's River Reach File 1 (RF1) (USEPA, 1996; DeWald and others, 1985) to support national and regional-scale surface water-quality modeling. Alexander and others (1999) developed ERF1, which assessed the hydrologic integrity of the digital reach traces and calculated the mean water time-of-travel in river reaches and reservoirs. ERF1-2 serves as the foundation for SPARROW (Spatially Referenced Regressions (of nutrient transport) On Watershed) modeling. Within the context of a Geographic Information System, SPARROW estimates the proportion of watersheds in the conterminous U.S. with outflow concentrations of several nutrients, including total nitrogen and total phosphorus, (Smith, R.A., Schwarz, G.E., and Alexander, R.B., 1997). This version of the network expands on ERF1 (version 1.2; Alexander et al. 1999), and includes the incremental and total drainage area derived from 1-kilometer (km) elevation data for North America. Previous estimates of the water time-of-travel were recomputed for reaches with water- quality monitoring sites that included two reaches. The mean flow and velocity estimates for these split reaches are based on previous estimation methods (Alexander et al., 1999) and are unchanged in ERF1-2. Drainage area calculations provide data used to estimate the contribution of a given nutrient to the outflow. Data estimates depend on the accuracy of node connectivity. Reaches split at water- quality or pesticide-monitoring sites indicate the source point for estimating the contribution and transport of nutrients and their loads throughout the watersheds. The ERF1-2 coverage extends the earlier ERF1 coverage by providing digital-elevation-model (DEM-based estimates of reach drainage area founded on the 1-kilometer data for North America (Verdin, 1996; Verdin and Jenson, 1996). A 1-kilometer raster grid of ERF1-2 projected to Lambert Azimuthal Equal Area, NAD 27 Datum (Snyder, 1987), was merged with the HYDRO1K flow direction data set (Verdin and Jenson, 1996) to generate a DEM-based watershed grid, ERF1_2WS. The watershed boundaries are maintained in a raster (grid cell) format as well as a vector (polygon) format for subsequent model analysis. Both the coverage, ERF1-2, and the grid, ERF1-2WS are available at: ""http://water.usgs.gov/orh/nrwww/sparrow_section5_nolan.pdf"". Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology." proprietary +USGS_etsite_Version 1.0 Evapotranspiration sites within the Ash Meadows and Oasis Valley discharge areas, Nevada CEOS_EXTRA STAC Catalog 1993-01-01 1999-01-01 -116.73254, 36.37027, -116.296814, 37.063698 https://cmr.earthdata.nasa.gov/search/concepts/C2231552240-CEOS_EXTRA.umm_json The digital data set was created to display site locations at which micrometeorological data were collected in Ash Meadows and Oasis Valley, Nev. The digital data set provides locations and general descriptions of sites instrumented to collect micrometeorological data from which mean annual ET rates were computed. Sites are located in Ash Meadows and Oasis Valley, Nevada. Data were collected December 1993 through present. Introduction The digital data set was created in cooperation with the U.S. Department of Energy. The data set was created as part of a study to refine current estimates of ground-water discharge from the major discharge areas of the Death Valley regional flow system. This digital data set provides locations and general descriptions of sites instrumented during recent studies of evapotranspiration in Ash Meadows and Oasis Valley, Nevada. Data were collected December 1993 through 2001. Reviews The digital data set has gone through a multi-level, quality-control process to ensure that the data are a reasonable representation of source points. Reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers also were asked to check the topological consistency, tolerances, projections, and geographic extent. Notes Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although the data set has been used by the U.S. Geological Survey, U.S. Department of the Interior, no warranty expressed or implied is made by the U.S. Geological Survey as to the accuracy of the data and related materials. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in non-proprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology. Users should exercise caution and judgment in applying these data, and be aware that errors may be present in any or all of the digital image data. If errors are encountered in this data set, it will be appreciated if the user would pass this information to the Metadata_Contact. proprietary USGS_etunit_Version 1.0 Classification of evapotranspiration units in major discharge areas of Death Valley regional flow system, Nevada and California CEOS_EXTRA STAC Catalog 1989-06-21 1992-09-01 -117.141106, 35.739525, -116.02616, 37.320343 https://cmr.earthdata.nasa.gov/search/concepts/C2231548554-CEOS_EXTRA.umm_json The data set was created to delineate the aerial extent and quantify acreage of the different ET units found within the many major discharge areas of the Death Valley regional flow system. The raster-based classification of evapotranspiration (ET) units is for nine major discharge areas in the Death Valley regional flow system. The ET units delineate general areas of similar vegetation and soil-moisture conditions. Classifications were derived from Landsat Thematic Mapper imagery data acquired June 13, 1992; Sept. 1, 1992; and June 21, 1989. Introduction The raster-based classification of ET units within the major discharge areas of the Death Valley regional flow system determined from Landsat Thematic Mapper (TM) imagery data acquired June 13, 1992, Sept. 1, 1992; and June 21, 1989. Background information on classification procedures can be found in American Society of Photogrammetry (1983). Except for Sarcobatus Flat, all discharge areas were classified using the 1992 TM imagery. An accurate classification of Sarcobatus Flat could not be attained from 1992 TM imagery because of extensive cloud cover over the area. Instead, Sarcobatus Flat was classified from TM data acquired June 21, 1989. Reviews The final classification of ET units within each major discharge area was checked for consistency and accuracy during data processing. Two external reviews were done. The reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers also were asked to check the topological consistency, tolerances, projections, and geographic extent. proprietary +USGS_gpwa_utm27f_met Mean annual precipitation for Ohio, 1931-80 CEOS_EXTRA STAC Catalog 1931-01-01 1980-12-31 -84.90978, 38.42526, -80.48513, 41.977966 https://cmr.earthdata.nasa.gov/search/concepts/C2231553599-CEOS_EXTRA.umm_json "This coverage is intended as a data layer representing the spatial distribution of mean annual precipitation in Ohio for the years 1931-80. Information contained in this coverage has been used to obtain values of mean annual precipitation at basin centroid locations. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology. This is a Triangulated Irregular Network (TIN) of mean annual precipitation for the period 1931-80 for Ohio. A 1:1,100,000 scale (approximate) paper isoline map of mean annual precipitation from Harstine (1991) was digitized as arcs directly into an Albers equal-area projection. The arc coverage was projected to the State Plane Coordinate system, zone 5001, and then converted to a TIN by means of the ""arctin"" command." proprietary USGS_ha24_hum COVERAGE HA24_HUM - 1:24,000-scale Hydrographic Areas for Humboldt River Basin, Nevada CEOS_EXTRA STAC Catalog 1998-11-30 1998-11-30 -119.04058, 38.720947, -114.694466, 41.848537 https://cmr.earthdata.nasa.gov/search/concepts/C2231551447-CEOS_EXTRA.umm_json This data set was created to display the topographic and administrative hydrographic area boundaries for the Humboldt River Basin at 1:24,000-scale. This data set contains the topographic and administrative hydrographic area boundaries for the Humboldt River Basin at 1:24,000-scale. Introduction The hydrographic area (HA) boundaries for the State of Nevada were delineated on 1:250,000-scale maps, in cooperation with the U.S. Geological Survey (USGS), and then redrawn and published at 1:500,000-scale (Cardinalli and others, 1968). This 1:500,000-scale map is the current reference for HAs in Nevada and is used as a guide in delineating the HAs at 1:24,000-scale. This data set contains the topographic and administrative HA boundaries for the Humboldt River Basin. The Humboldt River Basin HAs were delineated and digitized from 1993 to 1998 using 1:24,000-scale USGS topographic maps. Reviews The digital data in this data base has gone through a rigorous, multi-level, quality-control process that ensures the data set is a fair representation of the source map. If errors are encountered in this data set, it will be appreciated if the user would pass this information to the Metadata_Contact. Two reviews were done. The reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers were asked to check the topological consistency, tolerances, projections, and geographic extent. Notes It should be noted that, although the boundary lines between hydrographic areas generally coincide with true topographic basin divides, some of the lines are arbitrary divisions that have no basis in topography, but are administrative and specified by Nevada Division of Water Resources. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in non-proprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology. proprietary +USGS_herbicide2_1.0 Estimates of herbicide use for the twenty-first through the fortieth most-used herbicides in the conterminous United States CEOS_EXTRA STAC Catalog 1970-01-01 -128.07002, 22.67775, -65.25698, 48.26194 https://cmr.earthdata.nasa.gov/search/concepts/C2231549262-CEOS_EXTRA.umm_json The herbicide-use estimates in this coverage are intended for use as a means for estimating regional herbicide use, and for producing maps showing relative rates of herbicide use across broad regions of the United States. This coverage contains estimates of herbicide use for the twenty-first through the fortieth most-used herbicides in the conterminous United States as reported in Gianessi and Puffer (1991). Herbicide-use estimates in this coverage are reported for each county polygon as acres treated, pounds of active ingredient used, and pounds used per square mile. The herbicide-use estimates provided by Gianessi and Puffer (1991) list acres treated and pounds of active ingredient applied for a given crop in each county for which use has been estimated. Cropping data are from the 1987 Census of Agriculture, and are subject to occasional suppressions of acreage estimates at the county level due to problems of confidentiality and census disclosure rules. The herbicide-use estimates included in this coverage are totals of use on all crops treated in a given county. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). [Summary provided by USGS] proprietary +USGS_herbicide3_1.0 Estimates of herbicide use for the forty-first through the sixtieth most-used herbicides in the conterminous United States CEOS_EXTRA STAC Catalog 1970-01-01 -128.07002, 22.67775, -65.25698, 48.26194 https://cmr.earthdata.nasa.gov/search/concepts/C2231549121-CEOS_EXTRA.umm_json The herbicide-use estimates in this coverage are intended for use as a means for estimating regional herbicide use, and for producing maps showing relative rates of herbicide use across broad regions of the United States. This coverage contains estimates of herbicide use for the forty-first through the sixtieth most-used herbicides in the conterminous United States as reported in Gianessi and Puffer (1991). Herbicide-use estimates in this coverage are reported for each county polygon as acres treated, pounds of active ingredient used, and pounds used per square mile. The herbicide-use estimates provided by Gianessi and Puffer (1991) list acres treated and pounds of active ingredient applied for a given crop in each county for which use has been estimated. Cropping data are from the 1987 Census of Agriculture, and are subject to occasional suppressions of acreage estimates at the county level due to problems of confidentiality and census disclosure rules. The herbicide-use estimates included in this coverage are totals of use on all crops treated in a given county. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). [Summary provided by USGS] proprietary +USGS_herbicide4_1.0 Estimates of herbicide use for the sixty-first through the eightieth most-use herbicides in the conterminous United States CEOS_EXTRA STAC Catalog 1970-01-01 -128.07002, 22.67775, -65.25698, 48.26194 https://cmr.earthdata.nasa.gov/search/concepts/C2231550114-CEOS_EXTRA.umm_json The herbicide-use estimates in this coverage are intended for use as a means for estimating regional herbicide use, and for producing maps showing relative rates of herbicide use across broad regions of the United States. This coverage contains estimates of herbicide use for the sixty-first through the eightieth most-used herbicides in the conterminous United States as reported in Gianessi and Puffer (1991). Herbicide-use estimates in this coverage are reported for each county polygon as acres treated, pounds of active ingredient used, and pounds used per square mile. The herbicide-use estimates provided by Gianessi and Puffer (1991) list acres treated and pounds of active ingredient applied for a given crop in each county for which use has been estimated. Cropping data are from the 1987 Census of Agriculture, and are subject to occasional suppressions of acreage estimates at the county level due to problems of confidentiality and census disclosure rules. The herbicide-use estimates included in this coverage are totals of use on all crops treated in a given county. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). [Summary provided by USGS] proprietary +USGS_herbicidel_01_1.0 Estimates of herbicide use for the 20 most-used herbicides in the conterminous United States CEOS_EXTRA STAC Catalog 1970-01-01 -128.07002, 22.67775, -65.25698, 48.26194 https://cmr.earthdata.nasa.gov/search/concepts/C2231550842-CEOS_EXTRA.umm_json "The herbicide-use estimates in this coverage are intended for use as a means for estimating regional herbicide use, and for producing maps showing relative rates of herbicide use across broad regions of the United States. This coverage contains estimates of herbicide use for the 20 most-used herbicides in the conterminous United States as reported in Gianessi and Puffer (1991). Herbicide-use estimates in this coverage are reported for each county polygon as acres treated, pounds of active ingredient used, and pounds used per square mile. The herbicide-use estimates provided by Gianessi and Puffer (1991) list acres treated and pounds of active ingredient applied for a given crop in each county for which use has been estimated. Cropping data are from the 1987 Census of Agriculture, and are subject to occasional suppressions of acreage estimates at the county level due to problems of confidentiality and census disclosure rules. The herbicide-use estimates included in this coverage are totals of use on all crops treated in a given county. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). Herbicides Herbicide use Counties United States Procedures_Used: HERBICIDE-USE DATA An automated procedure was developed to process the raw herbicide-use data into ARC/INFO coverage attributes. The procedure is summarized below: (1) copy county2m coverage to coverage called herbicide%#%, and (2) run the AML herbadd.aml for each herbicide to be added. The herbadd.aml program runs a fortran program to total estimates of herbicide use on all crops by county, then processes these data, finally adding them as three columns of attribute data to the county coverage. Other programs were developed to calculate summary statistics of the herbicide-attribute data and to produce maps that show attribute values across the United States. COUNTY BOUNDARIES This series of maps was published as part of the National Atlas of the United States (U.S.Geological Survey, 1970). The maps for the conterminous United States were digitized in 15 sheets and published in the Digital Line Graph (DLG) format as described by Domeratz and others (1983). Each sheet was prepared by reading the DLG files of the political and water-bodies layers, converting them to ARC/INFO; extracting the county boundaries and the coastline, respectively; and joining the two layers. FIPS codes were assigned to all polygons by using available sources and were checked manually. Boundaries with adjacent sheets of the 15-sheet set were edgematched manually; one of the sheets was chosen arbitrarily as the ""correct"" border. Edgematching operations were used to adjust the linework as far as was necessary so that the coverages would fit to a tolerance of 100 meters (328.1 feet). The coverage (referred to herein as Version 1.0) was stored as 49 separate coverages (48 States and the District of Columbia) because the ARC/INFO software in use at the time could not process the entire coverage. Individual States could be joined by specifying a tolerance of 100 meters. From time to time, adjustments were made to the State coverages to reflect changes in counties. The accuracy of these adjustments is believed to be comparable to that of the original linework. For Version 2.0, all State coverages were rejoined and manually edited to produce a perfect edgematch between all States. For States on the original map-sheet boundaries, this adjustment averaged less than 20 meters and in no case was more than 100 meters. The whole coverage was Cleaned to a tolerance of 20 meters (65.6 feet), which resulted in few, if any, effects on small offshore islands. The coverage also was checked to ensure that it represented current counties or county equivalents. The coverage in Version 1.0 ended at the coastline. No attempt was made to depict offshore areas. This created problems when the coverage was used to assign county codes to sampling stations located near the coast. To help in this matter, Version 2.0 includes offshore extensions of the county polygons. The (water) boundaries of many of these polygons are arbitrary. The Canadian Great Lakes features are another new addition to Version 2.0. They were added to improve the utility of the coverage for visual displays. Although the Canadian Great Lakes are represented logically by a single polygon, practical considerations--the inability of some software to plot polygons with a large number of vertices--made it necessary to separate them into four polygons. The dividing lines are located in narrow channels to minimize interference with plotting patterns. Canadian islands within the Great Lakes also were included. All tick marks were relocated to places that are easily visible on maps of the United States, to help in registering maps that otherwise may not have adequate registration information. To expedite accessing parts of the coverage, certain items have been indexed with the procedure INDEX_COUNTY.AML. See Section 3 above. A spatial index also was created. When this coverage is used to clip or intersect other coverages, a tolerance as low as 2 meters (6.6 feet) can be used. The processing used to derive this coverage moved boundaries from their positions on the original maps. In cases of conflicting lines, preference was given to forming the correct topology. Strictly speaking, this coverage is not identical to the source materials. These changes were unavoidable in producing a continuous coverage of the conterminous United States." proprietary +USGS_hgmr_Version 1 Hydrogeomorphic Regions in the Chesapeake Bay Watershed. CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552541-CEOS_EXTRA.umm_json This data set was used to compare base-flow and ground-water nitrate loads to assess the significance of ground-water discharge as a source of nitrate load to non tidal streams in the Chesapeake Bay watershed. Generalized lithology (rock type) and physiography based on geologic formations were used to characterize hydrgeomorphic regions (HGMR) within the Chesapeake Bay watershed. These HGMRs were used in conjunction with existing data to assess the significance of ground-water discharge as a source of nitrate load to non tidal streams in the Chesapeake Bay watershed (Bachman and others, 1998). This work is part of the U.S. Geological Survey's (USGS) Chesapeake Bay initiative to develop an understanding and provide scientific information for the restoration of the Chesapeake Bay and its watershed (Phillips and Caughron, 1997). Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Geological Survey. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in non proprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology. The HGMR data set is the result of combining digital data sets of physiography and rock type from numerous sources. proprietary +USGS_hydmain_hum_Version 1.0, (September, 2001) Humboldt River main stem, Nevada CEOS_EXTRA STAC Catalog 1994-01-01 1994-12-31 -118.4891, 40.048546, -115.24806, 41.067688 https://cmr.earthdata.nasa.gov/search/concepts/C2231549056-CEOS_EXTRA.umm_json This dataset was created as a layer of a geographic information system (GIS) to calculate river miles on the Humboldt River. The currentness and accuracy of the digital orthophoto quadrangle (DOQ) source exceeded that of other available data. This data set contains the main stem of the Humboldt River as defined by Humboldt Project personnel of the U.S. Geological Survey Nevada District, 2001. The data set was digitized on screen using digital orthophoto quadrangles from 1994. Reviews The digital data in this data set has gone through a rigorous, multi-level, quality-control process that ensures the data set is a fair representation of the source map. If errors are found in this data set, it will be appreciated if the user would pass this information to the Metadata_Contact. Two formal reviews were done. The reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers were asked to check the topological consistency, tolerances, projections, and geographic extent. Notes Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology. proprietary +USGS_landfills_1.1 Map of landfill locations in United States CEOS_EXTRA STAC Catalog 1986-01-01 1986-12-31 -125.460815, 23.556513, -66.02105, 47.045097 https://cmr.earthdata.nasa.gov/search/concepts/C2231552322-CEOS_EXTRA.umm_json This is a point coverage of landfills shown in the 1986 National Water Summary Report (U.S. Geological Survey, 1987). proprietary +USGS_landuse_1 Digital map file of major land uses in the United States CEOS_EXTRA STAC Catalog 1991-01-01 1991-01-01 -127.87006, 23.24801, -65.40621, 48.20435 https://cmr.earthdata.nasa.gov/search/concepts/C2231552526-CEOS_EXTRA.umm_json "The intended use of this coverage was for the state sections of the 1990-91 National Water Summary on surface-water quality. Each state report contains a map of the state's major land uses and, where possible, discusses the influence of land use on water quality in the state. This is a polygon coverage of major land uses in the United States. The source of the coverage is the map of major land uses in the 1970 National Atlas of the United States, pages 158-159, which was adapted from U.S. Department of Agriculture, ""Major Land Uses in the United States,"" by Francis J. Marschner, revised by James R. Anderson, 1967." proprietary USGS_lfhbase_Version 1.0, July 09, 1998 COVERAGE LFHBASE -- Structure contours of base of Laramie-Fox Hills and Arapahoe aquifers CEOS_EXTRA STAC Catalog 1998-01-01 1998-12-31 -105.25684, 39.89908, -104.59373, 40.887665 https://cmr.earthdata.nasa.gov/search/concepts/C2231553268-CEOS_EXTRA.umm_json "This data set was created to display the altitude of the base of the Laramie-Fox Hills aquifer and the Arapahoe aquifer as depicted in the plates in Robson and others, (1988). This digital geospatial data set consists of structure contours of the base of the Laramie-Fox Hills aquifer and the base of the Arapahoe aquifer along the Front Range of Colorado. The U.S. Geological Survey developed this data set as part of a project described in the report, ""Structure, Outcrop, and Subcrop of the Bedrock Aquifers Along the Western Margin of the Denver Basin, Colorado"" (Robson and others, 1998)." proprietary USGS_lfhtop_Version 1.0 (July 20, 1998) COVERAGE LFHTOP -- Structure contours of top of Laramie-Fox Hills aquifer CEOS_EXTRA STAC Catalog 1998-01-01 1998-12-31 -104.96266, 40.105045, -104.59901, 40.884575 https://cmr.earthdata.nasa.gov/search/concepts/C2231555311-CEOS_EXTRA.umm_json "This data set was created to display maps of the altitude of the top of the Laramie-Fox Hills aquifer (Robson and others, 1998). This digital geospatial data set consists of structure contours of the top of the Laramie-Fox Hills aquifer along the Front Range of Colorado. The U.S. Geological Survey developed this data set as part of a project described in the report, ""Structure, Outcrop, and Subcrop of the Bedrock Aquifers Along the Western Margin of the Denver Basin, Colorado"" (Robson and others, 1998)." proprietary USGS_manure_1.0.0 County-based estimates of nitrogen and phosphorus content of animal manure in the United States for 1982, 1987, and 1992. CEOS_EXTRA STAC Catalog 1970-01-01 -127.84206, 23.254122, -65.385826, 48.187214 https://cmr.earthdata.nasa.gov/search/concepts/C2231554848-CEOS_EXTRA.umm_json These estimates are intended for large-scale ground- and surface-water analyses of nutrient sources or changes in these sources. These data on nutrients in manure can be compared to fertilizer inputs of nutrients. This data set contains county estimates of nitrogen and phosphorus content of animal wastes produced annually for the years 1982, 1987, and 1992. The estimates are based on animal populations for those years from the 1992 Census of Agriculture (U.S. Bureau of the Census, 1995) and methods for estimating the nutrient content of manure from the Soil Conservation Service (1992). The data set includes several components.. 1. Spatial component - generalized county boundaries in ARC/INFO format/1/, including nine INFO lookup tables containing animal counts and nutrient estimates keyed to the county polygons using county code. (The county lines were not used in the nutrient computations and are provided for displaying the data as a courtesy to the user.) The data is organized by 5-digit state/county FIPS (Federal Information Processing Standards) code. Another INFO table lists the county names that correspond to the FIPS codes. 2. Tabular component - Nine tab-delimited ASCII lookup tables of animal counts and nutrient estimates organized by 5-digit state/county FIPS (Federal Information Processing Standards) code. Another table lists the county names that correspond to the FIPS codes. The amount of nitrogen and phosphorus present in manure (in kilograms) has been calculated for each county of the United States. The procedure is identical to that of Smith and others (1997), which covered the year 1987. Nutrient estimates for the years 1982 and 1987 were computed again for the data set here, and the results were checked against the results computed previously by Alexander (written commun., 1992) for those years to ensure that they were identical. Limitations: The estimates are county level and are based on estimates of the nutrient content of animal manure produced per 1,000 pounds of animal weight on a daily basis. One important limitation of the animal population numbers from the Census of Agriculture is that for some counties and animal classes, no data are reported. This limitation reportedly is the result of restrictions on including animal population data for counties where animal production is dominated or limited to one business or farm. These data therefore are considered trade secrets and may not be included in the county-based data. This limitation on population data at the county level results in discrepancies when county-based data are summed and compared to national animal population totals. At the present we have no way of estimating animal populations for those counties with missing data and further have no way of determining which counties are missing data. Therefore, the animal manure, nitrogen and phosphorus estimates for some counties are an underestimate of the total nutrient form animal manure in those counties. proprietary +USGS_map-2653_1.0 Geologic Map of the Eminence Quadrangle, Shannon County, Missouri CEOS_EXTRA STAC Catalog 1996-01-01 1999-12-31 -91.3778, 37.1233, -91.2472, 37.2518 https://cmr.earthdata.nasa.gov/search/concepts/C2231553858-CEOS_EXTRA.umm_json The purpose of this geologic map and database is to support and be part of a three-dimensional geologic framework study of south-central Missouri. The framework will be used to assess environmental impacts of lead and zinc mining in the Mark Twain National Forest on the hydrologic system of the Ozark National Scenic Riverways. The geology of the Eminence 7 1/2-minute quadrangle , Shannon County, Missouri was mapped from 1996 through 1997 as part of the Midcontinent Karst Systems and Geologic Mapping Project, Eastern Earth Surface Processes Team. The map supports the production of a geologic framework that will be used in hydrogeologic investigations related to potential lead and zinc mining in the Mark Twain National Forest adjacent to the Ozark National Scenic Riverways (National Park Service). Digital geologic coverages will be used by other federal and state agencies in hydrogeologic analyses of the Ozark karst system and in ecological models. Bedrock, Quaternary , residual units, faults, and structural data are each stored in separate coverages. See readme.txt file for explanation of organization. proprietary +USGS_mapi-1300_Version 1.0 Geologic and structure map of the Choteau 1 x 2 degree quadrangle, western Montana: a digital database. CEOS_EXTRA STAC Catalog 2000-01-01 -114, 47.5, -112, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2231553685-CEOS_EXTRA.umm_json This dataset was developed to provide geologic map GIS of the Choteau 1:250,000 quadrangle for use in future spatial analysis by a variety of users. These data can be printed in a variety of ways to display various geologic features or used for digital analysis and modeling. This database is not meant to be used or displayed at any scale larger than 1:250,000 (e.g. 1:100,000 or 1:24,000). The geologic and structure map of Choteau 1 x 2 degree quadrangle (Mudge and others, 1982) was originally converted to a digital format by Jeff Silkwood (U.S. Forest Service and completed by the U.S. Geological Survey staff and contractor at the Spokane Field Office (WA) in 2000 for input into a geographic information system (GIS). The resulting digital geologic map (GIS) database can be queried in many ways to produce a variey of geologic maps. Digital base map data files (topography, roads, towns, rivers and lakes, etc.) are not included: they may be obtained from a variety of commercial and government sources. This database is not meant to be used or displayed at any scale larger than 1:250,000 (e.g. 1:100,000 or 1:24,000. The digital geologic map graphics and plot files (chot250k.gra/.hp/.eps and chot-map.pdf) that are provided in the digital package are representations of the digital database. They are not designed to be cartographic products. This GIS consists of two major Arc/Info datasets, a line and polygon file (chot250k) containing geologic contact and structures (lines) and geologic map rock units (polygons), and a point file (chot250kp) containing structural point data for plunging folds. proprietary +USGS_mapi-1509A_version 1.0 Geologic and structure maps of the Wallace 1 deg. x 2 deg. quadrangle, Montana and Idaho: A digital database CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -116, 47, -114, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2231551139-CEOS_EXTRA.umm_json This dataset was developed to provide a geologic map GIS of the Wallace 1x2 degree quadrangle for use in future spatial analysis by a variety of users. This database is not meant to be used or displayed at any scale larger than 1:250,000 (e.g., 1:100,000 or 1:24,000) This dataset was digitized by the U.S. Geological Survey EROS Data Center and U.S. Geological Survey Spokane Field Office for input into an Arc/Info geographic information system (GIS) The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps. This GIS consists of two major and Arc/Info datasets: one line and polygon file (wal250k) containing geologic contacts and structures (lines) and geologic map rock units (polygons), and one point file (wal250bc) containing breccia outcrops. proprietary +USGS_mapi-1803_1.0 Geologic map of the Dillon 1 x 2 degree quadrangle, Idaho and Montana CEOS_EXTRA STAC Catalog 1997-01-01 -114, 45, -112, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2231551047-CEOS_EXTRA.umm_json "This GIS database was prepared to provide digital geologic coverage for the Dillon 1 degree by 2 degree quadrangle of southwest Montana and east-central Idaho. The digital ARC/INFO databases included in this website provide a GIS database for the geologic map of the Dillon 1 degree by 2 degree quadrangle of southwest Montana and east-central Idaho. The geologic map was orginally published as U.S. Geological Survey Miscellaneous Investigations Series Map I-1803-H. This website directory contains ARC/INFO format files that can be used to query or display the geology of USGS Map I-1803-H with GIS software. (""http://pubs.usgs.gov/imap/1993/i-1803-h/"")" proprietary +USGS_mapi-1819_1.0 Geologic Map of the Challis 1 x 2 Degree Quadrangle, Idaho CEOS_EXTRA STAC Catalog 2000-01-01 -116, 44, -114, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2231553193-CEOS_EXTRA.umm_json This dataset was developed to provide a geologic map GIS database of Challis 1x2 Quadrangle, Idaho for use in spatial analysis. The paper version of The geology of the Challis 1 x 2 quadrangle, was compiled by Fred Fisher, Dave McIntyre and Kate Johnson in 1992. The geology was compiled on a 1:250,000 scale topographic base map. TechniGraphic System, Inc. of Fort Collins Colorado digitized this map under contract for N.Shock. G.Green edited and prepared the Challis digital version for publication as a geographic information system database. The digital geologic map database can be queried in many ways to produce a variety of geologic maps. proprietary +USGS_mapi-2267 Geologic and structure maps of the Kalispell 1:250,000 quadrangle, Montana, and Alberta and British Columbia: a digital database. CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -116, 48, -114, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231550407-CEOS_EXTRA.umm_json This dataset was developed to provide geologic map GIS of the Kalispell 1:250,000 quadrangle for use in the future spatial analysis by a variety of users. This database is not meant to be used or displayed at any scale larger than 1:250,000 (e.g., 1:100,000 or 1:24,000). This dataset was digitized by the U.S. Geological Survey EROS Data Center and U.S. Geological Survey Spokane Field Office for input into an Arc/Info geographic information system (GIS). The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps. This GIS dataset consists of one major Arc/Info dataset: a line and polygon file (kal250k) that contains geologic contacts and structures (lines) and geologic map rock units (polygons). proprietary +USGS_mapi-2395_1.0 Geologic map of the eastern part of the Challis National Forest and vicinity, Idaho CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -114.5, 43.5, -112.75, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2231554798-CEOS_EXTRA.umm_json This dataset was developed to provide a geologic map GIS database of Challis National Forest, Idaho for use in spatial analysis by a variety of users. The paper version of the Geologic Map of the eastern part of the Challis National Forest and vicinity, Idaho was compiled by Anna Wilson and Betty Skipp in 1994. The geology was compiled on a 1:250,000 scale topographic base map. TechniGraphic System, Inc. of Fort Collins Colorado digitized this map under contract for N.Shock. G.Green edited and prepared the digital version for publication as a GIS database. The digital geologic map database can be queried in many ways to produce a variety of geologic maps. proprietary +USGS_mapi-2494_1.0 Generalized Thermal Maturity Map of Alaska CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -175, 52, -130, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552378-CEOS_EXTRA.umm_json "The files in this directory are those that were used to create the Generalized Thermal Maturity Map of Alaska (USGS Miscellaneous Investigations Map I-2494), published in 1996. These files are necessary for importing the map in digital form into a Geographical Information System. Output files in several formats also are included in this directory; these can be used any time a digital version of the complete map is needed. The map is based, in large part, on the vitrinite-reflectance (VR) and conodont color-alteration-index (CAI) data in USGS Open-File Report 92-409, an updated version of which also is included on this CD-ROM. Alaska is a complex amalgamation of tectonic blocks with diverse histories. Sedimentary basins that are formed on these blocks both before amalgamation and as a result of collisions between them record the tectonic history of this complex region. Thermal-maturity data-indicators of maximum burial temperatures-provide important constraints both on basin evolution and on terrane amalgamation. To help elucidate these relations, and to provide constraints for hydrocarbon assessments, the U.S. Geological survey (USGS) has compiled thermal-maturity data from Alaska for many decades. This report is a digital release of our current understanding of thermal-maturity patters in Alaska. The 10 ARC/INFO coverages used to construct the map, together with the directory ""INFO"" (needed by ARC/INFO to support the coverages), are found in the ""coverages"" subdirectory. These coverages can be used by any GIS capable of importing files in ARC/INFO format. Export version of these coverages are found in the subdirectory ""export files."" These files can be imported into ARC/INFO with the ""import"" command. Shapefile versions of 9 of the ARC/INFO coverages are found in the subdirectory ""shapefiles."" Each shapefile actually consists of three files, with extensions .shp, .shx., and .dbf; all are needed for importation into a GIS supporting the shapefile format. Inset figures and text, as well as the map title, headers, and latitude-longitude ticks, were created as a separate file. This file is available in Adobe Illustrator 6.0 format (insets.ill) and in Encapsulated PostScript format (insets.eps) in the subdirectory ""insetfigures."" The subdirectory ""miscfiles"" contains many important files needed to use these coverages in ARC/INFO or another GIS. " proprietary +USGS_mapi-2634_2.0 Geologic map of the Sedan quadrangle, Gallatin and Park Counties, Montana CEOS_EXTRA STAC Catalog 1965-01-01 1971-12-31 -111, 45.75, -110.75, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2231553897-CEOS_EXTRA.umm_json "The geology of the Sedan quadrangle was mapped as part of a regional study of the western Crazy Mountains Basin. It was digitized for ease of production of the printed version and for greater distribution for analytical use. This quadrangle lies 6.4 km (4 mi) northeast of Bozeman, Mont., in southwestern Montana. Metamorphic, sedimentary, and volcanic rocks of Precambrian to Tertiary age are exposed in the Bridger Range and southwestern margin of the Crazy Mountains Basin in a crustal cross section and a structural triangle zone. Surface geology records Precambrian extension, Late Paleocene east-vergent contraction, including backthrusts, and Holocene basin-range extension. A preliminary map was published as a U.S. Geological Survey Open-File Report in 1971. The geologic data was interpreted 1965-93, the interpretation being informed by data from two wells in addition to the original field work. The digital files for the map were released in November 1998. The map-on-demand edition, released in January 2000, includes supplemental figures,three cross sections, and interpretive text. Users should be aware that of the many faults mapped, the only active one is the range front fault on the west side of the Bridger Range. The dataset for the Sedan quadrangle consists of 10 coverages: geo_net, geo_pnt, stru_net, stru_pnt, data_net, data_pnt, pnt_sym, pnt_graphic, stpnt_graphic, and dvalues. The three coverages pnt_graphic, stpnt_graphic, and dvalues are not ""true"" ARC/INFO coverages. They contain the graphic representations of symbols used on the geologic map: >pnt_sym = pnt_graphic, >stru_pnt and geo_pnt = stpnt_graphic, and >dvalues = annotation for stru_pnt and geo_pnt." proprietary +USGS_mapi-2645_version 1.0 Geologic Map of the Central Marysvale Volcanic Field, Southwestern Utah CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -112.625, 38.25, -112, 38.708332 https://cmr.earthdata.nasa.gov/search/concepts/C2231553120-CEOS_EXTRA.umm_json "This database was developed to improve upon previous mapping in the central Marysvale volcanic field and compile older mapping at a consistent scale. This area is an important mining district, and a regional understanding of the geology and mineral deposits will assist in understanding genesis of deposits and in exploration for new deposits. The area is also an important part of the transition zone between the Colorado Plateau to the east and the Great Basin to the west. This tectonically significant province may hold keys to the style and mechanisms of continent-scale deformation in the Western United States. The geologic map of the central Marysvale volcanic field, southwestern Utah, shows the geology at 1:100,000 scale of the heart of one of the largest Cenozoic volcanic fields in the Western United States. The map shows the area of 38 degrees 15' to 38 degrees 42'30"" N., and 112 degrees to 112 degrees 37'30"" W. The Marysvale field occurs mostly in the High Plateaus, a subprovince of the Colorado Plateau and structurally a transition zone between the complexly deformed Great Basin to the west and the stable, little-deformed main part of the Colorado Plateau to the east. The western part of the field is in the Great Basin proper. The volcanic rocks and their source intrusions in the volcanic field range in age from about 31 Ma (Oligocene) to about 0.5 Ma (Pleistocene). These rocks overlie sedimentary rocks exposed in the mapped area that range in age from Ordovician to early Cenozoic. The area has been deformed by thrust faults and folds formed during the late Mesozoic to early Cenozoic Sevier deformational event, and later by mostly normal faults and folds of the Miocene to Quaternary basin-range episode. The map revises and updates knowledge gained during a long-term U.S. Geological Survey investigation of the volcanic field, done in part because of its extensive history of mining. The investigation also was done to provide framework geologic knowledge suitable for defining geologic and hydrologic hazards, for locating hydrologic and mineral resources, and for an understanding of geologic processes in the area. A previous geologic map (Cunningham and others, 1983, U.S. Geological Survey Miscellaneous Investigations Series I-1430-A) covered the same area as this map but was published at 1:50,000 scale and is obsolete due to new data. This new geologic map of the central Marysvale field, here published as U.S. Geological Survey Geologic Investigations Series I-2645-A, is accompanied by gravity and aeromagnetic maps of the same area and the same scale (Campbell and others, 1999, U.S. Geological Survey Geologic Investigations Series I-2645-B)." proprietary +USGS_mapi-2690 Geologic map of the Ennis 30' X 60' quadrangle, Madison and Gallatin Counties, Montana, and Park County, Wyoming CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -112, 45, -111, 45.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231553215-CEOS_EXTRA.umm_json "This map forms part of the Montana State Geological Map. The Ennis 1:100,000 quadrangle lies within both the Laramide (Late Cretaceous to early Tertiary) foreland province of southwestern Montana and the northeastern margin of the middle to late Tertiary Basin and Range province. The oldest rocks in the quadrangle are Archean high-grade gneiss, and granitic to ultramafic intrusive rocks that are as old as about 3.0 Ga. The gneiss includes a supracrustal assemblage of quartz-feldspar gneiss, amphibolite, quartzite, and biotite schist and gneiss. The basement rocks are overlain by a platform sequence of sedimentary rocks as old as Cambrian Flathead Quartzite and as young as Upper Cretaceous Livingston Group sandstones, shales, and volcanic rocks. The Archean crystalline rocks crop out in the cores of large basement uplifts, most notably the ""Madison-Gravelly arch"" that includes parts of the present Tobacco Root Mountains and the Gravelly, Madison, and Gallatin Ranges. These basement uplifts or blocks were thrust westward during the Laramide orogeny over rocks as young as Upper Cretaceous. The thrusts are now exposed in the quadrangle along the western flanks of the Gravelly and Madison Ranges (the Greenhorn thrust and the Hilgard fault system, respectively). Simultaneous with the west-directed thrusting, northwest-striking, northeast-side-up reverse faults formed a parallel set across southwestern Montana; the largest of these is the Spanish Peaks fault, which cuts prominently across the Ennis quadrangle. Beginning in late Eocene time, extensive volcanism of the Absorka Volcanic Supergroup covered large parts of the area; large remnants of the volcanic field remain in the eastern part of the quadrangle. The volcanism was concurrent with, and followed by, middle Tertiary extension. During this time, the axial zone of the ""Madison-Gravelly arch,"" a large Laramide uplift, collapsed, forming the Madison Valley, structurally a complex down-to-the-east half graben. Basin deposits as thick as 4,500 m filled the graben. Pleistocene glaciers sculpted the high peaks of the mountain ranges and formed the present rugged topography. Compilation scale is 1:100,000. Geology mapped between 1988 and 1995. Compilation completed 1997. Review and revision completed 1997. Archive files prepared 1998-02." proprietary +USGS_mapi-2691_1.0 Geologic map of the Alligator Ridge area, White Pine County, Nevada CEOS_EXTRA STAC Catalog 2000-01-01 -115.625, 39.626, -115.41, 39.875 https://cmr.earthdata.nasa.gov/search/concepts/C2231553430-CEOS_EXTRA.umm_json Digital representation of geologic mapping facilitates the presentation and analysis of earth-science data. Digital maps may be displayed at any scale or projection, however the geologic data in this coverage is not intended for use at a scale larger that 1:24,000. Data set describes the geology of Paleozoic through Quaternary units in the Alligator Ridge area, which hosts disseminated gold deposits. These digital files were used to create the 1:24,000-scale geologic map of the Buck Mountain East and Mooney Basin Summit Quadrangles and parts of the Sunshine Well NE and Long Valley Slough Quadrangles, White Pine County, east-central Nevada. proprietary +USGS_mapi-2737 "Digital spatial data for the map ""Earthquakes in and near the northeastern United States, 1638-1998""" CEOS_EXTRA STAC Catalog 1638-06-11 1998-12-29 -81, 38, -66, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2231552226-CEOS_EXTRA.umm_json The map is an educational tool with which to inform the public about the existence and the broad, regional nature of earthquake hazard in the Northeast. The data were created digitally in order to ease and speed production and publication of the map. Text on the map cautions against using the map for scientific or engineering purposes, or to estimate hazard in small areas or at single sites. Entries in Lineage under Data_Quality_Information explain the reasons for this caution (see also Wheeler, 2000; reference in Lineage). The earthquake catalog was constructed in such a way that it should not be utilized in scientific, engineering, or hazards use (Wheeler, 2000; reference in Lineage). Accordingly, the catalog is not being published separately, in order to minimize the potential for misuse. It is available only as part of the digital files from which the entire map was made. The data are those used to make a large-format, colored map of earthquakes in the northeastern United States and adjacent parts of Canada and the Atlantic Ocean (Wheeler, 2000; Wheeler and others, 2001; references in Data_Quality_Information, Lineage). The map shows the locations of 1,069 known earthquakes of magnitude 3.0 or larger, and is designed for a non-technical audience. Colored circles represent earthquake locations, colored and sized by magnitude. Short descriptions, colonial-era woodcuts, newspaper headlines, and photographs summarize the dates, times of day, damage, and other effects of notable earthquakes. The base map shows color-coded elevation, shaded to emphasize relief. This metadata record describes the data on earthquakes and topography. Other data, such as for roads and urban areas, were obtained elsewhere and we lack metadata for them. Instead, this field cites the sources of these data that were obtained elsewhere. proprietary +USGS_mapi-2740_1.0 Geologic Map of Colorado National Monument and Adjacent Areas, Mesa County, Colorado CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -108.792, 38.958, -108.583, 39.125 https://cmr.earthdata.nasa.gov/search/concepts/C2231553872-CEOS_EXTRA.umm_json To update the interpretation and increase the scale of geologic mapping, provide a geologic map for the public to use at Colorado National Monument, and to provide sufficient geologic information for land-use and land-management decisions. New 1:24,000-scale geologic mapping in the Colorado National Monument Quadrangle and adjacent areas, in support of the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping Project, provides new interpretations of and data for the stratigraphy, structure, geologic hazards in the area from the Colorado River in Grand Valley onto the Uncompahgre Plateau. The plateau drops abruptly along northwest-trending structures toward the northeast 800 m to the Redlands area and the Colorado River in Grand Valley. In addition to common alluvial and colluvial deposits, surficial deposits include Holocene and late Pleistocene charcoal-bearing valley-fill deposits, late to middle Pleistocene river-gravel terrace deposits, Holocene to middle Pleistocene younger, intermediate, and old fan-alluvium deposits, late to middle Pleistocene local gravel deposits, Holocene to late Pleistocene rock-fall deposits, Holocene to middle Pleistocene young and old landslide deposits, Holocene to late Pleistocene sheetwash deposits and eolian deposits, and Holocene Cienga-type deposits. Only the lowest part of the Upper Cretaceous Mancos Shale is exposed in the map area near the Colorado River. The Upper and Lower Cretaceous Dakota Formation and the Lower Cretaceous Burro Canyon Formation form resistant dipslopes in the Grand Valley and a prominent ridge on the plateau. Less resistant strata of the Upper Jurassic Morrison Formation consisting of the Brushy Basin, Salt Wash, and Tidwell Members form slopes on the plateau and low areas below the mountain front of the plateau. The Middle Jurassic Wanakah Formation nomenclature replaces the previously used Summerville Formation. Because an upper part of the Middle Jurassic Entrada Formation is not obviously correlated with strata found elsewhere, it is therefore not formally named; however, the lower rounded cliff former Slickrock Member is clearly present. The Lower Jurassic silica-cemented Kayenta Formation forms the cap rock for the Lower Jurassic carbonate-cemented Wingate Sandstone, which forms the impressive cliffs of the monument. The Upper Triassic Chinle Formation was deposited on the eroded and weathered Middle Proterozoic meta-igneous gneiss, pegmatite dikes, and migmatitic gneiss. Structurally the area is deceptively challenging. Nearly flat-lying strata on the plateau are folded by northwest-trending fault-propagation folds into at least two S-shaped folds along the mountain front of the plateau. Strata under Grand Valley dip at about 6 degrees to the northeast. In the absence of local evidence, the uplifted plateau is attributed to Laramide deformation by dated analogous structures elsewhere in the Colorado Plateau. The major exposed fault records high-angle reverse relationships in the basement rocks but dissipates strain as a triangular zone of distributed microfractures and cataclastic flow into overlying Mesozoic strata that absorb the fault strain, leaving only folds. Evidence for younger, probably late Pliocene or early Pleistocene, uplift does exist at the antecedent Unaweep Canyon south and east of the map area. To what degree this younger deformation affected the map area is unknown. Several geologic hazards affect the area. Middle and late Pleistocene landslides involving the smectite-bearing Brushy Basin Member of the Morrison Formation are extensive on the plateau and common in the Redlands below the plateau. Expansive clay in the Brushy Basin and other strata create foundation stability problems for roads and homes. Flash floods create a serious hazard to people on foot in narrow canyons in the monument and to homes close to water courses downstream from narrow restrictions close to the monument boundary. Map political location: Mesa County, Colorado Compilation scale: 1:24,000 Geology mapped in 1998. Geospatial data files included in this data set: cnmpoly: geologic units cnmline: faults, fold axes, dikes, and other line features cnmpoint: strike and dip measurements and other point features cnmsym: cartographic decorations-strike/dip symbols, leaders, line decorations, etc. cnmtext: text labels for map units cnmborder: neatline of map cnmboundary: boundary of Colorado National Monument cnmhydro: hydrologic features cnmhypso: elevation contours cnmrailroads: railroads cnmroads: roads proprietary +USGS_mapi-797Scan_Version 1.0 Geologic map of the Stillwater Complex, Montana: scanned source map images CEOS_EXTRA STAC Catalog 1974-01-01 1974-12-31 -110.247635, 45.327175, -109.73657, 45.520306 https://cmr.earthdata.nasa.gov/search/concepts/C2231550921-CEOS_EXTRA.umm_json This set of images was developed to provide georeferenced digital images of the 1:12000-scale geologic map (Page and Nokleberg, 1974) These images can be used in conjunction with the vector data files now available as part of the I-797 dataset. This database is not meant to be used or displayed at any scale larger than 1:12000 (for example, 1:2000). This collection of four georeferenced MrSID files and one TIFF file provides raster images of the five map sheets comprising the Geologic map of the Stillwater Complex, Montana by Page and Nokleberg (1974). Paper copies of the four geologic map sheets and the explanation were scanned, and the geologic map sheets were georeferenced to the Montana State Plane South coordinate system. Each georeferenced MrSID image consists of a package of three files with the extensions: sid, .sdw (MrSID world file), and .aux (ArcInfo 8.1 georeferencing information). The MrSID and TIFF files are listed below: i797origs1.sid/.sdw/.aux - Sheet 1 - east end of the Stillwater Complex i797origs2.sid/.sdw/.aux - Sheet 2 - east central part of the Stillwater Complex i797origs3.sid/.sdw/.aux - Sheet 3 - west central part of the Stillwater Complex i797origs4.sid/.sdw/.aux - Sheet 4 - west end of the Stillwater Complex i797origs5.tif - Sheet 5 - explanation of map symbols, correlation of map units and map unit descriptions used on Sheets 1 through 4. proprietary +USGS_mapi-797Topo_Version 1.0 Geologic map of the Stillwater Complex, Montana: topographic base map image CEOS_EXTRA STAC Catalog 1943-01-01 1943-12-31 -110.247635, 45.327175, -109.73657, 45.520306 https://cmr.earthdata.nasa.gov/search/concepts/C2231555237-CEOS_EXTRA.umm_json This image was prepared for archival purposes and is not meant to be used or displayed at any scale larger than 1:12000 (for example. 1:2000). Four film positives of topography [which was prepared in 1943 and subsequently used by Page and Nokleberg (1974) for a base map for the geology of the Stillwater Complex, Montana] were scanned, and the resulting TIFF images were then geoferenced, rectified, and spliced together to create i797base.tif. proprietary +USGS_mapi-797_Version 1.0 Geologic map of the Stillwater Complex, Montana: a digital database CEOS_EXTRA STAC Catalog 2002-01-01 2002-12-31 -110.247635, 45.327175, -109.73657, 45.520306 https://cmr.earthdata.nasa.gov/search/concepts/C2231550321-CEOS_EXTRA.umm_json This dataset was developed to provide a spatial database of the 1:12,000 scale geologic map of the Stillwater Complex for use in future spatial analysis. These data can be printed in a variety of ways to display various geologic features or used for digital analysis and modeling. This database is not meant to be used or displayed at any scale larger than 1:12000. The digital geologic map graphics and plot files (i797.gra/ps and i797-map.pdf) that are provided in the digital package are representations of the digital database. They are not designed to be cartographic products. This report provides a digital version of the Geologic map of the Stillwater Complex, Montana originally published by N. Page and W. Nokleberg (1974). Paper copies of the four geologic map sheets from the original report were scanned and initially attributed by Optronics Specialty Company (Northridge, CA) and remitted to the U.S. Geological Survey for further attribution and publication of the geospatial digital files. The resulting digital geologic dataset can be queried in a geographic information system (GIS) in many ways to produce a variety of geological maps. This GIS dataset consists of two Arc/Info datasets. The first is a line and polygon file (i797) containing geologic contacts and structures (lines) and geologic map rock units (polygons). A second file contains structural point data (i797p). Since the topographic base map for the original publication is no longer readily available, a georeferenced image (tiff) of the original basemap is also included. proprietary +USGS_mdnet_Version 1.3 (July 06, 2001) Maryland Ground-Water Observation Well Network, 2001 CEOS_EXTRA STAC Catalog 2001-01-01 2001-01-01 -79.40369, 37.991196, -75.122604, 39.74173 https://cmr.earthdata.nasa.gov/search/concepts/C2231549594-CEOS_EXTRA.umm_json "The dataset MDNET was created to provide locations of Maryland ground-water observation wells for use within a Geographic Information System. MDNET is a point coverage that represents the locations and names of a network of observation wells for the State of Maryland. Additional information on water conditions at these sites can be found in the Ground-Water Site Inventory System (GWSI) database, which is maintained by the U.S. Geological Survey. Site information can be accessed on the internet at URL: ""http://waterdata.usgs.gov/nwis/"". Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology." proprietary +USGS_mdwu_98_Version 1.3, July 06, 2001 Maryland Water-Use Data, 1998 CEOS_EXTRA STAC Catalog 1998-01-01 1998-01-01 -79.40369, 37.991196, -75.122604, 39.74173 https://cmr.earthdata.nasa.gov/search/concepts/C2231552746-CEOS_EXTRA.umm_json The dataset MDWU98 was created to provide the locations of MDE permitted ground-water withdrawal sites in Maryland for use within a Geographic Information System. MDWU98 is a point coverage that represents the locations of wells for the State of Maryland that are permitted to withdraw 10,000 gallons or more per day by the Maryland Department of the Environment (MDE). Each site has the permit number, permit amount, reported withdrawal, aquifer code, and type of use. Information contained in the dataset comes from the U.S.Geological Survey site-specific water-use database (SWUDS). Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology. proprietary +USGS_msavi_92_Version 1.0 Modified soil adjusted vegetation index for the Death Valley regional flow system, Nevada and California, 1992 CEOS_EXTRA STAC Catalog 1992-06-13 1992-06-13 -117.550385, 35.378323, -115.251015, 37.653557 https://cmr.earthdata.nasa.gov/search/concepts/C2231551531-CEOS_EXTRA.umm_json "The data set was created to determine areas of regional plant-cover information for use in the report ""Ground-water discharge determined from estimates of evapotranspiration, Death Valley regional flow system, Nevada and California."" The raster-based Modified Soil Adjusted Vegetation Index was derived from Landsat Thematic Mapper imagery data acquired during June 1992 for the Death Valley regional flow system. The index has been shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a ""vegetation signal"" to ""soil noise"" ratio. The data set was used in determining phreatophyte boundaries for a ground-water evapotranspiration study and relative differences in vegetation density between discharge areas. Introduction The raster-based Modified Soil Adjusted Vegetation Index (MSAVI) was derived from Landsat Thematic Mapper (TM) imagery data acquired during June 1992 for the Death Valley regional flow system. Background and formulas of the MSAVI are detailed in Qi and others (1994). The MSAVI has been shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a ""vegetation signal"" to ""soil noise"" ratio. The MSAVI is a type of Soil Adjusted Vegetation Index (SAVI). The SAVI includes a constant soil adjustment factor L. The MSAVI uses the Normalized Difference Vegetation Index (NDVI) and Weighted Difference Vegetation Index (WDVI) to compute the L value in the SAVI for each picture element or pixel. This is referred to as a self-adjusting L function in Qi and others (1994, p. 123). The slope of the soil line used in the equations was 1.06. This was used by Qi and others (1994, p. 123) and was determined to be an acceptable value for this study. Reviews The MSAVI image for 1992 was checked for consistency and accuracy during the data processing. Two external reviews were done. The reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers also were asked to check the topological consistency, tolerances, projections, and geographic extent." proprietary +USGS_msavi_Version 1.0 Modified soil adjusted vegetation index of the Sarcobatus Flat area of the Death Valley regional flow system, Nevada and California, 1989 CEOS_EXTRA STAC Catalog 1989-06-21 1989-06-21 -117.216324, 36.997658, -116.66944, 37.40421 https://cmr.earthdata.nasa.gov/search/concepts/C2231553264-CEOS_EXTRA.umm_json "The data set was created to determine areas of regional plant-cover information for use in the report, ""Ground-water discharge determined from estimates of evapotranspiration, Death Valley regional flow system, Nevada and California."" The raster-based Modified Soil Adjusted Vegetation Index was derived from Landsat Thematic Mapper imagery data acquired during June 1989 for Sarcobatus Flat. The index has been shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a ""vegetation signal"" to ""soil noise"" ratio. The data set was used in determining phreatophyte boundaries for a ground-water evapotranspiration study and relative differences in vegetation density between discharge areas. Introduction The raster-based Modified Soil Adjusted Vegetation Index (MSAVI) was derived from Landsat Thematic Mapper (TM) imagery data acquired during June 1989 for the Sarcobatus Flat area of the Death Valley regional flow system. Background and formulas of the MSAVI are detailed in Qi and others (1994). The MSAVI has been shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a ""vegetation signal"" to ""soil noise"" ratio. The MSAVI is a type of Soil Adjusted Vegetation Index (SAVI). The SAVI includes a constant soil adjustment factor L. The MSAVI uses the Normalized Difference Vegetation Index (NDVI) and Weighted Difference Vegetation Index (WDVI) to compute the L value in the SAVI for each picture element or pixel. This is referred to as a self-adjusting L function in Qi and others (1994, p. 123). The slope of the soil line used in the equations was 1.06. This was used by Qi and others (1994, p. 123) and was determined to be an acceptable value for this study. Reviews The MSAVI image for 1989 was checked for consistency and accuracy during the data processing. Two external reviews were done. The reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers also were asked to check the topological consistency, tolerances, projections, and geographic extent." proprietary +USGS_nit85_1.0 Estimates of nitrogen-fertilizer sales for the conterminous United States in 1985 CEOS_EXTRA STAC Catalog 1970-01-01 -128.07002, 22.67775, -65.25698, 48.26194 https://cmr.earthdata.nasa.gov/search/concepts/C2231554641-CEOS_EXTRA.umm_json NITROGEN-FERTILIZER SALES DATA Estimates of nitrogen-fertilizer sales by county were generated by the U.S. Environmental Protection Agency (1990) and by Jerald Fletcher (West Virginia University, written commun., 1992) by using the following procedure: (1) compiling annual State fertilizer-sales data reported as tonnages to the National Fertilizer and Environmental Research Center of the TVA; (2) calculating the ratio of expenditures for commercial fertilizers by county to expenditures for commercial fertilizers by States from the 1987 Census of Agriculture (U.S. Department of Commerce, 1989a); and (3) computing annual county-level nitrogen-fertilizer sales, in tons, by multiplying estimates of annual States sales by the ratio of county expenditures to States expenditures. In some counties no fertilizer sales were reported, but some fertiliz use was reported in the Census Data. Although fertilizer expenditures estimates (in $1,000) represent the 1987 growing year, the nitrogen-fertilizer sales estimates (tons) generally reflect 1985 amounts. Estimates of nitrogen-fertilizer sales by county were constructed fro a combination of data reported to State regulatory agencies and from data in the 1987 Census of Agriculture. Fertilizer-sales data submitted annually to State regulatory agencies by fertilizer dealers reflect total sales without regard to the land use for which it was bought, or the State (or county) in which the fertilizer was actually used. In the Census of Agriculture sampling and statistics were used to account for non responding farm operations (U.S. Department of Commerce, 1989b) Thus, the information that describes county-level fertilizer sales is subject to sampling variability as well as reporting and coverage errors. Census disclosure rules also prevent the publication of information that would reveal the operation of individual farms. COUNTY BOUNDARIES The original files for this map were provided in 15 sections. Boundaries near the edges of sections have been adjusted in edgematching. Polygons that extend into the water (an ocean or the Great Lakes) should be considered arbitrary. Originating_Center: (required) Group: Reference End_Group Group: Summary The nitrogen-fertilizer sales estimates in this coverage are intended for use in estimating regional fertilizer sales, and in producing visual displays and mapping relative rates of fertilizer sales across broad regions of the United States. This coverage contains estimates of nitrogen-fertilizer sales for the conterminous United States in 1985 as reported by the U.S. Environmental Protection Agency (1990) and by Jerald Fletcher (West Virginia University, written commun., 1992). Nitrogen-fertilizer sales estimates in this coverage are reported for each county polygon in tons of actual nutrient sold (inorganic nitrogen, phosphate, and potash) as distinct from total tons of fertilizer product. The data are summarized for fertilizer years (i.e. the 1987 fertilizer year runs from July 1, 1986 to June 30, 1987). The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) file representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). proprietary USGS_ofr00-265-geol_Version 1.0, May 9, 2000 Bedrock Geology of the Turkey Creek Drainage Basin CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -105.40936, 39.499435, -105.17523, 39.670155 https://cmr.earthdata.nasa.gov/search/concepts/C2231552954-CEOS_EXTRA.umm_json This data set was created for analysis of the ground-water system of the study area. This geospatial data set describes bedrock geology of the Turkey Creek drainage basin in Jefferson County, Colorado. It was digitized from maps of fault locations and geologic map units based on age and lithology. Created for use in the Jefferson County Mountain Ground-Water Resources Study, it is to be used at a scale no more detailed than 1:50,000. The source materials for the generation of this data set consist of bedrock geology mapped on U.S. Geological Survey (USGS) topographic quadrangles at a scale of 1:24,000 by the USGS. The source materials were converted to digital format, topologically developed, and attributed on a quadrangle-by- quadrangle basis before being combined into one data set. The procedures for converting the materials to digital format differed for each quadrangle and are summarized as follows: Conifer The original camara-ready transparency of the map publication, Reconnaissance Geologic Map of the Conifer Quadrangle, Jefferson County, Colorado, was obtained from the USGS. A film-positive was made from this transparency. To simplify the linework, this film-positive was then traced by hand onto mylar. The mylar was then digitally scanned at 300 dots per inch (dpi) and stored as a TIFF image. Using Arc/INFO software from Environmental Systems Research Institute, the image was georeferenced to real-world coordinates and converted into an Arc/INFO raster data set format known as a grid, which was then vectorized into an Arc/INFO vector data set format known as a coverage. A quadrangle boundary outline that was generated from quadrangle boundary coordinates and then projected into real-world coordinates was added to the coverage, which was then converted to a coverage with polygon topology. Line features in the coverage were attributed according to fault type classification, and the polygon features were attributed according to bedrock geologic map unit and fault zone classification. Evergreen An incomplete collection of the original pre-press mylar separates for the map publication, Geologic Map of the Evergreen Quadrangle, Jefferson County, Colorado, was obtained from the USGS. Mylar separates of Quaternary geologic contacts and faults were identified and digitally scanned at 300 dpi into TIFF images. All other geologic contacts in the area of interest were traced onto mylar from a paper print of the map publication. Furthermore, an enclosing polygon outline outside of the area of interest was drawn on the mylar so that the traced contacts would form polygon features. The mylar was then digitally scanned at 300 dpi into a TIFF image. All the images were then georeferenced to real-world coordinates, converted into grids, and vectorized into three separate coverages, one for each of the two mylar sources, and one for the traced source. These coverages were then combined into one coverage. One of the authors of the map publication provided updated nomenclature for Precambrian map units (Bruce Bryant, U.S. Geological Survey, oral communication, 1998) so that the nomenclature would match that of adjacent quadrangles. The line features in the coverage were attributed according to fault type, and polygon features were attributed according to geologic map unit and fault zone classification. Indian Hills A paper print of the map publication, Geologic Map of the Indian Hills Quadrangle, Jefferson County, Colorado, was obtained from the USGS. For the area of interest on the quadrangle, two mylars were hand-traced from this paper print. One mylar consisted of geologic contacts and an enclosing polygon outline outside of the area of interest that was drawn so that the contacts would form polygon features. The other mylar consisted of fault traces. The two mylars were then digitally scanned at 300 dpi into TIFF images. These images were georeferenced to real-world coordinates and converted into grids which were then vectorized into coverages. The coverages were then combined into one coverage. One of the authors of the map publication provided updated nomencla- ture for Precambrian map units (Bruce Bryant, U.S. Geological Survey, oral communication, 1998) so that the nomenclature would match that of adjacent quadrangles. Line features in the coverage were attributed according to fault type, and polygon features were attributed according to geologic map unit and fault zone classification. Meridian Hill A paper photocopy of preliminary geologic mapping consisting of faults and geologic contacts for the Meridian Hill Quadrangle, Clear Creek, Jefferson, and Park Counties, was obtained from the USGS. For the area of interest on this quadrangle, all the linework was traced onto mylar. Furthermore, an enclosing polygon outline outside of the area of interest was drawn on the mylar so that the traced contacts would form polygon features. The mylar was then digitally scanned at 300 dpi into a TIFF image. The image was georeferenced to real-world coordinates and converted into a grid which was then vectorized into a coverage. Line features in the coverage were then attributed according to fault type, and the polygon features were attributed according to geologic map unit and fault zone classification. Morrison The original pre-press mylar separates for the map publication Geologic Map of the Morrison Quadrangle, Jefferson County, Colorado, were obtained from the USGS. The mylar separate of geologic contacts was digitally scanned at 300 dpi into a TIFF image. This image was georeferenced to real-world coordinates and converted into a grid which was then vectorized into a coverage. The fault linework was digitized into a coverage from another mylar separate of the same publication that had too many other themes on it and was therefore too difficult to scan and vectorize. The fault coverage was then transformed to real-world coordinates. The coverages were then combined into one coverage. An enclosing polygon outline outside of the area of interest was digitized into the coverage so that the geologic contacts would form polygon features. Line features in the coverage were attributed according to fault type, and polygons were attributed according to geologic map unit and fault zone classification. Once the polygon and vector topology was developed for each quadrangle, the individual coverages were combined into one coverage. No edgematching was performed. A study-area outline of the Turkey Creek Watershed was delineated in Arc/INFO with USGS Digital Elevation Model data sets. A 500-meter buffer polygon of this outline was used to clip the geology coverage. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. proprietary +USGS_ofr00-96_wlc80_97_1.0 Digital map of water-level changes in the High Plains Aquifer, 1980 to 1997 CEOS_EXTRA STAC Catalog 1999-01-01 1999-12-31 -106.015, 31.652, -96.26, 43.806 https://cmr.earthdata.nasa.gov/search/concepts/C2231552580-CEOS_EXTRA.umm_json This data set was created to document the original map (McGuire, V.L. and Fischer, B.C., 1999) produced by the High Plains Water-Level Monitoring Project and to make available the data on this map for use with geographic information systems. This data set consists of digital water-level-change contours for the High Plains aquifer in the central United States, 1980 to 1997. The High Plains aquifer extends from south of 32 degrees to almost 44 degrees north latitude and from 96 degrees 30 minutes to 104 degrees west longitude. The aquifer underlies about 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This digital data set was created from 5,233 wells measured in both 1980 and 1997. The water-level-change contours were drawn manually on mylar at a scale of 1:1,000,000. The contours then were converted to a digital map. Introduction -- The information provided in this introduction is found in U.S. Geological Survey Professional Paper 1400-B (Gutentag and others, 1984). This data set consists of digital water-level-change contours for the High Plains aquifer in the United States, 1980 to 1997. The High Plains aquifer, which underlies about 174,000 square miles in parts of eight states, is the principal water source in one of the nation's major agricultural areas. In 1980, about 170,000 wells pumped water from the aquifer to irrigate about 13 million acres. The High Plains aquifer is a regional water-table aquifer consisting mostly of near-surface sand-and-gravel deposits. In 1980, the maximum saturated thickness of the aquifer was about 1,000 feet and averaged about 200 feet. Hydraulic conductivity and specific yield of the aquifer depend on sediment types, which vary significantly both horizontally and vertically. Hydraulic conductivity ranged from less than 25 to greater than 300 feet per day and averaged 60 feet per day. Specific yields ranged from less than 10 to 30 percent and averaged about 15 percent. The High Plains aquifer boundaries were determined by erosional extent of associated geologic units and by hydraulic and physiographic boundaries where the High Plains aquifer extends eastward from the Great Plains physiographic province (Fenneman, 1931). In most of the area, the erosional extent of the hydraulically connected Tertiary and Quaternary deposits were used as the aquifer boundary. In eastern Nebraska, streams and physiographic boundaries were used as the aquifer boundary. Reviews Applied to Data -- This electronic report was subjected to the same review standards that apply to all U.S. Geological Survey reports. Reviewers were asked to check the topological consistency, tolerances, attribute frequencies and statistics, projection, and geographic extent. Reviewers were given digital data sets for checking against the source maps to verify the linework and attributes. The reviewers checked the metadata files for completeness and accuracy. proprietary +USGS_ofr00471_ddwdscon_Version 1.0, March 27, 2000 Geologic structure contours for the top of the Deadwood Formation, Black Hills, South Dakota CEOS_EXTRA STAC Catalog 1999-07-14 1999-07-14 -104.07985, 43.117573, -102.968216, 44.78429 https://cmr.earthdata.nasa.gov/search/concepts/C2231549083-CEOS_EXTRA.umm_json This data set was created as part of the Black Hills Hydrology Study (BHHS). The BHHS is a long-term investigation that was initiated in 1990 as a cooperative effort between the U.S. Geological Survey (USGS), the South Dakota Department of Environment and Natural Resources (DENR), and the West Dakota Water Development District. West Dakota represents various local and county cooperators. The purpose of the study is to assess the quantity, quality, and distribution of surface and ground water in the Black Hills area of western South Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer, Fall River, Lawrence, Meade, and Pennington counties in South Dakota. This data set represents geologic structure contours for the top of the Deadwood Formation, Black Hills, South Dakota. proprietary +USGS_ofr00471_hydrogeo_Version 1.0, April 27, 2000 Hydrogeologic Units in the Black Hills area, South Dakota CEOS_EXTRA STAC Catalog 1998-06-09 1998-06-09 -104.07994, 43.117134, -102.95288, 44.786495 https://cmr.earthdata.nasa.gov/search/concepts/C2231550572-CEOS_EXTRA.umm_json This data set was created as part of the Black Hills Hydrology Study (BHHS). The BHHS is a long-term investigation that was initiated in 1990 as a cooperative effort between the U.S. Geological Survey (USGS), the South Dakota Department of Environment and Natural Resources (DENR), and the West Dakota Water Development District. West Dakota represents various local and county cooperators. The purpose of the study is to assess the quantity, quality, and distribution of surface and ground water in the Black Hills area of western South Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer, Fall River, Lawrence, Meade, and Pennington counties in South Dakota. This data set represents surficial hydrogeology for the Black Hills of South Dakota. This electronic report was subjected to the same review standard that applies to all U.S. Geological Survey reports. Reviewers were asked to check the topological consistency, tolerances, attribute frequencies and statistics, projection, and geographic extent. The reviewers checked the metadata and a_readme.1st files for completeness and accuracy. proprietary +USGS_ofr00471_inkrscon_Version 1.0, March 17, 2000 Geologic structure contours for the top of the Inyan Kara Group, Black Hills, South Dakota CEOS_EXTRA STAC Catalog 1999-07-14 1999-07-14 -104.079315, 43.11753, -102.95605, 44.786076 https://cmr.earthdata.nasa.gov/search/concepts/C2231552963-CEOS_EXTRA.umm_json This data set was created as part of the Black Hills Hydrology Study (BHHS). The BHHS is a long-term investigation that was initiated in 1990 as a cooperative effort between the U.S. Geological Survey (USGS), the South Dakota Department of Environment and Natural Resources (DENR), and the West Dakota Water Development District. West Dakota represents various local and county cooperators. The purpose of the study is to assess the quantity, quality, and distribution of surface and ground water in the Black Hills area of western South Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer, Fall River, Lawrence, Meade, and Pennington counties in South Dakota. This data set represents geologic structure contours for the top of the Inyan Kara Group, Black Hills, South Dakota. proprietary +USGS_ofr00471_mnktscon_Version 1.0, March 21, 2000 Geologic structure contours for the top of the Minnekahta Limestone, Black Hills, South Dakota CEOS_EXTRA STAC Catalog 1999-07-14 1999-07-14 -104.07972, 43.11754, -102.95606, 44.784607 https://cmr.earthdata.nasa.gov/search/concepts/C2231552592-CEOS_EXTRA.umm_json This data set was created as part of the Black Hills Hydrology Study (BHHS). The BHHS is a long-term investigation that was initiated in 1990 as a cooperative effort between the U.S. Geological Survey (USGS), the South Dakota Department of Environment and Natural Resources (DENR), and the West Dakota Water Development District. West Dakota represents various local and county cooperators. The purpose of the study is to assess the quantity, quality, and distribution of surface and ground water in the Black Hills area of western South Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer, Fall River, Lawrence, Meade, and Pennington counties in South Dakota. This data set represents geologic structure contours for the top of the Minnekahta Limestone, Black Hills, South Dakota. proprietary +USGS_ofr00471_mnlsscon_Version 1.0, March 21, 2000 Geologic structure contours for the top of the Minnelusa Formation, Black Hills, South Dakota CEOS_EXTRA STAC Catalog 1999-07-14 1999-07-14 -104.07984, 43.1174, -102.957466, 44.784397 https://cmr.earthdata.nasa.gov/search/concepts/C2231551478-CEOS_EXTRA.umm_json This data set was created as part of the Black Hills Hydrology Study (BHHS). The BHHS is a long-term investigation that was initiated in 1990 as a cooperative effort between the U.S. Geological Survey (USGS), the South Dakota Department of Environment and Natural Resources (DENR), and the West Dakota Water Development District. West Dakota represents various local and county cooperators. The purpose of the study is to assess the quantity, quality, and distribution of surface and ground water in the Black Hills area of western South Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer, Fall River, Lawrence, Meade, and Pennington counties in South Dakota. This data set represents geologic structure contours for the Minnelusa Formation, Black Hills, South Dakota. proprietary +USGS_ofr00471_mnlssurf_Version 1.0, March 21, 2000 Geologic structure contours for the top of the Minnelusa Formation, Black Hills, South Dakota - USGS_ofr00471_mnlssurf CEOS_EXTRA STAC Catalog 1999-07-14 1999-07-14 -104.07984, 43.1174, -102.957466, 44.784397 https://cmr.earthdata.nasa.gov/search/concepts/C2231552095-CEOS_EXTRA.umm_json This data set was created as part of the Black Hills Hydrology Study (BHHS). The BHHS is a long-term investigation that was initiated in 1990 as a cooperative effort between the U.S. Geological Survey (USGS), the South Dakota Department of Environment and Natural Resources (DENR), and the West Dakota Water Development District. West Dakota represents various local and county cooperators. The purpose of the study is to assess the quantity, quality, and distribution of surface and ground water in the Black Hills area of western South Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer, Fall River, Lawrence, Meade, and Pennington counties in South Dakota. This data set represents geologic structure contours for the Minnelusa Formation, Black Hills, South Dakota. proprietary +USGS_ofr02-007_lithogeo_1.0, February, 2002 Lithogeochemical Character of Near-Surface Bedrock in the New England Coastal Basins CEOS_EXTRA STAC Catalog 2001-11-16 2001-11-16 -72.19068, 41.134434, -69.031586, 45.902214 https://cmr.earthdata.nasa.gov/search/concepts/C2232411626-CEOS_EXTRA.umm_json "The lithogeochemical data layer was compiled to provide the NECB NAWQA study area with digital geologic information that could be used in the analysis of surface- and ground-water quality. Goals of the NAWQA program are to describe the status and trends of a large representative part of the Nation's surface- and ground-water resources and to identify the natural and human factors that affect the quality of these resources (Leahy and others, 1990). The data layer presented here was intended to characterize the bedrock units in the study area in terms of mineralogic and chemical parameters relevant to water quality, such that the geologic data could be used in GIS to plan NAWQA study-unit activities, and to analyze and interpret water-quality and ecosystem conditions. This geographic information system (GIS) data layer shows the generalized lithologic and geochemical, termed lithogeochemical, character of near-surface bedrock in the New England Coastal Basins (NECB) study area of the U.S. Geological Survey's National Water Quality Assessment (NAWQA) Program. The area encompasses 23,000 square miles in western and central Maine, eastern Massachusetts, most of Rhode Island, eastern New Hampshire and a small part of eastern Connecticut. The NECB study area includes the Kennebec, Androscogginn, Saco, Merrimack, Charles, and Blackstone River Basins, as well as all of Cape Cod. Bedrock units in the NECB study area are classified into 38 lithogeochemical units based on the relative reactivity of their constituent minerals to dissolution and the presence of carbonate or sulfide minerals. The 38 lithogeochemical units are generalized into 7 major groups: (1) carbonate-bearing metasedimentary rocks; (2)primarily noncalcareous, clastic sedimentary rocks with restricted deposition in discrete fault-bounded sedimentary basins of Mississipian or younger age; (3) primarily noncalcareous, clastic sedimentary rocks at or above biotite-grade of regional metamorphism; (4) mafic igneous rocks and their metamorphic equivalents; (5) ultramafic rocks; (6) felsic igneous rocks and their metamorphic equivalents; and (7) unconsolidated and poorly consolidated sediments. The classification scheme used was first developed as part of the USGS's study of the Connecticut, Housatonic, and Thames River Basins (CONN), an adjacent NAWQA study area (Robinson and others, 1999). The classification scheme is based on geochemical principles, previous studies of the relations among water-quality and ecosystem characteristics and rock type, and the regional geology of New England. The classification scheme and data set are intended to provide a general, flexible framework for classifying and mapping bedrock units in the study area for all types of water-quality analysis. The data set is a lithologic map that has been coded to reflect the potential influence of geology on water quality. The classification scheme provides flexibility because the user can reclassify the 38 lithogeochemical units into other groups for other types of data analysis. The bedrock units in this study area have been mapped defined by time- stratigraphic and other geologic criteria which may not be directly relevant to water quality. Bedrock units depicted on the State geologic maps are inconsistent across state boundaries in some areas (See Data_Quality_Information section of this document for explanation on how these discrepancies were addressed with the classification scheme). Thus, a study-area-wide coding scheme was developed to classify the geologic map units according to mineralogical and chemical characteristics that are relevant for water-quality investigations. Bedrock units were classified for water-quality purposes according to the chemical composition and relative susceptibility to weathering of their constituent minerals. Although weathering rates may vary, the relative stability of different minerals during weathering in moist climates is generally consistent (Robinson, 1997). However, the degree to which a rock weathers reflects the proportions of its constituent mineral as well as many other factors such as degree of induration and relative amount of mineral surfaces exposed to water through primary and secondary porosity. Thus, although largely based on the relative stability of rock constituent minerals, the classification scheme to group bedrock units according to effects on water quality is more complex than mineral- stability sequences. Most common rock-forming minerals are only sparingly soluble, so that small amounts of highly reactive minerals can have large effects of water quality (Robinson, 1997). For example, carbonate minerals are more rapidly weathered and tend to produce higher solute concentrations in natural waters than other rock types. In contrast, granites, schists and quartzites, which are rich in alkali-feldspar, muscovites, and quartz, produce low solute concentrations because they react to a lesser degree and at slower rates than other rock types in humid temperate climates (Robinson, 1997). The lithogeochemical classification scheme used in this data set incorporates the relative stability of minerals classifications criteria such as used in previous studies, and the characteristics of bedrock geology specific to the study area (such as the presence of a discrete fault bounded sedimentary basins of Mississipian or younger age). Further description of the lithogeochemical classification scheme and the expected water- quality and ecosystem characteristics associated with each lithogeochemical unit is explained in Robinson (1997). Thirty-eight lithogeochemical units have been defined for the NECB study area based on the mineral and textural properties of the bedrock unit's constituent minerals, presence of carbonate and sulfide minerals and for some of the granitic units, relative age. The classification scheme used descriptions from State geologic maps (Osberg and others, 1985; Lyons and others, 1997; Zen and others, 1985;Hermes and others, 1994; and Rogers, 1985) of the lithology, mineralogy, and weathering characteristics of the bedrock units. For example, ""rusty-weathering"" serves as an indicator of sulfidic-bearing bedrock units (Robinson, 1997). Carbonate and sulfide minerals predominate in the classification scheme because these highly reactive minerals have a disproportionately large effect on water chemistry compared to other minerals commonly found in the rocks of this region. In the Maine data set, information about metamorphic grade was also used to classify bedrock units. A digital data layer of generalized regional metamorphic zones (Guidotti, 1985, shown in Osberg and others,1985), was obtained from the Maine Geological Survey. This layer was intersected with the digital bedrock geology to determine the regional metamorphic grade of each polygon in the bedrock geology data layer. Polygons lying within two metamorphic zones were split at the metamorphic-zone boundary. Metamorphic grade and geochemical composition of the protolith (pre-metamorphism source rock) were used to classify polygons into lithogeochemical units. For example, bedrock units with protoliths of ""limestone and(or) dolostone"" were classified as ""limestone, dolomite, and carbonate-rich clastic sediments"" (lithogeochemical unit ""11u"") in areas of none or weak regional metamorphism and as ""marble, may include some calc-silicate rock"" (lithogeochemical unit ""12u"") in areas of greenschist facies or high grade metamorphism. The 38 lithogeochemical units defined for the NECB study area result from the combination of a lithology code (numeric) with a modifier code (alphabetic). There are 17 lithology codes that represent the influences on water chemistry of lithology, metamorphic grade, and geologic setting. Each bedrock unit is assigned one of 17 lithology codes based on the description of the bedrock unit from the State bedrock geologic maps. There are 13 modifier codes used to identify minor amounts of carbonate and(or) sulfide minerals, and subdivide granitic units into subgroups based on their chemical and mineral characteristics and relative age. A description of the 38 lithogoechemical units in the NECB study area and their potental effects on water quality can be found in the Supplemental_Information section of this document. The 38 lithogeochemical units are generalized into 7 major groups that share similarities in overall geochemistry and lithology: (1) carbonate-bearing metasedimentary rocks; (2) primarily noncalcareous, clastic sedimentary rocks deposited in fault-bounded sedimentary basins of Mississipian or younger age; (3) primarily noncalcareous, clastic sedimentary rocks at or above biotite-grade of regional metamorphism; (4) mafic igneous rocks and their metamorphic equivalents; (5) ultramafic rocks; (6) felsic igneous rocks and their metamorphic equivalents; and (7) unconsolidated and poorly consolidated sediments. Major group 7 encompasses areas in the south-coastal part of the NECB study area where the bedrock is overlain by thick glacial sediments at the surface. These surficial glacial deposits are the primary aquifer for these areas. An example of how this data set has been used in study design strategies and in analyzing water-quality characteristic by lithogeochemical units and major groups is provided in Ayotte and others (1999). The bedrock units shown on the individual State maps for the NECB were classified according to a lithogeochemical scheme modified from Robinson and others (1999). Specifically, the modification included the subdivision of granitic bedrock units into additional lithogeochemical units with modifying attributes to indicate relative age. However, this modification to the classification system is evident in the lithogeochemical units. Thus, the CONN and the NECB data set can be readily merged together to create a larger regional product with these difference being more frequent when the data set is viewed with the lithogeochemical units showing and less frequent when the data set is viewed with the major groups showing. Overall, the bedrock units in the two study units are classified in a consistent manner to a create regional product that can be used to evaluate the influences of bedrock geology on water-quality characteristics. Quality Assurance procedures: The scientific content of this digital data set underwent technical review by two USGS scientists who have knowledge of the regional geology,and GIS and spatial-data production. The data set was evaluated on positional accuracy, contextual accuracy, attribute accuracy, and topological consistency." proprietary USGS_ofr02-338_depth2wt Depth/Colorado Front Range Infrastructure Resources Project (FRIRP) CEOS_EXTRA STAC Catalog 2002-10-07 2002-10-07 -105.277534, 39.91331, -104.5985, 40.750088 https://cmr.earthdata.nasa.gov/search/concepts/C2231548647-CEOS_EXTRA.umm_json This dataset was created by the U.S. Geological Survey (USGS) in the development of the USGS Front Range Infrastructure Resources Project. This dataset was used in the creation of 1:50,000-scale hydrogeologic contour maps. The U.S. Geological Survey developed this dataset as part of the Colorado Front Range Infrastructure Resources Project (FRIRP). One goal of the FRIRP was to provide information on the availability of those hydrogeologic resources that are either critical to maintaining infrastructure along the northern Front Range or that may become less available because of urban expansion in the northern Front Range. This dataset extends from the Boulder-Jefferson County line on the south, to the middle of Larimer and Weld Counties on the North. On the west, this dataset is bounded by the approximate mountain front of the Front Range of the Rocky Mountains; on the east, by an arbitrary north-south line extending through a point about 6.5 kilometers east of Greeley. This digital geospatial dataset consists of depth-to-water (unsaturated-thickness) contours that were generated from hydrogeologic data with Geographic Information System (GIS) software. proprietary USGS_ofr02-338_studyarea_Version 1.0, June 22, 1998 COVERAGE STUDY AREA -- Outline of study area boundary of Denver Basin CEOS_EXTRA STAC Catalog 1998-01-01 1998-12-31 -105.279755, 39.214413, -104.59333, 40.75322 https://cmr.earthdata.nasa.gov/search/concepts/C2231551626-CEOS_EXTRA.umm_json "This data set was created to display the outline of the study area as depicted in (Robson and others, 1998). This digital geospatial data set consists of outlines of the study area in the report ""Structure, Outcrop, and Subcrop of the Bedrock Aquifers Along the Western Margin of the Denver Basin, Colorado"" (Robson and others, 1998)." proprietary +USGS_ofr96-443_cond_1.0 Digital hydraulic conductivity values of the Antlers aquifer in southeastern Oklahoma CEOS_EXTRA STAC Catalog 1992-01-01 1992-12-31 -97.4976, 33.7288, -94.4684, 34.3644 https://cmr.earthdata.nasa.gov/search/concepts/C2231549511-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data set that could be used in ground-water vulnerability analysis. This data set consists of digitized polygons of a constant hydraulic conductivity value for the Antlers aquifer in southeastern Oklahoma. The Early Cretaceous-age Antlers Sandstone is an important source of water in an area that underlies about 4,400-square miles of all or part of Atoka, Bryan, Carter, Choctaw, Johnston, Love, Marshall, McCurtain, and Pushmataha Counties. The Antlers aquifer consists of sand, clay, conglomerate, and limestone in the outcrop area. The upper part of the Antlers aquifer consists of beds of sand, poorly cemented sandstone, sandy shale, silt, and clay. The Antlers aquifer is unconfined where it outcrops in about an 1,800-square-mile area. The hydraulic conductivity polygons were developed from the hydraulic conductivity value used as input into a ground-water flow model and from published digital data sets of the surficial geology of the Antlers Sandstone except in areas overlain by alluvial and terrace deposits near streams. Some of the lines were interpolated where the Antlers aquifer is overlain by alluvial and terrace deposits. The interpolated lines are very similar to the aquifer boundaries shown on maps published in a ground-water modeling report for the Antlers aquifer. The constant hydraulic conductivity value used as input to the ground-water flow model was estimated as 5.74 feet per day. Ground-water flow models are numerical representations that simplify and aggregate natural systems. Models are not unique; different combinations of aquifer characteristics may produce similar results. Therefore, values of hydraulic conductivity used in the model and presented in this data set are not precise, but are within a reasonable range when compared to independently collected data. proprietary +USGS_ofr96-444_cond_1.0 Digital hydraulic conductivity values of the Vamoosa-Ada aquifer in east-central Oklahoma CEOS_EXTRA STAC Catalog 1986-01-01 1986-12-31 -96.7807, 34.8562, -96.0003, 37.001 https://cmr.earthdata.nasa.gov/search/concepts/C2231550240-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digitized polygons of constant hydraulic conductivity values for the Vamoosa-Ada aquifer in east-central Oklahoma. The Vamoosa-Ada aquifer is an important source of water that underlies about 2,320-square miles of parts of Osage, Pawnee, Payne, Creek, Lincoln, Okfuskee, and Seminole Counties. Approximately 75 percent of the water withdrawn from the Vamoosa-Ada aquifer is for municipal use. Rural domestic use and water for stock animals account for most of the remaining water withdrawn. The Vamoosa-Ada aquifer is defined in a ground-water report as consisting principally of the rocks of the Late Pennsylvanian-age Vamoosa Formation and overlying Ada Group. The Vamoosa-Ada aquifer consists of a complex sequence of fine- to very fine-grained sandstone, siltstone, shale, and conglomerate interbedded with very thin limestones. The water-yielding capabilities of the aquifer are generally controlled by lateral and vertical distribution of the sandstone beds and their physical characteristics. The Vamoosa-Ada aquifer is unconfined where it outcrops in about an 1,700-square-mile area. The hydraulic conductivity of the Vamoosa-Ada aquifer was computed as 3 feet per day in a ground-water report. Most of the hydraulic conductivity polygons were extracted from published digital geology data sets. The lines in the digital geology data sets were scanned or digitized from maps published at a scale of 1:250,000 and represent geologic contacts. Some of the lines in the data set were interpolated in areas where the Vamoosa-Ada aquifer is overlain by alluvial and terrace deposits near streams and rivers. proprietary +USGS_ofr96-444_wlelev_1.0 Digital water-level elevation contours for the Vamoosa-Ada aquifer in east-central Oklahoma CEOS_EXTRA STAC Catalog 1986-01-01 1986-12-31 -96.8152, 34.8716, -96.0525, 37.0008 https://cmr.earthdata.nasa.gov/search/concepts/C2231552418-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digitized water-level elevation contours for the Vamoosa-Ada aquifer in east-central Oklahoma. The Vamoosa-Ada aquifer is an important source of water that underlies about 2,320-square miles of parts of Osage, Pawnee, Payne, Creek, Lincoln, Okfuskee, and Seminole Counties. Approximately 75 percent of the water withdrawn from the Vamoosa-Ada aquifer is for municipal use. Rural domestic use and water for stock animals account for most of the remaining water withdrawn. The Vamoosa-Ada aquifer is defined in a ground-water report as consisting principally of the rocks of the Late Pennsylvanian-age Vamoosa Formation and overlying Ada Group. The Vamoosa-Ada aquifer consists of a complex sequence of fine- to very fine-grained sandstone, siltstone, shale, and conglomerate interbedded with very thin limestones. The water-yielding capabilities of the aquifer are generally controlled by lateral and vertical distribution of the sandstone beds and their physical characteristics. The Vamoosa-Ada aquifer is unconfined where it outcrops in about an 1,700-square-mile area. The water-level elevation contours were digitized from a mylar map, at a scale of 1:250,000, used to publish a plate in a ground-water report about the Vamoosa-Ada aquifer. The water-level elevation contours in this data set extend west of the aquifer outcrop to areas where Vanoss Group rocks overlie the Ada Group. The data set also includes a water-level elevation contour for a terrace deposit east of the aquifer outcrop near the North Canadian River. Water-level elevations range from 800 to 1,000 feet above sea level for the Vamoosa-Ada aquifer. proprietary USGS_ofr96-445_aqbound_1.0 Digital boundaries of the alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie in northwestern Oklahoma CEOS_EXTRA STAC Catalog 1996-01-01 1996-01-01 -99.0874, 35.7774, -97.5243, 36.8974 https://cmr.earthdata.nasa.gov/search/concepts/C2231554346-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data set that could be used in ground-water vulnerability analysis. This data set consists of digital aquifer boundaries for the alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie in northwestern Oklahoma. Ground water in 1,305 square miles of Quaternary-age alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. Alluvial and terrace deposits are composed of interfingering lenses of clay, sandy clay, and cross-bedded poorly sorted sand and gravel. The aquifer is composed of hydraulically connected alluvial and terrace deposits that uncomfortably overlie the Permian-age Formations. The aquifer boundaries along geological contacts were extracted from published digital geology data sets. Additional boundaries defining the geographic limits of the aquifer and areas of less than 5 feet saturated thickness were digitized from a mylar map, at a scale of 1:250,000. The maps were published at a scale of 1:900,000. proprietary +USGS_ofr96-445_cond_1.0 Digital hydraulic conductivity values of the alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie in northwestern Oklahoma CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -99.0874, 35.7774, -97.5243, 36.8974 https://cmr.earthdata.nasa.gov/search/concepts/C2231550236-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data set that could be used in ground-water vulnerability analysis. This data set consists of digital polygons of constant hydraulic conductivity values for the alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie in northwestern Oklahoma. Ground water in 1,305 square miles of Quaternary-age alluvial and terrace deposits along the the Cimarron River from Freedom to Guthrie is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. Alluvial and terrace deposits are composed of interfingering lenses of clay, sandy clay, and cross-bedded poorly sorted sand and gravel. The aquifer is composed of hydraulically connected alluvial and terrace deposits that uncomfortably overlie the Permian-age Formations. The hydraulic-conductivity values for alluvial and terrace deposits used in this data set were published in a steady-state ground-water flow modeling report. The aquifer boundaries along geological contacts were extracted from published digital geology data sets. Boundaries defining the geographic limits of the aquifer were digitized from a mylar map, at a scale of 1:250,000. The maps were published at a scale of 1:900,000. The hydraulic conductivity values are 104.5 feet per day for the alluvial deposits and 47.5 feet per day for the terrace deposits. Ground-water flow models are numerical representations that simplify and aggregate natural systems. Models are not unique; different combinations of aquifer characteristics may produce similar results. Therefore, values of hydraulic conductivity used in the model and presented in this data set are not precise, but are within a reasonable range when compared to independently collected data. proprietary +USGS_ofr96-445_wlelev_1.0 Digital water-level elevation contours for the alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie in northwestern Oklahoma CEOS_EXTRA STAC Catalog 1985-01-01 1986-12-31 -99.0721, 35.8204, -97.5609, 36.8137 https://cmr.earthdata.nasa.gov/search/concepts/C2231549989-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data set that could be used in ground-water vulnerability analysis. This data set consists of digital water-level elevation contours for the alluvial and terrace deposits along the Cimarron River in northwestern Oklahoma during 1985-86. Ground water in 1,305 square miles of Quaternary-age alluvial and terrace deposits along the the Cimarron River from Freedom to Guthrie is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. Alluvial and terrace deposits are composed of interfingering lenses of clay, sandy clay, and cross-bedded poorly sorted sand and gravel. The aquifer is composed of hydraulically connected alluvial and terrace deposits that unconformably overlie the Permian-age Formations. Water-level elevations measured in 1985 and 1986 ranged from 1,650 feet to 950 feet above sea level. Regional ground-water flow is generally southeast to southwest towards the Cimarron River, except where the flow direction is affected by perennial tributaries. The water-level elevation contours were digitized from a mylar map at a scale of 1:250,000. The maps were published at a scale of 1:900,000. proprietary USGS_ofr96-446_aqbound_1.0 Digital boundaries of the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma CEOS_EXTRA STAC Catalog 1981-01-01 1981-12-31 -99.965, 36.0439, -98.5487, 36.9727 https://cmr.earthdata.nasa.gov/search/concepts/C2231552864-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digital aquifer boundaries for the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma. Ground water in 830 square miles of the Quaternary-age alluvial and terrace aquifer is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. The aquifer consists of poorly sorted, fine to coarse, unconsolidated quartz sand with minor amounts of clay, silt, and basal gravel. The hydraulically connected alluvial and terrace deposits unconformably overlie the Tertiary-age Ogallala Formation and Permian-age formations. The aquifer boundaries established in a ground-water flow model for the aquifer include areas: 1) where the terrace deposits pinch out against relatively impermeable Permian-age formations; 2) where the alluvium has been deposited against relatively impermeable Permian-age formations; 3) where the alluvial and terrace deposits have been eroded and underlying Permian-age formations are exposed at the surface; 4) where the aquifer extends beyond the geographic limit of the study area; and 5) where the aquifer has little or no saturated thickness. The lines in the data set representing aquifer boundaries along geological contacts were extracted from a published digital surficial geology data set based on a scale of 1:250,000. The geographic limits of the aquifer and areas of little or no saturated thickness were digitized from a folded paper map, at a scale of 1:250,000 from a ground-water modeling report. proprietary +USGS_ofr96-446_cond_1.0 Digital hydraulic conductivity values CEOS_EXTRA STAC Catalog 1981-01-01 1981-01-01 -99.965, 36.0439, -98.5487, 36.9727 https://cmr.earthdata.nasa.gov/search/concepts/C2231554806-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digital hydraulic conductivity values for the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma. Ground water in 830 square miles of the Quaternary-age alluvial and terrace aquifer is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. The aquifer consists of poorly sorted, fine to coarse, unconsolidated quartz sand with minor amounts of clay, silt, and basal gravel. The hydraulically connected alluvial and terrace deposits unconformably overlie the Tertiary-age Ogallala Formation and Permian-age formations. Six zones of ranges of hydraulic conductivity values for the alluvial and terrace deposits reported in a ground-water modeling report are used in this data set. The hydraulic conductivity values range from 0 to 160 feet per day, and average 59 feet per day. The features in the data set representing aquifer boundaries along geological contacts were extracted from a published digital surficial geology data set based on a scale of 1:250,000. The geographic limits of the aquifer and zones representing ranges of hydraulic conductivity values were digitized from folded paper maps, at a scale of 1:250,000 from a ground-water modeling report. Ground-water flow models are numerical representations that simplify and aggregate natural systems. Models are not unique; different combinations of aquifer characteristics may produce similar results. Therefore, values of hydraulic conductivity used in the model and presented in this data set are not precise, but are within a reasonable range when compared to independently collected data. proprietary +USGS_ofr96-446_recharg_1.0 Digital recharge rate for the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma CEOS_EXTRA STAC Catalog 1981-01-01 1981-12-31 -99.965, 36.0439, -98.5487, 36.9727 https://cmr.earthdata.nasa.gov/search/concepts/C2231550385-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digital polygons of a constant recharge value for the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma. Ground water in 830 square miles of the Quaternary-age alluvial and terrace aquifer is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. The aquifer consists of poorly sorted, fine to coarse, unconsolidated quartz sand with minor amounts of clay, silt, and basal gravel. The hydraulically connected alluvial and terrace deposits unconformably overlie the Tertiary-age Ogallala Formation and Permian-age formations. A recharge rate of 1 inch per year was estimated in the ground-water modeling report for the alluvial and terrace deposits and used in this data set. The recharge rate was estimated using a base-flow method and a monthly-water-balance method. The features in the data set representing boundaries along geological contacts were extracted from a published digital surficial geology data set based on a scale of 1:250,000. The geographic limits of the aquifer were digitized from a folded paper map, at a scale of 1:250,000 in the ground-water modeling report. Ground-water flow models are numerical representations that simplify and aggregate natural systems. Models are not unique; different combinations of aquifer characteristics may produce similar results. Therefore, values of recharge used in the model and presented in this data set are not precise, but are within a reasonable range when compared to independently collected data. proprietary +USGS_ofroo-300_SATTK9697_1.0 Digital map of the saturated thickness of the High Plains Aquifer, 1996-97 CEOS_EXTRA STAC Catalog 1999-01-01 1999-12-31 -106.015, 31.652, -96.26, 43.806 https://cmr.earthdata.nasa.gov/search/concepts/C2231549394-CEOS_EXTRA.umm_json This data set was created to document the original map (McGuire and Fischer, 1999) produced by the High Plains Water-level Monitoring project and make available the data on this map for use with geographic information systems. This digital data set consists of saturated thickness contours for the High Plains aquifer in Central United States, 1996-97. The High Plains aquifer extends from south of 32 degrees to almost 44 degrees north latitude and from 96 degrees 30 minutes to 104 degrees west longitude. The aquifer underlies about 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This data set was based on 10,085 water-level measurements, 49 stream elevations, (March 1997) and 10,036 water-level elevations from wells (1,370 from 1996 and 8,666 from 1997) and the base of aquifer value for each measurement location. The saturated thickness at each measurement location was determined by subtracting the water-level elevation from the base of aquifer at that location. Introduction -- The information provided in this introduction is found in U.S. Geological Survey Professional Paper 1400-B (Gutentag and others,1984). This data set consists of saturated thickness contours for the High Plains aquifer in Central United States, 1996-97 (modified from Weeks and Gutentag, 1981; Cederstrand and Becker, 1999). The High Plains aquifer, which underlies about 174,000 square miles in parts of eight states, is the principal water source in one of the nation's major agricultural areas. In 1980, about 170,000 wells pumped water from the aquifer to irrigate about 13 million acres. The High Plains aquifer is a regional water-table aquifer consisting mostly of near-surface sand and gravel deposits. In 1980, the maximum saturated thickness of the aquifer was about 1,000 feet and averaged about 200 feet. Hydraulic conductivity and specific yield of the aquifer depend on sediment types, which vary significantly both horizontally and vertically. Hydraulic conductivity ranged from less than 25 to greater than 300 feet per day and averaged 60 feet per day. Specific yields ranged from less than 10 to 30 percent and averaged about 15 percent. The High Plains aquifer boundaries were determined by erosional extent of associated geologic units and by hydraulic and physiographic boundaries where the High Plains aquifer extends eastward from the Great Plains physiographic province (Fenneman, 1931). In most of the area, the erosional extent of the hydraulically connected Tertiary and Quaternary deposits were used as the aquifer boundary. In eastern Nebraska, streams and physiographic boundaries were used as the aquifer boundary. Reviews Applied to Data -- This electronic report was subjected to the same review standard that applies to all U.S. Geological Survey reports. Reviewers were asked to check the topological consistency, tolerances, attribute frequencies and statistics, projection, and geographic extent. Reviewers were given digital data sets for checking against the source maps to verify the linework and attributes. The reviewers checked the metadata files for completeness and accuracy. proprietary USM_pCO2_0 University of Southern Mississippi (USM) - partial pressure of carbon dioxide (pCO2) project OB_DAAC STAC Catalog 2005-10-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360692-OB_DAAC.umm_json Measurements of pCO2 taken by the University of Southern Mississippi in the Gulf of Mexico near the Louisiana coast in 2005 and 2006 proprietary +US_FOREST_FRAGMENTATION Forest Fragmentation in the United States CEOS_EXTRA STAC Catalog 1970-01-01 -128, 24, -65, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231549003-CEOS_EXTRA.umm_json "National Land Cover Data (NLCD) was reclassified into three categories: forest, other natural (e.g., grassland and wetland), and anthropogenic use (e.g., agricultural and urban). Three new grids were created, one for each edge type (forest, forest, forest natural, and forest anthropogenic). The values in these grids were calculated as the number of edges with the appropriate type in the window divided by the total number of forest edges, regardless of neighbor. These grids represented forest connectivity (forest forest edges), naturally caused forest fragmentation (forest natural edges), and human-caused forest fragmentation (forest anthropogenic edges). In the map, forest connectivity is displayed in green, natural fragmentation in blue, and human fragmentation in red. Pure green identifies areas where most or all forest edges are shared by another forest pixel. Pure red areas are where forest edges are largely shared with human land use. Pure blue areas show where most or all forest edges are shared with another natural land cover type. Different mixes of the three edge types can produce other colors. Two common examples in the map are yellow and cyan. Yellow identifies areas with roughly equal amounts of forest connectivity and anthropogenic fragmentation. Cyan is where forest connectivity and natural fragmentation are approximately equal. Black represents areas with no forest in the window, and white represents ignored areas, mostly water, as well as state boundaries. With few exceptions, forest fragmentation by other natural land cover types is confined to the western United States, while most human-caused forest fragmentation is in the East and Midwest. The yellow and red areas around Yellowstone in northwest Wyoming are a result of the wildfires in 1988. The burned areas are classified as ""transitional"" in the NLCD, which are treated as anthropogenic use. The Mississippi River valley was largely forested at one time but has been almost entirely converted to agricultural use, resulting in a display of black and red. Las Vegas, Nevada, is visible as a patch of red in the Mojave Desert due to an ""urban forest"" effect from trees planted by residents. Riparian corridors are highly visible in arid and developed areas, especially the West and Midwest. In arid areas, climate often confines trees to riparian zones that are displayed in shades of blue. In the intensely farmed Midwest, intact and restored riparian vegetation is depicted in yellow or red. Southern Atlantic coastal plain riparian zones are wider; forest is better connected and is shown in green." proprietary US_MODIS_NDVI_1299_3 MODIS NDVI Data, Smoothed and Gap-filled, for the Conterminous US: 2000-2015 ORNL_CLOUD STAC Catalog 2000-01-01 2015-12-31 -129.89, 20.85, -62.56, 50.56 https://cmr.earthdata.nasa.gov/search/concepts/C2764637520-ORNL_CLOUD.umm_json This data set provides Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, smoothed and gap-filled, for the conterminous US for the period 2000-01-01 through 2015-12-31. The data were generated using the NASA Stennis Time Series Product Tool (TSPT) to generate NDVI data streams from the Terra satellite (MODIS MOD13Q1 product) and Aqua satellite (MODIS MYD13Q1 product) instruments. TSPT produces NDVI data that are less affected by clouds and bad pixels. proprietary US_MODIS_Veg_Parameters_1539_1 MODIS-derived Vegetation and Albedo Parameters for Agroecosystem-Climate Modeling ORNL_CLOUD STAC Catalog 2003-01-01 2010-12-31 -139.05, 15.15, -51.95, 49.15 https://cmr.earthdata.nasa.gov/search/concepts/C2517700524-ORNL_CLOUD.umm_json This dataset provides MODIS-derived leaf area index (LAI), stem area index (SAI), vegetation area fraction, dominant landcover category, and albedo parameters for the continental US (CONUS), parts of southern Canada, and Mexico at 30 km resolution. The data cover the period 2003-2010 and were developed to be used as surface input data for regional agroecosystem-climate models. MODIS Collection 5 products used to derive these parameters included the Terra yearly water mask, vegetation continuous field products, the combined Terra and Aqua yearly land-cover category (LCC) (MCD12Q1), 8-day composites for LAI (MCD15A2), and albedo parameter (MCD43B1) products. Please note that the MODIS Version 5 land data products used in this dataset have been superseded by Version 6 data products. proprietary UTC_1990countyboundaries 1990 County Boundaries of the United States CEOS_EXTRA STAC Catalog 1972-01-01 1990-12-31 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231550562-CEOS_EXTRA.umm_json This data set portrays the 1990 State and county boundaries of the United States, Puerto Rico, and the U.S. Virgin Islands. The data set was created by extracting county polygon features from the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey. These files were then merged into a single file and the boundaries were modified to what they were in 1990. This is a revised version of the March 2000 data set. proprietary +UTC_TNgeologicmaps Geologic Maps of Tennessee CEOS_EXTRA STAC Catalog 1966-01-01 1966-12-31 -90.31191, 34.983253, -81.64822, 36.679295 https://cmr.earthdata.nasa.gov/search/concepts/C2231549514-CEOS_EXTRA.umm_json This data set is a digital representation of the printed 1:250,000 geologic maps from the Tennessee Department of Environment and Conservation, Division of Geology. The coverage was designed primarily to provide a more detailed geologic base than the 1:2,500,000 King and Beikman (1974). 1:24,000 scale coverage of the state is available for about 40 percent of the state. Formation names and geologic unit codes used in the coverage are from the Tennessee Division of Geology published maps and may not conform to USGS nomenclature. The Tennessee Division of Geology can be contacted at (615) 532-1500. proprietary +UTC_TRIfacilities Facilities in the Toxic Release Inventory CEOS_EXTRA STAC Catalog 1997-12-31 -127.61431, 23.24277, -65.505165, 51.523094 https://cmr.earthdata.nasa.gov/search/concepts/C2231553589-CEOS_EXTRA.umm_json This data set is a subset of the U.S. Environmental Protection Agency (USEPA) Envirofacts point data set which includes facilities included in the the Toxic Release Inventory. Information on total pounds of volatile organic compounds released in 1995 (from USEPA's Toxic Release Inventory CD-ROM) has been included. This data set is designed to locate or plot manufacturing facilities included in the Toxic Release Inventory and display or analysis of volatile organic compounds releases in pounds per year. The following are the volatile organic compounds (VOC's) selected to calculate the total releases at each facility. Not all of these chemicals actually appear in the TRI data set, but this list was used to select releases to sum for each facility. CAS-ID Chemical name > ---------- ---------------------------- > 1 630-20-6 1,1,1,2-Tetrachloroethane > 2 71-55-6 1,1,1-Trichloroethane > 3 79-34-5 1,1,2,2-Tetrachloroethane > 4 76-13-1 1,1,2-Trichloro-1,2,2-trifluoroethane > 5 79-00-5 1,1,2-Trichloroethane > 6 75-34-3 1,1-Dichloroethane > 7 75-35-4 1,1-Dichloroethene > 8 563-58-6 1,1-Dichloropropene > 9 87-61-6 1,2,3-Trichlorobenzene > 10 96-18-4 1,2,3-Trichloropropane > 11 120-82-1 1,2,4-Trichlorobenzene > 12 95-63-6 1,2,4-Trimethylbenzene > 13 96-12-8 1,2-Dibromo-3-chloropropane > 14 106-93-4 1,2-Dibromoethane > 15 95-50-1 1,2-Dichlorobenzene > 16 107-06-2 1,2-Dichloroethane > 17 78-87-5 1,2-Dichloropropane > 18 108-67-8 1,3,5-Trimethylbenzene > 19 541-73-1 1,3-Dichlorobenzene > 20 142-28-9 1,3-Dichloropropane > 21 106-46-7 1,4-Dichlorobenzene > 22 95-49-8 1-Chloro-2-methylbenzene > 23 106-43-4 1-Chloro-4-methylbenzene > 24 594-20-7 2,2-Dichloropropane > 25 71-43-2 Benzene > 26 108-86-1 Bromobenzene > 27 74-97-5 Bromochloromethane > 28 75-27-4 Bromodichloromethane > 29 74-83-9 Bromomethane > 30 108-90-7 Chlorobenzene > 31 75-00-3 Chloroethane > 32 75-01-4 Chloroethene > 33 74-87-3 Chloromethane > 34 124-48-1 Dibromochloromethane > 35 74-95-3 Dibromomethane > 36 75-71-8 Dichlorodifluoromethane > 37 75-09-2 Dichloromethane > 38 1330-20-7 Dimethylbenzenes > 39 100-42-5 Ethenylbenzene > 40 100-41-4 Ethylbenzene > 41 87-68-3 Hexachlorobutadiene > 42 98-82-8 Isopropylbenzene > 43 1634-04-4 Methyl tert-butyl ether > 44 108-88-3 Methylbenzene > 45 91-20-3 Naphthalene > 46 127-18-4 Tetrachloroethene > 47 56-23-5 Tetrachloromethane > 48 75-25-2 Tribromomethane > 49 79-01-6 Trichloroethene > 50 75-69-4 Trichlorofluoromethane > 51 67-66-3 Trichloromethane > 52 156-59-2 cis-1,2-Dichloroethene > 53 10061-01-5 cis-1,3-Dichloropropene > 54 104-51-8 n-Butylbenzene > 55 103-65-1 n-Propylbenzene > 56 99-87-6 p-Isopropyltoluene > 57 135-98-8 sec-Butylbenzene > 58 98-06-6 tert-Butylbenzene > 59 156-60-5 trans-1,2-Dichloroethene > 60 10061-02-6 trans-1,3-Dichloropropene Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology. proprietary +UTC_USdams Major Dams in the United States CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 -162.93422, 18.016077, -66.01461, 68.06759 https://cmr.earthdata.nasa.gov/search/concepts/C2231555196-CEOS_EXTRA.umm_json "This data set portrays major dams of the United States, including Puerto Rico and the U.S. Virgin Islands. The data set was created by extracting dams 50 feet or more in height, or with a normal storage capacity of 5,000 acre- feet or more, or with a maximum storage capacity of 25,000 acre-feet or more, from the 75,187 dams in the U.S. Army Corps of Engineers National Inventory of Dams. These data are intended for geographic display and analysis at the national level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:2,000,000-scale data. No responsibility is assumed by the U.S. Geological Survey in the use of these data. In the online, interactive National Atlas of the United States, at scales smaller than 1:4,850,000 the data is thinned for display purposes. For scales between 1: 4,850,000 and 1:22,000,000, dams are only shown if they have a height of 500 feet or more, or a normal storage capacity of 50,000 acre-feet or more, or a maximum storage capacity of 250,000 acre-feet or more (1173 dams). At scales smaller than 1:22,000,000, dams are only shown if they have a height of 5000 feet or more, or a normal storage capacity of 500,000 acre-feet or more, or a maximum storage capacity of 2,500,000 acre-feet or more (240 dams). The dams in this file were selected from the National Inventory of Dams (NID). First, a subset of the attributes contained in the NID was selected based on input from the Army Corps of Engineers. Using an ArcView query, the dams with a height of 50 feet or more were selected, along with the dams with a normal storage capacity of 5,000 acre-feet or more, and those with a maximum storage capacity of 25,000 acre-feet or more. (The International Committee on Large Dams considers dams over 50 feet to be large dams. The USGS Water Resources Division considers large reservoirs to be those with a normal storage capacity of 5,000 acre-feet or more, or with a maximum storage capacity of 25,000 acre-feet or more.) The resulting data set was converted to an ArcView shape file using the ""Convert to Shapefile"" command. 33 dams that fell outside the 50 States were deleted (1 in Guam, 1 in the Trust Territories, and 31 in Puerto Rico), and 78 dams without coordinates were also deleted. Several misspelled county names were corrected, and the entries in the FIPS_cnty (County FIPS) field were cleaned up. For all dams with a valid county name but no County FIPS, the FIPS code was added based on the listed county name. If two county names were given, the FIPS code used was for the first one listed, or for the county in the listed State. Where the county name was invalid or missing, the county was determined by comparing the dam location to the National Atlas counties file. If the dam fell on a State line, the county name and FIPS code used were those appropriate for the listed State. The shape file was converted to an Arc/Info coverage and then converted to NAD 83 for display purposes. The result was then converted back to shapefile format." proprietary +UTC_hydrography Hydrographic Features of the United States CEOS_EXTRA STAC Catalog 1995-01-01 1999-12-31 -177.1, 17, -64, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231551647-CEOS_EXTRA.umm_json The data set portrays the polygon and line water features of the United States, Puerto Rico, and the U.S. Virgin Islands. The file was produced by joining the individual State hydrographic layers from the 1:2,000,000- scale Digital Line Graph (DLG) data produced by the USGS. This is a revised version of the March 1999 data set. These data are intended for geographic display and analysis at the national level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:2,000,000-scale data. No responsibility is assumed by the U.S. Geological Survey in the use of these data. proprietary +UTC_landpolygonfeatures Federally Owned Land Polygon Features of the United States CEOS_EXTRA STAC Catalog 1972-01-01 2000-12-31 -179.13339, 17.674692, 179.78821, 71.3407 https://cmr.earthdata.nasa.gov/search/concepts/C2231554023-CEOS_EXTRA.umm_json This data set consists of federally owned land polygon features of the United States. The data set was created by extracting federal land polygon features from the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey. These files were then appended into a single coverage. This is a revised version of the June 1998 data set. There may be private in holdings within the boundaries of Federal Lands in this data set. These data are intended for geographic display and analysis at the national level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:2,000,000-scale data. No responsibility is assumed by the U.S. Geological Survey in the use of these data. proprietary UTC_majorgeologicunits Boundaries and Tags for Major Geologic Units in the United States CEOS_EXTRA STAC Catalog 1998-03-01 1999-06-01 -122.21224, 30.833223, -72.71876, 44.773125 https://cmr.earthdata.nasa.gov/search/concepts/C2231551856-CEOS_EXTRA.umm_json This data set contains boundaries and tags for major geologic units in the conterminous United States. In addition to the polygons representing the areal extent of geologic units, it identifies boundaries of metamorphic provinces, major faults, calderas, impact structures, and the limits of continental glaciation. The data depict the geology of the bedrock that lies at or near the land surface, but not the distribution of surficial materials such as soils, alluvium, and glacial deposits. The data are generalized from a compilation prepared for use in the Geologic Map of North America, to be published in hard copy by the Geological Society of America and released as a digital file by the U.S. Geological Survey. These data have been prepared with a degree of detail appropriate for viewing at a scale of 1:7,500,000. Because of the degree of generalization required (generalization based on compilation scale), the data are intended primarily for display and for regional and national analysis, rather than for more detailed analysis in specific areas. No responsibility is assumed by the U.S. Geological Survey in the use of these data. proprietary UV_DMS_MINICOSM_1 DMS, DMSP, DMSO measurements Minicosm Experiment, Davis 2002-03 AU_AADC STAC Catalog 2002-11-12 2003-06-26 77.82166, -68.62655, 78.33801, -68.5222 https://cmr.earthdata.nasa.gov/search/concepts/C1214311421-AU_AADC.umm_json Dimethylsulfide and its precursors and derivatives constitute a major sulfate aerosol source. This dataset incorporates the potential for increased UV radiation effects due to stratospheric ozone depletion over spring and summer in Antarctica, using large-scale incubation systems and 13-14 day incubation periods. Surface seawater (200 micron filtered) from the Davis coastal embayment was incubated during four experiments over the 2002-03 Antarctic Summer. The data incorporates seawater measurements of DMS, DMSP and DMSO over a temporal progression during each incubation experiment. Six polyethylene tanks of varying PAR and UV irradiances were incubated. Water was collected stored and analysed by gas chromatography according to a specific sampling protocol, employed by all investigators associated with the project. The data are organised according to analysis day, with each days calibration data displayed at the top of each sheet. The sample code is followed by GC run number and then the raw count data from the GC. This is calculated to nanomoles DMS, DMSP or DMSO. Sample Codes: Codes for temporal data follow format X.XXXX 1st X gives experiment number, 1 to 4. 2nd X gives sampling day, 0, 0.5, 1, 2, 4, 7, 14 (will result in digit code for day no. less than 10 3rd X gives tank number relating to irradiance level(one to six) 4th and 5th X is replicate number, (01, 02, 03, DMS), (04, 05,06, DMSP total), (07, 08, 09, DMSP dissolved), (10, 11, 12, DMSO total). The fields in this dataset are: Sample Code Run Number from the GC Counts - GC generated raw data Log Counts - logarithmic conversion of the count data Log -c - logarithmic conversion minus the y-intercept determined by calibration of the GC. (log -c)/m - log -c divided by m, determined by calibration of the GC. ngS anti log - nanograms of Sulfur NaOH - NaOH adjustment ngS/L - adjustment per litre nM-DMSP/L - nanoMol's DMSP per litre nm-DMS/L - nanoMol's DMS per litre September 2013 Update: DMSO was analysed in these experiments according to an adaptation of the sodium borohydride (NaBH4) reduction method of Andreae (1980). The method has since been superseded and the data here probably displays inaccuracies as a result of the analytical method used. This DMSO data should be treated with caution. proprietary UV_Davis_1 Erythemal UV radiation at Davis station summer 1998 AU_AADC STAC Catalog 1998-01-08 1998-01-15 78.12378, -68.63655, 78.37097, -68.52429 https://cmr.earthdata.nasa.gov/search/concepts/C1214311420-AU_AADC.umm_json Data provides the date, time and 10 min cumulative erythemal irradiance at Davis Station for downwelling solar radiation and the irradiance at depth beneath neutral density screen (ND-polythene) and 3.3, 5.5, and 9.0 mm borosilicate glass. These light treatments simulated water column depths of 1.0, 2.0, 3.0, and 3.6 m depth (calculated using Beer's Law and the average UV transmittance of Antarctic seawater at 4 ice-edge sites). A no erythemal UV control (transmittance greater than 375 nm) was also used in which samples were incubated beneath UV-stabilised polycarbonate. The fields in this dataset are: date Davis solar time downwell wnd w3.3 w5.5 w9 proprietary @@ -14217,6 +14909,7 @@ Umiat_Veg_Plots_1370_1 Arctic Vegetation Plots at Umiat, North Slope, Alaska, 19 Unalaska_Veg_Plots_1375_1 Arctic Vegetation Plots on Unalaska Island, Aleutian Islands, Alaska, 2007 ORNL_CLOUD STAC Catalog 2007-08-03 2007-08-27 -166.52, 53.84, -166.44, 53.91 https://cmr.earthdata.nasa.gov/search/concepts/C2170969919-ORNL_CLOUD.umm_json This data set provides environmental, soil, and vegetation data collected during August 2007 from 69 study plots at the Unalaska Island research site, and one plot on Amaknak Island. The study sites are within the eastern Aleutian Islands, Alaska, USA. Data includes the plot information for vegetation, soils, and site characteristics for the study plots subjectively located in 11 plant communities that occur in six broad habitat types. Specific attributes include: dominant vegetation species and cover, soil chemistry, moisture, organic matter, topography, and elevation. Cover-abundance was estimated for all vascular plants, bryophytes, and macrolichens according to the nine-point ordinal scale of Westhoff and van der Maarel (1973). proprietary Uncertainty_US_Coastal_GHG_1650_1 Coastal Wetland Elevation and Carbon Flux Inventory with Uncertainty, USA, 2006-2011 ORNL_CLOUD STAC Catalog 2006-01-01 2011-12-31 -135.03, 20.38, -56.66, 48.99 https://cmr.earthdata.nasa.gov/search/concepts/C2389101052-ORNL_CLOUD.umm_json This dataset provides maps of coastal wetland carbon and methane fluxes and coastal wetland surface elevation from 2006 to 2011 at 30 m resolution for coastal wetlands of the conterminous United States. Total coastal wetland carbon flux per year per pixel was calculated by combining maps of wetland type and change with soil, biomass, and methane flux data from a literature review. Uncertainty in carbon flux was estimated from 10,000 iterations of a Monte Carlo analysis. In addition to the uncertainty analysis, this dataset also provides a probabilistic map of the extent of tidal elevation, as well as the geospatial files used to create that surface, and a land cover and land cover change map of the coastal zone from 2006 to 2011 with accompanying estimated median soil, biomass, methane, and total CO2 equivalent annual fluxes, each with reported 95% confidence intervals, at 30 m resolution. Land cover was quantified using the Coastal Change Analysis Program (C-CAP), a Landsat-based land cover mapping product. proprietary Understory_Veg_Biomass_Alaska_2340_1 Understory Vegetation Biomass from Selected Burned and Unburned sites in Alaska ORNL_CLOUD STAC Catalog 2016-08-09 2018-07-11 -150.28, 63.88, -146.56, 68.99 https://cmr.earthdata.nasa.gov/search/concepts/C3140252003-ORNL_CLOUD.umm_json This dataset provides measurements of vegetation biomass from 11 locations across Alaska during 2016 to 2018. Vegetation was harvested from plots that were located at the end of previously established 30-m transects at each site, except at one site where plots were randomly selected. Vascular vegetation was clipped from 50 cm x 50 cm plots, and non-vascular vegetation was clipped from 25 cm x 25 cm plots. All harvested vegetation was sorted by functional group or by species where identification was possible. The sorted vegetation was dried and then weighed to determine biomass. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma separated values (CSV) format. proprietary +VATECH_VAdust Dust Deposition in Southern Nevada and California CEOS_EXTRA STAC Catalog 1984-01-01 1989-12-31 -118, 32.5, -114, 38.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231548569-CEOS_EXTRA.umm_json Dust samples taken annually for five years from 55 sites in southern Nevada and California provide an unparalleled source of information on modern rates of dust deposition, grain size, and mineralogical and chemical composition. The relations of modern dust to climatic factors, type and lithology of dust source, and regional wind patterns shed new light on the processes of dust entrainment and deposition. The average silt-plus-clay flux in southern Nevada and southeastern California ranges from 4.3 to 15.7 g/m2/yr, but in southwestern California the average flux is as high is 30 g/m2/yr. These rates are generally less than those of previous studies in the arid southwestern United States, probably due to differences in measurement techniques (other studies mostly used traps at lower heights and did not exclude bird- derived sediment). The climatic factors that affect dust flux interact with each other and with the factors of source type, source lithology, geographic area, and human disturbance. For example, average dust flux increases with mean annual temperature but is only weakly related to decreases in mean annual precipitation, because the prevailing winds bring dust to relatively wet areas. In contrast, annual dust flux mostly reflects changes in annual precipitation rather than temperature. Although playa and alluvial sources emit about the same amount of dust per unit area, the volume of dust from the more extensive alluvial sources is much larger. In addition, playa and alluvial sources respond differently to annual changes in precipitation. Most playas emit dust that is richer in soluble salts and carbonate than that from alluvial sources (except carbonate-rich alluvial fans), but the dust-deposition rates do not reflect this trend: salt flux tends to be larger in mountain ranges, and gypsum flux parallels carbonate flux. Gypsum dust may be produced by the interaction of carbonate dust and anthropogenic sulfates. Cultivated areas generally yield about 20 percent more dust than uncultivated areas. The dust flux in an arid urbanizing area may be as much as twice that before disturbance, but decreases when construction stops. The mineralogic and major-oxide composition of the dust samples indicate that sand and some silt is locally derived and deposited, whereas clay and some silt from different sources can be far-travelled. Dust deposited in the Transverse Ranges of California by the Santa Ana winds appears to be mainly derived from sources to the north and east. The sampling design for this study was not statistically based; rather, sites were chosen to provide data on dust influx at soil-study sites and to answer specific questions about the relations of dust to local source lithology and type, distance from source, and climate. Some sites were chosen for their proximity to potential dust sources of different lithologic composition (for example, playas versus granitic, calcic, or mafic alluvial fans). Other sites were placed along transects crossing topographic barriers downwind from a dust source. These transects include sites east of Tonopah (43-46) crossing the rhyolitic Kawich Range, sites downwind of northern (40, 35, 36) and central Death Valley (38, 39, 11-14) crossing the mixed-lithology Grapevine and Funeral Mountains, respectively, and sites downwind of Desert Dry Lake crossing the calcareous Sheep Range (47-50) north of Las Vegas. In addition, some sites were chosen for their proximity to weather stations. Specific locations for dust traps were chosen on the basis of the above criteria plus accessibility, absence of dirt roads or other artificially disturbed areas upwind, and inconspicuousness. The last factor is important because the sites are not protected or monitored; hence, most sites are at least 0.5 mile from a road or trail. Despite these precautions, dust traps are sometimes tampered with, often violently. This is a particular problem in areas close to population centers, and most of these sites (52-55 near Los Angeles and 17-19 and 22 near Las Vegas) have been abandoned. A few other sites, mostly those that appeared to be greatly influenced by nearby farming (20, 21, and 41), were eliminated in 1989. Dust traps were also generally placed in flat, relatively open areas to mitigate wind-eddy effects created by tall vegetation or topographic irregularities. The 55 sites established in 1984 and 1985 were sampled annually through 1989 in order to establish an adequate statistical basis to calculate annual dust flux. Sampling continues at 37 of these sites (many sites now have two or more dust traps) every two or three years as opportunity and funding permit. proprietary VBEMI2AE_002 MISR Level 2 TOA/Cloud Aerosol Product subset for the VBBE region V002 LARC STAC Catalog 2007-08-01 2007-11-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1626189758-LARC.umm_json Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Aerosol Product subset for the VBBE region V002 contains Aerosol optical depth and particle type, with associated atmospheric data. proprietary VBEMI2LS_002 MISR Level 2 Land Surface Product subset for the VBBE region V002 LARC STAC Catalog 2007-08-01 2007-11-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1626189760-LARC.umm_json Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 Land Surface Product subset for the VBBE region V002 contains information on land directional reflectance properties; albedos (spectral and photosynthetically active radiation (PAR) integrated); fraction of absorbed photosynthetically active radiation (FPAR); asssociated radiation parameters; and terrain-referenced geometric parameters. proprietary VBEMI2ST_002 MISR Level 2 TOA/Cloud Stereo Product subset for the VBBE region V002 LARC STAC Catalog 2007-07-24 2007-11-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1626189762-LARC.umm_json Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Stereo Product subset for the VBBE region V002 contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, Reflecting Level Reference Altitude (RLRA), with associated data. proprietary @@ -14740,7 +15433,12 @@ Vulcan_V3_Hourly_Emissions_1810_1 Vulcan: High-Resolution Hourly Fossil Fuel CO2 WACS2_0 Western Atlantic Climate Study II OB_DAAC STAC Catalog 2014-05-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360697-OB_DAAC.umm_json Sea spray aerosol (SSA) impacts the Earth’s radiation budget indirectly by altering cloud properties including albedo, lifetime, and extent, and directly by scattering solar radiation. Characterization of the properties of SSA in its freshly emitted state is needed for accurate model calculations of climate impacts. In addition, simultaneous measurements of surface seawater are required to assess the impact of ocean properties on sea spray aerosol and to develop accurate parameterizations of the SSA number production flux for use in regional and global scale models.Sea spray aerosol (SSA) impacts the Earth’s radiation budget indirectly by altering cloud properties including albedo, lifetime, and extent, and directly by scattering solar radiation. Characterization of the properties of SSA in its freshly emitted state is needed for accurate model calculations of climate impacts. In addition, simultaneous measurements of surface seawater are required to assess the impact of ocean properties on sea spray aerosol and to develop accurate parameterizations of the SSA number production flux for use in regional and global scale models. proprietary WAF_DEALIASED_SASS_L2_1 SEASAT SCATTEROMETER DEALIASED OCEAN WIND VECTORS (Wentz et al.) POCLOUD STAC Catalog 1978-07-07 1978-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2617197640-POCLOUD.umm_json Contains Seasat-A Scatterometer (SASS) wind vector measurements for the entire Seasat mission, from July 1978 until October 1978. The data are global and presented chronologically in by swath. Each record contains data binned in 100 km cells. No wind vectors are computed for the cells along the left and right edges of the swath. Wind direction ambiguities are resolved using a global weather prediction model. This complete dataset is the result of the reprocessing efforts on behalf of Frank Wentz, Robert Atlas, and Michael Freilich. proprietary WARd0002_108 Administration Division Maps Of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232846827-CEOS_EXTRA.umm_json Administration division of Poland created on a basis of digitization with manual generalisation proper for specific scales. Projection Albers; points and polygons; ARC/INFO and SINUS systems proprietary +WARd0004_108 Land Use Division Maps of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232848834-CEOS_EXTRA.umm_json Land use map of Poland acquisited form interpreted Landsat TM, MSS images by digitization. 24 classes of land use grouped in subjects (agriculture, grass lands, settlements and communication areas, forests, surface waters, industry, not used areas). Vector and raster format; projection Albers; ARC/INFO and SINUS systems proprietary +WARd0005_108 Geomorphology Forms of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232848304-CEOS_EXTRA.umm_json Geomorphological forms of Poland created within Central Scientific Programme 10.4/1989. Digitized from the map of relief types in Poland; Scale 1:1 000 000. proprietary +WARd0006_108 Hunting Unit Border Maps of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232849207-CEOS_EXTRA.umm_json Borders of hunting units digitized from the maps prepared by Polish Hunting Association within Central Scientific Programme 10.4/1989. proprietary WARd0010_108 Catchments Division Maps of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232848090-CEOS_EXTRA.umm_json "Four-level hydrographic division of Poland prepared in accordance to a new scheme of catchment division elaborated by the Institute of Meteorology and Water Management (IMGW). Scanned from the ""Hydrological Atlas of Poland""." proprietary +WARd0011_108 Ecological Hazards in Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232847578-CEOS_EXTRA.umm_json Ecological hazards digitized from the map of protected landscape. proprietary +WARd0012_108 Digitized Maps of Main Cities in Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232846804-CEOS_EXTRA.umm_json Main cities in Poland digitized from the Review Map of Poland proprietary WATVP_D3_VIIRS_SNPP_1 VIIRS/SNPP Water Vapor Level-3 daily 0.5 x 0.5 degree grid LAADS STAC Catalog 2012-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1682045802-LAADS.umm_json The VIIRS/SNPP Water Vapor Level-3 daily 0.5 x 0.5 degree grid Product provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and Cross-track Infrared Sounder (CrIS) plus Advanced Technology Microwave Sounder (ATMS) water vapor soundings to continue the depiction of global moisture at a higher spatial resolution started with MODIS on the Terra and Aqua platforms. Level-3 global 0.5 degree by 0.5 degree spatial resolution daily mean data products (called WATVP_D3_VIIRS_SNPP) is derived by using a gridding software (called Yori) developed at the University of Wisconsin, Madison, Space Science and Engineering Center (Veglio et al., 2018), and implemented by the NASA VIIRS Atmosphere Science Investigator-led Processing System (SIPS). The Yori has been adapted for the VIIRS TPW products and is processed using the VIIRS Level-2 Water Vapor products (WATVP_L2_VIIRS_SNPP) separated by day and night. The mean and the standard deviation of each Level-2 water vapor product are calculated for each grid cell. The sum, the square of the sum of each product, and the number of pixels in the cells are also stored in the Level-3 (daily) output files for further aggregation purposes. proprietary WATVP_L2_VIIRS_SNPP_1 VIIRS/SNPP Level-2 Water Vapor Products 6-min Swath 750m LAADS STAC Catalog 2012-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1681179895-LAADS.umm_json The Suomi NPP VIIRS Water Vapor Products provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and CrIS plus ATMS water vapor soundings to continue the depiction of global moisture at high spatial resolution started with MODIS on the Terra and Aqua platforms. While MODIS has two water vapor channels within the 6.5 μm H2O absorption band and four channels within the 15 micrometer CO2 absorption band, VIIRS has no channels in either IR absorption band. The VNPWATVP algorithm is similar to the MODIS MOD07 synthetic regression algorithm. It uses the three VIIRS longwave IR window bands in a regression relation and adds the NUCAPS (CrIS+ATMS) water vapor product to compensate for the absence of VIIRS water vapor channels. The Level-2 6-minute granule and 750 m spatial resolution VIIRS TPW product file includes the collocated NUCAPS background TPW, the VIIRS-only TPW, and VIIRS+NUCAPS TPW retrievals with quality flags. proprietary WATVP_M3_VIIRS_SNPP_1 VIIRS/SNPP Water Vapor Level-3 monthly 0.5 x 0.5 degree grid LAADS STAC Catalog 2012-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1682050863-LAADS.umm_json The VIIRS/SNPP Water Vapor Level-3 monthly 0.5 x 0.5 degree grid Product provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and Cross-track Infrared Sounder (CrIS) plus Advanced Technology Microwave Sounder (ATMS) water vapor soundings to continue the depiction of global moisture at a higher spatial resolution started with MODIS on the Terra and Aqua platforms. Level-3 global 0.5 degree by 0.5 degree spatial resolution daily mean data products (called WATVP_M3_VIIRS_SNPP) is derived by using a gridding software (called Yori) developed at the University of Wisconsin, Madison, Space Science and Engineering Center (Veglio et al., 2018), and implemented by the NASA VIIRS Atmosphere Science Investigator-led Processing System (SIPS). The Yori has been adapted for the VIIRS TPW products and is processed using the VIIRS Level-2 Water Vapor products (WATVP_L2_VIIRS_SNPP) separated by day and night. The mean and the standard deviation of each Level-2 water vapor product are calculated for each grid cell. The sum, the square of the sum of each product, and the number of pixels in the cells are also stored in the Level-3 (monthly) output files for further aggregation purposes. proprietary @@ -14752,6 +15450,8 @@ WBD_Marine_Mammals_1 Marine Mammals of the World - World Biodiversity Database C WBD_Planarians_1 Marine Planarians of the World - World Biodiversity Database CD-ROM Series AU_AADC STAC Catalog 1794-01-01 1996-12-31 -180, -80, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214314113-AU_AADC.umm_json This metadata record has been created to describe a commercial CD product of the Expert Center for Taxonomic Identification. This CD has in no way been produced by the Australian Antarctic Division (AAD), and the metadata record is only intended as a reference for AAD employees. From the CD booklet: This CD-ROM volume is dedicated to Marine Planarians of the World and is based upon work by Dr Ronald Sluys of the Expert Center for Taxonomic Identification. It comprises a complete guide to all species in this Turbellarian group. It is based on the treatise 'A Monograph on Marine Triclads'. The introduction section of the program describes the general characteristics of Turbellarian flatworms and provides the user with details about the morphology, ecology and anatomy of the Maricola. The higher taxa section gives information on 58 higher taxa, which are described in a similar way to the species. In total, 404 drawings and pictures have been added to the CD-ROM, giving a complete and detailed overview of all taxa described. The Expert Center for Taxonomic Identification (ETI) is a Non-governmental Organisation (NGO) in operational relation with UNESCO, dedicated to the production of scientific and educational computer software for the preservation of knowledge concerning the worlds plants and animals. ETI is supported by The Netherlands Government, the University of Amsterdam, UNESCO, and other international organisations. It cooperates with major scientific institutes around the world. proprietary WCMC_149 Biodiversity Data Sourcebook from the World Conservation Monitoring Centre (WCMC) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848848-CEOS_EXTRA.umm_json This sourcebook of biodiversity data was released in part as a contribution to the First Conference of the Parties to the Convention on Biological Diversity (Bahamas, 28 November - 9 December) in anticipation that it will provide information of interest and relevance. An extended introduction to many theoretical and applied aspects of biological diversity was provided in Global Biodiversity: status of the Earth's living resources (WCMC, 1992; funded largely by the UK Overseas Development Administration). That document, which benefitted from collaboration with many organisations and individual scientists, was produced at the time of the United Nations Conference on Environment and Development held in 1992 in Rio de Janeiro. The purpose of the book was to provide conceptual background and baseline data both to practitioners in the biodiversity field, and to all concerned persons who needed a guide into that complex and highly topical area. Given the grounding previously provided in Global Biodiversity, the present volume concentrates on data rather than text and provides an illustrative set of data tables, in part revised and expanded from the earlier volume. The choice of data to be included and the manner of presentation have been determined with the likely end-users borne strongly in mind. With this aim, most data are presented in standardised tables by country, so that they are immediately available to users working at a national level but can also be placed easily in regional and global contexts. Overall, they give a good indication of the global availability of information on many aspects of biodiversity, drawing attention to some of the gaps that exist and to the regional imbalances in the distribution of biodiversity and the resources that have been devoted to its assessment and study. proprietary WCMC_155 Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) CEOS_EXTRA STAC Catalog 1975-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848136-CEOS_EXTRA.umm_json "WCMC has provided services to the Convention on International Trade in Endangered Species CITES since 1980, computerising the trade records of species listed in the CITES Appendices, as reported by the Parties. This computer database is the largest of its kind, currently holding over 2 million records on trade in wildlife species and their derivative products. The information spans from 1975, when a mere 148 trade records were reported, to the present and is constantly being updated as further annual reports are received from CITES Parties. Since 1986 more than 200,000 trade records have been reported annually. In addition to the trade records, the database holds some 29,000 scientific names and synonyms. ""http://www.cites.org/"". The annual report data arrive in many different formats, ranging from copies of permits, hand-written or printed reports, to computer tapes, diskettes and electronic mail. The information is entered into the database, either manually or by direct electronic transfer, and customised translation programmes written in Perl at WCMC now enable automatic loading of most reports received on magnetic media. Now that WCMC is connected to the Internet, it is expected that many countries will be able to submit their information directly via the network, this process having already been successfully carried out by the Management Authority of Brazil. In order to investigate the further potential of this type of data collection WCMC has devised a questionnaire that the CITES Secretariat is circulating to all Party States. At the beginning of 1993 the trade data were transferred from a WANG computer to an Ingres relational database held on a Sunsparc 10/30. A large suite of custom-built programs allow sophisticated control, maintenance and manipulation of the data; for example, information on a species Appendix-listing is linked to the taxonomic file thus possible errors at the data input stage are reduced to a minimum. Current work at WCMC is linking the trade data with species distribution information and with the Centre's Geographical Information Systems (GIS), known as the Biodiversity Map Library, to ensure that the information, so laboriously collected, can be used in the best way to promote species conservation. Further links with information on national and international legislation may be possible in the future. In addition to input and maintenance of the trade data, WCMC collects information on protected areas, habitats and species of conservation concern, and can therefore provide comprehensive analyses and reports. The trade outputs usually comprise one of three standard formats: Gross/Net Trade Tabulation - will provide gross or net import/export data for a specified year(s), country, species and/or product, thus allowing yearly trends to be monitored. Comparative Tabulation - produces data from corresponding importing and exporting countries for a specific year, species, product, etc., thus allowing a comparison of the reporting between the two Parties and a chance to identify any potentially illegal trade. Annual Report - format will provide a complete printout of all CITES trade for a particular year reported by a specific CITES Party. Where Parties are unable to produce their own annual report, WCMC can produce one based on that country's returned permits. Regular requests for information from the database are made by the CITES Secretariat, Management and Scientific Authorities, the TRAFFIC Network, WWF, IUCN, Universities, NGO's, researchers, journalists and teachers, etc. With permission from the CITES Secretariat, WCMC can provide data in any of the above formats although a fee is charged to cover the production costs of the work. WCMC have carried out detailed analyses of the status and trade data have included the following: * selected species listed in Appendix II * Green and Hawksbill turtles * world trade in raw and worked ivory * Asian monitor lizards * South East Asian pythons * Crocodile farming and ranching LANGUAGE: English STATISTICAL INFORMATION: ACCESS AND DISTRIBUTION: WCMC makes information available through published media, through provision of datasets, and through the provision of either standard or customised reports. WCMC is committed to the principle of the free exchange of data with other institutions and users. In so far as is practical, it places its data in the public domain and encourages their wide distribution. However, costs may be incurred in accessing and distributing datasets, and where analysis and assessment provide an added-value service. Such costs are passed on to the user." proprietary +WCMC_157 East Africa Biodiversity Metadatabase from the World Conservation Monitoring Centre (WCMC) CEOS_EXTRA STAC Catalog 1992-01-01 28, -12, 42, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2232847994-CEOS_EXTRA.umm_json "Availability of Biodiversity Information for East Africa Launched in 1992 at the Conference Conservation of Biodiversity in Africa, Nairobi, the project represents a survey of the sources and types of information held on biodiversity for Kenya, Tanzania and Uganda by organisations both within and outside East Africa. The project came about in response to the need for a systematic review of data holdings for the region in support of conservation and sustainable development. This need has been subsequently underscored by provisions in the Convention on Biological Diversity calling for contracting parties to develop national strategies, plans or programmes, assisted by such baseline information. The study was a collaborative venture between the IUCN Regional Office for Eastern Africa (EARO), the World Conservation Monitoring Centre (WCMC), and key national institutions in each of the three countries. Funding was provided by The European Commission (B7-5040 Contract 92/11) and through a contract with the Food and Agriculture Organization of the United Nations, executing agency for a GEF/UNDP project entitled Institutional Support for the Protection of East African Biodiversity (UNO/RAF/006/GEF). Information for the study was collected using a standard questionnaire design, administered by means of face to face interviews for the majority of the 100 institutions surveyed within East Africa, by mailing the questionnaire to more than 1000 institutions outside the region, and by posting the questionnaire on ""News"", a global electronic bulletin board, with the potential of reaching another 1 million+ subscribers. A total of 350 questionnaires were completed and returned, the results of which were used in the production of the following outputs: The creation of a ""data sources"" database (metadatabase) of the sources and types of biodiversity information held for the region The production of a printed report including (1) summary information and analysis of results in terms of taxonomic and geographic coverage of biodiversity information; and (2) a catalogue of questionnaire entries Presentation of catalogue entries in Folio Views text-retrieval software, with accompanying User's Guide Important follow-up to this study includes forthcoming publication and distribution of hard-copy and electronic outputs, maintenance and updating of the database, ongoing provision of training in information collection and database use, and general support for biodiversity initiatives by promoting networking between institutions and accessibility to information. Consideration is being given to extending the study to other regions of Africa and elsewhere, and the experience gained from this initiative is being used in support of larger institutional capacity building projects currently being undertaken at WCMC. LANGUAGE: English STATISTICAL INFORMATION: ACCESS AND DISTRIBUTION: WCMC makes information available through published media, through provision of datasets, and through the provision of either standard or customised reports. WCMC is committed to the principle of the free exchange of data with other institutions and users. In so far as is practical, it places its data in the public domain and encourages their wide distribution. However, costs may be incurred in accessing and distributing datasets, and where analysis and assessment provide an added-value service. Such costs are passed on to the user." proprietary +WCMC_158 List of Threatened Animals from the World Conservation Monitoring Centre (WCMC) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848347-CEOS_EXTRA.umm_json As part of the species activities, WCMC maintains nomenclature, distribution and conservation status information on some 18,000 animal species and subspecies worldwide. WCMC stores animal information in a series of Foxpro database files which include data on single country endemics, globally threatened species, and species included on various International Conventions. These animal data are currently being converted from Foxpro to A-Rev. For further details of structure see Plant Species Database description. The basic data elements on species conservation include scientific and common names, distribution by country and conservation status. Additional information on population size, trends and habitat are sought wherever possible. For species subject to wildlife trade, information on levels of trade, impact on wild populations, protection and management measures are important. WCMC publishes the Red List of Threatened Animals in collaboration with IUCN and the Species Survival Commission. All the data are held on computer, including nomenclature, common names, distribution, conservation status, threats, etc. The Centre is actively seeking collaboration to prepare digital distribution maps for threatened animals and plants, but this work is at an early stage. LANGUAGE: English STATISTICAL INFORMATION: ACCESS AND DISTRIBUTION: WCMC makes information available through published media, through provision of datasets, and through the provision of either standard or customised reports. WCMC is committed to the principle of the free exchange of data with other institutions and users. In so far as is practical, it places its data in the public domain and encourages their wide distribution. However, costs may be incurred in accessing and distributing datasets, and where analysis and assessment provide an added-value service. Such costs are passed on to the user. proprietary WC_LSMEM_SOILM_025_001 AMSR-E/Aqua surface soil moisture (LSMEM) L3 1 day 0.25 degree x 0.25 degree V001 (WC_LSMEM_SOILM_025) at GES DISC GES_DISC STAC Catalog 2002-06-19 2011-09-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1242287803-GES_DISC.umm_json AMSR-E/Aqua surface soil moisture (LSMEM) L3 1 day 0.25 degree x 0.25 degree V001 is a global, 10-year (2002-2011) data set. It is created from soil moisture retrieved from passive microwave brightness temperatures measured by the 10.65 and 36.5 GHz radiometers on the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the NASA Aqua satellite. The retrieval algorithm is based on Princeton's Land Surface Microwave Emission Model (LSMEM), a physically-based radiative transfer model, and serves as the core algorithm in the estimation procedure. To retrieve surface soil moisture, two unknowns, the soil moisture and the effective vegetation optical depth, are simultaneously solved from two radiative transfer equations in LSMEM, one for the 10.65 GHz horizontally-polarized brightness temperature and the other for the 10.65 GHz vertically-polarized brightness temperature. The land surface temperature required in the estimation procedure is estimated from the 36.5 GHz vertically-polarized brightness temperature, using a regression relationship. This soil moisture product does not include areas covered by snow, so the snow model is not described. Also, the atmosphere is assumed to have constant brightness temperatures; therefore, the atmosphere model is also not described. proprietary WC_MULTISEN_PREC_025_001 TMI/TRMM precipitation and uncertainty (TMPA) L3 3 hour 0.25 degree x 0.25 degree V001 (WC_MULTISEN_PREC_025) at GES DISC GES_DISC STAC Catalog 1998-01-01 2010-12-31 -180, -50, 180, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1242287793-GES_DISC.umm_json TMI/TRMM precipitation and uncertainty (TMPA) L3 3 hour 0.25 degree x 0.25 degree V001 provides estimates of accumulated precipitation from the Tropical Rainfall Measuring Mission (TRMM) and Other Data Precipitation Data Set (TRMM 3B42; Huffman et al., 2007), along with estimates of the uncertainty in the TRMM 3B42 made by Bytheway and Kummerow (2013). The data set covers both ocean and land from 50 degree North to 50 degree South. proprietary WC_PM_ET_050_1 SRB/GEWEX evapotranspiration (Penman-Monteith) L4 3 hour 0.5 degree x 0.5 degree V1 (WC_PM_ET_050) at GES DISC GES_DISC STAC Catalog 1984-01-01 2007-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1371013470-GES_DISC.umm_json SRB/GEWEX evapotranspiration (Penman-Monteith) L4 3 hour 0.5 degree x 0.5 degree V1 is a global, 24-year (1984-2007), satellite-derived evapotranspiration over land data set. It is based on the Penman-Monteith model. Evapotranspiration provides the critical link between the water and energy cycles within the Earth system. Better representation of the spatial distribution and temporal development of surface evapotranspiration is needed not only to improve the description of water vapor exchanges for global water budget estimation but also to advance our understanding of the climate system. Input data sets include (1) vegetation index data, i.e., Leaf Area Index (LAI), derived from the Advanced Very High Resolution Radiometer (AVHRR) sensors onboard the NOAA-7, NOAA-9, NOAA-11, NOAA-14, and NOAA-16 satellites and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the EOS-Terra and EOS-Aqua satellites; (2) meteorology data from the latest version of the Princeton University global forcing data sets and from the Variable Infiltration Capacity (VIC) land surface model output; and (3) radiative data from the NASA Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget Project. proprietary @@ -14760,6 +15460,7 @@ WENTZ_SASS_SIGMA0_L2_1 SEASAT SCATTEROMETER BINNED 50KM SIGMA-0 DATA (Wentz) POC WHITECAPS_0 Influence of Whitecaps on Aerosol and Ocean-Color Remote Sensing OB_DAAC STAC Catalog 2011-02-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360700-OB_DAAC.umm_json The influence of whitecaps on ocean color and aerosol remote sensing from space were invistigated onboard the R/V Melville (MV1102) from Cape Town, South Africa to Valparaiso, Chile from February 2, 2011 to March 14, 2011. Satellite imagery has revealed relatively large amounts of aerosols and particulate organic and inorganic carbon in the Southern oceans, but it is not clear whether this is real or the result of not taking into account properly whitecap effects in the retrieval algorithms. By measuring whitecap optical properties and profiles of marine reflectance and backscattering and absorption coefficients, a bulk whitecap reflectance model will be developed. The measurements will allow comparisons of the aerosol optical thickness and marine reflectance one should retrieve (i.e., in the absence of whitecaps and bubbles) with the satellite-derived estimates. The parameters/variables that will be measured include whitecap coverage, surface reflectance, aerosol optical thickness, in situ profiles of marine reflectance, backscattering and attenuation coefficients, and particle size distribution, and absorption and backscattering coefficients and HPLC pigments from water samples. The backscattering and absorption measurements from water samples will characterize conditions without whitecaps. Cruise information can be found in the R2R repository: https://www.rvdata.us/search/cruise/MV1102. proprietary WILKS_2018_Chatham_sedimenttraps_specieslist_3 Diatom and coccolithophore assemblages from archival sediment trap samples of the Subtropical and Subantarctic Southwest Pacific AU_AADC STAC Catalog 1996-06-17 1997-05-07 174.90234, -45.39845, 179.73633, -40.71396 https://cmr.earthdata.nasa.gov/search/concepts/C1459701888-AU_AADC.umm_json "This spreadsheet contains species lists and counts from four sediment trap records. The sediment traps were deployed for ~1 year north and south of the Chatham Rise, New Zealand, between 1996 and 1997. Sheets 1a and 1b refer to North Chatham Rise (NCR). 1a = the 300m trap. 1b = the 1000m trap. Sheets 2a and 2b are for the South Chatham Rise traps (SCR). 2a= 300m, 2b= 1000m. Counting was undertaken on 1/16th splits. Material was cleaned of organics before diatom counting under light microscopy. Coccolith counting on uncleaned material was only undertaken at the 300m traps. Radiolarians and silicoflagellates were counted but not identified. Diatoms and coccoliths were counted along non-overlapping transects until 300 specimens had been counted per sample, or until 10 transects had been made. This dataset includes counts of diatom, coccolithophores, radiolarians and silicoflagellates for four sediment trap records- North Chatham Rise (NCR) and South Chatham Rise (SCR) at two trap depths each (300 m and 1000 m). It is intended as supplementary material to Wilks et al. 2018 (submitted) ""Diatom and coccolithophore assemblages from archival sediment trap samples of the Subtropical and Subantarctic Southwest Pacific."" Numbers are raw count per sample cup. Authorities are given. Coordinates of traps given in degrees, minutes and seconds." proprietary WIND_3DP 3-D Plasma and Energetic Particle Investigation on WIND SCIOPS STAC Catalog 1994-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214615352-SCIOPS.umm_json The main purpose of the Wind spacecraft is to measure the incoming solar wind, magnetic fields and particles, although early on it will also observe the Earth's foreshock region. Wind, together with Geotail, Polar, SOHO, and Cluster projects, constitute a cooperative scientific satellite project designated the International Solar Terrestrial Physics (ISTP) program which aims at gaining improved understanding of the physics of solar terrestrial relations. This experiment is designed to measure the full three-dimensional distribution of suprathermal electrons and ions at energies from a few eV to over several hundred keV on the WIND spacecraft. Its high sensitivity, wide dynamic range, and good energy and angular resolution make it especially capable of detecting and characterizing the numerous populations of particles that are present in interplanetary space at energies above the bulk of the solar wind particles and below the energies typical of most cosmic rays. Data consists of ion moments, energy spectra, electron spectra, electron and ion omni directional energy spectra. Data are available from SSL at University of California, Berkeley (http://sprg.ssl.berkeley.edu/wind3dp/esahome.html) and at the NSSDC CDAWeb (http://cdaweb.gsfc.nasa.gov/cdaweb/) proprietary +WIR_98_4105 Major-Ion, Nutrient, and Trace-Element Concentrations in the Steamboat Creek Basin CEOS_EXTRA STAC Catalog 1996-09-09 1996-09-13 -122.7, 42.3, -122.5, 43.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231554333-CEOS_EXTRA.umm_json In September 1996, a water-quality study was done by the U.S. Geological Survey, in coordination with the U.S. Forest Service, in headwater streams of Steamboat Creek, a tributary to the North Umpqua River Basin in southwestern Oregon. Field measurements were made in and surface-water and bottom-sediment samples were collected from three tributaries of Steamboat Creek-Singe Creek, City Creek, and Horse Heaven Creek-and at one site in Steamboat Creek upstream from where the three tributaries flow into Steamboat Creek. Water samples collected in Singe Creek had larger concentrations of most major-ion constituents and smaller concentrations of most nutrient constitu ents than was observed in the other three creeks. City Creek, Horse Heaven Creek, and Steamboat Creek had primarily calcium bicarbonate water, whereas Singe Creek had primarily a calcium sulfate water; the calcium sulfate water detected in Singe Creek, along with the smallest observed alkalinity and pH values, suggests that Singe Creek may be receiving naturally occurring acidic water. Of the 18 trace elements analyzed in filtered water samples, only 6 were detected-aluminum, barium, cobalt, iron, manganese, and zinc. All six of the trace elements were detected in Singe Creek, at concentrations generally larger than those observed in the other three creeks. Of the detected trace elements, only iron and zinc have chronic toxicity criteria established by the U.S. Environmental Protection Agency (USEPA) for the protection of aquatic life; none exceeded the USEPA criterion. Bottom-sediment concentrations of antimony, arsenic, cadmium, copper, lead, mercury, zinc, and organic carbon were largest in City Creek. In City Creek and Horse Heaven Creek, concentrations for 11 constituents--antimony, arsenic, cadmium, copper, lead, manganese (Horse Heaven Creek only), mercury, selenium, silver, zinc, and organic carbon (City Creek only)--exceeded concentrations considered to be enriched in streams of the nearby Willamette River Basin, whereas in Steamboat Creek only two trace elements--antimony and nickel--exceeded Willamette River enriched concentrations. Bottom-sediment concentrations for six of these constituents in City Creek and Horse Heaven Creek--arsenic, cadmium, copper, lead, mercury, and zinc--also exceeded interim Canadian threshold effect level (TEL) concentrations established for the protection of aquatic life, whereas only four constituents between Singe Creek and Steamboat Creek--arsenic, chromium, copper (Singe Creek only), and nickel--exceeded the TEL concentrations. The data set checked for the concentrations of major ions, nutrients, and trace elements in water and bottom sediments collected in the four tributaries during the low-flow conditions of September 9-13, 1996. Stream-water chemistry results were contrasted, and trace-element concentrations were compared with U.S. Environmental Protection Agency chronic aquatic life toxicity criteria. Bottom-sediment trace-element results were also contrasted and compared with concentrations considered to be enriched in streams of the nearby Willamette River Basin and to interim Canadian threshold level (TEL) concentrations established for the protection of aquatic life. The area of study was Headwater streams of Steamboat Creek, a tributary to the north Umpqua River Basin in southwestern Oregon Field measurements and surface-water and bottom-sediment samples at each of the four sites included streamflow, stream temperature, specific conductance, dissolved oxygen, pH, alkalinity, major ions in filtered water (8 constituents), low-level concentrations of trace elements in filtered water (18 elements), and trace elements and carbon in bottom sediment (47 elements). Stream temperature, specific conductance, dissolved oxygen, and pH were measured using a calibrated Hydrolab multiparameter unit. Because stream widths were less than 8 feet, field measurements were made only near the center of flow at 1 foot or less below water surface. The Hydrolab unit was calibrated at each site before and after sampling. Stream temperatures were recorded to the nearest 0.1 degree Centigrade; specific conductance to the nearest 1 microsiemen per centimeter at 25 degrees Centigrade ; dissolved oxygen to the nearest 0.1 milligrams per liter; and pH to the nearest 0.1 pH units. Measurements of streamflow were made in accordance with standard USGS procedures (Rantz and others, 1982). Alkalinity measurements were made on filtered water samples using an incremental titration method (Shelton, 1994), and results were reported to the nearest 1 milligram per liter as calcium carbonate (CaCO3). Water samples were collected using 1-liter narrow-mouth acid-rinsed polyethylene bottles from a minimum of eight verticals in the cross section, suing an equal-width-increment method described by Edwards and Glysson (1988), and composited into a 8-liter polyethylene acid-rinsed churn splitter. Sample and compositing containers were prerinsed with native water prior to sample collection. Water samples were collected using clean procedures as outlined by Horowitz and others (1994). Samples were chilled on ice from time of sample collection until analysis, except when samples were processed. Processing of the field samples was accomplished either in the mobile field laboratory or in an area suitably clean for carrying out the filtering and preservation procedures. Samples for major ions, nutrients, and trace elements in filtered water (operationally defined as dissolved) were passed through 0.45 micrometer pore-size capsule filters into polyethylene bottles using procedures outlined by Horowitz and others (1994). Filtered-water trace-element samples were preserved with 0.5 milliliter of ultra-pure nitric acid per 250 mL of sample; nutrient samples were placed in dark brown polyethylene bottles and were chilled for preservation. All chemical samples were shipped to the USGS National Water Quality Laboratory (NWQL) in Arvada, Colorado, for analysis according to methods outlined by Fishman (1993). The information for this metadata was taken from the Online Publications of the Oregon District at http://oregon.usgs.gov/pubs_dir/online_list.html . proprietary WISPMAWSON04-05_1 A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season AU_AADC STAC Catalog 2004-12-10 2005-04-25 62.18384, -67.68587, 63.40759, -67.47282 https://cmr.earthdata.nasa.gov/search/concepts/C1214314124-AU_AADC.umm_json Very little information is known about the distribution and abundance of Wilson's storm petrels at the regional and local scales. This dataset contains locations of Wilson's storm petrel nests, mapped in the Mawson region during 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile(ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in description of the shapefile. A text file also provide the attribute information (formatted for input into R statistical software). This work has been completed as part of ASAC project 2704 (ASAC_2704). Fields recorded Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred proprietary WLDAS_NOAHMP001_DA1_D1.0 WLDAS Noah-MP 3.6 Land Surface Model L4 Daily 0.01 degree x 0.01 degree Version D1.0 (WLDAS_NOAHMP001_DA1) at GES DISC GES_DISC STAC Catalog 1979-01-02 -124.925, 25.065, -89.025, 52.925 https://cmr.earthdata.nasa.gov/search/concepts/C2789781977-GES_DISC.umm_json The Western Land Data Assimilation System (WLDAS), developed at Goddard Space Flight Center (GSFC) and funded by the NASA Western Water Applications Office, provides water managers and stakeholders in the western United States with a long-term record of near-surface hydrology for use in drought assessment and water resources planning. WLDAS leverages advanced capabilities in land surface modeling and data assimilation to furnish a system that is customized for stakeholders’ needs in the region. WLDAS uses NASA’s Land Information System (LIS) to configure and drive the Noah Multiparameterization (Noah-MP) Land Surface Model (LSM) version 3.6 to simulate land surface states and fluxes. WLDAS uses meteorological observables from the North American Land Data Assimilation System (NLDAS-2) including precipitation, incoming shortwave and longwave radiation, near surface air temperature, humidity, wind speed, and surface pressure along with parameters such as vegetation class, soil texture, and elevation as inputs to a model that simulates land surface energy and water budget processes. Outputs of the model include soil moisture, snow depth and snow water equivalent, evapotranspiration, soil temperature, as well as derived quantities such as groundwater recharge and anomalies of the state variables. proprietary WOCE91_Chlorophyll_1 Chlorophyll a data collected on the 1991 WOCE voyage of the Aurora Australis AU_AADC STAC Catalog 1991-10-08 1991-10-26 136.393, -62.294, 154.937, -45.183 https://cmr.earthdata.nasa.gov/search/concepts/C1214314037-AU_AADC.umm_json Chloropyll a data were collected along the WOCE transect on voyage 1 of the Aurora Australis, during October of 1991. These data were collected as part of ASAC project 40 (The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms). proprietary @@ -14768,6 +15469,8 @@ WOV_PRYDZ_BIRD_COMMUNITIES_1 Community composition of seabirds in the Prydz Bay WRF_STILT_Footprints_Boston_1572_1 WRF-STILT Gridded Footprints for Boston, MA, USA, 2013-2014 ORNL_CLOUD STAC Catalog 2013-07-01 2014-12-31 -169.5, 10.5, -50.5, 69.5 https://cmr.earthdata.nasa.gov/search/concepts/C2517698238-ORNL_CLOUD.umm_json "This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) footprint data products for two receptors located in Boston, Massachusetts, USA, for July 2013 - December 2014. The data are gridded footprints on a 1-km grid congruent with the ACES emissions inventory. Meteorological fields from version 3.5.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the ""receptor"" location), to create the adjoint of the transport model in the form of a ""footprint"" field. The footprint, with units of mixing ratio, quantifies the influence of upwind surface fluxes on CO2 and CH4 concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume." proprietary WRF_STILT_Particles_Boston_1596_1 WRF-STILT Particle Trajectories for Boston, MA, USA, 2013-2014 ORNL_CLOUD STAC Catalog 2013-07-01 2014-12-31 -81.78, 34.51, -65.93, 49.19 https://cmr.earthdata.nasa.gov/search/concepts/C2517667717-ORNL_CLOUD.umm_json "This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data and footprint products for two receptors located in Boston, Massachusetts, USA, for July 2013 - December 2014. Meteorological fields from version 3.6.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the ""receptor"" location), to create the adjoint of the transport model in the form of a ""footprint"" field. The footprint, with units of mixing ratio (ppm) per surface flux (umol m-2 s-1), quantifies the influence of upwind surface fluxes on CO2 and CH4 concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. Footprints are provided for the two receptors at two temporal and spatial scales: three days of surface influence over the whole North American coverage area at 1-degree resolution and 24 hours of surface influence within a smaller region close to the measurement locations ('near field') at 0.1-degree resolution." proprietary WRIR_01_4005 Data Collection Methods, Quality Assurance Data, and Site Considerations for Total Dissolved Gas Monitoring, Lower Columbia River, Oregon and Washington, 2000. CEOS_EXTRA STAC Catalog 2000-03-01 2000-09-15 -124, 44.75, -120, 46.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552786-CEOS_EXTRA.umm_json Excessive total dissolved gas pressure can cause gas-bubble trauma in fish downstream from dams on the Columbia River. In cooperation with the U.S. Army Corps of Engineers, the U.S. Geological Survey collected data on total dissolved gas pressure, barometric pressure, water temperature, and probe depth at eight stations on the lower Columbia River from the John Day forebay (river mile 215.6) to Camas (river mile 121.7) in water year 2000 (October 1, 1999, to September 30, 2000). These data are in the databases of the U.S. Geological Survey and the U.S. Army Corps of Engineers. Methods of data collection, review, and processing, and quality-assurance data are presented in this report. The purpose of TDG monitoring is to provide USACE with (1) real-time data for managing streamflows and TDG levels upstream and downstream from its project dams in the lower Columbia River and (2) reviewed and corrected TDG data to evaluate conditions in relation to water-quality criteria and to develop a TDG data base model for modeling the effect of various management scenarios of stream flow and spill on TDG levels. Instrumentation at each fixed station consisted of a TDG probe, an electronic barometer, a data-collection platform (DCP), and a power supply. The TDG probe was manufactured by Hydrolab Corporation. The probe had individual sensors for TDG, temperature, and probe depth (unvented sensor). The TDG sensor consisted of a cylindrical framework wound with a length of Silastic (dimethyl silicon) tubing. The tubing was tied off at one end and the other end was connected to a pressure transducer. After the TDG pressure in the river equilibrated with the gas pressure inside the tubing (about 15 to 20 minutes), the pressure transducer produced a measure of the TDG presure in the River. The water-temperature sensor was a thermocouple. The barometer was contained in the display unit of the Model TBO-L, a total dissolved gas meter manufactured by Common Sensing, Inc. More information abou the TDG probe is provided by Tanner, D. Q. And Johnston and M.W. 2001. The fixed station monitors were calibrated every 2 weeks from March 10 to September 15, 2000, and every three weeks for the remainder of the year, at which time Warrendale and Bonneville forebay were the only sites in operation. The general procedure was to check the operation of the TDG probe in the field without disturbing it, replace the field probe with one that had just been calibrated in the laboratory, and then check the operation of the newly deployed field probe. The details of the laboratory calibration procedure are outlined in Tanner and Johnston, 2001. Information for this metadata was obtained from the Technical Reports of the Oregon District available at http://oregon.usgs.gov/pubs_dir/online_list.html . proprietary +WRIR_97_4268 Distribution of Dissolved Pesticides and Other Water Quality Constituents in Small Streams, and their Relation to Land Use, Willamette River Basin, Oregon CEOS_EXTRA STAC Catalog 1996-03-01 1996-11-30 -124, 43.5, -121.5, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2231555249-CEOS_EXTRA.umm_json Water quality samples were collected at sites in 16 randomly selected agricultural and 4 urban subbasins as part of Phase III of the Willamette River Basin Water Quality Study in Oregon during 1996. Ninety-five samples were collected and analyzed for suspended sediment, conventional constituents (temperature, dissolved oxygen, pH, specific conductance, nutrients, biochemical oxygen demand, and bacteria) and a suite of 86 dissolved pesticides. The data were collected to characterize the distribution of dissolved pesticide concentrations in small streams (drainage areas 2.6? 13 square miles) throughout the basin, to document exceedances of water quality standards and guidelines, and to identify the relative importance of several upstream land use categories (urban, agricultural, percent agricultural land, percent of land in grass seed crops, crop diversity) and seasonality in affecting these distributions. A total of 36 pesticides (29 herbicides and 7 insecticides) were detected basinwide. The five most frequently detected compounds were the herbicides atrazine (99% of samples), desethylatrazine (93%), simazine (85%), metolachlor (85%), and diuron (73%). Fifteen compounds were detected in 12?35% of samples, and 16 compounds were detected in 1?9% of samples. Water quality standards or criteria were exceeded more frequently for conventional constituents than for pesticides. State of Oregon water quality standards were exceeded at all but one site for the indicator bacteria E. coli, 3 sites for nitrate, 10 sites for water temperature, 4 sites for dissolved oxygen, and 1 site for pH. Pesticide concentrations, which were usually less than 1 part per billion, exceeded State of Oregon or U.S. Environmental Protection Agency aquatic life toxicity criteria only for chlorpyrifos, in three samples from one site; such criteria have been established for only two other detected pesticides. However, a large number of unusually high concentrations (1?90 parts per billion) were detected, indicating that pesticides in the runoff sampled in these small streams were more highly concentrated than in the larger streams sampled in previous studies. These pulses could have had short term toxicological implications for the affected streams; however, additional toxicological assessment of the detected pesticides was limited because of a lack of available information on the response of aquatic life to the observed pesticide concentrations. Six pesticides, including atrazine, diuron, and metolachlor, had significantly higher (p<0.08 for metolachlor, p<0.05 for the other five) median concentrations at agricultural sites than at urban sites. Five other compounds ?carbaryl, diazinon, dichlobenil, prometon, and tebuthiuron?had significantly higher (p<0.05) concentrations at the urban sites than at the agricultural sites. Atrazine, metolachlor, and diuron also had significantly higher median concentrations at southern agricultural sites (dominated by grass seed crops) than northern agricultural sites. Other compounds that had higher median concentrations in the south included 2,4-D and metribuzin, which are both used on grass seed crops, and triclopyr, bromacil, and pronamide. A cluster analysis of the data grouped sites according to their pesticide detections in a manner that was almost identical to a grouping made solely on the basis of their upstream land use patterns (urban, agricultural, crop diversity, percentage of basin in agricultural production). In this way inferences about pesticide associations with different land uses could be drawn, illustrating the strength of these broad land use categories in determining the types of pesticides that can be expected to occur. Among the associations observed were pesticides that occurred at a group of agricultural sites, but which have primarily noncropland uses such as vegetation control along rights-of-way. Also, the amount of forested land in a basin was negatively associated with pesticide occurrence proprietary +WRIR_99_4196 Inorganic Chemistry of Water and Bed Sediment in Selected Tributaries of the South Umpqua River, Oregon, 1998 CEOS_EXTRA STAC Catalog 1998-08-01 1998-09-01 -122.83, 42.66, -122.5, 43.33 https://cmr.earthdata.nasa.gov/search/concepts/C2231551961-CEOS_EXTRA.umm_json Ten sites on small South Umpqua River tributaries were sampled for inorganic constituents in water and streambed sediment. In aqueous samples, high concentrations (concentrations exceeding U.S. Environmental Protection Agency criterion continuous concentration for the protection of aquatic life) of zinc, copper, and cadmium were detected in Middle Creek at Silver Butte, and the concentration of zinc was high at Middle Creek near Riddle. Similar patterns of trace-element occurrence were observed in streambed-sediment samples.The dissolved aqueous load of zinc carried by Middle Creek along the stretch between the upper site (Middle Creek at Silver Butte) and the lower site (Middle Creek near Riddle) decreased by about 0.3 pounds per day. Removal of zinc from solution between the upper and lower sites on Middle Creek evidently was occurring at the time of sampling. However, zinc that leaves the aqueous phase is not necessarily permanently lost from solution. For example, zinc solubility is pH-dependent, and a shift between solid and aqueous phases towards release of zinc to solution in Middle Creek could occur with a perturbation in stream-water pH. Thus, at least two potentially significant sources of zinc may exist in Middle Creek: (1) the upstream source(s) producing the observed high aqueous zinc concentrations and (2) the streambed sediment itself (zinc-bearing solid phases and/or adsorbed zinc). Similar behavior may be exhibited by copper and cadmium because these trace elements also were present at high concentrations in streambed sediment in the Middle Creek Basin. proprietary WUS_UCLA_SR_1 Western United States UCLA Daily Snow Reanalysis V001 NSIDC_ECS STAC Catalog 1984-10-01 2021-09-30 -125, 31, -102, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2253727823-NSIDC_ECS.umm_json This Western United States snow reanalysis data set contains daily estimates of posterior snow water equivalent (SWE), fractional snow-covered area (fSCA) and snow depth (SD) at 16 arc-second (~500 m) resolution from water years 1985 to 2021. This data set was developed to be compared to SnowEx data sets but its utility reaches beyond that since its spatial and temporal bounds extend over the entire Western U.S. and over several decades. proprietary WV01_Pan_L1B_1 WorldView-1 Level 1B Panchromatic Satellite Imagery CSDA STAC Catalog 2007-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497387766-CSDA.umm_json The WorldView-1 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Panchromatic imagery is collected by the DigitalGlobe WorldView-1 satellite using the WorldView-60 camera across the global land surface from September 2007 to the present. Data have a spatial resolution of 0.5 meters at nadir and a temporal resolution of approximately 1.7 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program. proprietary WV02_MSI_L1B_1 WorldView-2 Level 1B Multispectral 8-Band Satellite Imagery CSDA STAC Catalog 2009-10-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497404794-CSDA.umm_json The WorldView-2 Level 1B Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-2 satellite using the WorldView-110 camera across the global land surface from October 2009 to the present. This satellite imagery is in the visible and near-infrared waveband range with data in the coastal, blue, green, yellow, red, red edge, and near-infrared (2 bands) wavelengths. It has a spatial resolution of 1.85m at nadir and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program. proprietary @@ -14834,6 +15537,7 @@ a0d9764a3068439b997c42928ef739d2_NA ESA Greenland Ice Sheet Climate Change Initi a1fdc436-0c81-43c4-93f0-b7b1abafe4da_NA Resourcesat-2 - Multispectral Images (LISS-IV) - Europe, Multispectral Mode FEDEO STAC Catalog 2004-01-18 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458025-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 23.5 km x 23.5 km IRS LISS-IV multispectral data provide a cost effective solution for mapping tasks up to 1:25'000 scale. proprietary a386504aa8ae492f9f2af04c109346e9_NA ESA Sea Level Climate Change Initiative (Sea_Level_cci): A database of coastal sea level anomalies and associated trends from Jason satellite altimetry from 2002 to 2018 FEDEO STAC Catalog 2002-01-15 2018-05-30 -30, -45, 160, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2548142874-FEDEO.umm_json This dataset contains 17-year-long (June 2002 to May 2018 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of six regions: Mediterranean Sea, Northeast Atlantic, West Africa, North Indian Ocean, Southeast Asia and Australia. Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. This dataset has been derived from the ESA SL_cci+ v1.1 dataset of coastal sea level anomalies (also available in the catalogue, DOI:10.5270/esa-sl_cci-xtrack_ales_sla-200206_201805-v1.1-202005), which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series. This large amount of coastal sea level estimates has been further analysed to produce the present dataset: it consists in a selection of 429 portions of satellite tracks crossing land for which valid sea level time series are provided at monthly interval together with the associated sea level trends over the 17-year time span at each along-track 20-Hz point, from 20 km offshore to the coast.The main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.This dataset has a DOI: https://doi.org/10.17882/74354 proprietary a6efcb0868664248b9cb212aba44313d_NA ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 2 aerosol products from MERIS (ALAMO algorithm), Version 2.2 FEDEO STAC Catalog 2008-01-01 2008-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142742-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 2 aerosol products from MERIS for 2008, using the ALAMO algorithm, version 2.2. The data have been provided by Hygeos.For further details about these data products please see the linked documentation. proprietary +a78f0eb5-a146-4129-9066-519378e22fd8_1 IUCN PROTECTED AREAS OF AFRICA CEOS_EXTRA STAC Catalog 1986-01-01 1986-01-01 -17.3, -34.6, 51.1, 38.2 https://cmr.earthdata.nasa.gov/search/concepts/C2232848904-CEOS_EXTRA.umm_json "The Protected Areas of Africa were provided by the International Union for the Conservation of Nature and Natural Resources (IUCN) - World Conservation Monitoring Center (WCMC) of Gland, Switzerland and Cambridge, UK, to UNEP/GRID-Geneva for digitization into computer form in 1986. The map was digitized in ARC/INFO and subsequently rasterized to a two-minute cell size in the ELAS software format. Today, the same data set resides at GRID on an IBM mainframe computer, but it has not been updated since the initial work was carried out.* The Protected Areas of Africa data set shows a series of 11 different types of parks, reserves and other unique areas which had some degree of protected status. The various types of Protected Areas are all shown as squares of varying size on a background map of Africa, with four square sizes which are proportional to the actual size of each area, and with center points approximately equal to the actual central location of each Protected Area. Thus, this data set is perhaps most useful for showing the general distribution of African Protected Areas by type, circa 1986. There are two versions of the Protected Areas of Africa data set (two data files) at UNEP/GRID-Geneva. Because there is significant overlap between the Protected Area squares, the first shows the various squares superimposed with the size of Protected Areas used as the criterion for which take precedence over others. The second version shows Protected Areas ranked by importance; that is, squares take precedence according to the order in which they appear in the legend, with the more highly ranked Protected Areas overlaying others of lower rank. Following is the legend which applies to the Protected Areas of Africa (categories were formulated by IUCN-WCMC): Values Category of Protected Area ------ -------------------------- 1 Scientific Reserve 2 National Park 3 National Monument 4 Wildlife Sanctuary 5 Protected Landscape 6 Resource Reserve 7 Anthropological Reserve 8 Multiple Use Management Area 9 Biosphere Reserve 10 World Heritage Site 11 Unclassified The Protected Areas data set from IUCN-WCMC covers the entire African continent at a spatial resolution of two minutes (120 seconds) of latitude/longitude, or approximately 3.7 kilometers. The data file consists of 2191 rows (lines/records) by 2161 columns (elements/pixels/ samples). Its upper-left or northwest corner origin is 38 degrees, 0 minutes and 45 seconds North latitude (38d 00' 45"" N), and -20 degrees, 1 minute and 15 seconds West longitude (-20d 01' 15"" W); and it extends to -35 degrees, 1 minute and 15 seconds South latitude (-35d 01' 15"" S), and 52 degrees, 0 minutes and 45 seconds East longitude (52d 00' 45"" E) at its terminal point in the lower-right or southeast corner. The data file comprises 4.74 Megabytes. The source of the Protected Area data is, as mentioned above, the International Union for the Conservation of Nature and Natural Resources (IUCN's) World Conservation Monitoring Center (WCMC) in Cambridge, UK. There is no published reference for this data set. * - Another more recent version of Protected Areas for Africa, with actual protected area boundaries, exists at UNEP/GRID-Nairobi. " proprietary a7b87a912c494c03b4d2fa5ab8479d1c_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change 1992-2014, v1.2 FEDEO STAC Catalog 1992-01-01 2014-12-31 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142730-FEDEO.umm_json This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from satellite (ERS‐1, ERS‐2, Envisat and Cryosat) radar altimetry. The ice mask is based on the GEUS/GST land/ice/ocean mask provided as part of national mapping projects, and based on 1980’s aerial photography. The data from ERS and Envisat are based on a 5‐year running average, using combined algorithms of repeat‐track (RT), along‐track (AT) or cross‐over (XO) algorithms, and include propagated error estimates. It is important to note that different processing algorithms were applied to the ERS‐1, ERS‐2, Envisat and CryoSat data; for details see the Product User Guide (PUG), available on the CCI website and in the documentation section here. For ERS‐1, the radar data were processed using a cross‐over algorithm (XO) only. For ERS‐2 data and Envisat data in repeat mode, a combination of RT and XO algorithms was applied, followed by filtering. For across‐mission (i.e. ERS‐2‐Envisat) combinations, and for Envisat operating in a drifting orbit, an AT and XO combination was applied (the difference between RT and AT algorithms is that AT use reference tracks and searches for data in the vicinity of this track). For CryoSat data a binning/gridding and plane fit method has been applied, following by weak filtering (0.05 degree resolution). proprietary a7e11745933a4f37b5aa1d4b23d71a83_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ADV algorithm), Version 2.31 FEDEO STAC Catalog 1995-06-01 2002-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142909-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ADV algorithm, version 2.31. Data are available for the period 1995-2002.For further details about these data products please see the linked documentation. proprietary a86bc574-3f22-44f8-a1f6-8d5bcc1c8a72_NA IRS-1C - Panchromatic Images (PAN) - Europe FEDEO STAC Catalog 1996-01-28 2004-01-31 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458068-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS PAN data provide a cost effective solution for mapping tasks up to 1:25'000 scale. proprietary @@ -15037,6 +15741,7 @@ b1f1ac03077b4aa784c5a413a2210bf5_NA ESA Sea Ice Climate Change Initiative (Sea_I b25d4a6174de4ac78000d034f500a268_NA ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v3.0 FEDEO STAC Catalog 1997-01-01 2019-12-31 -180, 30, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143089-FEDEO.umm_json This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m).Case A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year. proprietary b382ebe6679d44b8b0e68ea4ef4b701c_NA ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7 FEDEO STAC Catalog 1992-01-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143154-FEDEO.umm_json As part of the ESA Land Cover Climate Change Initiative (CCI) project a new set of Global Land Cover Maps have been produced. These maps are available at 300m spatial resolution for each year between 1992 and 2015.Each pixel value corresponds to the classification of a land cover class defined based on the UN Land Cover Classification System (LCCS). The reliability of the classifications made are documented by the four quality flags (decribed further in the Product User Guide) that accompany these maps. Data are provided in both NetCDF and GeoTiff format.Further Land Cover CCI products, user tools and a product viewer are available at: http://maps.elie.ucl.ac.be/CCI/viewer/index.php . Maps for the 2016-2020 time period have been produced in the context of the Copernicus Climate Change service, and can be downloaded from the Copernicus Climate Data Store (CDS). proprietary b431fbecf73c4442ad5d7bcf80929b03_NA ESA Ozone Climate Change Initiative (Ozone CCI): GOMOS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 FEDEO STAC Catalog 2002-01-01 2011-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143149-FEDEO.umm_json This dataset comprises gridded limb ozone monthly zonal mean profiles from the GOMOS instrument. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-GOMOS_ENVISAT-MZM-2008.nc” contains monthly zonal mean data for GOMOS in 2008. proprietary +b480d7c8-3694-4772-8294-941f3d3ede9f_1 European remote sensing forest/non-forest digital map CEOS_EXTRA STAC Catalog 1992-01-01 1993-09-28 -12, 38, 44, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2232847861-CEOS_EXTRA.umm_json "The European Remote Sensing Forest/Non-forest Digital Map was originally prepared for the European Space Agency (ESA) as a contribution to the World Forest Watch project of the International Space Year (ISY), 1992. The actual production of the map was carried out by a consortium of four companies, GAF mbH (Munich FRG), the Swedish Space Corporation (Kiruna), SCOT Conseil (France) and the National Land Survey of Finland (Helsinki). It is based entirely on the digital classification of NOAA/AVHRR-HRPT* one-kilometer resolution multispectral data, approximately 70 scenes from the summer periods only of 1990 to 1992. As such, the European Forest/Non-forest Digital Map is reasonably up-to- date and based on a homogeneous data source. Because the methodology used to produce the digital map is documented and was ""economically"" accomplished, the product is presumably replicable and could therefore be updated and/or used for monitoring purposes at scales of up to 1:2 million (ESA/ESTEC, 1992). The following steps are a summary of those actually used by the consortium in the production of the digital map: - Satellite data selection (minimal cloud cover)/acquisition; - Data pre-processing for a) geometric correction and b) cloud masking; - Data subset stratification into homogeneous spectral zones; - Data subset classification (Bayesian maximum likelihood); - Accuracy assessment (using classified Landsat MSS); - Mosaicking of classified data subsets; - Merging of final results and overlays; - Cartographic preparation. The producers of the digital map used only data from AVHRR channels 1, 2 and 3 with ""maximal geometric and radiometric resolution""; that is, the central 1200 to 1600 pixels of any given scan line, to map European forest areas greater than one square kilometer. Because the AVHRR sensor is not capable of distinguishing among different European forest types, many broad classes (Boreal, Central European and Mediterranean) are grouped together as ""forest"" in the digital map. * - the National Oceanic and Atmospheric Administration (NOAA) / satellite's Advanced Very High Resolution Radiometer (AVHRR) sensor - and High Resolution Picture Transmission (HRPT) data. For the 32 Landsat scenes compared with the NOAA/AVHRR forest/non-forest classification, the overall accuracy (percentage of pixels ""correctly"" classified) was calculated as 82.5%, and the surface area accuracy (degree of agreement in areal extent between the NOAA/AVHRR results and the Landsat MSS used as ""ground truth"") was found to be 93.8%. Format of the Original ESA/ESTEC-Provided Data Set The European Forest/Non-Forest Digital Map was provided to GRID on a single 150-Mb data cartridge, as a total of seven ARC/INFO-format data files for separate parts of the continent as follows: Northwest; North; Central; Southwest and Southeast Europe; the Commonwealth of Independent States (CIS, up to the Ural Mountains only); and North Africa. A total of 53 countries are included altogether. Within this original digital map, data are coded by country and category (i.e. forest, non-forest or water), but ""overall"" selections of one category or another are rendered difficult because the codes are in combination (i.e. country + category). Also, the large size of the seven individual ARC/INFO coverages all but prohibits working with the digital data for the entire pan-European area. Explanation of the Data Processing done by GRID GRID's objective in data processing of the European Forest/Non-forest Digital Map was to create a single seamless product covering most of the continent, for forestry and GIS studies at a pan-European level. The assemblage of the seven original individual coverages prepared for ESA/ESTEC into a single entity proved impractical due to both hardware and software limitations; thus, the seventh and largest portion for the Commonwealth of Independent States (CIS) was left out of the overall assemblage. Even so, it was still necessary to generalize the data somewhat, given the total number of polygons (>100000) and arcs (>170000) in the remaining six original coverages. Thus, the following methodology was followed to reduce the amount of data and assemble the six coverages into a single product (all data processing was done using commands in the ARC/INFO software): - Polygon elimination based on area - After several experiments, polygons with an area smaller than four square kilometers (sq. km.) were eliminated. This minimum area proved to be a good compromise between original forest patterns and number of polygons eliminated (total of 70%). The equivalent of four sq. km. at a central latitude within each of the six original coverages was calculated, and this value was used in the 'ELIMINATE' command. It would have been more accurate to perform the 'ELIMINATEs' with the data in an equal-area projection, but for practical reasons (space and time) they were not. - Assembling six coverages into one - The six coverages were put together using the 'MAPJOIN' command. The software limitation of a maximum 10000 arcs per polygon was circumvented by splitting the outer polygon of Europe into three separate parts. - Editing errors produced by step (2) - The 'MAPJOIN' command puts adjacent coverages together and recreates topology using an assigned distance known as the ""fuzzy tolerance"" factor. Any reasonable factor forces some lines to converge, creating dangling arcs and new polygons without IDs. As a result, interactive editing of the new coverage was necessary to delete dangling arcs, and to assign proper polygon IDs. - Update of the topology - After the modifications made in step (3), it was necessary to re-create the polygon topology using 'CLEAN'. - Addition of INFO item 'classes' - A new numeric item (format 3 3 I) was added in the polygon attribute table (.PAT) to contain the following values: 1) Forest; 2) Non-forest; and 3) Water. This item allows a user to select e.g. all of the European forested area polygons, as opposed to just those within a single country, in one simple INFO command. The European Forest/Non-forest data set is available from GRID as one ARC/INFO 'EXPORT'-format data file in the Geographic Projection, which covers an area from 20 to 80 degrees North latitude, and -30 degrees West to 60 degrees East longitude. The single data file ""EURO_FOR.E00"" comprises 77.25 Mb., but after being 'IMPORTed' to the equivalent ARC/INFO coverage, is reduced to 19.7 Mb in size. There is also the separate, original (non-generalized) data file which covers the CIS area alone; this additional 'EXPORT'-format data file ""CIS.E00"" comprises 68.262 Mb. Users who would prefer to have other original portions of the European Forest/Non-forest Digital Map listed above, as opposed to the GRID version documented herein, are requested to contact ESA/ESTEC at the address listed below. Reference and Source The source of the data set is the ESA/ESTEC ISY Office*, as modified by UNEP/GRID-Geneva. The proper reference to the data set is ""ESA, 1992, Remote sensing forest map of Europe (brochure), ESA/ESTEC, 18 pages."" ESA/ESTEC also provides a paper entitled ""Digital data set of the remote sensing forest map of Europe; guidelines for data handling (as prepared by GAF-Munich in April 1993)"", which contains much useful information about their original digital data product and the seven individual data files they distribute as one entity. In addition, ESA/ESTEC distributes a paper map of the original product having the same name as above, at a scale of 1:6 000 000 (the paper map uses the Lambert Azimuthal Equal-Area projection). * - the European Space Agency/European Space Research and Technology Centre - the International Space Year; P. O. Box 299; 2200 AG Noordwijk; The Netherlands (Mr. K. Pseiner; fax = 01719-17400). " proprietary b64b1a0ad7874fb39791e99c57b944bc_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 3.1 FEDEO STAC Catalog 1997-09-03 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142812-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 3.1 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection). proprietary b673f41b-d934-49e4-af6b-44bbdf164367_NA AVHRR - Land Surface Temperature (LST) - Europe, Daytime FEDEO STAC Catalog 1998-02-23 -24, 28, 57, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2207458008-FEDEO.umm_json "The ""Land Surface Temperature derived from NOAA-AVHRR data (LST_AVHRR)"" is a fixed grid map (in stereographic projection ) with a spatial resolution of 1.1 km. The total size covering Europe is 4100 samples by 4300 lines. Within 24 hours of acquiring data from the satellite, day-time and night-time LSTs are calculated. In general, the products utilise data from all six of the passes that the satellite makes over Europe in each 24 hour period. For the daily day-time LST maps, the compositing criterion for the three day-time passes is maximum NDVI value and for daily night-time LST maps, the criterion is the maximum night-time LST value of the three night-time passes. Weekly and monthly day-time or night-time LST composite products are also produced by averaging daily day-time or daily night-time LST values, respectively. The range of LST values is scaled between –39.5°C and +87°C with a radiometric resolution of 0.5°C. A value of –40°C is used for water. Clouds are masked out as bad values. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/" proprietary bark-and-wood-boring-insects-in-pines_1.0 Infestation of Scots pines with different vitalities by bark and wood boring insects ENVIDAT STAC Catalog 2022-01-01 2022-01-01 7.5627136, 46.2249145, 7.8984833, 46.3184179 https://cmr.earthdata.nasa.gov/search/concepts/C2789814729-ENVIDAT.umm_json After a major dieback of Scots pines in the Valais, an inner Alpine valley in Switzerland, the colonization of differently vigorous pines by stem and branch insects was investigated to assess their role in tree mortality. At 2 locations, the needle loss (defoliation) of some 500 pine trees was assessed twice a year. Of these trees, 34-36 trees were cut each year between 2001-2005 across all defoliation classes. From each tree, two 75-cm bolts were cut from both the stem and thick branches. They were incubated in photo-eclectors (metal cabinets) set up in a greenhouse where the insects could develop under the bark. The emerged adults were collected in water-filled eclector boxes and identified to species level by specialists. Attack time was estimated from the development time of each insect species emerged. The colonisation densities of the trees were related to the transparency level of each host tree at the time of attack. proprietary @@ -15090,6 +15795,8 @@ book-of-abstracts-from-plans-to-land-change-dynamics-of-urban-regions_1.0 From P boreas_aeshrday_235_2 BOREAS AES Canadian Hourly and Daily Surface Meteorological Data, R1 ORNL_CLOUD STAC Catalog 1975-01-01 1997-01-01 -107.87, 52.17, -97.83, 57.35 https://cmr.earthdata.nasa.gov/search/concepts/C2759278030-ORNL_CLOUD.umm_json This data set contains hourly and daily meteorological data from 23 meteorological stations across Canada from January 1975 to January 1997. The surface meteorology parameters include: date, time, temperature, precipitation, snow, snow depth, sea level pressure, station pressure, dew point, wind direction, wind speed, dry and wet bulb temperature, relative humidity, cloud opacity and cloud amount. proprietary box_hill_ice_compression_1 Box Hill Ice Compression Tests AU_AADC STAC Catalog 1977-04-15 1982-03-15 144, -38, 145, -37 https://cmr.earthdata.nasa.gov/search/concepts/C1214308311-AU_AADC.umm_json "A series of ice compression tests were carried out by Jo Jacka in 1977, and again by J.S.Birch in 1979-82, all aimed at determining how ice reacted under different circumstances. For each series of experiments, five different ""Box Hill"" rigs were set up, and kept at -10C (1977) or -30 (1979) for the duration of the experiments. The experiments in 1977 came to an early end when the cold room being used failed. The setup and method for each experiment, along with the results, were recorded in log books and have been archived at the Australian Antarctic Division. Logbook(s): Glaciology Box Hill Compression Rig Experiments, Book 1 - Initial notes on setup, and recordings of early results for the 1977 experiments. Glaciology Box Hill Compression Rig Experiments, Book 2 - More results from 1977. Glaciology Ice Compression Logbook - Setup and results for the 1979 experiments." proprietary bratts_penguin_gis_1 Islands NE of Brattstrand Bluff penguin GIS dataset AU_AADC STAC Catalog 1981-11-01 1982-04-01 77, -69, 77, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313310-AU_AADC.umm_json "Aerial photography (35mm film) of penguin colonies was acquired over some islands north east of Brattstrand Bluff islands (Eric Woehler). The penguin colonies were traced, then digitised (John Cox), and saved as DXF-files. Using the ArcView extension 'Register and Transform' (Tom Velthuis), The DXF-files were brought into a GIS and transformed to the appropriate islands. Update May 2015 - This dataset has been rename from ""Brattstrand Bluff penguin GIS dataset"" to ""Islands NE of Brattstrand Bluff penguin GIS dataset"" to better describe the location of the colonies. The penguin colonies are on a small group of islands approximately 12km north east of Brattstrand Bluff. Latitude 69.148 south and longitude 77.268 east. The Data Centre does not have a copy of the original photographs or described GIS data. In May 2015, the Data Centre has attached the following to this record: The DXF file produced by John Cox by digitising the aerial photography. Note this document is not georeferenced. Four photographs taken in 2009 by Barbara Wienecke, Seabird Ecologist, showing penguin colonies on these islands. A shapefile exists of the digitised colonies. The digitising by Ursula Harris, Australian Antarctic Data Centre, was done by georeferencing the DXF drawing over unprocessed Quickbird Image 05NOV15042413-M1BS-052187281010_01_P002. It was done in two parts, the largest island and then the two smaller islands. This allowed for better matching. The accuracy of this data is unknown." proprietary +brdglsc0001 Great Lakes Commercial Fishing Database CEOS_EXTRA STAC Catalog 1929-01-01 2011-12-31 -93, 41, -76, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231554766-CEOS_EXTRA.umm_json The Great Lakes Commercial Fishing Database contains commercial fishing data from the United States. The states of Illinois, Indiana, Michigan, Minnesota, New York, Ohio, Pennsylvania, and Wisconsin gather monthly fishing reports and forward them to the Great Lakes Center. The database provides the fisherman's name, information about the vessel, the estimated weight and estimated dollar value. Methodology: The database is not a scientific one. The data is reported by individual licensed fishermen to each state juridsdiction. The states gather monthly fishing reports and forward them to the Great Lakes Science Center. GLSC then compiles all of the information into a database each year and produces an annual summary that is called the NOAA report. It is sent to the National Marine Fishery Service (NMFS) and is included with commercial fishing data from the entire United States into a publication. proprietary +brdlsc0007 Efficiency Of Adaptive Cluster Sampling for Estimating Density of Wintering Waterfowl CEOS_EXTRA STAC Catalog 1992-12-13 1992-12-15 -85, 25, -80, 30 https://cmr.earthdata.nasa.gov/search/concepts/C2231554639-CEOS_EXTRA.umm_json An evaluation of adaptive cluster sampling was based on a simulation experiment where samples were drawn from an enumeration of three species of waterfowl wintering in central Florida. The initial samples were taken either by simple random sampling or with probability proportional to available habitat. Efficiency of adaptive cluster sampling relative to simple random sampling was highest when 1) the within-network variance was close to the population variance, and 2) the final sampling fraction was close to the initial sampling fraction. The within-network variance is determined by the spatial distribution of the population, quadrat size, and the condition that determined when to adapt sampling. The final sampling fraction depends on the previous factors as well as the size and selection of the initial sample. Some combinations of these factors led to increased precision compared to simple random sampling and some did not. Geographic Description: Central Florida (5,000 km2). The study region extended 100 km east and 50 km north from the southwest corner at 0438000, 3056000 (Universal Transverse Mercator coordinates; zone 17). 1.5.2 Bounding Rectangle Coordinates Methodology: An effort was made to count every individual duck of the three waterfowl species in a 5,000 km2 area of central Florida by making systematic flights over the entire study region. Two biologists counted waterfowl from separate helicopters (Bell Jet Rangers) during 13-15 December, 1992 and used the LORAN-C and GPS systems to determine flock locations Field. proprietary brdpier0004 Aspects of the Life History and Foraging Ecology of the Endangered Akiapolaau CEOS_EXTRA STAC Catalog 1976-11-01 1982-01-31 -155, 19, -155, 19 https://cmr.earthdata.nasa.gov/search/concepts/C2231554551-CEOS_EXTRA.umm_json Relative abundance, breeding ecology, annual survival, home range, and foraging ecology of the Akiapolaau (Hemignathus munroi), an endangered Hawaiian honeycreeper, were studied on the island of Hawaii. The species is a specialist; Akiapolaau used koa (Acacia koa) for foraging much more than expected based on koa availability, and most Akiapolaau occurred in old-growth koa and ohia (Metrosideros polymorpha forests. Male Akiapolaau most often foraged on the trunks and large branches of koa, whereas females used small branches and twigs. The longer bill of males is apparently adapted to the greater bark thickness of larger branches. Lichen-covered and dead branches were preferred feeding sites. Akiapolaau showed serial monogamy and had a relatively low reproductive rate of 0.86 young per par per year, with a long parental dependency period. Home range sizes averaged 10.7 ha and did not differ between males and females. Annual survival for adults was 0.71. Avian diseases appear to restrict Akiapolaau to higher elevation forests where mosquitos are rare. Protection of remaining old-growth koa and ohia forests above the mosquito zone are critical to the survival of the species. Geographic Description: Akiapolaau were studied at five study sites on Hawaii: Keauhou Ranch (19.52, -155.33; 1,740 m elevation), Kilauea Forest (19.52, -155.32 1,630 m), Hamakua (19.78 -155.33; 1,770 m), Kau Forest (19.22, -155.65; 1,750 m), and a Dry Forest site (19.82, -155.55 1,865-2,800 m). 1.5.2 Bounding Rectangle Coordinates Methodology: 25-162 stations were surveyed each month at each study site using the variable circular-plot method with 8-minute counts (Scott et al. 1986) to obtain indices of Akiapolaau abundance. proprietary brdpier0006 Demography and Movements of the Endangered Akepa and Hawaii Creeper CEOS_EXTRA STAC Catalog 1976-11-01 1982-01-31 -155, 19, -155, 19 https://cmr.earthdata.nasa.gov/search/concepts/C2231555263-CEOS_EXTRA.umm_json Populations of the endangered Akepa (Loxops coccineus coccineus) and Hawaii Creeper (Oreomystis mana) at four sites on the island of Hawaii. Mean monthly density (+/-) of Akepa was 5.74 +/- 0.87, 1.35 +/-0.41, 0.96 +/- 0.13, and 0.76 +/- 0.12 Akepa/ha at Kau Forest, Hamakua, Keauhou Ranch, and Kilauea Forest study areas, respectively. Hawaii creepers were found at densities of 1.68 +/- 0.53, 1.79 +/- 0.42, 0.48 +/- 0.06, and 0.54 +/- 0.08 birds /ha, respectively , at the four study areas. Highest capture rates and numbers of birds counted from stations occurred from August through November and February through March. Hatching-year birds were captured from May through December for Akepa and April through December for Hawaii Creeper. Annual survival for adults at Keauhou Ranch was 0.70 +/- 0.27 SE fro 61 Akepa and 0.73 +/- 0.12 SE for 49 Hawaii Creepers. Lowest rates of mortality and emigration occurred between May and August. Both species appeared to defend Type-B territories typical of cardueline finches, retained mates for more than one year, and showed strong philopatry. Home ranges for Hawaii Creepers ( x = 7.48 ha) were larger than those for Akepa (x = 3.94 ha). No difference was found between home range sizes of males and females for either species. Geographic Description: Hawaii Creepers and Akepa were studied at four sites on the island of Hawaii. The Keauhou Ranch study area (19.50, 155.33; 1800 m elevation) had a discontinuous canopy dominated by ohia and naio (Myoporum sandwicense) and had a long history of grazing by cattle and logging for koa and ohia. A 16-ha grid marked at 50-m intervals was established at this wet (ca 2000 mm annual rainfall) forest site. The 16-ha Kilauea Forest study area (19 .52, 155.32; 1600-1650 m) was in a relatively pristine, closed-canopy, wet forest dominated by 20-30m tall koa and 15-25 m tall ohia trees, and was approximately 1.8 km NW of the Keauhou Ranch study area. This site was described in detail by Mueller-Dombois et al. (1981).. The 50-ha Hamakua study area near mor continuous canopy and an almost complete lack of native understory plants because of intensive grazing by cattle The 50-ha Kau Forest study area (19.22, 155.65; 1750 m) had a closed canopy of ohia and a largely ungrazed understory of kolea (Myrsine lessertiana), olapa (Cheirodendron trigynum), kawau (Ilex anomala), and native ferns. 1.5.2 Bounding Rectangle Coordinates Methodology: Estimated densities of Hawaii Creepers and Akepa at each of the four study areas by the variable circular-plot method (Reynolds et al. 1980, Ramsey and Scott 1979) during eight min count periods as described in Ralph (1981). proprietary brdpier0008 Determining age and sex of Oma'o (Myadestes obscurus) CEOS_EXTRA STAC Catalog 1976-01-01 1982-12-31 -155, 19, -155, 19 https://cmr.earthdata.nasa.gov/search/concepts/C2231549047-CEOS_EXTRA.umm_json Methods to determine the age and sex of 'Oma'o (Myadestes obscurus) were developed on the basis of 66 museum speciments and 149 live 'Oma'o captured in mist nets on the island of Hawaii. 'Oma'o in juvenile plumage are heavily spotted with scalloped greater coverts and tertials and are easily distinguished from adults. Birds in their first prebasic plumage usually retain one or more scallped wing coverts or tertials. Wing lengths of adult and immature male 'Oma'o were significantly longer than those of females, but only 80% of adult specimens were accurately sexed by wing length. Geographic Description: Island of Hawaii, Keauhou Ranch (19.50, -155.33; 1800 m elevation) and Kilauea Forest (19.52, -155.32; 1600-1650 m). 1.5.2 Bounding Rectangle Coordinates Methodology: Recorded plumage characteristics and exteral measurements of 55 'Oma'o specimens at the Bernice P. Bishop Museum and 11 'Oma'o specimens loaned by the American Museum of Natural History. 'Oma'op juvenal plumage are dark and below and are easily distinguished from adults. The feathers of the breast, belly, and flanks are buffy white in the center but are broadly bordered with blackish brown, giving the feathers a scalloped pattern (Berger 1981, Pratt 1982). proprietary @@ -15111,6 +15818,7 @@ bvoc_flux_759_1 SAFARI 2000 BVOC Measurements at Skukuza and Maun Flux Towers, W c05fa2267e6e03d0e5b9bb6429fdbb974a8194a1 3 year daily average solar exposure map Mali 3Km GRAS August 2008-2011 SCIOPS STAC Catalog 2008-03-01 2011-03-31 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214604040-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for August. proprietary c0b9f42f-640a-44e0-9080-7e80081942c9_NA MERIS - Water Parameters - North Sea, Daily FEDEO STAC Catalog 2005-04-22 2010-03-18 -6.10393, 49.9616, 11.4301, 61.9523 https://cmr.earthdata.nasa.gov/search/concepts/C2207458012-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides daily maps. proprietary c183044b88734442b6d37f5c4f6b0092_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ensemble product), Version 2.6 FEDEO STAC Catalog 2002-01-01 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143201-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily, monthly and yearly gridded aerosol products from the AATSR instrument on the ENVISAT satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 2002 to 2012. In the early period, it also contains data from the ATSR-2 instrument on the ERS-2 satellite. A separate ATSR-2 product covering the period 1995-2001 is also available, and together these form a continuous timeseries from 1995-2012.For further details about these data products please see the documentation. proprietary +c241e665-5175-4c26-b0cd-f0dfee32afdb Earthquakes events from ANSS 1970-March 2011 CEOS_EXTRA STAC Catalog 1970-01-02 2011-04-01 -180, -58, 180, 85.03594 https://cmr.earthdata.nasa.gov/search/concepts/C2232847370-CEOS_EXTRA.umm_json This dataset includes earthqakes events with magnitudes higher than 5.0 as reported by the Advanced national Seismic System (ANSS) Catalogue over the period 1970 - March 2011. UNEP/GRID-Europe processed the intensity buffer of each event following a methodology developped in GRAVITY I and II (http://www.grid.unep.ch/product/publication/download/ew_gravity1.pdf and http://www.grid.unep.ch/product/publication/download/ew_gravity2.pdf). Credit: Earthquakes events (USGS/ANSS), Intensity buffers UNEP/GRID-Europe. Attributes descriptions: EV_ID: Event ID ISO3YEAR: Country and year ISO3: Country ISO3 ID_NAT: Event ID and ISO3 ID_CAT: ANSS ID YEAR: Year START_DATE: Year, Month and Day (YYYYMMDD) MAG: Earthquake magnitude DEPTH: Earthquake depth (kilometer) RADIUS_M: Buffer radius following Gravity I and II methodology (meter) LATITUDE: Latitude (decimal degrees) proprietary c2af8764c84744de87a69db7fecf7af9_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.1 FEDEO STAC Catalog 1991-08-05 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142704-FEDEO.umm_json The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v06.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001 proprietary c2d1361c3fcbc6c7b60b35791f7bbc45bf8079dc 3 year daily average solar exposure map Mali 3Km GRAS January 2008-2011 SCIOPS STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603977-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for January. proprietary c4_percent_1deg_932_1 ISLSCP II C4 Vegetation Percentage ORNL_CLOUD STAC Catalog 1993-01-01 1998-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784880272-ORNL_CLOUD.umm_json The photosynthetic composition (C3 or C4) of vegetation on the land surface is essential for accurate simulations of biosphere-atmosphere exchanges of carbon, water, and energy. C3 and C4 plants have different responses to light, temperature, CO2, and nitrogen; they also differ in physiological functions like stomatal conductance and isotope fractionation. A fine-scale distribution of these plant types is essential for earth science modeling.The C4 percentage is determined from data sets that describe the continuous distribution of plant growth forms (i.e., the percent of a grid cell covered by herbaceous or woody vegetation), climate classifications, the fraction of a grid cell covered in croplands, and national crop type harvest area statistics. The staff from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II have made the original data set consistent with the ISLSCP-2 land/water mask. This data set contains a single file in ArcInfo ASCIIGRID format.This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets. proprietary @@ -15157,6 +15865,7 @@ c4p3flt_1 CAMEX-4 NOAA WP-3D FLIGHT LEVEL DATA V1 GHRC_DAAC STAC Catalog 2001-09 c4p3rad_1 CAMEX-4 NOAA WP-3D RADAR V1 GHRC_DAAC STAC Catalog 2001-09-03 2001-09-19 -100, 10, -60, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1995553989-GHRC_DAAC.umm_json The CAMEX-4 NPAA WP-3D Radar dataset used the NOAA WP-3D Orion aircraft, which has two separate research radars to collect meteorological data. One is mounted on the lower fuselage (C-band), and the other is located in the tail (X-band). CAMEX-4 focused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using NASA-funded aircraft and surface remote sensing instrumentation. Data from these radars consist of reflectivity in range and azimuth coordinates collected either in the horizontal (lower fuselage) or vertical (tail radar) planes. Doppler radial velocity is also collected by the tail radar. For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov proprietary c4p3vid_1 CAMEX-4 NOAA WP-3D VIDEO V1 GHRC_DAAC STAC Catalog 2001-09-03 2001-09-19 -100, 10, -60, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1995554052-GHRC_DAAC.umm_json The CAMEX-4 NOAA WP-3D Video dataset was collected during the fourth field campaign in the CAMEX series (CAMEX-4), which ran from 16 August to 25 September, 2001 and was based out of Jacksonville Naval Air Station, Florida. An important addition to CAMEX-4 was the participation of the NOAA weather reconnaissance WP-3D that collected radar, video and microphysical data.The NOAA WP-3D Videos were created giving a forward, left, right and downward views relative to the aircraft. Each view is a separate tape. All are recoreded in SVHS format in compressed time mode. That means that the video shows time passing at a rate approximately 12.5 times that of normal speed (e.g. 1 minute real time takes 5 seconds on the video). For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov proprietary c4sg8_1 CAMEX-4 GOES-8 PRODUCTS V1 GHRC_DAAC STAC Catalog 2001-08-03 2001-09-21 -130, 15, -10, 75 https://cmr.earthdata.nasa.gov/search/concepts/C1979102757-GHRC_DAAC.umm_json The CAMEX-4 GOES-8 Products dataset was collected during the CAMEX-4 field campaign, which ocused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using NASA-funded aircraft and surface remote sensing instrumentation. In support of the fourth Convection and Moisture Experiment (CAMEX-4), imagery from the Geostationary Operational Environmental Satellite 8 (GOES-8) was collected and archived. Three channels were archived: channel 1-- visible (0.65 microns), channel 2-- infrared (11 microns) and channel 3-- known as the water vapor channel (6.75 microns). Data files are available in McIDAS format, and browse imagery is also available. proprietary +c5064da0-ce61-47fc-b17f-c837bd2847be Flood events CEOS_EXTRA STAC Catalog 1970-01-01 -101.242195, -34.5123, 127.74636, 52.267735 https://cmr.earthdata.nasa.gov/search/concepts/C2232848956-CEOS_EXTRA.umm_json This dataset includes an estimate of flood events. It is based on two sources: 1) A GIS modeling using a statistical estimation of peak-flow magnitude and a hydrological model using HydroSHEDS dataset and the Manning equation to estimate river stage for the calculated discharge value. 2) Observed flood from 1999 to 2007, obtained from the Dartmouth Flood Observatory (DFO). This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing UNEP/GRID-Europe, with key support from USGS EROS Data Center, Dartmouth Flood Observatory 2008. proprietary c65ce27928f34ebd92224c451c2a8bed_NA ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI): Analysis long term product version 1.1 FEDEO STAC Catalog 1991-08-31 2010-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143126-FEDEO.umm_json The ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI) dataset accurately maps the surface temperature of the global oceans over the period 1991 to 2010, using observations from many satellites. The data provides an independently quantified SST to a quality suitable for climate research.The ESA SST CCI Analysis Long Term Product consists of daily, spatially complete fields of sea surface temperature (SST), obtained by combining the orbit data from the AVHRR and ATSR ESA SST CCI Long Term Products, using optimal interpolation to provide SSTs where there were no measurements. These data cover the period between 09/1991 and 12/2010.The Version 1.1 data is an update of the Version 1.0 dataset.Version 1.0 of this dataset is cited in: Merchant, C. J., Embury, O., Roberts-Jones, J., Fiedler, E., Bulgin, C. E., Corlett, G. K., Good, S., McLaren, A., Rayner, N., Morak-Bozzo, S. and Donlon, C. (2014), Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI). Geoscience Data Journal. doi: 10.1002/gdj3.20 proprietary c88_data_1 Fish biological and stomach contents data - Casey 1988 AU_AADC STAC Catalog 1988-01-01 1988-12-31 110, -67, 112, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313382-AU_AADC.umm_json Data from fish captured by Erwin, Casey 1988. Includes fish size, weight, sex, reproductive stage data as well as quantitative stomach contents data and qualitative position data. Approximate locations where fish were caught are provided in the database. Additionally an approximate image map is also provided as a visual reference. These data are stored in an Access Database. Additionally, another Microsoft Access database containing data from this cruise, plus several others is available for download from the URL given below. The Entry ID's of the other metadata records also related to this data are: AADC-00038 AADC-00068 AADC-00073 AADC-00075 AADC-00080 AADC-00082 c88_data The fields in this dataset are: Cruises Date Location Latitude Longitude Species Gear Length Weight Sex Gonad Eye Otolith Stomach Lifestage Family proprietary calibgas_500_1 BOREAS Calibration Gas Standards ORNL_CLOUD STAC Catalog 1994-05-01 1996-11-30 -111, 49, -93, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2761665325-ORNL_CLOUD.umm_json In order to improve the comparability of trace gas measurements made by various science teams, the BOReal Ecosystem-Atmosphere Study (BOREAS) obtained several cylinders of carbon dioxide (CO2) and methane (CH4) that were used as calibration standards. proprietary @@ -15180,6 +15889,7 @@ catchment-biodiversity-vaud-edna_1.0 Vertebrate and plant taxa recovered from 10 causal-effect-of-lup_1.0 Causal effect of LUP ENVIDAT STAC Catalog 2022-01-01 2022-01-01 116.4484859, 23.9449666, 118.6127926, 25.7295192 https://cmr.earthdata.nasa.gov/search/concepts/C2789814593-ENVIDAT.umm_json Title: Does zoning contain built-up land expansion? Causal evidence from Zhangzhou City, China. Research objective: Built-up land zoning is an imporatant policy measure of land use planning (LUP) to contain built-up land expansion in China. We used a difference-indifference model with propensity score matching to estimate the average and annual effect of built-up land zoning on built-up land expansion in Zhangzhou City, China between 2010 and 2020. Data: Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020. XZQDM2 is villages' unique administrative ID; Area is the land area of village i; Dis2water is the Euclidean distance from village i to the nearest waterbody; Dis2coastl is the Euclidean distance from village i to the nearest coastline; Dis2city is the the Euclidean distance from village i to the city center; Dis2county is the the Euclidean distance from village i to the nearest county center; Elevation is the the average elevation within village i; Dis2road is the the Euclidean distance from village i to the nearest road; Nei_Built_ is the the area of built-up land (Nei Built.upit) in the neighboring villages of village i in year t; Treated is a binary variable, Treated = 1 to the villages that were partially or entirely located inside the development-permitted zones, and Treated = 0 to the villages that were entirely located outside the development-permitted zones; Intensity is the percentage of land that was assigned to the development-permitted zones in village i; Year represent the year in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020; BuLE is the dependent variable, representing built-up land expansion in village i in year t; Town is town' unique administrative ID. Method: First, we employed propensity score matching to overcome the selection bias and satisfy the parallel trend assumption. Second, we built four Difference-in-Difference models to estimate the average and annual effect. proprietary causal-effect-of-mfoz_1.0 Causal effect of MFOZ ENVIDAT STAC Catalog 2022-01-01 2022-01-01 116.2441398, 23.5080862, 120.7485344, 27.512657 https://cmr.earthdata.nasa.gov/search/concepts/C2789814608-ENVIDAT.umm_json Title: Closer to causality: How effective is spatial planning in governing built-up land expansion in Fujian Province, China? Research objective: The Major Function Oriented Zone (MFOZ), the first strategic spatial plan in China, is developed to achieve a coordinated regional development, through spatial regulation and zoning of development. The MFOZ he MFOZ divided land into four major function-oriented zones: The development-optimized zone, the development-prioritised zone, the development-restricted zone, and the development-prohibited zone. We used propensity score marching to evaluate the effect of the MFOZ on built-up land expansion in Fujian Province over three time intervals (2013–2015, 2013–2018 and 2013–2020). Data: Data.xlsx contains the variables of 954 towns in Fujian Province. Town_ID is the town unique ID; County_ID is the county unique ID; City_ID is the city unique ID; MFOZ is the the development-prioritised zone and the development-restricted zone (The development-optimized zone and the development-prohibited zone are excluded); Builtup_13_15 is the built-up land expansion from 2013 to 2015; Builtup_13_18 is the built-up land expansion from 2013 to 2018; Builtup_13_20 is the built-up land expansion from 2013 to 2020; Dis2water is the Euclidean distance from the town to the nearest waterbody; Slope is the the average slope within the town; GDP is the average GDP in 2010 within the town; Pop is the average population in 2010 within the town; Road is the average population in 2010 within the town; Dis2city is the Euclidean distance from the town to the nearest prefectural city centre; Nei_Arable, Nei_Forest, and Nei_Built.up are the area of arable land, forest land, and built-up land neighbouring town i in 2010. Method: we used the propensity score matching to compare the changes in the amount of built-up land in the towns of the development-prioritised zone with the matched towns of the development-restricted zone. Additionally, we used three evaluation intervals (2013–2015, 2013–2018 and 2013–2020) to evaluate temporal variation in the causal effect of the MFOZ on built-up land expansion. proprietary cb54bd70826842a9acf658ebabe4a104_NA ESA Ozone Climate Change Initiative (Ozone CCI): SCIAMACHY Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 FEDEO STAC Catalog 2002-01-01 2011-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143053-FEDEO.umm_json This dataset comprises gridded limb ozone monthly zonal mean profiles from the SCIAMACHY instrument on ENVISAT. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-SCIAMACHY_ENVISAT-MZM-2008-fv0001.nc” contains monthly zonal mean data for SCIAMACHY in 2008. proprietary +cc4d85ee-6c72-4249-8775-a96e359457ad_1 Global template for the GLASOD digital database CEOS_EXTRA STAC Catalog 1991-07-01 1991-07-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848766-CEOS_EXTRA.umm_json "The Global Assessment of Human Induced Soil Degradation (GLASOD) was conducted by the International Soil Reference and Information Centre (ISRIC) at Wageningen, The Netherlands, as commissioned by the United Nations Environment Programme (UNEP). ISRIC produced a 1:10 million scale wall chart in 1990 and subsequently produced a digital data set. In essence, the GLASOD database contains information on soil degradation within map units as reported by numerous soil experts around the world through a questionnaire. It includes the type, degree, extent, cause and rate of soil degradation. From these data, the GRID-Nairobi center produced digital and hardcopy maps and made area calculations. The GLASOD database includes a topographic basemap or global template of continental coastlines, islands and lakes, which GRID-Nairobi extracted from the digital version of GLASOD's 1:10 million wall map. All of the boundaries that defined oceans and lakes were selected to create a new ARC/INFO coverage, which was subsequently used as a basemap for all the maps in UNEP's World Atlas of Desertification (see reference below). The global boundaries template contains 306 polygons of four types, which are coded in the data set as follows: 1) Oceans; 2) Lakes; 3) Continents; and 4) Islands. It is available from GRID as a single ARC/INFO 'EXPORT'-format file comprising 1.7 Mb when uncompressed. While the original projection ISRIC used for the GLASOD wall map was the Mercator to display the various continents with as little distortion as possible, it is distributed by GRID in either the Van der Grinten (a variation of Mercator) or the Geographic projection. The sources of the global boundaries template are ISRIC and UNEP/GRID, and the proper references are as follows: Oldeman, L. R., Hakkeling, R. T. A. and W. G. Sombroek. October 1990. ""World Map of the Status of Human-Induced Soil Degradation; Explanatory Note"". (The) Global Assessment of Soil Degradation, ISRIC and UNEP in cooperation with the Winand Staring Centre, ISSS, FAO and ITC; 27 pages. Deichmann, Uwe and Lars Eklundh. July 1991. ""Global digital data sets for land degradation studies: a GIS approach"". GRID Case Study Series No. 4; UNEP/GEMS & GRID; Nairobi, Kenya; 103 pages (mostly pp. 29-32). An additional reference is UNEP's 1992 World Atlas of Desertification (Edward Arnold, London, UK, 69 pages - see pages vii to ix). " proprietary ccamlr_subareas_gis_1 CCAMLR Statistical Reporting Subareas GIS Dataset. AU_AADC STAC Catalog 2002-06-01 -180, -90, 180, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214313406-AU_AADC.umm_json CCAMLR (Commission for the Conservation of Antarctic Marine Living Resources) Statistical Reporting Subareas. GIS data representing the boundary (line) and centroid (point with the area name as an attribute) of each area. The southern boundary of the areas adjacent to Antarctica is the coastline of Antarctica. The coastline has not been included with this data. This dataset is no longer maintained by the Australian Antarctic Data Centre as the CCAMLR Statistical Reporting Subarea boundaries are now available from CCAMLR's Online GIS (see the Related URL). proprietary ccbeb356a88847058159049678fe5c35_NA ESA Ozone Climate Change Initiative (Ozone CCI): ACE Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 FEDEO STAC Catalog 2004-01-01 2010-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142673-FEDEO.umm_json This dataset comprises gridded limb ozone monthly zonal mean profiles from the ACE FTS instrument on the SCISAT satellite. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-ACE_FTS_SCISAT-MZM-2008-fv0001.nc” contains monthly zonal mean data for ACE in 2008. proprietary ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch_1.0 CCN, hygroscopicity, predicted cloud droplet numbers Weissfluhjoch ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814623-ENVIDAT.umm_json __Cloud Condensation Nuclei (CCN) data:__ A Droplet Measurement Technologies (DMT) single-column continuous-flow streamwise thermal gradient chamber (CFSTGC; Roberts and Nenes, 2005) was deployed at the measurement site Weissfluhjoch (2700 m a.s.l., LON: 9.806475, LAT: 46.832964) to record the in-situ CCN number concentrations between February 24 and March 8 2019 for different supersaturations (SS). To account for the difference between the ambient (~735 mbar) and the calibration pressure (~800 mbar), the SS reported by the instrument is adjusted by a factor of 0.92. The CFSTGC was cycled between 6 discrete SS values ranging from 0.09% to 0.74%, producing a full CCN spectrum every hour. The raw CCN measurements are filtered to discount periods of transient operation and whenever the room temperature housing the instrument changed sufficiently to induce a reset in column temperature. Additional information can be found in Section 2.1.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/). __Hygroscopicity data:__ The CCN number concentration measurements were directly related to the size distribution and total aerosol concentration data measured by the Scanning Mobility Particle Size Spectrometer (SMPS) instrument at the same station (https://www.envidat.ch/dataset/aerosol-data-weissfluhjoch) to infer the particles hygroscopicity parameter (kappa). For each SMPS scan, the particles critical dry diameter (Dcr) is estimated by integrating backward the SMPS size distribution, until the aerosol number matches the CCN concentration observed for the same time period as the SMPS scan. Assuming the particle chemical composition is internally mixed, the kappa is determined from Dcr and SS, applying Köhler theory. Additional information can be found in Section 2.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/). __Predicted cloud droplet numbers:__ Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the “characteristic velocity” approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from the SMPS instrument deployed at Weissfluhjoch. The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at Davos Wolfgang and are extracted for the altitude of interest, being 1100 m above ground level for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/). proprietary @@ -15689,38 +16399,225 @@ gem2_1.0 GEM2: Meteorological and snow station at Gemsstock (3021 m asl), Canton generalised-stand-descriptions-within-the-swiss-nfi_1.0 Generalised stand descriptions in Switzerland ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815225-ENVIDAT.umm_json "The files refer to the data and R code used in Mey et al. ""From small forest samples to generalised uni- and bimodal stand descriptions"" (2021) _Methods in Ecology and Evolution_. __Generalised stand descriptions__ are coming from the simultaneous examination of samples that are representative for a specific target area (here, Switzerland) and link available information about forest stand attributes. They combine the modelling of uni- or bimodal diameter distributions and species compositions, i.e. the shares of stems of individual species. Generalised stand descriptions may be used to interpret tree species diversity, regeneration and harvest potentials on a plot-level basis, and to initialise forest models with representative stand data. The data stored here were derived from the fourth campaigns of the Swiss National Forest Inventory (NFI). The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). --------------------------------------- The file 'Data Figures 2 and 4' is publicly available and contains the data used to produce the Figures 2 and 4 published in the paper. The files 'Data diameter modelling' and 'Data species modelling' contain all the data required to reproduce the diameter and species model building. The access to these two files is restricted as they contain raw data from the fourth Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement. The files 'Script diameter and species modelling' and 'Functions diameter modelling' are publicly available and provide the R code used to derive the generalised stand descriptions from the Swiss NFI data." proprietary geocoord_556_1 BOREAS Site and Area Geographic Coordinate Information ORNL_CLOUD STAC Catalog 1992-01-01 1997-12-31 -111, 49, -88, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2808093396-ORNL_CLOUD.umm_json Geographic coordinate and other site information from several sources throughout the experiment period. The final set of information is organized into two data sets that provide geographic coordinate and site characteristic information for single sites and corner coordinates for standard geographic areas. proprietary geodata_0001 Cereals - Production CEOS_EXTRA STAC Catalog 1961-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846694-CEOS_EXTRA.umm_json Cereals also includes other cereals such as mixed grains and buckwheat. Crop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption). When the production data available refers to a production period falling into two successive calendar years and it is not possible to allocate the relative production to each of them, it is usual to refer production data to that year into which the bulk of the production falls. Crop production data are stored in tonnes (T). proprietary +geodata_0028 Improved Sanitation Coverage - Rural Population CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846604-CEOS_EXTRA.umm_json Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. proprietary +geodata_0032 Improved Drinking Water Coverage - Urban Population CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849241-CEOS_EXTRA.umm_json Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. Improved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine. proprietary +geodata_0048 Improved Drinking Water Coverage - Rural Population CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847812-CEOS_EXTRA.umm_json Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. Improved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine. proprietary +geodata_0049 Fertility CEOS_EXTRA STAC Catalog 1950-01-01 2050-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847840-CEOS_EXTRA.umm_json The average number of children a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates of a given period and if they were not subject to mortality. It is expressed as children per woman. proprietary +geodata_0052 Improved Sanitation Coverage - Urban Population CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848815-CEOS_EXTRA.umm_json Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. Improved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine. proprietary +geodata_0058 Earthquake Intensity Zones CEOS_EXTRA STAC Catalog 1988-01-01 1988-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847971-CEOS_EXTRA.umm_json The dataset shows earthquake intensity zones in accordance with the 1956 version of the Modified Mercalli Scale (MM). The intensity describes exclusively the effects of an earthquake on the surface of the earth and integrates numerous parameters (such as ground acceleration, duration of an earthquake, subsoil effects). It also includes historical earthquake reports. The risk grading is based on expectations for a period of 50 years, corresponding to the mean service life of modern buildings. The probability that degrees of intensity shown on the map will be exceeded in 50 years is 20 per cent. This probability figure varies with time; i.e., it is lower for shorter periods and higher for longer periods. In ARC/INFO, the item ZONE in the polygon attribute table (PAT) contains the following earthquake intensity values: Zone Probable maximum intensity once in 50 years (MM Scale) 0 V and below 1 VI 2 VII 3 VIII 4 IX and above 10 indicates main waterbodies proprietary +geodata_0059 Ecological Zones (Holdridge Lifezones) CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847949-CEOS_EXTRA.umm_json "The Holdridge Life Zones data set is from the International Institute for Applied Systems Analyses (IIASA) in Laxenburg, Austria. The data set shows the Holdridge Life Zones of the World, a combination of climate and vegetation (ecological) types, under current, so-called ""normal"" climate conditions, as well as under a presumed doubling of atmospheric CO2. The Life Zones were devised using three indicators: biotemperature (based on the growing season length and temperature); mean annual precipitation; and a potential evapotranspiration ratio, linking biotemperature with annual precipitation to define humidity provinces. The data set has a spatial resolution of one-half degree latitude/longitude, and a total of 38 life-zone classes which are listed on the accompanying legend sheet. The Holdridge Life Zones data set includes a total of four data files. The first (HOLDNORM) is as described in the paragraph above; the second (HOLDDOUB) shows how the Life Zones would change given an assumed doubling of atmospheric CO2 (according to a General Circulation Model from the U.K. Office of Meteorology). The third and fourth data files show only those portions of the Life Zones which would undergo changes, that is for both the old classification (HOLDCHFR) before and the new classification (HOLDCHTO) after the theoretical doubling of CO2 (in effect, these areas have the appearance of 'sliver' polygons)." proprietary +geodata_0060 Human Induced Soil Degradation (GLASOD) CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847802-CEOS_EXTRA.umm_json Soil degradation Severity : Overall severity by which the polygon is affected by soil degradation. This item takes the degree and extent of both types into account. For the classification from 1 (low) to 4 (very high), a look-up table created by ISRIC was used. This item should be used for mapping only, not for area calculations! proprietary +geodata_0063 Global Humidity Index CEOS_EXTRA STAC Catalog 1991-01-01 1991-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847604-CEOS_EXTRA.umm_json The Global Humidity Index is based on a ratio of annual precipitation and potential evapotrans- piration (these data layers are described elsewhere) as P/PET, and largely follows the classification used in a 1984 UNESCO study. The Global Humidity Index surface shows mean annual potential moisture availability for the period 1951-1980, classified into four aridity zones and one humid zone, defined in this data set as follows: Hyper-Arid Zone P/PET less than 0.05 Arid Zone 0.05 less equal P/PET less than 0.20 Semi-Arid Zone 0.20 less equal P/PET less than 0.50 Dry-Subhumid Zone 0.50 less equal P/PET less than 0.65 Humid Zone 0.65 less equal P/PET proprietary +geodata_0065 Matthews Cultivation Intensity CEOS_EXTRA STAC Catalog 1991-01-01 1991-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847950-CEOS_EXTRA.umm_json Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro-magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow-free conditions except for permanently snow-covered continental ice. proprietary +geodata_0066 Matthews Vegetation CEOS_EXTRA STAC Catalog 1991-01-01 1991-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848148-CEOS_EXTRA.umm_json Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro-magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow-free conditions except for permanently snow-covered continental ice proprietary geodata_0067 Annual Precipitation CEOS_EXTRA STAC Catalog 1970-01-01 2002-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848191-CEOS_EXTRA.umm_json The original data took the form of a value for each month and each box on a 0.5 degree latitude / longitude grid. The annual values are the average of their constituent months, they have been calculated by GRID-Geneva. Original Data Station observations were first collected by national meteorological, hydrological and related services, and were acquired through the free and unrestricted exchange of meteorological and related data. These observations were gridded by collaborators at the Climatic Research Unit (www.cru.uea.ac.uk). The gridded data-set is publicly available, and has been published in a peer-reviewed scientific journal. Data Source: CRU TS 2.10 Jan 2004 T. D. Mitchell, Tyndall Centre Reference: Mitchell T.D. and Jones P.D. 2005 An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol. 25: 693-712 proprietary geodata_0100 Central Government Debt CEOS_EXTRA STAC Catalog 1990-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847771-CEOS_EXTRA.umm_json Debt is the entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the government. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year. Source: International Monetary Fund, Government Finance Statistics Yearbook and data files. proprietary geodata_0123 Agricultural Production Index Base 1999-2001 - Total CEOS_EXTRA STAC Catalog 1961-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848356-CEOS_EXTRA.umm_json The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 1999-2001. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. Net Production Index Number (PIN) base 1999-2001 Presents Net Production (Production - Feed - Seed) indices. All indices are calculated by the Laspeyres formula. Net production quantities of each commodity are weighted by 1999-2001average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 1999-2001. Indices are calculated from net production data presented on a calendar year basis. proprietary geodata_0162 Biogeographical Provinces CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -33.19, -41.41, 62.62, 40.04 https://cmr.earthdata.nasa.gov/search/concepts/C2232847219-CEOS_EXTRA.umm_json "Biogeographical realms were established by Udvardy on the basis of geographic and historic elements, utilizing ground-breaking work as appears on this topic in the published literature. Udvardy's paper makes reference to at least three preceding reports on this topic, and also includes an extensive bibliography of five pages. There are 8 biogeographical realms recognized by Udvardy in Paper #18: the Palearctic, the Nearctic, the Afrotropical, the Indomalayan, the Oceanian, the Australian, the Antarctic and the Neotropical. The proper reference for this data set is ""Udvardy, Miklos D. F. 1975. A Classification of the Biogeographical Provinces of the World. IUCN Occasional Paper No. 18, prepared as a contribution to UNESCO's Man and the Biosphere (MAB) Program, Project No. 8. International Union for the Conservation of Nature and Natural Resources, Morges (now Gland), Switzerland, 49 pages."" A source citation should include IUCN, as digitized by UNEP/GRID in 1986." proprietary +geodata_0165 Mean Annual Rainfall CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -33.19, -41.41, 62.62, 40.04 https://cmr.earthdata.nasa.gov/search/concepts/C2232847515-CEOS_EXTRA.umm_json "Precipitation is ""average annual"", and is expressed in terms of millimeters (mm.) per year; Average Days of Precipitation (""Wet Days"") is number of days per year; and Average Windspeed is expressed in terms of meters per second (note that this is not maximum windspeed, nor is there any directional content included in this data set). It is GRID's assumption that the definition of a ""wet day"" is one in which enough precipitation occurred on a given day so as to be recordable by a gauging station at a particular location." proprietary +geodata_0179 Forests - Original CEOS_EXTRA STAC Catalog 9999-01-01 9999-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849373-CEOS_EXTRA.umm_json UNEP-WCMC has been gathering and compiling spatial data on the extent and conservation status of forests since 1987. Until 1995, WCMC's work focused on tropical moist forests because of their high species diversity. GIS data were first assembled for closed moist tropical forests and used to publish the three volumes of the Conservation Atlas of Tropical Forests, covering Asia (1991), Africa (1992) and the Americas (1996). Because digital data were rare at this time, the process of assembling the forest cover data sets involved digitizing manually many paper maps. Continuing on from the tropical moist forest mapping, the next major initiative was to create the first 'World Forest Map'. This was produced in 1996 and was the first digital global forest map showing actual forest extent and protected areas with forested land. Since this achievement, significant work has been carried out to improve data sources and fill in gaps which occurred in this first attempt. This led to the production of the 'Global Overview of Forest Conservation CDROM' in 1997. proprietary +geodata_0180 Forests - Current CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849191-CEOS_EXTRA.umm_json UNEP-WCMC has been gathering and compiling spatial data on the extent and conservation status of forests since 1987. Until 1995, WCMC's work focused on tropical moist forests because of their high species diversity. GIS data were first assembled for closed moist tropical forests and used to publish the three volumes of the Conservation Atlas of Tropical Forests, covering Asia (1991), Africa (1992) and the Americas (1996). Because digital data were rare at this time, the process of assembling the forest cover data sets involved digitising manually many paper maps. proprietary +geodata_0181 Mangroves CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849096-CEOS_EXTRA.umm_json The World Mangrove Atlas is the first significant attempt to provide an overview of the distribution of mangroves worldwide. Mapped data showing the extent of mangroves in over 100 countries have been gathered from a wide range of sources. Mangrove plants include trees, shrubs, ferns and palms. These plants are found in the tropics and sub- tropics on river banks and along coastlines, being unusually adapted to anaerobic conditions of both salt and fresh water environments. These plants have adapted to muddy, shifting, saline conditions. They produce stilt roots which project above the mud and water in order to absorb oxygen. Mangrove plants form communities which help to stabilise banks and coastlines and become home to many types of animals. proprietary +geodata_0199 Forest Plantation Extent CEOS_EXTRA STAC Catalog 1990-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847295-CEOS_EXTRA.umm_json Forest plantation is a forest established by planting and/or seeding in the process of afforestation or reforestation. It consists of introduced species or, in some cases, indigenous species. Forest plantation and natural forests are included in the term forest, a term that refers to land with a tree cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 m. Young stands that have not yet reached, but are expected to reach, a crown density of 10m percent and tree height of 5 m are included under forest, as are temporarily unstocked areas. The term includes forests used for purposes of production, protection, multiple use or conservation (i.e. forest in national parks, nature reserves and other protected areas), as well as forest stands on agricultural lands (e.g. windbreaks and shelterbelts of trees with a width of more than 20 m) and rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems. proprietary +geodata_0200 Forest Plantation Annual Change CEOS_EXTRA STAC Catalog 1990-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847185-CEOS_EXTRA.umm_json Plantation Average Annual Change - is the annual change of a forest established by planting and/or seeding in the process of afforestation or reforestation. It consists of introduced species or, in some cases, indigenous species. Forest plantation and natural forests are included in the term forest, a term that refers to land with a tree cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 m. Young stands that have not yet reached, but are expected to reach, a crown density of 10m percent and tree height of 5 m are included under forest, as are temporarily unstocked areas. The term includes forests used for purposes of production, protection, multiple use or conservation (i.e. forest in national parks, nature reserves and other protected areas), as well as forest stands on agricultural lands (e.g. windbreaks and shelterbelts of trees with a width of more than 20 m) and rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems. proprietary +geodata_0201 Forests Certified by FSC- Accredited Certification Bodies CEOS_EXTRA STAC Catalog 2002-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847173-CEOS_EXTRA.umm_json FSC-endorsed certification of a forest site signifies that an independent evaluation by one of several FSC accredited certification bodies has shown that its management meets the internationally recognised FSC Principles and Criteria of Forest Stewardship. proprietary +geodata_0227 General Government Final Consumption Expenditure CEOS_EXTRA STAC Catalog 1960-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849338-CEOS_EXTRA.umm_json General government final consumption expenditure (formerly general government consumption) includes all government current expenditures for purchases of goods and services (including compensation of employees). It also includes most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. Data are in current U.S. dollars. Source: World Bank national accounts data, and OECD National Accounts data files. proprietary +geodata_0231 Military Expenditures - Percent of GDP CEOS_EXTRA STAC Catalog 1988-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849225-CEOS_EXTRA.umm_json Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.) Source: Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security. Note: Data for some countries are based on partial or uncertain data or rough estimates. proprietary +geodata_0237 Life Expectancy CEOS_EXTRA STAC Catalog 1950-01-01 2050-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848978-CEOS_EXTRA.umm_json Life expectancy: The average number of years of life expected by a hypothetical cohort of individuals who would be subject during all their lives to the mortality rates of a given period. It is expressed as years. proprietary +geodata_0261 Groundwater Produced Internally CEOS_EXTRA STAC Catalog 1958-01-01 2012-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847626-CEOS_EXTRA.umm_json Groundwater produced internally: Long-term annual average groundwater recharge, generated from precipitation within the boundaries of the country. Renewable groundwater resources of the country are computed either by estimating annual infiltration rate (in arid countries) or by computing river base flow (in humid countries). proprietary +geodata_0271 Fishery Production - Marine CEOS_EXTRA STAC Catalog 1960-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848156-CEOS_EXTRA.umm_json TOTAL PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. * includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals proprietary +geodata_0278 Exclusive Fishing Zone (EFZ) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848807-CEOS_EXTRA.umm_json The exclusive fishing zone or fishery zone refers to an area beyond the outer limit of the territorial sea (12 nautical miles from the coast) in which the coastal State has the right to fish, subject to any concessions which may be granted to foreign fishermen. Some countries have made no claim beyond the territorial sea. Some States have claimed an exclusive fishing zone instead of the more encompassing 200 nautical mile Exclusive Economic Zone (EEZ). The United Nations Convention on the Law of the Sea (UNCLOS) is an international agreement that sets conditions and limits on the use and exploitation of the oceans. This Convention also sets the rules for the maritime jurisdictional boundaries of the different member states. The UNCLOS was opened for signature on 10 December 1982 in Montego Bay, Jamaica, and it entered into force on 16 November 1994. As of January 2000, there are 132 countries that have ratified UNCLOS. Under UNCLOS, coastal States can claim sovereign rights in a 200-nautical mile exclusive economic zone (EEZ). This allows for sovereign rights over the EEZ in terms of exploration, exploitation, conservation and management of all natural resources in the seabed, its subsoil, and overlaying waters. UNCLOS allows other states to navigate and fly over the EEZ, as well as to lay submarine cables and pipelines. The inner limit of the EEZ starts at the outer boundary of the Territorial Sea (i.e., 12 nautical miles from the low-water line along the coast). Some States have not ratified UNCLOS and many have not yet claimed their EEZ. Given the uncertainties surrounding much of the delimitation of the fishing zones, these figures should be used with caution. Further information on the Web site: http://www.maritimeboundaries.com/ proprietary geodata_0279 Claimed Exclusive Economic Zone (EEZ) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848800-CEOS_EXTRA.umm_json "The United Nations Convention on the Law of the Sea (UNCLOS) is an international agreement that sets conditions and limits on the use and exploitation of the oceans. This Convention also sets the rules for the maritime jurisdictional boundaries of the different member states. The UNCLOS was opened for signature on 10 December 1982 in Montego Bay, Jamaica, and it entered into force on 16 November 1994. As of January 2000, there are 132 countries that have ratified UNCLOS. Under UNCLOS, coastal States can claim sovereign rights in a 200-nautical mile exclusive economic zone (EEZ). This allows for sovereign rights over the EEZ in terms of exploration, exploitation, conservation and management of all natural resources in the seabed, its subsoil and overlaying waters. UNCLOS allows other states to navigate and fly over the EEZ, as well as to lay submarine cables and pipelines. The inner limit of the EEZ starts at the outer boundary of the Territorial Sea (i.e., 12 nautical miles from the low-water line along the coast). In cases where a country's low-water lines is within 400 nautical miles of each other the EEZ boundaries are generally established by treaty, though there are many cases where these are in dispute. Under UNCLOS, ""land-locked and geographically disadvantaged States have the right to participate on an equitable basis in exploitation of an appropriate part of the surplus of the living resources of the EEZ's of coastal States of the same region or sub-region."" Some States have not ratified UNCLOS and many have not yet claimed their EEZ. These areas of unclaimed EEZ are the areas that a State has the right to claim under UNCLOS, but has not done so yet. Given the uncertainties surrounding much of the delimitation of the EEZ, these figures should be used with caution. Further information on the Web site: http://www.maritimeboundaries.com/ " proprietary geodata_0290 Administrative Boundaries - First Level (ESRI) CEOS_EXTRA STAC Catalog 1998-01-01 1998-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848662-CEOS_EXTRA.umm_json The sub Country Administrative Units 1998 GeoDataset represents a small-scale political map of the world. The data are generalized and were designed for display at scales to about 1:10,000,000. The data were generalized from ESRI's ArcWorld Supplement Map data. Country codes are from U.S. Federal Information Processing Standards (FIPS) version 10-4. proprietary geodata_0291 Dams CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848575-CEOS_EXTRA.umm_json Construction of reservoirs became a worldwide activity in the second half of the twentieth century. The total storage capacity of the large reservoirs is more than 100 million cubic meters, which makes up more than 95% of water accumulated in all the reservoirs of the world. The total area of the more than 60,000 reservoirs that have been built in the last 50 years exceeds more than 100,000 square kilometers. This is an area equivalent to 11 water bodies the size of the Sea of Azov or five the size of Lake Superior. These man-made lakes affect natural and economic conditions over an area of 1.5 million square kilometers. Many of the world's large rivers, such as the Volga, Angara, Missouri, Colorado, and Parana Rivers, have been transformed into cascades of reservoirs. Construction and use of reservoirs cause inevitable changes in the environment, both positive and negative. Environmental changes can include overflowing and swamping; transformation of coasts; changes of soil, vegetation, and fauna; and changes of reproduction and habitat conditions of various aquatic organisms, especially fish and blue-green algae. The impact of reservoirs on the environment is diverse and contradictory. proprietary +geodata_0295 Global Vegetation Index 1983-1990 CEOS_EXTRA STAC Catalog 1991-01-01 1991-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848459-CEOS_EXTRA.umm_json "The NOAA/GVI (Global Vegetation Index; see reference pg. 3) Eight-Year Mean Maximum data set was developed in the following manner. First, eight years of NOAA/GVI Monthly Maximum data were obtained from GRID's Geneva archive of these data*. At GRID-Nairobi, an analyst then used these data files (12 per year) to calculate yearly mean maximum images, and the eight yearly mean images were averaged in their turn, in order to create a single eight-year mean maximum image. The original idea had been to produce an eight-year :hp2.maximum:ehp2. value image, but this was abandoned due to the accretion of ""noise"" from spurious maximum-value pixels in the individual data files (UNEP/GRID, 1990). * - GRID-Geneva has compiled an archive of NOAA/GVI Weekly data from the U.S. National Oceanic and Atmospheric Administration / National Environmental Satellite Data and Information Service / National Climate Data Center / Satellite Data Services Division (or the NOAA / NESDIS / NCDC / SDSD). This collection covers the period from April 1982 to present. At GRID-Geneva, the Weekly data are used to create Monthly, Seasonal and Annual Maximum images, in addition to the archived NOAA/GVI Weekly data. " proprietary geodata_0331 Agriculture Value Added - Percent of GDP CEOS_EXTRA STAC Catalog 1960-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848745-CEOS_EXTRA.umm_json Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Source: World Bank national accounts data, and OECD National Accounts data files. proprietary +geodata_0335 Industry Value Added - Percent of GDP CEOS_EXTRA STAC Catalog 1960-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848389-CEOS_EXTRA.umm_json Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Source: World Bank national accounts data, and OECD National Accounts data files. proprietary +geodata_0337 Fish Catch - Total CEOS_EXTRA STAC Catalog 1960-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848427-CEOS_EXTRA.umm_json CAPTURE PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is excluded. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals proprietary +geodata_0344 Energy Production - Total (IEA) CEOS_EXTRA STAC Catalog 1971-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849265-CEOS_EXTRA.umm_json Total energy production is the production of primary energy, from, the total of all energy sources : hard coal, lignite/brown coal, peat, crude oil, NGLs, natural gas, combustible renewables and wastes, nuclear, hydro, geothermal, solar and the heat from heat pumps that is extracted from the ambient environment. Production is calculated after removal of impurities (e.g. sulphur from natural). proprietary +geodata_0365 European Remote Sensing Forest/Non-forest Digital Map CEOS_EXTRA STAC Catalog 1992-01-01 1992-12-31 -27.72, 34.56, 68.1, 85.21 https://cmr.earthdata.nasa.gov/search/concepts/C2232847449-CEOS_EXTRA.umm_json "As such, the European Forest/Non-forest Digital Map is reasonably up-to- date and based on a homogeneous data source. Because the methodology used to produce the digital map is documented and was ""economically"" accomplished, the product is presumably replicable and could therefore be updated and/or used for monitoring purposes at scales of up to 1:2 million (ESA/ESTEC, 1992). The following steps are a summary of those actually used by the consortium in the production of the digital map: - Satellite data selection (minimal cloud cover)/acquisition; - Data pre-processing for a) geometric correction and b) cloud masking; - Data subset stratification into homogeneous spectral zones; - Data subset classification (Bayesian maximum likelihood); - Accuracy assessment (using classified Landsat MSS); - Mosaicking of classified data subsets; - Merging of final results and overlays; - Cartographic preparation " proprietary +geodata_0368 Map of the Natural Vegetation CEOS_EXTRA STAC Catalog 1987-01-01 1987-12-31 -27.72, 34.56, 68.1, 85.21 https://cmr.earthdata.nasa.gov/search/concepts/C2232849091-CEOS_EXTRA.umm_json The digital version of the Vegetation Map of the European Communities and the Council of Europe held by GRID covers all of Belgium, Denmark, France, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, the United Kingdom and the former West Germany, although the original map also covers Iceland, Norway, Sweden, Finland, Turkey and Cyprus. proprietary +geodata_0418 Diseases of the Respiratory System - Number of Deaths CEOS_EXTRA STAC Catalog 1979-01-01 2003-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849038-CEOS_EXTRA.umm_json "Diseases of the Respiratory System: Effects are generally irritation and reduced lung function with increased incidence of respiratory disease, especially in more susceptible members of the population such as young children, the elderly and asthmatics. Diseases of the Respiratory System includes: ICD-9 BTL codes B31-B32, ICD-9 code CH08 for some ex-USSR countries, ICD-9 code C052 for China, ICD-10 codes J00-J99, European mortality indicator database (HFA-MDB), available at www.euro.who.int for missing figures for some european countries: indicator ""3250 Deaths, Diseases of the Respiratory System""" proprietary +geodata_0436 Disasters of Natural Origin - Affected People CEOS_EXTRA STAC Catalog 1975-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849260-CEOS_EXTRA.umm_json "Affected: People requiring immediate assistance during a period of emergency, i.e. requiring basic survival needs such as food, water, shelter, sanitation and immediate medical assistance (included in the field ""total affected""); Appearance of a significant number of cases of an infectious disease introduced in a region or a population that is usually free from that disease. (100 or more people affected). Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms. " proprietary +geodata_0438 Disasters of Natural Origin - Killed People CEOS_EXTRA STAC Catalog 1975-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846577-CEOS_EXTRA.umm_json Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms. proprietary geodata_0449 Amphibians - Number of Threatened Species CEOS_EXTRA STAC Catalog 1996-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847919-CEOS_EXTRA.umm_json Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. proprietary geodata_0450 Birds - Number of Threatened Species CEOS_EXTRA STAC Catalog 1996-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848521-CEOS_EXTRA.umm_json Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. proprietary +geodata_0458 Mammals - Number of Threatened Species CEOS_EXTRA STAC Catalog 1996-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847714-CEOS_EXTRA.umm_json Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. proprietary +geodata_0465 Emissions of CO2 - from Cement Production (CDIAC) CEOS_EXTRA STAC Catalog 1960-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847865-CEOS_EXTRA.umm_json Emissions of CO2 - from Cement Production (CDIAC) is the amount of C02 created by the conversion of calcium carbonate to calcium oxide inside the kilns, and by burning large quantities of fossil fuels to heat the kilns to the 1450 C necessary for roasting limestone. The sum of emissions estimates for all countries is not equal to the estimate of global total emissions because: 1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals. 2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not. 3. National totals include annual changes in fuel stocks whereas the global total does not. 4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers. proprietary +geodata_0469 Emissions of CO2 - from Gas Flaring (CDIAC) CEOS_EXTRA STAC Catalog 1960-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848710-CEOS_EXTRA.umm_json Emissions of CO2 - from Gas Flaring (CDIAC): Annual estimations of CO2 emissions from Gas Flaring, primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration, Rotty (1974), and with a few national estimates provided by G. Marland. For information about the data collection methodology used by Rotty (1974) and G. Marland see http://cdiac.esd.ornl.gov. The sum of emissions estimates for all countries is not equal to the estimate of global total emissions because : 1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals. 2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not. 3. National totals include annual changes in fuel stocks whereas the global total does not. 4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers. proprietary +geodata_0470 Emissions of CO2 - from Gas Fuels Consumption (CDIAC) CEOS_EXTRA STAC Catalog 1960-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847712-CEOS_EXTRA.umm_json Emissions of CO2 - from Gas Fuel Consumption (CDIAC): Annual estimations of CO2 emissions from gas Gas Fuel Consumption, primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration, Rotty (1974), and with a few national estimates provided by G. Marland. For information about the data collection methodology used by Rotty (1974) and G. Marland see http://cdiac.esd.ornl.gov. The sum of emissions estimates for all countries is not equal to the estimate of global total emissions because : 1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals. 2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not. 3. National totals include annual changes in fuel stocks whereas the global total does not. 4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers. proprietary +geodata_0473 Emissions of CO2 - from Liquid Fuels Consumption (CDIAC) CEOS_EXTRA STAC Catalog 1960-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847566-CEOS_EXTRA.umm_json Emissions of CO2 - from Liquid Fuel Consumption (CDIAC): Annual estimations of CO2 emissions from Liquid Fuel Consumption, primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration, Rotty (1974), and with a few national estimates provided by G. Marland. For information about the data collection methodology used by Rotty (1974) and G. Marland see http://cdiac.esd.ornl.gov. The sum of emissions estimates for all countries is not equal to the estimate of global total emissions because : 1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals. 2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not. 3. National totals include annual changes in fuel stocks whereas the global total does not. 4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers. proprietary +geodata_0476 Emissions of CO2 - from Solid Fuels Consumption (CDIAC) CEOS_EXTRA STAC Catalog 1960-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848108-CEOS_EXTRA.umm_json Emissions of CO2 - from Solid Fuel Consumption (CDIAC): Annual estimations of CO2 emissions from Solid Fuel Consumption, primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration, Rotty (1974), and with a few national estimates provided by G. Marland. For information about the data collection methodology used by Rotty (1974) and G. Marland see http://cdiac.esd.ornl.gov. The sum of emissions estimates for all countries is not equal to the estimate of global total emissions because : 1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals. 2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not. 3. National totals include annual changes in fuel stocks whereas the global total does not. 4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers. proprietary +geodata_0480 Emissions of CO2 - from Fossil Fuels - Total (CDIAC) CEOS_EXTRA STAC Catalog 1960-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848729-CEOS_EXTRA.umm_json Total emissions of CO2 from fossil fuels are the sum of CO2 produced during the consumption of solid, liquid, and gaseous fuels, and from gas flaring, and cement manufacturing. The data is primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration (Rotty) and with a few national estimates provided by G. Marland. The sum of emissions estimates for all countries is not equal to the estimate of global total emissions because : 1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals. 2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not. 3. National totals include annual changes in fuel stocks whereas the global total does not. 4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers. proprietary +geodata_0543 Floods - Killed People CEOS_EXTRA STAC Catalog 1980-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849227-CEOS_EXTRA.umm_json Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Flood: Significant rise of water level in a stream, lake, reservoir or coastal region. proprietary +geodata_0588 Extreme Temperatures - Killed People CEOS_EXTRA STAC Catalog 1980-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847385-CEOS_EXTRA.umm_json "Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Extreme temperature: Disaster type term comprising the two disaster subsets ""heat wave"" and ""cold wave"" (Long lasting period with extremely high or low surface temperature). " proprietary geodata_0613 Crude Birth Rate CEOS_EXTRA STAC Catalog 1950-01-01 2050-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846733-CEOS_EXTRA.umm_json Crude birth rate: number of births over a given period divided by the person-years lived by the population over that period. It is expressed as number of births per 1,000 population. proprietary +geodata_0633 Household Final Consumption Expenditure - Total CEOS_EXTRA STAC Catalog 1960-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848942-CEOS_EXTRA.umm_json Household final consumption expenditure (formerly private consumption) is the market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. Here, household consumption expenditure includes the expenditures of nonprofit institutions serving households, even when reported separately by the country. Data are in constant 2000 U.S. dollars. Source: World Bank national accounts data, and OECD National Accounts data files. proprietary geodata_0686 Arable Land CEOS_EXTRA STAC Catalog 1961-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848372-CEOS_EXTRA.umm_json "Arable Land: land under temporary crops (double-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for ""Arable land"" are not meant to indicate the amount of land that is potentially cultivable. " proprietary +geodata_0758 Gross Domestic Product - Purchasing Power Parity CEOS_EXTRA STAC Catalog 1980-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847187-CEOS_EXTRA.umm_json PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2005 international dollars. Source: World Bank, International Comparison Program database. proprietary geodata_0771 Arable and Permanent Crops CEOS_EXTRA STAC Catalog 1961-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846928-CEOS_EXTRA.umm_json "Arable Land: land under temporary crops (double-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for ""Arable land"" are not meant to indicate the amount of land that is potentially cultivable. Permanent Crops: land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee and rubber; this category includes land under flowering shrubs, fruit trees, nut trees and vines, but excludes land under trees grown for wood or timber. " proprietary +geodata_0776 Infant Mortality Rate CEOS_EXTRA STAC Catalog 1950-01-01 2050-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847531-CEOS_EXTRA.umm_json Infant mortality: probability of dying between birth and exact age 1. It is expressed as deaths per 1,000 births. proprietary geodata_0839 Cereals - Yield CEOS_EXTRA STAC Catalog 1961-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849290-CEOS_EXTRA.umm_json Cereals also includes other cereals such as mixed grains and buckwheat. The data reported under this element represent the harvested production per unit of harvested area for crop products. In most of the cases yield data are not recorded but obtained by dividing the data stored under production element by those recorded under element: area harvested. Data are recorded in hectogramme (100 grammes) per hectare (HG/HA). proprietary +geodata_0879 Droughts - Killed People CEOS_EXTRA STAC Catalog 1980-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848029-CEOS_EXTRA.umm_json Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Drought: Period of deficiency of moisture in the soil such that there is inadequate water required for plants, animals and human beings. proprietary +geodata_0885 Disasters of Natural Origin - Total Affected People CEOS_EXTRA STAC Catalog 1975-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848224-CEOS_EXTRA.umm_json Total affected: In EM-DAT, people that have been injured, affected and left homeless after a disaster are included in this category. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms. proprietary +geodata_0927 Energy Production - Combustible Renewables and Waste (IEA) CEOS_EXTRA STAC Catalog 1971-01-01 2008-12-31 62, -85, 64, -84 https://cmr.earthdata.nasa.gov/search/concepts/C2232848131-CEOS_EXTRA.umm_json Production refers to the quantities of fuels extracted or produced, calculated after any operation for removal of inert matter or impurities (e.g. sulphur from natural gas). Waste refers to the quantities of fuels extracted or produced from Industrial waste and Municipal waste. Industrial waste consists of solid and liquid products (e.g. tyres) combusted directly, usually in specialised plants, to produce heat and/or power and that are not reported in the category solid biomass. Municipal waste consists of products that are combusted directly to produce heat and/or power and comprises wastes produced by the residential, commercial and public services sectors that are collected by local authorities for disposal in a central location. Hospital waste is included in this category. A KTOE is defined as 41.868 TJ (Terajoules) or 11.630 GWh (Gigawatt-Hour). proprietary +geodata_0930 Forest Average Annual Change CEOS_EXTRA STAC Catalog 1990-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847940-CEOS_EXTRA.umm_json Forest Average Annual Change – Total is the net change in forests and includes expansion of forest plantations and losses and gains in the area of natural forests. Total Forest includes natural forests and forest plantations. The term is used to refer to land with a tree cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 m. Young stands that have not yet reached, but are expected to reach, a crown density of 10m percent and tree height of 5 m are included under forest, as are temporarily unstocked areas. The term includes forests used for purposes of production, protection, multiple use or conservation (i.e. forest in national parks, nature reserves and other protected areas), as well as forest stands on agricultural lands (e.g. windbreaks and shelterbelts of trees with a width of more than 20 m) and rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems. proprietary geodata_0932 Aquaculture Production - Total CEOS_EXTRA STAC Catalog 1960-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848199-CEOS_EXTRA.umm_json AQUACULTURE PRODUCTION The annual series of aquaculture production begin in 1950 for the quantities and in 1984 for the values. Aquaculture is the farming of aquatic organisms including fish, mollusks, and crustaceans*. Farming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual or corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by the public as a common property resource, with or without appropriate licenses, are the harvest of fisheries. Production of fish, crustaceans and molluscs is expressed in live weight that is the nominal weight of the aquatic organisms at the time of capture Quantities are given in tonnes. *includes all FAOSTAT group excluded aquatic animals nei, aquatic plants, aquatic mammals proprietary +geodata_0938 Fish Catch - Inland Waters CEOS_EXTRA STAC Catalog 1960-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848676-CEOS_EXTRA.umm_json CAPTURE PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is excluded. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. * includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals proprietary +geodata_0940 Fish Catch - Marine CEOS_EXTRA STAC Catalog 1960-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846722-CEOS_EXTRA.umm_json CAPTURE PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is excluded. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. * includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals proprietary +geodata_0960 Forest Fire Extent CEOS_EXTRA STAC Catalog 2005-01-01 2005-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848961-CEOS_EXTRA.umm_json Forest Fire Extent - Annual Average comprises the reported forest areas exposed to fire. Total Forest includes natural forests and forest plantations. The term is used to refer to land with a tree cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 m. Young stands that have not yet reached, but are expected to reach, a crown density of 10m percent and tree height of 5 m are included under forest, as are temporarily unstocked areas. The term includes forests used for purposes of production, protection, multiple use or conservation (i.e. forest in national parks, nature reserves and other protected areas), as well as forest stands on agricultural lands (e.g. windbreaks and shelterbelts of trees with a width of more than 20 m) and rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems. proprietary +geodata_0992 Energy Capacity - Nuclear CEOS_EXTRA STAC Catalog 1996-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847536-CEOS_EXTRA.umm_json Energy Capacity - Nuclear is the actual capacity of the nuclear electric power industry to describe the size of generating plants. “MWe” is the symbol for the actual output of a generating station in megawatts of electricity. proprietary geodata_1011 Carbon to the Atm. From Land-Use Change - Ann. Net Flux CEOS_EXTRA STAC Catalog 1960-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848705-CEOS_EXTRA.umm_json Carbon to the Atmosphere from Land-Use Change - Annual Net Flux is a numeric database with annual estimations, from 1850 through 1990, of the net flux of carbon between terrestrial ecosystems and the atmosphere. The data is the result of deliberate changes in land cover and land use, especially forest clearing for agriculture and the harvest of wood for wood products or energy. proprietary geodata_1018 Desalinated Water Produced CEOS_EXTRA STAC Catalog 1968-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848039-CEOS_EXTRA.umm_json Desalinated water corresponds to the annual amount of fresh water generated by desalination of sea or brackish waters (annually estimated on the basis of the total capacity of water desalination installations). proprietary +geodata_1029 Improved Sanitation Coverage - Total Population CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848426-CEOS_EXTRA.umm_json Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. Improved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine. proprietary +geodata_1034 Improved Drinking Water Coverage - Total Population CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847727-CEOS_EXTRA.umm_json Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. Improved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine. proprietary geodata_1085 Continental Shelf Area CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847265-CEOS_EXTRA.umm_json According to the UN Convention of the Law of the Sea, the Continental Shelf is the area of the seabed and subsoil which extends beyond the territorial sea to a distance of 200 nautical miles from the territorial sea baseline and beyond that distance to the outer edge of the continental margin. Areas of continental shelf that are disputed by overlaping claims by one or more nations have been excluded from this table. Areas that are of cooperative joint development between two or more nations have also been excluded. Coastal States have sovereign rights over the continental shelf (the national area of the seabed) for exploring and exploiting it; the shelf can extend at least 200 nautical miles from the shore, and more under specified circumstances. The United Nations Convention on the Law of the Sea (UNCLOS) is an international agreement that sets conditions and limits on the use and exploitation of the oceans. This Convention also sets the rules on how the maritime jurisdictional boundaries of the different member states are set. The UNCLOS was opened for signature on 10 December 1982 in Montego Bay, Jamaica, and it entered into force on 16 November 1994. As of January 2000, there are 132 countries that have ratified UNCLOS. Further information on the Web site: http://www.maritimeboundaries.com/ proprietary +geodata_1088 Length of Coastline CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849208-CEOS_EXTRA.umm_json The measurement of an irregular and curving feature such as a nation's coastal length is scale-dependent and very difficult to measure. Maps of individual islands for example, frequently show great detail, whereas regional maps summarize complex coastlines into a few simple lines. In addition, coastal features are constantly changing due to erosion, etc. The only way to derive comparable statistics on coastline length is to use a single source which uses a constant scale. This is what has been attempted with the data presented in this table, however, highly complex coastlines will appear longer at higher resolutions. Estimates may differ from other published sources. Because of the difficulty in trying to measure coastline length, these figures should be interpreted as approximations and should be used with caution. Coastline length was derived from the World Vector Shoreline database at 1:250,000 kilometers. The estimates presented here were calculated using a Geographic Information System (GIS) and an underlying database consistent for the entire world. The methodology used to estimate length is based on the following: 1) A country's coastline is made up of individual lines, and an individual line has two or more vertices and/or nodes. 2) The length between two vertices is calculated on the surface of a sphere. 3) The sum of the lengths of the pairs of vertices is aggregated for each individual line, and 4) the sum of the lengths of individual lines was aggregated for a country. In general, the coastline length of islands that are part of a country, but are not overseas territories, are included in the coastline estimate for that country (i.e., Canary Islands are included in Spain). Coastline length for overseas territories and dependencies are listed separately. Disputed areas are not included in country or regional totals. proprietary +geodata_1147 Emissions of CO2 - from Public Electricity and Heat Production (IEA) CEOS_EXTRA STAC Catalog 1960-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848176-CEOS_EXTRA.umm_json Emissions of CO2 from public electricity and heat production contain the sum of emissions from public electricity generation, public combined heat and power generation, and public heat plants. Public utilities are defined as those undertakings whose primary activity is to supply the public. They may be publicly or privately owned. Emissions from own on-site use of fuel should be included. This corresponds to IPCC Source/Sink Category 1 A 1 a. proprietary +geodata_1150 Emissions of CO2 - from Manufacturing Industries and Construction (IEA) CEOS_EXTRA STAC Catalog 1960-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847956-CEOS_EXTRA.umm_json Emissions of CO2 from manufacturing industries and construction contain the emissions from combustion of fuels (coal, oil and gas) in industry. The IPCC Source/Sink Category 1 A 2 includes these emissions. However, in the Guidelines, the IPCC category also includes emissions from industry autoproducers that generate electricity and/or heat. The IEA data are not collected in a way that allows the energy consumption to be split by specific end-use and therefore, autoproducers are shown as a separate item (Unallocated Autoproducers). Manufacturing Industries and Construction also includes emissions from coke inputs into blast furnaces, which may be reported either in the transformation sector, the industry sector or the separate IPCC Source/Sink Category 2, Industrial Processes. proprietary +geodata_1153 Emissions of CO2 - from Transport (IEA) CEOS_EXTRA STAC Catalog 1960-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848166-CEOS_EXTRA.umm_json Emissions of CO2 from transport contain emissions from the combustion of fuel (coal, oil and gas) for all transport activity, regardless of the sector, except for international marine and aviation bunkers. This corresponds to IPCC Source/Sink Category 1 A 3. In addition, the IEA data are not collected in a way that allows the autoproducer consumption to be split by specific end-use. proprietary +geodata_1156 Emissions of CH4 - from Agriculture (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1970-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847635-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). Emissions of CH4 - from Agriculture (RIVM). Emissions of CH4 (Methane) from the agricultural sector include emissions from: Rice cultivation (IPCC 4C); Animal breeding: enteric fermentation and animal waste management (IPCC 4A and 4B,); Savannah burning (IPCC 4E); Agricultural waste burning (IPCC 4F). The emissions from deforestation (IPCC 5A1) and vegetation fires (IPCC 5A2,3) are not included. proprietary +geodata_1162 Emissions of CH4 - Total (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1970-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848292-CEOS_EXTRA.umm_json "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of CH4 - Total (RIVM) include ""Energy"", ""Agriculture"", ""Waste"" and ""Others"" EDGAR subdivisions. ""Energy"" comprises production, handling, transmission and combustion of fossil fuels and biofuels (IPCC category 1A and 1B); ""Agriculture"" comprises animals, animal waste, rice production, agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); ""Waste"" comprises landfills, wastewater treatment, human wastewater disposal and waste incineration (non-energy) (IPCC category 6); ""Others"" include industrial process emissions and tropical and temperate forest fires (IPCC categories 2 and 5). " proprietary +geodata_1165 Emissions of CH4 - Waste (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1970-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848626-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of CH4 - from Waste (RIVM) include emissions from: Landfills (including CH4 recovery) (IPCC 6A1,2); Wastewater treatment (including CH4 recovery) (IPCC 6B1,2); Human wastewater disposal (IPCC 6B2); Waste incineration (non-energy) (IPCC 6C). proprietary +geodata_1198 Emissions of Total GHG (CO2, CH4, N2O, HFCs, PFCs and SF6) (National Reports, UNFCCC), Including Land Use, Land-Use Change and Forestry CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848797-CEOS_EXTRA.umm_json Emissions of Total GHG (CO2, CH4, N2O, HFCs, PFCs and SF6) (UNFCCC), Excluding Land-Use Change and Forestry The Global Warming Potential (GWP) is an index used to translate the level of emissions of various gases into a common measure in order to compare the relative radiative forcing of different gases without directly calculating the changes in atmospheric concentrations. GWPs are calculated as the ratio of the radiative forcing that would result from the emissions of one kilogram of a greenhouse gas to that from the emission of one kilogram of carbon dioxide over a period of time (usually 100 years). Gases involved in complex atmospheric chemical processes have not been assigned GWPs. proprietary +geodata_1204 Emissions of N2O - from Agriculture (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1970-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848853-CEOS_EXTRA.umm_json Emissions of N2O - from Agriculture (RIVM). Emissions of CH4 (Methane) from the agricultural sector include emissions from: Arable Land (fertilizer use) (IPCC 4D); Animal waste management (IPCC 4B); Savannah burning (IPCC 4E); Agricultural waste burning (IPCC 4F); Crop production (IPCC 4D); Animal waste (deposited on soil - N2O) (IPCC 4B); Atmospheric deposition (IPCC 4D); Leaching and Run-off (IPCC 4D). The emissions from deforestation (IPCC 5A1), vegetation fires (IPCC 5A2,3) and deforestation post burn effects (IPCC 5B1) are not included. proprietary +geodata_1210 Emissions of N2O - Total (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1970-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848860-CEOS_EXTRA.umm_json "Emissions of N2O - Total (RIVM) include ""Energy"", ""Agriculture"", ""Waste"" and ""Others"" EDGAR subdivisions. ""Energy"" comprises combustion of fossil fuels and biofuels (IPCC category 1A and 1B); ""Agriculture"" comprises fertilizer use (synthetic and animal manure), animal waste management, agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); ""Waste"" comprises human sewage discharge and waste incineration (non-energy) (IPCC category 6); ""Others"" include industrial process emissions, N2O usage and tropical and temperate forest fires (IPCC categories 2, 3 and 5). " proprietary +geodata_1213 Emissions of CO - Total (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1990-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848686-CEOS_EXTRA.umm_json "Emissions of CO (carbon monoxide) - Total (RIVM) include the following EDGAR subdivisions: ""Fuel combustion"", “Biofuel combustion”, “Fugitive”, “Industry”, “Solvent use”, ""Agriculture"", ""Waste"" and ""Others"". ""Fuel combustion"" refers to fossil fuel combustion and evaporation of NMVOC in road transport (part of IPCC category 1A); ""Biofuel combustion"" refers to traditional biofuels as well as to wood waste, paper, ethanol, etc. (part of IPCC category 1A); ""Fugitive"" comprises flaring and venting of associated gas in oil and gas production, handling / transmission losses of oil and charcoal production (IPCC category 1B); ""Industry"" refers to non-combustion industrial processes, excluding solvent use (IPCC category 2); ""Solvent use"" refers to solvent use in industry and non-industry sectors (IPCC category 3); ""Agriculture"" comprises agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); ""Waste"" comprises waste incineration (non-energy) (uncontrolled residential burning and controlled non-residential burning) and hazardous waste handling (IPCC category 6); ""Others"" comprises tropical forest fires and temperate forest fires (IPCC category 5A). " proprietary +geodata_1216 Emissions of NMVOC - Total (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1990-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848501-CEOS_EXTRA.umm_json "Emissions of NMVOC (Non-Methane Volatile Organic Compounds) - Total (RIVM) include the following EDGAR subdivisions: ""Fuel combustion"", “Biofuel combustion”, “Fugitive”, “Industry”, “Solvent use”, ""Agriculture"", ""Waste"" and ""Others"". ""Fuel combustion"" refers to fossil fuel combustion and evaporation of NMVOC in road transport (part of IPCC category 1A); ""Biofuel combustion"" refers to traditional biofuels as well as to wood waste, paper, ethanol, etc. (part of IPCC category 1A); ""Fugitive"" comprises flaring and venting of associated gas in oil and gas production, handling / transmission losses of oil and charcoal production (IPCC category 1B); ""Industry"" refers to non-combustion industrial processes, excluding solvent use (IPCC category 2); ""Solvent use"" refers to solvent use in industry and non-industry sectors (IPCC category 3); ""Agriculture"" comprises agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); ""Waste"" comprises waste incineration (non-energy) (uncontrolled residential burning and controlled non-residential burning) and hazardous waste handling (IPCC category 6); ""Others"" comprises tropical forest fires and temperate forest fires (IPCC category 5A)." proprietary +geodata_1219 Emissions of NOx - Total (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1990-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848211-CEOS_EXTRA.umm_json "Emissions of NOx (Nitrogen Oxides) - Total (RIVM) include the following EDGAR subdivisions: ""Fuel combustion"", “Biofuel combustion”, “Fugitive”, “Industry”, “Solvent use”, ""Agriculture"", ""Waste"" and ""Others"". ""Fuel combustion"" refers to fossil fuel combustion and evaporation of NMVOC in road transport (part of IPCC category 1A); ""Biofuel combustion"" refers to traditional biofuels as well as to wood waste, paper, ethanol, etc. (part of IPCC category 1A); ""Fugitive"" comprises flaring and venting of associated gas in oil and gas production, handling / transmission losses of oil and charcoal production (IPCC category 1B); ""Industry"" refers to non-combustion industrial processes, excluding solvent use (IPCC category 2); ""Solvent use"" refers to solvent use in industry and non-industry sectors (IPCC category 3); ""Agriculture"" comprises agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); ""Waste"" comprises waste incineration (non-energy) (uncontrolled residential burning and controlled non-residential burning) and hazardous waste handling (IPCC category 6); ""Others"" comprises tropical forest fires and temperate forest fires (IPCC category 5A). " proprietary +geodata_1222 Emissions of SO2 - Total (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1990-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847778-CEOS_EXTRA.umm_json "Emissions of SO2 (Sulfur dioxide) - Total (RIVM) include the following EDGAR subdivisions: ""Fuel combustion"", “Biofuel combustion”, “Fugitive”, “Industry”, “Solvent use”, ""Agriculture"", ""Waste"" and ""Others"". ""Fuel combustion"" refers to fossil fuel combustion and evaporation of NMVOC in road transport (part of IPCC category 1A); ""Biofuel combustion"" refers to traditional biofuels as well as to wood waste, paper, ethanol, etc. (part of IPCC category 1A); ""Fugitive"" comprises flaring and venting of associated gas in oil and gas production, handling / transmission losses of oil and charcoal production (IPCC category 1B); ""Industry"" refers to non-combustion industrial processes, excluding solvent use (IPCC category 2); ""Solvent use"" refers to solvent use in industry and non-industry sectors (IPCC category 3); ""Agriculture"" comprises agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); ""Waste"" comprises waste incineration (non-energy) (uncontrolled residential burning and controlled non-residential burning) and hazardous waste handling (IPCC category 6); ""Others"" comprises tropical forest fires and temperate forest fires (IPCC category 5A). " proprietary +geodata_1253 Mangroves Forest Extent - Total Area CEOS_EXTRA STAC Catalog 1980-01-01 2005-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847510-CEOS_EXTRA.umm_json Mangroves are commonly found along sheltered coastlines in the tropics and subtropics where they fulfil important socio-economic and environmental functions. These include the provision of a large variety of wood and non-wood forest products; coastal protection against the effects of wind, waves and water currents; conservation of biological diversity, including a number of endangered mammals, reptiles, amphibians and birds; protection of coral reefs, sea-grass beds and shipping lanes against siltation; and provision of habitat, spawning grounds and nutrients for a variety of fish and shellfish, including many commercial species. Mangrove trees and shrubs, including ferns and palms, are found along river banks and coastlines in tropical and subtropical countries. Their main characteristic is that they can tolerate salt and brackish water environments. Globally, there are seventy known species of mangroves. proprietary +geodata_1256 Mangroves Forest Extent - Protected Area CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846595-CEOS_EXTRA.umm_json The World Mangrove Atlas is the first significant attempt to provide an overview of the distribution of mangroves worldwide. Mapped data showing the extent of mangroves in over 100 countries have been gathered from a wide range of sources. Mangrove trees and shrubs, including ferns and palms, are found along river banks and coastlines in tropical and subtropical countries. Their main characteristic is that they can tolerate salt and brackish water environments. Globally, there are seventy known species of mangroves. proprietary +geodata_1261 Emissions of CO2 - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849166-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1262 Emissions of CO2 - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1262 CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847196-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1264 Emissions of CO2 - from Cement Production (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849248-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1265 Emissions of CO2 - from Power Generation (Model Estimations, RIVM-MNP) - geodata_1265 CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846672-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from Power Generation (public and auto, including cogeneration) corresponds to IPCC category 1A1a. proprietary +geodata_1266 Emissions of CO2 - from Power Generation (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849296-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from Power Generation (public and auto, including cogeneration) corresponds to IPCC category 1A1a. proprietary +geodata_1267 Emissions of CO2 - from Residentials, Commercials and Other Sector (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849370-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from Residential, Commercials and Other sector corresponds to IPCC category 1A4. proprietary +geodata_1268 Emissions of CO2 - from Residentials, Commercials and Other Sector (Model Estimations, RIVM-MNP) - geodata_1268 CEOS_EXTRA STAC Catalog 1990-12-31 1995-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847928-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from Residential, Commercials and Other sector corresponds to IPCC category 1A4. proprietary +geodata_1269 Emissions of CO2 - from Transport Road (Model Estimations, RIVM-MNP) - geodata_1269 CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847835-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from transport road corresponds to IPCC category 1A3b. proprietary +geodata_1270 Emissions of CO2 - from Transport Road (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849247-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from transport road; corresponds to IPCC category 1A3b. proprietary +geodata_1271 Emissions of CH4 - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1271 CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847506-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1272 Emissions of CH4 - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849293-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1273 Emissions of CH4 - from Animal Breeding: Enteric Fermentation (Model Estimations, RIVM-MNP) - geodata_1273 CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847615-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Methane production from herbivores is a by-product of enteric Emissions of CH4 from animal breeding corresponds to IPCC category 4A. All Anthropogenic Sources also includes international air traffic and international shipping. proprietary +geodata_1274 Emissions of CH4 - from Animal Breeding: Enteric Fermentation (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849107-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Methane production from herbivores is a by-product of enteric,fermentation, a digestive process by which carbohydrates are broken down by micro-organisms into simple molecules for absorption into the bloodstream. Both ruminant (e.g. cattle, sheep) and non-ruminant animals (e.g. pigs, horses) produce CH 4 , although ruminants are the largest source (per unit of feed intake). proprietary +geodata_1275 Emissions of N2O - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849164-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1276 Emissions of N2O - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1276 CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847278-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1277 Emissions of N2O - from Fertilizer Use in Arable Land (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848949-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of N2O from fertilizer use in arable land: synthetic and animal waste handling. proprietary +geodata_1278 Emissions of N2O - from Fertilizer Use in Arable Land (Model Estimations, RIVM-MNP) - geodata_1278 CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846606-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of N2O from fertilizer use in arable land: synthetic and animal waste handling. proprietary +geodata_1279 Emissions of CO - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1279 CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847548-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1280 Emissions of CO - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846739-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1281 Emissions of SO2 - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846592-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1282 Emissions of SO2 - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1282 CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847945-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) proprietary +geodata_1283 Emissions of NOx - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847241-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1284 Emissions of NOx - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1284 CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847681-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1285 Emissions of NMVOC (Non-Methane Volatile Organic Compounds) - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847549-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). proprietary +geodata_1286 Emissions of NMVOC (Non-Methane Volatile Organic Compounds) - from All Anthropogenic Sources (Model Estimations, RIVM-MNP) - geodata_1286 CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847440-CEOS_EXTRA.umm_json A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) proprietary +geodata_1315 Mean Annual Precipitation CEOS_EXTRA STAC Catalog 1961-01-01 1990-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849152-CEOS_EXTRA.umm_json "For the purpose of Desertification Atlas map production, the GRID-Nairobi data analysts required data from a fairly dense network of global climate stations. They therefore obtained both precipitation and temperature station data from UEA/CRU for two 30-year periods, 1930-59 and 1960-89. While the CRU database contained 950 precipitation station values, this number was not sufficient for interpolating two separate global surfaces to be used in a climate change study, for reasons of both temporal instability and inaccuracies of eventual area estimates. Thus, GRID decided in conjunction with UEA/CRU to produce a single, high-resolution preci- pitation surface for one time period only, using the maximum number of station means available. For this surface, data from the time period 1951-1980 were selected, both in order to avoid creation of a ""timeless"" data set, and to better match the period of the GLASOD study whose data were compiled in the late 1980s. " proprietary geodata_1316 Annual Temperature CEOS_EXTRA STAC Catalog 1970-01-01 2002-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849004-CEOS_EXTRA.umm_json The original data took the form of a value for each month and each box on a 0.5 degree latitude / longitude grid. The annual values are the average of their constituent months, they have been calculated by GRID-Geneva. Original Data Station observations were first collected by national meteorological, hydrological and related services, and were acquired through the free and unrestricted exchange of meteorological and related data. These observations were gridded by collaborators at the Climatic Research Unit (www.cru.uea.ac.uk). The gridded data-set is publicly available, and has been published in a peer-reviewed scientific journal. Data Source: CRU TS 2.10 Jan 2004 T. D. Mitchell, Tyndall Centre Reference: Mitchell T.D. and Jones P.D. 2005 An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol. 25: 693-712. proprietary +geodata_1351 Modis Blue Marble Land Surface (Africa) CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 -33.19, -41.41, 62.62, 40.04 https://cmr.earthdata.nasa.gov/search/concepts/C2232848194-CEOS_EXTRA.umm_json "The ""blue marble"" image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. Much of the information contained in this image came from a single remote-sensing device-NASA’s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor’s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey’s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration’s AVHRR sensor—the Advanced Very High Resolution Radiometer. " proprietary +geodata_1352 Modis Blue Marble Land Surface (Middle East) CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 29.09, 9.24, 63.99, 38.67 https://cmr.earthdata.nasa.gov/search/concepts/C2232847977-CEOS_EXTRA.umm_json "The ""blue marble"" image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. Much of the information contained in this image came from a single remote-sensing device-NASA’s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor’s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey’s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration’s AVHRR sensor—the Advanced Very High Resolution Radiometer. " proprietary +geodata_1353 Modis Blue Marble Land Surface (Asia) CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 26.35, -10.61, 179.66, 89.32 https://cmr.earthdata.nasa.gov/search/concepts/C2232847943-CEOS_EXTRA.umm_json Ther “blue marble” image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. Much of the information contained in this image came from a single remote-sensing device-NASA’s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor’s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey’s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration’s AVHRR sensor—the Advanced Very High Resolution Radiometer. proprietary +geodata_1354 Modis Blue Marble Land Surface (South America) CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 -122.85, -55.78, -18.14, 30.46 https://cmr.earthdata.nasa.gov/search/concepts/C2232847662-CEOS_EXTRA.umm_json "The ""blue marble"" image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. Much of the information contained in this image came from a single remote-sensing device-NASA’s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor’s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey’s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration’s AVHRR sensor—the Advanced Very High Resolution Radiometer. " proprietary +geodata_1355 Modis Blue Marble Land Surface (North America) CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 -178.97, 8.56, -11.98, 87.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232847610-CEOS_EXTRA.umm_json The “blue marble” image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. Much of the information contained in this image came from a single remote-sensing device-NASA’s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor’s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey’s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration’s AVHRR sensor—the Advanced Very High Resolution Radiometer. proprietary +geodata_1356 Modis Blue Marble Land Surface (Europe) CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 -27.72, 34.56, 68.1, 85.21 https://cmr.earthdata.nasa.gov/search/concepts/C2232847810-CEOS_EXTRA.umm_json "The ""blue marble"" image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. Much of the information contained in this image came from a single remote-sensing device-NASA’s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor’s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey’s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration’s AVHRR sensor—the Advanced Very High Resolution Radiometer. " proprietary +geodata_1358 Global Forest Canopy Density (Africa) CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 -33.19, -41.41, 62.62, 40.04 https://cmr.earthdata.nasa.gov/search/concepts/C2232848820-CEOS_EXTRA.umm_json Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover). proprietary +geodata_1359 Global Forest Canopy Density (Asia) CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 26.35, -10.61, 179.66, 89.32 https://cmr.earthdata.nasa.gov/search/concepts/C2232848781-CEOS_EXTRA.umm_json Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover). proprietary +geodata_1360 Global Forest Canopy Density (South America) CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 -122.85, -55.78, -18.14, 30.46 https://cmr.earthdata.nasa.gov/search/concepts/C2232848276-CEOS_EXTRA.umm_json Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover). proprietary +geodata_1361 Global Forest Canopy Density (North America) CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 -178.97, 8.56, -11.98, 87.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232848328-CEOS_EXTRA.umm_json Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover). proprietary +geodata_1362 Global Forest Canopy Density (Europe) CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 -27.72, 34.56, 68.1, 85.21 https://cmr.earthdata.nasa.gov/search/concepts/C2232848473-CEOS_EXTRA.umm_json Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover). proprietary +geodata_1363 Global Forest Canopy Density (Middle East) CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 29.09, 9.24, 63.99, 38.67 https://cmr.earthdata.nasa.gov/search/concepts/C2232848449-CEOS_EXTRA.umm_json Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover). proprietary +geodata_1364 Global Forest Cover (Middle East) CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 29.09, 9.24, 63.99, 38.67 https://cmr.earthdata.nasa.gov/search/concepts/C2232848843-CEOS_EXTRA.umm_json The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. proprietary +geodata_1365 Global Forest Cover (Africa) CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 -33.19, -41.41, 62.62, 40.04 https://cmr.earthdata.nasa.gov/search/concepts/C2232848777-CEOS_EXTRA.umm_json The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. proprietary +geodata_1366 Global Forest Cover (Asia) CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 26.35, -10.61, 179.66, 89.32 https://cmr.earthdata.nasa.gov/search/concepts/C2232848561-CEOS_EXTRA.umm_json The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. proprietary +geodata_1367 Global Forest Cover (South America) CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 -122.85, -55.78, -18.14, 30.46 https://cmr.earthdata.nasa.gov/search/concepts/C2232848671-CEOS_EXTRA.umm_json The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. proprietary +geodata_1368 Global Forest Cover (North America) CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 -178.97, 8.56, -11.98, 87.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232847667-CEOS_EXTRA.umm_json The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. proprietary +geodata_1369 Global Forest Cover (Europe) CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 -27.72, 34.56, 68.1, 85.21 https://cmr.earthdata.nasa.gov/search/concepts/C2232847605-CEOS_EXTRA.umm_json The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. proprietary +geodata_1370 Global Burnt Area CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848826-CEOS_EXTRA.umm_json Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal). proprietary +geodata_1371 Global Burnt Area (Middle East) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 29.09, 9.24, 63.99, 38.67 https://cmr.earthdata.nasa.gov/search/concepts/C2232848779-CEOS_EXTRA.umm_json Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC) http://www.gvm.sai.jrc.it/fire/default.htm In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal) Biomass burning contributes up to 50%, 40% and 16% of the total emissions of anthropogenic origin for carbon monoxide, carbon dioxide and methane respectively. Both the scientific community and the policy makers are looking for reliable and quantitative information on the magnitude and spatial distribution of biomass burning. Please visit the original source site at: http://www.grid.unep.ch/activities/earlywarning/preview/ims/gba/ where you can find more detailed information, downloads and the GBA2000-IMS application. The GBA2000-IMS application informs users of the status of the project. In addition, the user can overlay burnt area maps with other sources of information such as country borders, national park boundaries and a land cover map. Moreover users can zoom in and out, change the background, the month of observation in 2000, as well as download the data and access statistics. NOTE: The GBA2000 products are currently under development. Burnt area maps are still prototype versions and might be modified/improved to take into account the comments received from the scientific community. proprietary +geodata_1372 Global Burnt Area (Africa) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -33.19, -41.41, 62.62, 40.04 https://cmr.earthdata.nasa.gov/search/concepts/C2232848559-CEOS_EXTRA.umm_json Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal). proprietary +geodata_1373 Global Burnt Area (Asia) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 26.35, -10.61, 179.66, 89.32 https://cmr.earthdata.nasa.gov/search/concepts/C2232848673-CEOS_EXTRA.umm_json Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC) http://www.gvm.sai.jrc.it/fire/default.htm In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal). proprietary +geodata_1374 Global Burnt Area (South America) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -122.85, -55.78, -18.14, 30.46 https://cmr.earthdata.nasa.gov/search/concepts/C2232848280-CEOS_EXTRA.umm_json Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal). proprietary +geodata_1375 Global Burnt Area (North America) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -178.97, 8.56, -11.98, 87.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232848322-CEOS_EXTRA.umm_json Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal) . proprietary +geodata_1376 Global Burnt Area (Europe) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -27.72, 34.56, 68.1, 85.21 https://cmr.earthdata.nasa.gov/search/concepts/C2232848483-CEOS_EXTRA.umm_json Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal). proprietary +geodata_1395 Length of Available Growing Period CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847793-CEOS_EXTRA.umm_json The concept of the length of the available growing period (LGP) combines temperature and moisture considerations to determine the length of time crops are able to grow, hence excluding periods which are too cold or too dry or both. LGP refers to the number of days within the period of temperatures above 5°C when moisture conditions are considered adequate. Under rain-fed conditions, the begin of the LGP is linked to the start of the rainy season. The growing period for most crops continues beyond the rainy season and, to a greater or lesser extent, crops mature on moisture stored in the soil profile. proprietary +geodata_1398 Dominant Type of Problem Lands CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848678-CEOS_EXTRA.umm_json Problem soils have been defined as soils with inherent physical or chemical constraints to agricultural production. In these soils degradation hazards are more severe and adequate soil management measures are more difficult or costly to apply. Such soils, if improperly used or inadequately managed will degrade rapidly, sometimes irreversibly. As a result the land itself might go out of production. The analysis is carried out in a sequential way. proprietary +geodata_1399 Easy Available Water CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848554-CEOS_EXTRA.umm_json This is an indicator for the amount of stored soil moisture readily available to crops.The water retention at 2 bar suction is used to separate easily available water (EAV) from water which is more tightly held at higher suctions and difficult to abstract, especially from deeper subsoils; and in the use of a conceptual model of effective rooting depth. proprietary +geodata_1425 Net Reproduction Rate CEOS_EXTRA STAC Catalog 1950-01-01 2050-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847838-CEOS_EXTRA.umm_json Net reproduction rate: The average number of daughters a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates and the mortality rates of a given period. It is expressed as number of daughters per woman. proprietary +geodata_1458 Emissions of Organic Water Pollutants (BOD) - Total CEOS_EXTRA STAC Catalog 1989-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849007-CEOS_EXTRA.umm_json Emissions of organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants. Source: 1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, Industrial Pollution in Economic Development: Kuznets Revisited (available at www.worldbank.org/nipr). The data were updated through 2005 by the World Bank's Development Research Group using the same methodology as the initial study. proprietary +geodata_1459 Emissions of Organic Water Pollutants (BOD) - per Worker CEOS_EXTRA STAC Catalog 1989-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848968-CEOS_EXTRA.umm_json Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants. Source: World Bank and UNIDO's industry database. proprietary +geodata_1474 Healthy Life Expectancy (HALE) CEOS_EXTRA STAC Catalog 2000-01-01 2002-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849320-CEOS_EXTRA.umm_json Healthy life expectancy (HALE) is based on life expectancy (LEX), but includes an adjustment for time spent in poor health. This indicator measures the equivalent number of years in full health that a newborn child can expect to live based on the current mortality rates and prevalence distribution of health states in the population. proprietary +geodata_1480 Economically Active Population CEOS_EXTRA STAC Catalog 1980-01-01 2020-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847424-CEOS_EXTRA.umm_json The economically active population comprises all persons of either sex who furnish the supply of labour for the production of economic goods and services as defined by the United Nations systems of national accounts and balances during a specified time-reference period. According to these systems the production of economic goods and services includes all production and processing of primary products whether for the market for barter or for own consumption, the production of all other goods and services for the market and, in the case of households which produce such goods and services for the market, the corresponding production for own consumption. proprietary +geodata_1498 Drylands - Total Area CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849159-CEOS_EXTRA.umm_json The concept of drylands continues to be debated. In this data set, drylands are taken as areas with a potential hazard of desertification. The hyperarid zone is not subject to desertification and is therefore excluded. Hence drylands are defined as the arid, semi-arid and dry subhumid zones, or areas with lengths of growing periods of 1-179 days. proprietary +geodata_1501 Drylands - Percent of Total Area CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849375-CEOS_EXTRA.umm_json The concept of drylands continues to be debated. In this data set, drylands are taken as areas with a potential hazard of desertification. The hyperarid zone is not subject to desertification and is therefore excluded. Hence drylands are defined as the arid, semi-arid and dry subhumid zones, or areas with lengths of growing periods of 1-179 days. proprietary +geodata_1525 Energy use (kg oil equivalent) per $1,000 GDP (Constant 2005 PPP $) CEOS_EXTRA STAC Catalog 1980-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846723-CEOS_EXTRA.umm_json Energy use per GDP (Constant 2005 PPP $) is the kilogram of oil equivalent of energy use per gross domestic product converted to 2005 constant international dollars using purchasing power parity rates. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport. Gross Domestic Product (GDP) is the sum of gross value added by all resident producers in the economy plus any product taxes (less subsidies) not included in the valuation of output. Value added is the net output of an industry after adding up all outputs and subtracting intermediate inputs. The purchasing power parity (PPP) conversion factor is the number of units of a country's currency required to buy the same amounts of goods and services in the domestic market as the United States (U.S.) dollar would buy in the United States. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States. proprietary +geodata_1540 Hazardous Waste - Production CEOS_EXTRA STAC Catalog 1990-01-01 2002-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848009-CEOS_EXTRA.umm_json Definitions used in these data refer to the waste streams to be controlled according to the Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and their Disposal (seeAnnex IV of the convention for complete definition and methods of treatment, movement and disposal). proprietary geodata_1571 Consumption of Ozone-Depleting Substances - Chlorofluorocarbons (CFCs) CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848439-CEOS_EXTRA.umm_json Consumption of Ozone-Depleting CFCs is the sum of the consumption of the weighted tons of the individual substances in the group—metric tons of the individual substance (defined in the Montreal Protocol on Substances That Deplete the Ozone Layer) multiplied by its ozone-depleting potential. Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. The consumption of CFCs is the national production plus imports, minus exports, minus destroyed quantities, minus feedstock uses of individual CFCs. National annual consumption of CFCs is the sum of the weighted tons (consumption in metric tons multiplied by the estimated ozone-depleting potential) of the individual CFCs. proprietary +geodata_1624 Disasters of Natural Origin - Total Affected per Million People CEOS_EXTRA STAC Catalog 1975-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847656-CEOS_EXTRA.umm_json Total affected: In EM-DAT, people that have been injured, affected and left homeless after a disaster are included in this category. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms. proprietary +geodata_1627 Disasters of Natural Origin - Killed per Million People CEOS_EXTRA STAC Catalog 1975-01-01 2005-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847829-CEOS_EXTRA.umm_json Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms. proprietary geodata_1628 Animal Species - Threatened CEOS_EXTRA STAC Catalog 2004-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848803-CEOS_EXTRA.umm_json """Threatened"" includes species listed as Critically Endangered (CR), Endangered (EN) and Vulnerable (VU). A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered and it is therefore considered to be facing an extremely high risk of extinction in the wild. A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered and it is therefore considered to be facing a very high risk of extinction in the wild. A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable and it is therefore considered to be facing a high risk of extinction in the wild." proprietary geodata_1644 Aquaculture Production - Marine CEOS_EXTRA STAC Catalog 1960-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847176-CEOS_EXTRA.umm_json AQUACULTURE PRODUCTION The annual series of aquaculture production begin in 1950 for the quantities and in 1984 for the values. Aquaculture is the farming of aquatic organisms including fish, mollusks, and crustaceans*. Farming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual or corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by the public as a common property resource, with or without appropriate licenses, are the harvest of fisheries. Production of fish, crustaceans and molluscs is expressed in live weight that is the nominal weight of the aquatic organisms at the time of capture Quantities are given in tonnes. *includes all FAOSTAT group excluded aquatic animals nei, aquatic plants, aquatic mammals proprietary +geodata_1646 Irrigated Areas CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846631-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). proprietary +geodata_1647 Irrigated Areas (Africa) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -33.19, -41.41, 62.62, 40.04 https://cmr.earthdata.nasa.gov/search/concepts/C2232846641-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). proprietary +geodata_1648 Irrigated Areas (Asia) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 26.35, -10.61, 179.66, 89.32 https://cmr.earthdata.nasa.gov/search/concepts/C2232849231-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format) proprietary +geodata_1649 Irrigated Areas (Europe) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -27.72, 34.56, 68.1, 85.21 https://cmr.earthdata.nasa.gov/search/concepts/C2232849226-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). proprietary +geodata_1650 Irrigated Areas (South America) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -122.85, -55.78, -18.14, 30.46 https://cmr.earthdata.nasa.gov/search/concepts/C2232847182-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). proprietary +geodata_1651 Irrigated Areas - geodata_1651 CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -178.97, 8.56, -11.98, 87.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232848914-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). proprietary +geodata_1652 Irrigated Areas (Middle East) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 29.09, 9.24, 63.99, 38.67 https://cmr.earthdata.nasa.gov/search/concepts/C2232846636-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). proprietary geodata_1672 Agricultural Area CEOS_EXTRA STAC Catalog 1961-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848984-CEOS_EXTRA.umm_json "Agricultural area, this category is the sum of areas under a) arable land - land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for Arable land are not meant to indicate the amount of land that is potentially cultivable; (b) permanent crops - land cultivated with long-term crops which do not have to be replanted for several years (such as cocoa and coffee); land under trees and shrubs producing flowers, such as roses and jasmine; and nurseries (except those for forest trees, which should be classified under ""forest""); and (c) permanent meadows and pastures - land used permanently (five years or more) to grow herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land)." proprietary +geodata_1685 Land Area CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846605-CEOS_EXTRA.umm_json Land area is the total area of the country excluding area under inland water bodies. Possible variations in the data may be due to updating and revisions of the country data and not necessarily to any change of area. proprietary geodata_1706 Consumption of Ozone-Depleting Substances - Methyl Bromide CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849036-CEOS_EXTRA.umm_json Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. Methyl bromide (CH3Br) is used as a fumigant for high-value crops, pest control, and quarantine treatment of agricultural commodities awaiting export. Total world annual consumption is about 70,000 tonnes, most of it in the industrialized countries. It takes about 0.7 years to break down. proprietary geodata_1708 Consumption of Ozone-Depleting Substances - Hydrochlorofluorocarbons (HCFCs) CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847289-CEOS_EXTRA.umm_json Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. Hydrochlorofluorocarbons (HCFCs) were developed as the first major replacement for CFCs. While much less destructive than CFCs, HCFCs also contribute to ozone depletion. They have an atmospheric lifetime of about 1.4 to 19.5 years. proprietary +geodata_1717 Disasters of Natural Origin (Excluding: Earthquakes, Insect Infestation, Volcanic Eruptions) - Total Affected per Million People CEOS_EXTRA STAC Catalog 1975-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849404-CEOS_EXTRA.umm_json Total affected: In EM-DAT, people that have been injured, affected and left homeless after a disaster are included in this category. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Extreme Temperatures, Floods, Slides, Wave/surges, Wild fires and Wind storms. proprietary +geodata_1720 Disasters of Natural Origin (Excluding: Earthquakes, Insect Infestation, Volcanic Eruptions) - Total Affected People CEOS_EXTRA STAC Catalog 1975-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847273-CEOS_EXTRA.umm_json Total affected: In EM-DAT, people that have been injured, affected and left homeless after a disaster are included in this category. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Extreme Temperatures, Floods, Slides, Wave/surges, Wild fires and Wind storms. proprietary +geodata_1723 Disasters of Natural Origin (Excluding: Earthquakes, Insect Infestation, Volcanic Eruptions) - Killed per Million People CEOS_EXTRA STAC Catalog 1975-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847492-CEOS_EXTRA.umm_json Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Extreme Temperatures, Floods, Slides, Wave/surges, Wild fires and Wind storms. proprietary +geodata_1726 Disasters of Natural Origin (Excluding: Earthquakes, Insect Infestation, Volcanic Eruptions) - Killed People CEOS_EXTRA STAC Catalog 1975-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846578-CEOS_EXTRA.umm_json Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Extreme Temperatures, Floods, Slides, Wave/surges, Wild fires and Wind storms. proprietary +geodata_1730 Energy Consumption for Total Transport Sector - Total (IEA) CEOS_EXTRA STAC Catalog 1971-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847246-CEOS_EXTRA.umm_json The transport sector includes all fuels for transport except international marine bunkers [ISIC Divisions 60, 61 and 62]. It includes transport in the industry sector and covers road, railway, air, internal navigation (including small craft and coastal shipping not included under marine bunkers), fuels used for transport of materials by pipeline and non-specified transport. Fuel used for ocean, coastal and inland fishing should be included in agriculture. For many countries, the split between international civil aviation and domestic air appears to allocate fuel use for both domestic and international departures of domestically owned carriers to domestic air. proprietary +geodata_1741 Energy Production - Crude Oil (IEA) CEOS_EXTRA STAC Catalog 1971-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848235-CEOS_EXTRA.umm_json Energy production comprises crude oil, natural gas liquids, refinery feedstocks, and additives as well as other hydrocarbons such as synthetic oil, mineral oils extracted from bituminous minerals (in the row production) and oils from coal and natural gas liquefaction (in the row liquefaction). Production is calculated after removal of impurities (e.g. sulphur from natural gas). A TOE is defined as 41.868 gigajoules or 10 Exp 7 kilocalories. One terawatt-hour = 0.086 MTOE. proprietary +geodata_1744 Energy Production - Natural gas (IEA) CEOS_EXTRA STAC Catalog 1971-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847687-CEOS_EXTRA.umm_json "Gas includes natural gas (excluding natural gas liquids) and gas works gas. The latter appears as a positive figure in the ""gas works"" row but is not part of production. A TOE is defined as 41.868 gigajoules or 10 Exp 7 kilocalories. One terawatt-hour = 0.086 MTOE." proprietary +geodata_1745 Energy Production - Nuclear (IEA) CEOS_EXTRA STAC Catalog 1971-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847568-CEOS_EXTRA.umm_json Energy production Nuclear shows the primary heat equivalent of the electricity produced by a nuclear power plant with an average thermal efficiency of 33 per cent. proprietary +geodata_1761 Diseases of the Respiratory System - Number of Deaths per 100,000 Population CEOS_EXTRA STAC Catalog 1979-01-01 2003-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848737-CEOS_EXTRA.umm_json "Diseases of the Respiratory System: Effects are generally irritation and reduced lung function with increased incidence of respiratory disease, especially in more susceptible members of the population such as young children, the elderly and asthmatics. Diseases of the Respiratory System includes: ICD-9 BTL codes B31-B32, ICD-9 code CH08 for some ex-USSR countries, ICD-9 code C052 for China, ICD-10 codes J00-J99, European mortality indicator database (HFA-MDB), available at http://www.euro.who.int, for missing figures for some european countries: indicator ""3250 Deaths, Diseases of the Respiratory System"" " proprietary +geodata_1786 Global Lakes and Wetlands Database (GLWD-3) - Level 3 CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847563-CEOS_EXTRA.umm_json Drawing upon a variety of existing maps, data and information, a new Global Lakes and Wetlands Database (GLWD) has been created. Level 3 of the Global Lakes and Wetlands Database (GLWD) comprises lakes, reservoirs, rivers, and different wetland types in the form of a global raster map at 30-sec resolution. proprietary +geodata_1787 Global Lakes and Wetlands Database (GLWD-2) - Level 2 CEOS_EXTRA STAC Catalog 9999-01-01 9999-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847695-CEOS_EXTRA.umm_json Drawing upon a variety of existing maps, data and information, a new Global Lakes and Wetlands Database (GLWD) has been created. Level 2 of the Global Lakes and Wetlands Database (GLWD) comprises the shoreline polygons of permanent open water bodies with a surface area greater equal 0.1 square km, excluding the water bodies contained in GLWD-1. proprietary +geodata_1788 Global Lakes and Wetlands Database (GLWD-1) - Level 1 CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848716-CEOS_EXTRA.umm_json Drawing upon a variety of existing maps, data and information, a new Global Lakes and Wetlands Database (GLWD) has been created.Level 1 of the Global Lakes and Wetlands Database (GLWD) comprises the shoreline polygons of the largest lakes (area greater equal 50 square km) and reservoirs (storage capacity greater equal 0.5 cubic km) worldwide, including extensive attribute data. proprietary geodata_1824 Concentration of Biochemical Oxygen Demand (BOD) in Rivers, Lakes and Groundwater CEOS_EXTRA STAC Catalog 1979-01-01 2005-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848658-CEOS_EXTRA.umm_json BOD, Biological Oxygen Demand, gives an indication of the amount of organic matter present in water bodies. A certain amount of BOD is always present in water bodies, usually around 2 mg/l O2, while higher levels of BOD could imply that the water is contaminated with bacteria and thus pose a risk to human health. proprietary geodata_1825 Concentration of Nitrogen (NO3+NO2) in Rivers, Lakes and Groundwater CEOS_EXTRA STAC Catalog 1979-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848595-CEOS_EXTRA.umm_json In water, nitrogen (N) occurs as nitrates (NO3-) and nitrites (NO2-). These are naturally occurring ions that are part of the nitrogen cycle. Nitrate is used mainly in inorganic fertilizers, and sodium nitrite is used as a food preservative, especially in cured meats. In most countries, nitrate levels in drinking-water derived from surface water do not exceed 10 mg/liter, although nitrate levels in well water often exceed 50 mg/liter; nitrite levels are normally lower, less than a few milligrams per liter (WHO 2004). proprietary +geodata_1835 Emissions of CO - Total (National Reports, UNFCCC) CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848463-CEOS_EXTRA.umm_json "National figures only include data from UNFCCC Greenhouse Gas Inventory (GHG) Annex I Parties. These inventory data are provided in the national communications under the Convention by Annex I and non-Annex I Parties, and in addition Annex I Parties submit annual national greenhouse gas inventories. We use the category ""National Total"" from the GHG Database; this category does not include emissions resulting from fuel sold to ships or aircraft engaged in international transport (international bunker fuel emissions). Furthermore, the ""National Total"" does not include emissions from biomass burning or emissions or removals from the land-use change and forestry sector. " proprietary +geodata_1840 Emissions of SO2 - Total (National Reports, UNFCCC) CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848973-CEOS_EXTRA.umm_json "Sulfur dioxide is an air pollutant produced when fossil fuels containing sulfur are burned. It contributes to acid rain and can damage human health, particularly that of the young and the elderly. National figures only include data from UNFCCC Greenhouse Gas Inventory (GHG) Annex I Parties. These inventory data are provided in the national communications under the Convention by Annex I and non-Annex I Parties, and in addition Annex I Parties submit annual national greenhouse gas inventories. We use the category ""National Total"" from the GHG Database; this category does not include emissions resulting from fuel sold to ships or aircraft engaged in international transport (international bunker fuel emissions). Furthermore, the ""National Total"" does not include emissions from biomass burning or emissions or removals from the land-use change and forestry sector. " proprietary +geodata_1843 Emissions of NMVOC - Total (National Reports, UNFCCC) CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849123-CEOS_EXTRA.umm_json "National figures only include data from UNFCCC Greenhouse Gas Inventory (GHG) Annex I Parties. These inventory data are provided in the national communications under the Convention by Annex I and non-Annex I Parties, and in addition Annex I Parties submit annual national greenhouse gas inventories. We use the category ""National Total"" from the GHG Database; this category does not include emissions resulting from fuel sold to ships or aircraft engaged in international transport (international bunker fuel emissions). Furthermore, the ""National Total"" does not include emissions from biomass burning or emissions or removals from the land-use change and forestry sector." proprietary +geodata_1846 Emissions of NOX - Total (National Reports, UNFCCC) CEOS_EXTRA STAC Catalog 2008-12-31 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849222-CEOS_EXTRA.umm_json "Nitrogen dioxide is a poisonous, pungent gas formed when nitric oxide combines with hydrocarbons and sunlight, producing a photochemical reaction. These conditions occur in both natural and anthropogenic activities. Nitrogen dioxide is emitted by bacteria, motor vehicles, industrial activities, nitrogenous fertilizers, combustion of fuels and biomass, and aeorbic decomposition of organic matter in soils and oceans. National figures only include data from UNFCCC Greenhouse Gas Inventory (GHG) Annex I Parties. These inventory data are provided in the national communications under the Convention by Annex I and non-Annex I Parties, and in addition Annex I Parties submit annual national greenhouse gas inventories. We use the category ""National Total"" from the GHG Database; this category does not include emissions resulting from fuel sold to ships or aircraft engaged in international transport (international bunker fuel emissions). Furthermore, the ""National Total"" does not include emissions from biomass burning or emissions or removals from the land-use change and forestry sector. " proprietary +geodata_1848 Global Land Cover 2000 (GLC 2000) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846682-CEOS_EXTRA.umm_json Each regional partner used the VEGA2000 dataset, providing a daily global image from the Vegetation sensor onboard the SPOT4 satellite. Each partner also used the Land Cover Classification System (LCCS) produced by FAO and UNEP (Di Gregorio and Jansen, 2000), which ensured that a standard legend was used over the globe. This hierarchical classification system allowed each partner to choose the most appropriate land cover classes which best describe their region, whilst also providing the possibility to translate regional classes to a more generalised global legend. proprietary +geodata_1890 Emissions of Particulates Smaller than 2.5 Microns CEOS_EXTRA STAC Catalog 1990-01-01 2002-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849230-CEOS_EXTRA.umm_json Particulate matter contributes significantly to visibility reduction and, as a carrier of toxic metals and other toxic substances, exerts pressures on human health, especially fine particulates. An effort has been made to present data on particulates smaller than 2.5 microns. proprietary +geodata_1896 Human Poverty Index for Developing Countries (HPI-1) CEOS_EXTRA STAC Catalog 2003-01-01 2003-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848988-CEOS_EXTRA.umm_json While the HDI measures average achievement, the HPI-1 measures deprivations in the three basic dimensions of human development captured in the HDI: - A long and healthy life vulnerability to death at a relatively early age, as measured by the probability at birth of not surviving to age 40. - Knowledge exclusion from the world of reading and communications, as measured by the adult illiteracy rate. - A decent standard of living lack of access to overall economic provisioning, as measured by the unweighted average of two indicators, the percentage of the population without sustainable access to an improved water source and the percentage of children under weight for age. proprietary +geodata_1897 Ecological Footprint CEOS_EXTRA STAC Catalog 1961-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848979-CEOS_EXTRA.umm_json The Ecological Footprint (EF) is a measure of the consumption of renewable natural resources by a human population, be it that of a country, a region or the whole world. A population's EF is the total area of productive land or sea required to produce all the crops, meat, seafood, wood and fibre it consumes, to sustain its energy consumption and to give space for its infrastructure. The EF can be compared with the biologically productive capacity of the land and sea available to that population. proprietary geodata_1930 Concentrations of Particulate Matter less than 10 microns (PM10) CEOS_EXTRA STAC Catalog 1990-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849405-CEOS_EXTRA.umm_json "Particulate matter concentrations refer to fine suspended particulates less than 10 microns in diameter (PM10) that are capable of penetrating deep into the respiratory tract and causing significant health damage. Data for countries and aggregates for regions and income groups are urban-population weighted PM10 levels in residential areas of cities with more than 100,000 residents. The estimates represent the average annual exposure level of the average urban resident to outdoor particulate matter. The state of a country’s technology and pollution controls is an important determinant of particulate matter concentrations. Source: Kiren Dev Pandey, David Wheeler, Bart Ostro, Uwe Deichmann, Kirk Hamilton, and Katherine Bolt. ""Ambient Particulate Matter Concentrations in Residential and Pollution Hotspot Areas of World Cities: New Estimates Based on the Global Model of Ambient Particulates (GMAPS),"" World Bank, Development Research Group and Environment Department (2006). " proprietary +geodata_1933 Global Mean Sea Level CEOS_EXTRA STAC Catalog 1870-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2226653620-CEOS_EXTRA.umm_json The reconstruction has used monthly-mean tide gauge data from the Permanent Service for Mean Sea Level (PSMSL) database [Woodworth and Player, 2003], together with Empirical Orthogonal Functions (EOFs) from a 12-year TOPEX/Poseidon + Jason-1 satellite altimeter data set to 'reconstruct' a GMSL curve from January 1870 to December 2001. proprietary +geodata_1965 Growing Stock in Forest CEOS_EXTRA STAC Catalog 2010-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848060-CEOS_EXTRA.umm_json Growing stock Volume over bark of all living trees more than X cm in diameter at breast height. Includes the stem from ground level or stump height up to a top diameter of Y cm, and may also include branches to a minimum diameter of W cm. Explanatory notes: 1. The countries must indicate the three thresholds (X, Y, W in cm) and the parts of the tree that are not included in the volume. The countries must also indicate whether the reported figures refer to volume above ground or above stump. 2. The diameter is measured at 30 cm above the end of the buttresses if these are higher than 1 meter. 3. Includes windfallen living trees. 4. Excludes: Smaller branches, twigs, foliage, flowers, seeds, and roots. Forest: Land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use. Explanatory notes 1. Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate. 2. Includes areas with bamboo and palms provided that height and canopy cover criteria are met. 3. Includes forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest. 4. Includes windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 ha and width of more than 20 m. 5. Includes plantations primarily used for forestry or protection purposes, such as rubberwood plantations and cork oak stands. 6. Excludes tree stands in agricultural production systems, for example in fruit plantations and agroforestry systems. The term also excludes trees in urban parks and gardens. The term is mainly related to FRA 2005 National Reporting Table T1. proprietary +geodata_1966 Forest Harvest Rate CEOS_EXTRA STAC Catalog 1990-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848238-CEOS_EXTRA.umm_json Forest harvest rates expressed as the ratio of roundwood production and growing stock in forests. After decades of increases, harvesting of roundwood from forests appears to have levelled off in recent years. However, roundwood production is still very high and largely exceeds growth of forest stock in Asia and the Pacific. Data source: GEO Data Portal, compiled from FAO, FAOStat forestry 2010, Forest Resources Assessment 2005 for 1990, 2000 and 2005, Forest Resources Assessment 2010 for 2010 proprietary +geodata_1967 Energy Production - Hydro (IEA) CEOS_EXTRA STAC Catalog 1971-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848081-CEOS_EXTRA.umm_json Production is the production of primary energy, i.e. hard coal, lignite/brown coal, peat, crude oil, NGLs, natural gas, combustible renewables and waste, nuclear, hydro, geothermal, solar and the heat from heat pumps that is extracted from the ambient environment. Production is calculated after removal of impurities (e.g. sulphur from natural gas). Hydro shows the energy content of the electricity produced in hydro power plants. Hydro output excludes output from pumped storage plants. proprietary +geodata_1977 Emissions of NO2 (National Reports, UNFCCC), Including Land Use, Land-Use Change and Forestry CEOS_EXTRA STAC Catalog 2008-12-31 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847701-CEOS_EXTRA.umm_json Emissions of NO2, With LULUCF correspond to total emissions of NO2 and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land) proprietary +geodata_1980 Emissions of NO2 (National Reports, UNFCCC), Excluding Land Use, Land-Use Change and Forestry CEOS_EXTRA STAC Catalog 2008-12-31 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848249-CEOS_EXTRA.umm_json Emissions of NO2, Without LULUCF correspond to total emissions of NO2 without emissions and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land) proprietary +geodata_1982 Emissions of GHGs - from Waste (National Reports, UNFCCC) CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848524-CEOS_EXTRA.umm_json Emissions of ghgs from waste correspond to the total emissions from solid waste disposal on land, wastewater, waste incineration and any other waste management activity. Any CO2 emissions from fossil-based products (incineration or decomposition) are not included here. CO2 from organic waste handling and decay are not included here. proprietary +geodata_1986 Emissions of GHGs - from Industrial Processes (National Reports, UNFCCC) CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848538-CEOS_EXTRA.umm_json Emissions of ghgs from industrial processes corresponds to emissions by-product or fugitive emissions of greenhouse gases from industrial processes. Emissions from fuel combustion in industry are included under Fuel Combustion. proprietary +geodata_1988 Emissions of GHGs - from Agriculture (National Reports, UNFCCC) CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847690-CEOS_EXTRA.umm_json Emission ghgs from agriculture correspond to all anthropogenic emissions from agriculture except for fuel combustion and sewage emissions. proprietary +geodata_1993 Emissions of GHGs - from Transport (National Reports, UNFCCC) CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848703-CEOS_EXTRA.umm_json Emissions of ghgs from transport correspond to the emissions from the combustion and evaporation of fuel for all transport activity, regardless of the sector. Emissions from fuel sold to any air or marine vessel engaged in international transport (international bunker fuels) are not included. proprietary +geodata_1995 Emissions of CO2 - (National Reports, UNFCCC), Including Land Use, Land-Use Change and Forestry CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848391-CEOS_EXTRA.umm_json Emissions of CO2 with LULUCF corresponds to total emissions and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land). proprietary +geodata_1998 Emissions of CO2 - (National Reports, UNFCCC), Excluding Land Use, Land-Use Change and Forestry CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848100-CEOS_EXTRA.umm_json Emissions of CO2 without LULUCF corresponds to total emissions and removals without activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land). proprietary +geodata_2001 Emissions of CH4 - (National Reports, UNFCCC), Excluding Land Use, Land-Use Change and Forestry CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847544-CEOS_EXTRA.umm_json Emissions of CH4 without LULUCF: Total emissions and removals without emissions from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land). proprietary +geodata_2004 Emissions of CH4 - (National Reports, UNFCCC), Including Land Use, Land-Use Change and Forestry CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846732-CEOS_EXTRA.umm_json Emissions of CH4 with LULUCF: Total emissions and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land). proprietary +geodata_2018 Nitrogen (N Total Nutrients) - Production CEOS_EXTRA STAC Catalog 2002-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848954-CEOS_EXTRA.umm_json Fertilizer consumption refers to the application of nutrients in terms of nitrogen (N), phosphate (P2O5), and potash (K2O) consumed in agriculture by a country. All figures are given in Metric Tonnes (T) of plant nutrients (N total nutriens). Production P = (– M) + X + NF + C; Production = less Imports + Exports + Non fertilizer use + Consumption When the data of a country are all available then P = the country actual production; M = actual imports, X = actual exports, C =actual consumption and NF = actual non fertilizer use. proprietary +geodata_2021 Nitrogen (N Total Nutrients) - Consumption CEOS_EXTRA STAC Catalog 2002-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849307-CEOS_EXTRA.umm_json Fertilizer consumption refers to the application of nutrients in terms of nitrogen (N), phosphate (P2O5), and potash (K2O) consumed in agriculture by a country. All figures are given in Tonnes (T) of plant nutrients (N total nutrients). Consumption (C) = Production (P) + Imports (M) - Exports (X) - Non-Fertilizer use (NF); P + M – X – NF = C When data is not known for either fertilizer production or consumption, then the other items are used to derive the residual data. When this occurs, the data is labelled as apparent (e.g. apparent production). Apparent Consumption AC = P + M – (NF + X); Apparent consumption = production + imports - (non-fertilizer use + exports). Apparent consumption figures are developed based on the underlying assumption that supply equals consumption. However, actual apparent consumption may be underestimated due to the following: ( Non-fertilizer use assumed to be zero in the absence of data; Stocks of fertilizer assumed to be zero or stable; Country imports or exports of fertilizer data not available and assumed to be zero). proprietary geodata_2024 Crustaceans - Number of Threatened Species CEOS_EXTRA STAC Catalog 1996-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848936-CEOS_EXTRA.umm_json Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. proprietary +geodata_2026 Molluscs - Number of Threatened Species CEOS_EXTRA STAC Catalog 1996-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849175-CEOS_EXTRA.umm_json Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. proprietary +geodata_2027 Fishes - Threatened Species as Percent of Species Evaluated CEOS_EXTRA STAC Catalog 2007-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849102-CEOS_EXTRA.umm_json Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. A part from the mammals, birds, amphibians and gymnosperms (i.e., those groups completely or almost completely evaluated), the figures in the last column are gross over-estimates of the percentage threatened due to biases in the assessment process towards assessing species that are thought to be threatened, species for which data are readily available, and under-reporting of Least Concern species. The true value for the percentage threatened lies somewhere in the range indicated by the two right-hand columns. In most cases this represents a very broad range. For example, the true percentage of threatened insects lies somewhere between 0.07% and 50%. Hence, although 39% of all species on the IUCN Red List are listed as threatened, this figure needs to be treated with extreme caution given the biases described above. proprietary geodata_2028 Amphibians - Threatened Species as Percent of Species Evaluated CEOS_EXTRA STAC Catalog 2007-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847513-CEOS_EXTRA.umm_json Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. A part from the mammals, birds, amphibians and gymnosperms (i.e., those groups completely or almost completely evaluated), the figures in the last column are gross over-estimates of the percentage threatened due to biases in the assessment process towards assessing species that are thought to be threatened, species for which data are readily available, and under-reporting of Least Concern species. The true value for the percentage threatened lies somewhere in the range indicated by the two right-hand columns. In most cases this represents a very broad range. For example, the true percentage of threatened insects lies somewhere between 0.07% and 50%. Hence, although 39% of all species on the IUCN Red List are listed as threatened, this figure needs to be treated with extreme caution given the biases described above. proprietary geodata_2029 Birds - Threatened Species as Percent of Species Evaluated CEOS_EXTRA STAC Catalog 2007-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847547-CEOS_EXTRA.umm_json Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. A part from the mammals, birds, amphibians and gymnosperms (i.e., those groups completely or almost completely evaluated), the figures in the last column are gross over-estimates of the percentage threatened due to biases in the assessment process towards assessing species that are thought to be threatened, species for which data are readily available, and under-reporting of Least Concern species. The true value for the percentage threatened lies somewhere in the range indicated by the two right-hand columns. In most cases this represents a very broad range. For example, the true percentage of threatened insects lies somewhere between 0.07% and 50%. Hence, although 39% of all species on the IUCN Red List are listed as threatened, this figure needs to be treated with extreme caution given the biases described above. proprietary +geodata_2031 Mammals - Threatened Species as Percent of Species Evaluated CEOS_EXTRA STAC Catalog 2007-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849019-CEOS_EXTRA.umm_json Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). CRITICALLY ENDANGERED (CR) A taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. ENDANGERED (EN) A taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. VULNERABLE (VU) A taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild. A part from the mammals, birds, amphibians and gymnosperms (i.e., those groups completely or almost completely evaluated), the figures in the last column are gross over-estimates of the percentage threatened due to biases in the assessment process towards assessing species that are thought to be threatened, species for which data are readily available, and under-reporting of Least Concern species. The true value for the percentage threatened lies somewhere in the range indicated by the two right-hand columns. In most cases this represents a very broad range. For example, the true percentage of threatened insects lies somewhere between 0.07% and 50%. Hence, although 39% of all species on the IUCN Red List are listed as threatened, this figure needs to be treated with extreme caution given the biases described above. proprietary +geodata_2032 Human Impact on Marine Ecosystems CEOS_EXTRA STAC Catalog 9999-01-01 9999-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849178-CEOS_EXTRA.umm_json The global map of Human Impact on Marine Ecosystems is an ecosystem-specific, multiscale spatial model to synthesize 17 global data sets of anthropogenic drivers of ecological change for 20 marine ecosystems. proprietary geodata_2034 Anthropogenic Drivers of Change on Marine Ecosystems: Nutrient Pollution (Fertilizer) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849367-CEOS_EXTRA.umm_json The Nutrient Pollution (Fertilizer) dataset represents an anthropogenic driver of ecological change for marine ecosystem. proprietary +geodata_2039 Energy Production - Biodiesel (IEA) CEOS_EXTRA STAC Catalog 1992-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846597-CEOS_EXTRA.umm_json Biodiesels includes biodiesel (a methyl-ester produced from vegetable or animal oil, of diesel quality), biodimethylether (dimethylether produced from biomass), Fischer Tropsh (Fischer Tropsh produced from biomass), cold pressed bio-oil (oil produced from oil seed through mechanical processing only) and all other liquid biofuels which are added to, blended with or used straight as transport diesel. Biodiesels includes the amounts that are blended into the diesel - it does not include the total volume of diesel into which the biodiesel is blended. A KTOE is defined as 41.868 TJ (Terajoules) or 11.630 GWh (Gigawatt-Hour). proprietary +geodata_2045 Energy Production - Biogasoline (IEA) CEOS_EXTRA STAC Catalog 1992-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848504-CEOS_EXTRA.umm_json Biogasoline includes bioethanol (ethanol produced from biomass and/or the biodegradable fraction of waste), biomethanol (methanol produced from biomass and/or the biodegradable fraction of waste), bioETBE (ethyl-tertio-butyl-ether produced on the basis of bioethanol; the percentage by volume of bioETBE that is calculated as biofuel is 47%) and bioMTBE (methyl-tertio-butyl-ether produced on the basis of biomethanol: the percentage by volume of bioMTBE that is calculated as biofuel is 36%). Biogasoline includes the amounts that are blended into the gasoline - it does not include the total volume of gasoline into which the biogasoline is blended. A KTOE is defined as 41.868 TJ (Terajoules) or 11.630 GWh (Gigawatt-Hour). proprietary +geodata_2048 Energy Production - Other Liquid Biofuels (IEA) CEOS_EXTRA STAC Catalog 1990-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847933-CEOS_EXTRA.umm_json Other liquid biofuels includes liquid biofuels used directly as fuel other than biogasoline or biodiesels. proprietary geodata_2063 Average Monthly Maximum Temperature - January CEOS_EXTRA STAC Catalog 1950-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848120-CEOS_EXTRA.umm_json average monthly maximum temperature (°C * 10) These layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds. WorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. proprietary geodata_2064 Average Monthly Maximum Temperature - February CEOS_EXTRA STAC Catalog 1950-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847848-CEOS_EXTRA.umm_json average monthly maximum temperature (°C * 10) These layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds. WorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978 proprietary geodata_2065 Average Monthly Maximum Temperature - March CEOS_EXTRA STAC Catalog 1950-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847775-CEOS_EXTRA.umm_json average monthly maximum temperature (°C * 10) These layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds. WorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. proprietary @@ -15746,20 +16643,53 @@ geodata_2084 Average Monthly Minimum Temperature - October CEOS_EXTRA STAC Catal geodata_2085 Average Monthly Minimum Temperature - November CEOS_EXTRA STAC Catalog 1950-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848375-CEOS_EXTRA.umm_json average monthly minimum temperature (°C * 10) These layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds. WorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978 proprietary geodata_2086 Average Monthly Minimum Temperature - December CEOS_EXTRA STAC Catalog 1950-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848515-CEOS_EXTRA.umm_json average monthly minimum temperature (°C * 10) These layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds. WorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978 proprietary geodata_2125 Aquaculture Production - Inland Waters CEOS_EXTRA STAC Catalog 1960-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848493-CEOS_EXTRA.umm_json AQUACULTURE PRODUCTION The annual series of aquaculture production begin in 1950 for the quantities and in 1984 for the values. Aquaculture is the farming of aquatic organisms including fish, mollusks, and crustaceans*. Farming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual or corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by the public as a common property resource, with or without appropriate licenses, are the harvest of fisheries. Production of fish, crustaceans and molluscs is expressed in live weight that is the nominal weight of the aquatic organisms at the time of capture Quantities are given in tonnes. *includes all FAOSTAT group excluded aquatic animals nei, aquatic plants, aquatic mammals. proprietary +geodata_2126 Fishery Production - Inland Waters CEOS_EXTRA STAC Catalog 1960-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848333-CEOS_EXTRA.umm_json TOTAL PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. * includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals proprietary geodata_2127 Cadmium (Cd) Production CEOS_EXTRA STAC Catalog 1999-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848271-CEOS_EXTRA.umm_json World metal production proprietary geodata_2128 Cadmium (Cd) Consumption CEOS_EXTRA STAC Catalog 1999-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847947-CEOS_EXTRA.umm_json World metal consumption proprietary +geodata_2129 Lead (Pb) Production CEOS_EXTRA STAC Catalog 2003-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847966-CEOS_EXTRA.umm_json Lead production refers to World mine production (metal content). proprietary +geodata_2130 Lead (Pb) Consumption CEOS_EXTRA STAC Catalog 2003-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848445-CEOS_EXTRA.umm_json Lead Consumption refers to World refined lead consumption proprietary +geodata_2131 Mercury (Hg) Production CEOS_EXTRA STAC Catalog 1999-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848487-CEOS_EXTRA.umm_json World metal production (primary metal) proprietary geodata_2134 Agricultural Area Irrigated CEOS_EXTRA STAC Catalog 2001-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848664-CEOS_EXTRA.umm_json Agricultural area irrigated, part of the full or partial control irrigated Agricultural land which is actually irrigated in a given year. Often, part of the equipped area is not irrigated for various reasons, such as lack of water, absence of farmers, land degradation, damage, organizational problems etc. proprietary geodata_2135 Country Area CEOS_EXTRA STAC Catalog 1961-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848571-CEOS_EXTRA.umm_json Country area, area of the country including area under inland water bodies, but excluding offshore territorial waters. Possible variations in the data may be due to updating and revisions of the country data and not necessarily to any change of area. proprietary +geodata_2136 Forest Area CEOS_EXTRA STAC Catalog 1990-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848786-CEOS_EXTRA.umm_json Forest area is the land spanning more than 0.5 hectares with trees higher than 5 metres and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use. Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 metres (m) in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate. Includes: areas with bamboo and palms provided that height and canopy cover criteria are met; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry or protective purposes, such as: rubber-wood plantations and cork, oak stands. Excludes: tree stands in agricultural production systems, for example in fruit plantations and agroforestry systems. The term also excludes trees in urban parks and gardens. proprietary geodata_2169 Consumption of Ozone-Depleting Substances - All CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849311-CEOS_EXTRA.umm_json Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. Hydrochlorofluorocarbons (HCFCs) were developed as the first major replacement for CFCs. While much less destructive than CFCs, HCFCs also contribute to ozone depletion. They have an atmospheric lifetime of about 1.4 to 19.5 years. proprietary geodata_2170 Consumption of Ozone-Depleting Substances - Halons CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846698-CEOS_EXTRA.umm_json Consumption of Ozone-Depleting CFCs is the sum of the consumption of the weighted tons of the individual substances in the group—metric tons of the individual substance (defined in the Montreal Protocol on Substances That Deplete the Ozone Layer) multiplied by its ozone-depleting potential. Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. The consumption of CFCs is the national production plus imports, minus exports, minus destroyed quantities, minus feedstock uses of individual CFCs. National annual consumption of CFCs is the sum of the weighted tons (consumption in metric tons multiplied by the estimated ozone-depleting potential) of the individual CFCs. proprietary geodata_2171 Consumption of Ozone-Depleting Substances - Carbon Tetrachloride CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846600-CEOS_EXTRA.umm_json Consumption of Ozone-Depleting CFCs is the sum of the consumption of the weighted tons of the individual substances in the group—metric tons of the individual substance (defined in the Montreal Protocol on Substances That Deplete the Ozone Layer) multiplied by its ozone-depleting potential. Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. The consumption of CFCs is the national production plus imports, minus exports, minus destroyed quantities, minus feedstock uses of individual CFCs. National annual consumption of CFCs is the sum of the weighted tons (consumption in metric tons multiplied by the estimated ozone-depleting potential) of the individual CFCs. proprietary geodata_2172 Consumption of Ozone-Depleting Substances - Methyl Chloroform CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846969-CEOS_EXTRA.umm_json Consumption of Ozone-Depleting CFCs is the sum of the consumption of the weighted tons of the individual substances in the group—metric tons of the individual substance (defined in the Montreal Protocol on Substances That Deplete the Ozone Layer) multiplied by its ozone-depleting potential. Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. The consumption of CFCs is the national production plus imports, minus exports, minus destroyed quantities, minus feedstock uses of individual CFCs. National annual consumption of CFCs is the sum of the weighted tons (consumption in metric tons multiplied by the estimated ozone-depleting potential) of the individual CFCs. proprietary +geodata_2173 Emissions of CO2 per GDP (PPP) CEOS_EXTRA STAC Catalog 1980-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847194-CEOS_EXTRA.umm_json PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2005 international dollars. Source: World Bank, International Comparison Program database. proprietary +geodata_2195 Large Marine Ecosystem (LME) CEOS_EXTRA STAC Catalog 2002-01-01 2002-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849215-CEOS_EXTRA.umm_json LMEs are natural regions of ocean space encompassing coastal waters from river basins and estuaries to the seaward boundary of continental shelves and the outer margins of coastal currents. They are relatively large regions of 200,000 km2 or greater, the natural boundaries of which are based on four ecological criteria: bathymetry, hydrography, productivity, and trophically related populations. proprietary +geodata_2197 Improved Sanitation Coverage - Total Population with Shared Sanitation CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849360-CEOS_EXTRA.umm_json Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved. Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection. Improved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine. proprietary geodata_2199 Carbon Stock in Living Forest Biomass CEOS_EXTRA STAC Catalog 1990-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846805-CEOS_EXTRA.umm_json CARBON STOCK The quantity of carbon in a “pool”, meaning a reservoir or system which has the capacity to accumulate or release carbon. Examples of carbon pools are Living biomass (including Above and below-ground biomass); Dead organic matter (including dead wood and litter); Soils (soils organic matter). The units are mass. proprietary +geodata_2200 Forest Primary Designated Function - Production CEOS_EXTRA STAC Catalog 2010-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847443-CEOS_EXTRA.umm_json DESIGNATED FUNCTIONS (of Forest and Other wooded land) the designated function refers to the function or purpose assigned to a piece of land either by legal prescriptions or by decision of the land owner/manager. It applies to land classified as “Forest” and as “Other wooded land”. Conservation of biodiversity: Forest/Other wooded land designated for conservation of biological diversity. It includes, but is not limited to, Protected Areas. Production: Forest/Other wooded land designated for production and extraction of forest goods, including both wood and non-wood forest products. proprietary +geodata_2201 Forest Primary Designated Function - Conservation of Biodiversity CEOS_EXTRA STAC Catalog 2010-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847285-CEOS_EXTRA.umm_json DESIGNATED FUNCTIONS (of Forest and Other wooded land) the designated function refers to the function or purpose assigned to a piece of land either by legal prescriptions or by decision of the land owner/manager. It applies to land classified as “Forest” and as “Other wooded land”. Conservation of biodiversity: Forest/Other wooded land designated for conservation of biological diversity. It includes, but is not limited to, Protected Areas. Production: Forest/Other wooded land designated for production and extraction of forest goods, including both wood and non-wood forest products. proprietary +geodata_2202 Forest within Protected Areas CEOS_EXTRA STAC Catalog 2010-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847496-CEOS_EXTRA.umm_json As part of FRA 2010, countries were asked to provide information on the area of forest contained in protected areas systems. This is not an easy task where spatially explicit information is missing or outdated since not all protected areas are fully forested. However, most of the large, forest-rich countries did provide this information for all four reporting years. proprietary +geodata_2203 Forest Revenue CEOS_EXTRA STAC Catalog 2005-01-01 2005-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847552-CEOS_EXTRA.umm_json Public expenditure and revenue collection from forestry are measures of the financial flows between government and the forestry sector. In FRA 2010 forest revenue was defined to include all taxes, fees, charges and royalties collected specifically from the domestic production and trade of forest products, but it excluded general taxes collected from all sectors of the economy (e.g. corporation tax and sales tax). proprietary +geodata_2206 Food Supply Quantity - Cereals CEOS_EXTRA STAC Catalog 1961-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846748-CEOS_EXTRA.umm_json Food: total calories Refers to the total amount of food available for human consumption expressed in kilocalories (kcal). Caloric content is derived by applying the appropriate food composition factors to the quantities of the commodities and shown in million units. proprietary +geodata_2207 Livestock Production Index Base 1999-2001 - Total CEOS_EXTRA STAC Catalog 1961-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846571-CEOS_EXTRA.umm_json The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 1999-2001. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. proprietary geodata_2208 Cereals - Area Harvested CEOS_EXTRA STAC Catalog 1961-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849210-CEOS_EXTRA.umm_json Cereals also includes other cereals such as mixed grains and buckwheat. Crop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption). When the production data available refers to a production period falling into two successive calendar years and it is not possible to allocate the relative production to each of them, it is usual to refer production data to that year into which the bulk of the production falls. Crop production data are stored in tonnes (T). proprietary +geodata_2215 Hazardous Pesticides - Exports CEOS_EXTRA STAC Catalog 2007-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847283-CEOS_EXTRA.umm_json Refers to the value of the type of pesticide (put up in forms or packings for retail sale or as preparations or articles), provided to (exports) or received (imported) from the rest of the world. Differences between figures given for total exports and total imports at the world level may be due to several factors, e.g. the time lag between the dispatch of goods from exporting country and their arrival in the importing country; the use of different classification of the same product by different countries; or the fact that some countries supply data on general trade while others give data on special trade. proprietary +geodata_2216 Hazardous Pesticides - Imports CEOS_EXTRA STAC Catalog 2007-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847501-CEOS_EXTRA.umm_json Refers to the value of the type of pesticide (put up in forms or packings for retail sale or as preparations or articles), provided to (exports) or received (imported) from the rest of the world. Differences between figures given for total exports and total imports at the world level may be due to several factors, e.g. the time lag between the dispatch of goods from exporting country and their arrival in the importing country; the use of different classification of the same product by different countries; or the fact that some countries supply data on general trade while others give data on special trade. proprietary geodata_2217 Agricultural Area Certified Organic CEOS_EXTRA STAC Catalog 2003-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847551-CEOS_EXTRA.umm_json Land area exclusively dedicated to organic agriculture and managed by applying organic agriculture methods. It refers to the land area fully converted to organic agriculture. It is the portion of land area (including arable lands, pastures or wild areas) managed (cultivated) or wild harvested in accordance with specific organic standards or technical regulations and that has been inspected and approved by a certification body. proprietary geodata_2222 Adjusted Human Water Security Threat CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849396-CEOS_EXTRA.umm_json Rivers maintain unique biotic resources and provide critical water supplies to people. The Earth's limited supplies of fresh water and irreplaceable biodiversity are vulnerable to human mismanagement of watersheds and waterways. Multiple environmental stressors, such as agricultural runoff, pollution and invasive species, threaten rivers that serve 80 percent of the world’s population. These same stressors endanger the biodiversity of 65 percent of the world’s river habitats putting thousands of aquatic wildlife species at risk. Efforts to abate fresh water degradation through highly engineered solutions are effective at reducing the impact of threats but at a cost that can be an economic burden and often out of reach for developing nations. proprietary +geodata_2223 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2000 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849273-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary +geodata_2224 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2001 CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849194-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary +geodata_2225 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2003 CEOS_EXTRA STAC Catalog 2003-01-01 2003-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849086-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary +geodata_2226 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2004 CEOS_EXTRA STAC Catalog 2004-01-01 2004-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848916-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary +geodata_2227 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2005 CEOS_EXTRA STAC Catalog 2005-01-01 2005-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849045-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary +geodata_2228 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2006 CEOS_EXTRA STAC Catalog 2006-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847468-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary +geodata_2229 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2007 CEOS_EXTRA STAC Catalog 2007-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847271-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary +geodata_2230 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2008 CEOS_EXTRA STAC Catalog 2008-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849203-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary +geodata_2231 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2009 CEOS_EXTRA STAC Catalog 2009-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849060-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary +geodata_2232 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2002 CEOS_EXTRA STAC Catalog 2002-01-01 2002-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848919-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary +geodata_2237 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2010 CEOS_EXTRA STAC Catalog 2010-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849274-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary geodata_2240 Arsenic in Groundwater - Probability of Occurrence CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848741-CEOS_EXTRA.umm_json Global assessment of the probability of occurrence of excessive Arsenic concentrations proprietary +geodata_2244 Mineral Resources Outside the United States CEOS_EXTRA STAC Catalog 2006-01-01 2010-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848393-CEOS_EXTRA.umm_json Mineral facilities and operations outside the United States compiled by the National Minerals Information Center of the USGS. This representation combines source data from five previous publications. National Minerals Information Center (NMIC) makes available a wide variety of commodity statistics and other mineral resource supply and production information both within the United States and internationally. These databases complement aggregate commodity statistics collected by the NMIC. proprietary +geodata_2245 Mineral Resources Data System (MRDS) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848248-CEOS_EXTRA.umm_json Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. This product is a digest in which the fields chosen are those most likely to contain valid information. This digest of the complex mineral resources database is intended for use as reference material supporting mineral resource and environmental assessments on local to regional scale worldwide. proprietary +geodata_2246 Forests Certified by PEFC Chain of Custody - Number of Certifications CEOS_EXTRA STAC Catalog 2001-01-01 2011-01-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848423-CEOS_EXTRA.umm_json PEFC's Chain of Custody certification is a mechanism for tracking certified material from the forest to the final product to ensure that the wood, wood fibre or non-wood forest produce contained in the product or product line can be traced back to certified forests. It is an essential part of the PEFC system which ensures that claims about products originating in sustainably managed forests are credible and verifiable throughout the whole supply chain. It is used to certify entities all along the value-chain of forest-based products proprietary +geodata_2247 Forests Certified by PEFC Chain of Custody - Area CEOS_EXTRA STAC Catalog 2000-01-01 2011-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848522-CEOS_EXTRA.umm_json PEFC's Chain of Custody certification is a mechanism for tracking certified material from the forest to the final product to ensure that the wood, wood fibre or non-wood forest produce contained in the product or product line can be traced back to certified forests. It is an essential part of the PEFC system which ensures that claims about products originating in sustainably managed forests are credible and verifiable throughout the whole supply chain. It is used to certify entities all along the value-chain of forest-based products proprietary +geodata_2251 Green Water Footprint of National Production CEOS_EXTRA STAC Catalog 1996-01-01 2005-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848254-CEOS_EXTRA.umm_json Green water footprint :Volume of rainwater consumed during the production process. This is particularly relevant for agricultural and forestry products (products based on crops or wood), where it refers to the total rainwater evapotranspiration (from fields and plantations) plus the water incorporated into the harvested crop or wood. Water footprint of national production ? Another term for the ?water footprint within a nation?: Water footprint within a nation Is defined as the total freshwater volume consumed or polluted within the territory of the nation. proprietary geodata_2252 Blue Water Footprint of National Production CEOS_EXTRA STAC Catalog 1996-01-01 2005-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848422-CEOS_EXTRA.umm_json Blue water footprint ? Volume of surface and groundwater consumed as a result of the production of a good or service. Consumption refers to the volume of freshwater used and then evaporated or incorporated into a product. It also includes water abstracted from surface or groundwater in a catchment and returned to another catchment or the sea. It is the amount of water abstracted from ground- or surface water that does not return to the catchment from which it was withdrawn. Water footprint of national production ? Another term for the ?water footprint within a nation?: Water footprint within a nation Is defined as the total freshwater volume consumed or polluted within the territory of the nation. proprietary +geodata_2253 Grey Water Footprint of National Production CEOS_EXTRA STAC Catalog 1996-01-01 2005-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848523-CEOS_EXTRA.umm_json Grey water footprint – The grey water footprint of a product is an indicator of freshwater pollution that can be associated with the production of a product over its full supply chain. It is defined as the volume of freshwater that is required to assimilate the load of pollutants based on existing ambient water quality standards. It is calculated as the volume of water that is required to dilute pollutants to such an extent that the quality of the water remains above agreed water quality standards. Water footprint of national consumption – Is defined as the total amount of fresh water that is used to produce the goods and services consumed by the inhabitants of the nation. The water footprint of national consumption can be assessed in two ways. The bottom-up approach is to consider the sum of all products consumed multiplied with their respective product water footprint. In the top-down approach, the water footprint of national consumption is calculated as the total use of domestic water resources plus the gross virtual-water import minus the gross virtual-water export. Water footprint of national production – Another term for the ‘water footprint within a nation’: Water footprint within a nation Is defined as the total freshwater volume consumed or polluted within the territory of the nation. proprietary geoecology_R1_656_1 Geoecology: County-Level Environmental Data for the United States, 1941-1981 ORNL_CLOUD STAC Catalog 1941-01-01 1981-12-31 -124.76, 24.5, -66.95, 49.38 https://cmr.earthdata.nasa.gov/search/concepts/C2761762895-ORNL_CLOUD.umm_json The Geoecology database is a compilation of environmental data for the period 1941 to 1981. The Geoecology database contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files. Data represent the conterminous United States at the county level. These historical data are provided as a source of 1970s baseline environmental conditions for the United States. proprietary geomorphology_australasian_seafloor_1 Geomorphology Map of the Australasian Seafloor AU_AADC STAC Catalog 2008-01-01 2011-12-31 78.1651, -68.6815, -99.8011, 23.9811 https://cmr.earthdata.nasa.gov/search/concepts/C1214313437-AU_AADC.umm_json "A geomorphology map of the Australasian seafloor was created as a Geographic Information System layer for the study described in Torres, Leigh G., et al. ""From exploitation to conservation: habitat models using whaling data predict distribution patterns and threat exposure of an endangered whale."" Diversity and Distributions 19.9 (2013): 1138-1152. The geomorphology map was generated using parameters derived from the General Bathymetric Chart of the World (GEBCO 2008, http://www.gebco.net/), with 30 arc-second grid resolution. Geomorphology features were delineated manually with a consistent spatial resolution. Each feature was assigned a primary attribute of depth zone and a secondary attribute of morphological feature. The following feature classes are defined: shelf, slope, rise, plain, valley, trench, trough, basin, hills(s), mountains(s), ridges(s), plateau, seamount. Further information (methods, definitions and an illustration of the geomorphology map) is provided in Appendix S2 of the paper which is available for download (see related URLs)." proprietary gfscpex_1 Global Forecast System (GFS) CPEX GHRC_DAAC STAC Catalog 2017-05-24 2017-07-20 -100, 5, -45, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2611060678-GHRC_DAAC.umm_json The Global Forecast System (GFS) CPEX dataset includes model data simulated by the Global Forecast System (GFS) model for the Convective Process Experiment (CPEX) field campaign. The NASA Convective Processes Experiment (CPEX) aircraft field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May-25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May-24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 24, 2017 through July 20, 2017 and are available in netCDF-3 format. proprietary @@ -17133,9 +18063,11 @@ increment_star-162_1.0 Increment* ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.9 individual-tree-tls-point-clouds-for-tree-volume-estimation_1.0 Individual tree TLS point clouds for tree volume estimation ENVIDAT STAC Catalog 2023-01-01 2023-01-01 7.6011658, 47.2560873, 8.6585999, 47.609095 https://cmr.earthdata.nasa.gov/search/concepts/C3226082133-ENVIDAT.umm_json ## Dataset This dataset is based on terrestrial laser scanning (TLS) data acquired during winter 2020/2021 in leaf-off conditions, with a Leica BLK 360 instrument following a tree-centric scanning pattern. The data was acquired on two sites (47.42°N 8.49°E and 47.504°N, 7.78°E), both of which were managed mixed temperate forest stands. Individual trees were semi-automatically segmented from the co-registered TLS point clouds. ## Background Accurate estimates of individual tree volume or biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data. Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, with existing approaches ranging from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories. In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM). Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total tree, stem, coarse wood and fine branch volume. proprietary induced-rockfall-dataset-chant-sura_1.0 Induced Rockfall Dataset #2 (Chant Sura Experimental Campaign), Flüelapass, Grisons, Switzerland ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.9632928, 46.7401819, 9.9743649, 46.7500628 https://cmr.earthdata.nasa.gov/search/concepts/C2789815108-ENVIDAT.umm_json The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 46, 200, 800 and 2670 kg of mass. Additionally available are all the reconstructed data sets for all trajectories with videogrammetry installed comprising StoneNode data streams for rocks equipped with a sensor. The data set consists of: # Resources (individual zip-archives) __ExperimentalRuns__: Archive with all available StoneNode data streams and its respective figure (.mat files) __Input__: Archive containing folders * GNSS: 182 Deposition points of all different weight and shape classes, shape files for release point, cliff and scree line, * UAS: UAS generated pre- and post-experimental digital surface models and orthophoto of the four most important experimental days and * VG_Coord: Reconstruction input: Videogrammetry based coordinate list along side with the corresponding sensor/video times __EOTA__: Point cloud of cubic EOTA(111) and platy EOTA(221) rock as input for RAMMS::ROCKFALL or other suitable rockfall simulation codes incorporating complex shape files. __Output__: Reconstruced trajectory information for all 82 reconstructed trajectories __Video__: available video streams for all runs ## Further information Preceeding publications concering the deployed sensors and the reconstruction methods are found in the subsequent references: A. Caviezel et al., Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/ P. Niklaus et al., StoneNode: A low-power sensor device for induced rockfall experiments, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/ Caviezel, A., Demmel, S. E., Ringenbach, A., Bühler, Y., Lu, G., Christen, M., Dinneen, C. E., Eberhard, L. A., von Rickenbach, D., and Bartelt, P.: Reconstruction of four-dimensional rockfall trajectories using remote sensing and rock-based accelerometers and gyroscopes, Earth Surf. Dynam., 7, 199–210, https://doi.org/10.5194/esurf-7-199-2019, 2019 proprietary inishell-2-0-4_2.0.4 Inishell-2.0.4 ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815121-ENVIDAT.umm_json This is the source code of the Inishell-2.0.4 flexible Graphical User Interface. It is configured through an XML file for applications that themselves need to be configured via ini-files. It allows to set constraints regarding the sections, keys and values that may be present in the ini-files that are produced by the end user. It is released under the GPL-v3 or later license. Precompiled binaries are available at https://models.slf.ch/p/inishell-ng/downloads/ while the development takes place at https://code.wsl.ch/snow-models/inishell (gitlab forge). proprietary +inpe_CPTEC_GLOBAl_FORECAST Global Meteorological Model Analysis and Forecast Images (INPE/CPTEC) CEOS_EXTRA STAC Catalog 1970-01-01 -120, -60, 0, 30 https://cmr.earthdata.nasa.gov/search/concepts/C2227456094-CEOS_EXTRA.umm_json "CPTEC offers global model analysis and forecast images for twelve meteorological parameters. Forecast time steps range from the initial analysis each day out to six days. The user may choose forecasts from the most recent forecast run back to the previous 36 hours. Parameters Forecasted: Mean Sea Level Pressure Temperature at 1000 hPa Relative Humidity at 925 hPa, 850 hPa Vertical p_Velocity at 850 hPa, 500 hPa, 200 hPa Velocity Potential at 925 hPa, 200 hPa Stream Function at 925 hPa, 200 hPa 500/1000 hPa Thickness Advection of Temperature at 925 hPa, 850 hPa, 500 hPa Advection of Vorticity at 925 hPa, 850 hPa, 500 hPa Convergence of Humidity Flux at 925 hPa, 850 hPa Streamlines and Wind Speed at 925 hPa, 850 hPa, 200 hPa Total Precipitation Last 24 Hours All forecast images can be obtained via World Wide Web from the CPTEC Home Page. Link to: ""http://www.cptec.inpe.br/""" proprietary input-data-for-break-point-detection-of-swiss-snow-depth-time-series_1.0 Input data for break point detection of Swiss snow depth time series ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815138-ENVIDAT.umm_json Data set consists of monthly mean values for snow depth and days with snow on the ground intended for the use of break detection with ACMANT, Climatol and HOMER. List and coordinates of stations used as well as metadata and break detection results from all three methods is included. ## Columns Monthly means for each hydrological year: Nov, Dec, Jan, Feb, Mar, Apr with May to Oct set to zero proprietary input-data-for-impact-assessment-of-homogenised-snow-series_1.0 Input data for impact assessment of homogenised snow series ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226082287-ENVIDAT.umm_json # Input data for the following research article: Impact assessment of homogenised snow depth series on trends The data consists of separate output files from the following homogenisation methods: * Climatol * HOMER * interpQM The variable is seasonal mean snow depth (HSavg) plot.data is an additional data frame containing trends of HSavg (station, method, value, pvalue, altitude) proprietary insects_subsaharanAfrica A Checklist of the Insects of Subsaharan Africa SCIOPS STAC Catalog 2000-01-01 13.68, -35.9, 33.98, -21.27 https://cmr.earthdata.nasa.gov/search/concepts/C1214611706-SCIOPS.umm_json "One of the most basic needs for inventorying, exploiting and monitoring the changes in the insect diversity of Africa is a complete list of species which are already know to occur in Africa. Surprisingly, such a basic list does not exist, despite some 250 years of formal scientific description of life on earth. The International Centre of Insect Physiology and Ecology (ICIPE), along with the National Museum of Natural History, is therefore sponsoring the production of the list, which will provide a reliable platform of 'standard' names for species on which many other projects depend. This list, or authority file, will greatly enhance communication both among scientists and between scientists and users of scientific data. The African list will also be a major contribution toward the proposed list of world species (e.g. the Global Biodiversity Information Facility (GBIF) and Species 2000 initiative of DIVERSITAS). A demonstration database is provided for the species of the orders Odonata (dragonflies and damselflies), Ephemeroptera (mayflies), Plecoptera (stoneflies), Megaloptera (alderflies), Hemiptera-Heteroptera (true bugs), Homoptera (cicadas, leafhoppers, planthoppers, scales, and others), and Trichoptera (caddisflies). Invitation to collaboration: Compilation of the checklist is being coordinated by Nearctica (formerly Entomological Information Specialists), because of their experience with Nomina Insecta Nearctica. We are attempting to collaborate with known specialists as contributors and reviewers, but we welcome additional suggestions of collaborators. Inquires can be directed to Scott Miller (miller.scott@nmnh.si.edu). Information was obtained from ""http://entomology.si.edu/""." proprietary +instm_trawl National Institute of Marine Sciences and Technologies - Trawling Surveys CEOS_EXTRA STAC Catalog 1983-04-16 2006-11-03 5.14, 17.1, 13.37, 38.1 https://cmr.earthdata.nasa.gov/search/concepts/C2232477692-CEOS_EXTRA.umm_json The National Institute of Marine Sciences and Technologies (INSTM) fo Tunisia has four laboratories. Regular trawl surveys are done by the Laboratory of Marine Living Resources to assess the exploitable resource stocks. This dataset consists of 7664 records of 90 families. proprietary intercomparison-of-photogrammetric-platforms_1.0 Photogrammetric snow depth maps from satellite-, airplane-, UAS and terrestrial platforms from the Davos region (Switzerland) ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.7544861, 46.6485877, 10.0428772, 46.844319 https://cmr.earthdata.nasa.gov/search/concepts/C2789815195-ENVIDAT.umm_json "This data set contains the produced snow depth maps as well as the reference data set (manual and snow pole measurements) from our paper ""Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping"". __Abstract.__ Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability of snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas, and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques, as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellites (Pléiades), airplane (Ultracam Eagle M3), Unmanned Aerial System (eBee+ with S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D), were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while Unmanned Aerial Systems (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing, as well as using remote observations of fixed snow poles. When comparing snow depth maps with manual and snow pole measurements the root mean square error (RMSE) values and the normalized median deviation (NMAD) values were 0.52 m and 0.47 m respectively for the satellite snow depth map, 0.17 m and 0.17 m for the airplane snow depth map, 0.16 m and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with 4 manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 m and 0.38 m for the satellite snow depth map, 0.12 m and 0.11 m for the airplane snow depth map, 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km2), the snow depth map from the satellite yielded a RMSE value of 0.92 m and a NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas." proprietary interview-guide-and-transcripts_1.0 Interview guide and transcripts (CONCUR Aim 2 on Governance) ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815227-ENVIDAT.umm_json This dataset is composed of an interview guide used to conduct 43 in-depth, qualitative, and in-person interviews with planning experts, academics and practitioners, in 14 European urban regions and the corresponding interview transcripts (verbatim). These interviews were conducted in the selected urban regions between March and September 2016. They were first digitally recorded and later thoroughly transcribed. proprietary intratrait_1.0 intratrait ENVIDAT STAC Catalog 2022-01-01 2022-01-01 6.02, -46.64, 178.52, 53.46 https://cmr.earthdata.nasa.gov/search/concepts/C3226082491-ENVIDAT.umm_json This data set was used to test whether species specialized to high elevations or with narrow elevational ranges show more conservative (i.e. less variable) trait responses across their elevational distribution, or in response to neighbours, than species from lower elevations or with wider elevational ranges. We did so by studying intraspecific trait variation of 66 species along 40 elevational gradients in four countries (Switzerland, Australia, New Zealand, China) in both hemispheres. As an indication of potential neighbour interactions that could drive trait variation, we also analysed plant species’ height ratio, its height relative to its nearest neighbour. The following traits and parameters were measured and are available in this data set: As an indication of plant stature, we measured vegetative and generative height, where vegetative height was distance from soil to highest vegetative leaf and generative height was distance to the highest point on the reproductive shoot. As a measure of reproductive investment, we noted the presence of flowers on the randomly chosen individuals (see below). As a measure of individual and genet basal area, we measured individual plant and patch diameters, in two dimensions (along the largest diameter and perpendicular to it). In clonal plant species, plant diameter was equivalent to an individual rosette, whereas patch diameter referred to the whole genet and could represent the size of a tuft, tussock or cushion. For genera with more singular growth forms (e.g., some Gentiana species) plant and patch diameter were the same. The two diameter measurements were made at right angles, allowing estimates of patch and plant areas to be calculated as an ellipse (i.e., area = 0.5 a 0.5 b Π). All traits were measured on ten randomly selected individuals per site. Flower count data was considered in a binary fashion on a per individual basis (because for some species individuals only produce one flower when flowering) so that the presence or absence of flower(s) was a nominal value between 0 and 10 for each species at each site. We then collected at least three leaves (up to 30 for small and light leaves) from each of the first three individuals selected from each species for determination of leaf dry matter content (LDMC) and specific leaf area (SLA). For calculations of LDMC and SLA, fresh leaves were scanned on a flatbed scanner to determine leaf area. Leaves were then weighed on a balance to a precision of +/- 0.001g, prior to being air dried and reweighed with a balance to a precision of +/- 0.0001g. LDMC was calculated by dividing dry leaf mass by fresh leaf mass. SLA was calculated by dividing leaf area by dry leaf mass. Additionally, within an area of 10 cm diameter around the target individual, we determined the tallest neighbouring species and measured its vegetative and generative height, and estimated the percent cover of the target species, other vegetation, rock, and bare soil. For more details see Rixen et al. 2022, Journal of Ecology. proprietary @@ -17144,6 +18076,9 @@ islscp2_soils_1deg_1004_1 ISLSCP II Global Gridded Soil Characteristics ORNL_CLO isotope-lab_1.0 Stable Isotope Research Lab WSL ENVIDAT STAC Catalog 2019-01-01 2019-01-01 8.45634, 47.360992, 8.45634, 47.360992 https://cmr.earthdata.nasa.gov/search/concepts/C2789815291-ENVIDAT.umm_json "![alt text](https://www.envidat.ch/dataset/6480bbef-06bf-4da8-8502-96f4def23358/resource/0a9d712c-38ad-4f55-842e-36b21a7e1b97/download/isotopelab_wsl.jpg ""Isotope Laboratory WSL"") The lab uses stable isotope ratios of the light elements hydrogen, carbon, nitrogen and oxygen as a universal tool for studying physical, chemical and biological processes in forests and other ecosystems. Due to natural isotope fractionations, environmental changes leave unique fingerprints in organic matter, like tree-rings. It is, therefore, possible to detect the influence of ongoing climate changes on plant physiology. By applying isotopically labelled substrate, matter fluxes through plants and soil can be traced and better understood. The facility has isotope-Ratio mass-spectrometers and dedicated periphery for the analysis of organic matter, gas samples and water samples. With HPLC and GC we apply compound-specific isotope ratio analysis of sugars and organic acids. Additional isotope mass-spectrometers are operated by the Zentrallabor WSL." proprietary isslis_v2_fin_2 Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Science Data V2 GHRC_DAAC STAC Catalog 2017-03-01 2023-11-16 -180, -55, 180, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2303212754-GHRC_DAAC.umm_json The Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Science Data dataset was collected by the LIS instrument mounted on the ISS and are used to detect the distribution and variability of total lightning occurring in the Earth’s tropical and subtropical regions. This dataset consists of quality controlled science data. This data collection can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. The data are available in both HDF-4 and netCDF-4 formats, with corresponding browse images in GIF format. proprietary isslisg_v2_fin_2 Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Backgrounds V2 GHRC_DAAC STAC Catalog 2017-03-01 2023-11-16 -180, -55, 180, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2303219035-GHRC_DAAC.umm_json The Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Backgrounds dataset was collected by the LIS instrument mounted on the ISS and are used to detect the distribution and variability of total lightning occurring in the Earth’s tropical and subtropical regions. This dataset consists of quality controlled science data. This data collection can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. The data are available in both HDF-4 and netCDF-4 formats. proprietary +iziko_Crustaceans iziko South African Museum - Crustacean Collection CEOS_EXTRA STAC Catalog 1883-01-01 2003-12-31 -97.22, -74.58, 172.7, 34.62 https://cmr.earthdata.nasa.gov/search/concepts/C2232477683-CEOS_EXTRA.umm_json The iziko South African Museum houses the most important crustacean (crabs, lobsters, shrimps, barnacles) collection in South Africa. Significant past contributions were made by K.H. Barnard, J.R. Grindley and B.F. Kensley (Crustacea). It currently contains 5101 records of 274 families. proprietary +iziko_molluscs iziko South African Museum - Mollusc Collection CEOS_EXTRA STAC Catalog 1881-01-01 2000-12-31 -159.56, -59.45, 165.95, 50.6 https://cmr.earthdata.nasa.gov/search/concepts/C2232477686-CEOS_EXTRA.umm_json The iziko South African Museum's mollusc collection of southern African species is the second largest mollusc collection in southern Africa. Significant additions were made in the past by K.H. Barnard. It currently contains 6078 records. The families were not provided. proprietary +iziko_sharks iziko South African Museum - Shark Collection CEOS_EXTRA STAC Catalog 1828-04-01 2005-12-31 179.73, 62, 179.25, 73.3 https://cmr.earthdata.nasa.gov/search/concepts/C2232477682-CEOS_EXTRA.umm_json This collection has global holdings. It includes numerous representatives of eight of the shark groups, most representatives of the Batoids and Chimaeras, including rare species. Significant material is being acquired from, fisheries research and tooth fish long-lining and fishing company by-catches. proprietary jetty_sat_1 Jetty Peninsula Satellite Image Map 1:500 000 AU_AADC STAC Catalog 1991-09-01 1991-09-30 65, -72, 73, -70 https://cmr.earthdata.nasa.gov/search/concepts/C1214313527-AU_AADC.umm_json Satellite image map of Jetty Peninsula, Mac. Robertson Land, Antarctica. This map is part (d) of a series of four north Prince Charles Mountains maps. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991. The map is at a scale of 1:500000, and was produced from Landsat TM and Landsat MSS scenes. It is projected on a Lambert Conformal Conic projection, and shows traverses/routes/foot tracks, glaciers/ice shelves, and stations/bases. The map has only geographical co-ordinates. proprietary jfetzer-phosphatase-leaching_1.0 Phosphatase leaching ENVIDAT STAC Catalog 2020-01-01 2020-01-01 7.8588858, 49.9488636, 13.7036124, 53.3024328 https://cmr.earthdata.nasa.gov/search/concepts/C2789815303-ENVIDAT.umm_json Data on phosphomonoesterase activity in forest topsoil leachates and soil extracts as well as P forms in the leachate. Leachate samples were taken in Feb./Mar. and July 2019 with zero-tension lysimeters at two sites in Germany of contrasting phosphorus availability from the litter, the Oe/Oa, and the A horizon in beech forest. Soil samples were taken in July 2019. For methods see publication. proprietary jornada_albedo_667_1 PROVE Surface albedo of Jornada Experimental Range, New Mexico, 1997 ORNL_CLOUD STAC Catalog 1997-05-22 1997-05-27 -106.75, 32.5, -106.75, 32.5 https://cmr.earthdata.nasa.gov/search/concepts/C2804796522-ORNL_CLOUD.umm_json The objective of this study was to determine the spatial variations in field measurements of broadband albedo as related to the ground cover and under a range of solar conditions during the Prototype Validation Exercise (PROVE) at the Jornada Experimental Range in New Mexico on May 20-30, 1997. proprietary @@ -17165,6 +18100,7 @@ kdoximpacts_1 KDOX NEXRAD IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-01 2020-03-0 kdtximpacts_1 KDTX NEXRAD IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-01 2020-03-01 -89.0956, 38.5699, -77.8477, 46.8301 https://cmr.earthdata.nasa.gov/search/concepts/C2020898934-GHRC_DAAC.umm_json The KDTX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary kdvnimpacts_1 KDVN NEXRAD IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-01 2020-03-01 -96.109, 37.482, -85.053, 45.742 https://cmr.earthdata.nasa.gov/search/concepts/C2025219690-GHRC_DAAC.umm_json The KDVN NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary kenximpacts_1 KENX NEXRAD IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-01 2020-03-01 -79.677, 38.457, -68.451, 46.716 https://cmr.earthdata.nasa.gov/search/concepts/C2025220226-GHRC_DAAC.umm_json The KENX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary +kenya_marine Kenya Marine and Fisheries Research Institute - Marine Species CEOS_EXTRA STAC Catalog 1970-01-01 37.90619, -4.71, 41.74052, -0.0235591 https://cmr.earthdata.nasa.gov/search/concepts/C2232477677-CEOS_EXTRA.umm_json Kenya Marine and Fisheries Research Institute (KMFRI) is a State Corporation in the Ministry of Fisheries Development of the Government of Kenya. It is mandated to conduct aquatic research covering all the Kenyan waters and the corresponding riparian areas including the Kenyan's EEZ in the Indian Ocean waters. This collection was compiled from publications, and it currently consists of 3080 records of 533 families. proprietary kerg_ant_bathy_1 Bathymetric Grid for the region 60E to 90E, 48S to 70S AU_AADC STAC Catalog 1985-01-01 2007-05-01 60, -70, 90, -48.45 https://cmr.earthdata.nasa.gov/search/concepts/C1214311162-AU_AADC.umm_json This dataset is a bathymetric grid of the region 60E to 90E and 48.45S to 70S, created in a geographic coordinate system based on a WGS84 horizontal datum. The grid has a cell size of 0.005 degrees. Most of the work involved creating a bathymetric grid of the region 60E to 90E and 55S to 70S which was generated from the latest available multibeam swath bathymetry, fisheries' surveys and satellite altimetry data. A report outlining the development of this grid is available for download (see the related url below). This grid was then merged with the bathymetric grid described by the metadata record 'Bathymetric Grid of Heard Island - Kerguelen Plateau Region (2005)', which covers the region 68E to 80E and 48S to 56S. Hence the final grid has two 'No data' areas between 48.45S to 55S: 60E to 68E and 80E to 90E. The final grid is available for download as a geotiff and ArcInfo ascii file and contours derived from the grid are available for download as a shapefile (see the related urls below). proprietary kfcximpacts_1 KFCX NEXRAD IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-01 2020-03-01 -85.449, 32.895, -75.099, 41.154 https://cmr.earthdata.nasa.gov/search/concepts/C2025222404-GHRC_DAAC.umm_json The KFCX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary kgrbimpacts_1 KGRB NEXRAD IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-01 2020-03-01 -93.906, 40.369, -82.316, 48.629 https://cmr.earthdata.nasa.gov/search/concepts/C2025222762-GHRC_DAAC.umm_json The KGRB NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary @@ -17372,6 +18308,10 @@ macquarie_quickbird_mapping_1 Macquarie Island mapping from Quickbird satellite macquarie_sma_gis_1 Macquarie Island Special Management Areas AU_AADC STAC Catalog 2003-11-01 2003-11-30 158.77, -54.78, 158.95, -54.49 https://cmr.earthdata.nasa.gov/search/concepts/C1214313610-AU_AADC.umm_json Macquarie Island Nature Reserve Special Management Areas were originally defined for 2003/04 and have since been updated. Special Management areas are declared from year to year to protect vulnerable species, vegetation communities or sites extremely vulnerable to human disturbance. Related URLs provide: 1 the download of a shapefile with the boundaries of the Special Management Areas; and 2 a link to the website of Parks and Wildlife Service, Tasmania with information about the Special Management Areas. proprietary macquarie_taspaws_grid_1 A grid system used by the Parks and Wildlife Service, Tasmania, for Macquarie Island, 1974 to June 2001 AU_AADC STAC Catalog 1974-01-01 2001-06-02 158.7322, -54.8011, 158.9781, -54.4714 https://cmr.earthdata.nasa.gov/search/concepts/C1214313536-AU_AADC.umm_json "This metadata record describes a grid system for Macquarie Island formerly used by the Parks and Wildlife Service, Tasmania. The grid was first adopted by Irynej Skira in 1974 and was based on the 1:50000 scale map of the island published by Australia's Division of National Mapping in 1971. Data was continually recorded on this system up to June 2001 when the Universal Transverse Mercator (UTM) grid was adopted. The dataset available for download from this metadata record includes a map with the grid system and a document compiled by Geoff Copson with details about converting from the Parks and Wildlife grid to the UTM grid. Geoff states in the document ""The 1971 map was particularly inaccurate in the centre two quarters of the island. The grid for the Parks and Wildlife Service system was hand drawn and fairly variable. Conversion values are averaged out on coastal points around the island.""" proprietary macquarie_tracks_1 Macquarie Island walking tracks AU_AADC STAC Catalog 1997-09-01 2012-06-30 158.77, -54.78, 158.95, -54.48 https://cmr.earthdata.nasa.gov/search/concepts/C1214311191-AU_AADC.umm_json This GIS dataset represents walking tracks on Macquarie Island and was compiled by the Australian Antarctic Data Centre from surveys and other sources. This data is displayed in a pair of A3 1:50000 maps of Macquarie Island (see a Related URL). proprietary +madagascar_diatoms MADAGASCAR National Oceanographic Data Centre - Diatoms CEOS_EXTRA STAC Catalog 2003-10-01 2004-10-31 43.61, -23.38, 43.68, -23.35 https://cmr.earthdata.nasa.gov/search/concepts/C2232477687-CEOS_EXTRA.umm_json The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar’s northeast coast. This dataset of diatoms has been collected at three stations in Toliara Bay, and it currently consists of 2754 records of 19 families. proprietary +madagascar_dinoflagelles MADAGASCAR National Oceanographic Data Centre - Dinoflagellates CEOS_EXTRA STAC Catalog 2002-12-01 2003-12-31 43.61, -23.38, 43.68, -23.35 https://cmr.earthdata.nasa.gov/search/concepts/C2232477667-CEOS_EXTRA.umm_json The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar’s northeast coast. This dataset of dinoflagellates has been collected at three stations in Toliara Bay, and it currently consists of 1297 records of 15 families. proprietary +madagascar_fish MADAGASCAR National Oceanographic Data Centre - Fish CEOS_EXTRA STAC Catalog 2001-07-29 2001-08-18 43.58, -23.38, 43.58, -23.38 https://cmr.earthdata.nasa.gov/search/concepts/C2232477670-CEOS_EXTRA.umm_json The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar’s northeast coast. This dataset has been collected in Toliara Bay, and includes bony fish, cartilagenous fish, mammals and reptiles. It currently consists of 721 records of 49 families. proprietary +madagascar_invertebrates MADAGASCAR National Oceanographic Data Centre - Invertebrates CEOS_EXTRA STAC Catalog 2001-07-29 2001-08-18 43.58, -23.38, 43.58, -23.38 https://cmr.earthdata.nasa.gov/search/concepts/C2232477672-CEOS_EXTRA.umm_json The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar’s northeast coast. This dataset has been collected in Toliara Bay, and includes mollusks, echinoderms, crustaceans, sponges and annelids. It currently consists of 230 records of 7 phylums. proprietary madcrypto-bryophyte-and-macrolichen-diversity-in-laurel-forests-of-madeira_1.0 MadCrypto – Bryophyte and macrolichen diversity in laurel forests of Madeira ENVIDAT STAC Catalog 2019-01-01 2019-01-01 -17.2883606, 32.6278099, -16.6676331, 32.8726671 https://cmr.earthdata.nasa.gov/search/concepts/C2789815340-ENVIDAT.umm_json This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species). The dataset was used for the paper Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science 30: 1122–1133. The excel file contains 5 sheets: 1) Plot information 2) Bryophyte data with species specific information, separated per substrate 3) Macrolichen data with species specific information, separated per substrate 4) Bryophyte data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 2 and 4 might therefore differ slightly. 5) Macrolichen data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 3 and 5 might therefore differ slightly. proprietary magnetic_domec_1977_1 Magnetic Readings Along Pioneerskaya - Dome C Traverses, 1977 and 1978 AU_AADC STAC Catalog 1977-01-01 1978-12-31 90, -75, 125, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214311206-AU_AADC.umm_json Magnetic readings taken along the Russian traverse from Pioneerskaya to Dome C in 1977 and 1978. Copies of these documents have been archived in the records store of the Australian Antarctic Division. proprietary manual-measuring-network_1.0 Manual measuring network ENVIDAT STAC Catalog 2023-01-01 2023-01-01 6.842866, 45.933188, 10.42462, 47.272886 https://cmr.earthdata.nasa.gov/search/concepts/C3226082890-ENVIDAT.umm_json The SLF avalanche warning service operates an extensive network of manual measuring sites. The sites are distributed throughout the Swiss Alps and predominantly situated in intermediate altitude zones, between 1000 and 2000 m. Some of the measurement series already span very long periods and are therefore highly valued; the data are also used for climatological and hydrological purposes. The measuring sites are in fixed locations, which are flat and wind-protected. The observers who perform the measurements are trained and paid by the SLF. Data is collected, as far as possible, from the beginning of November until the end of April and after that until half of the measuring site is snow-free. On some measuring sites event-based measurements are also collected during the summer months. If possible, measurements take place between 7 and 7.30 am local time. The following variables are measured at all measuring sites: - snow depth and 24-hour new snow at numerous sites this additional variable is measured: - water equivalent of 24-hour new snow (height of the water column in millimeters, if the new snow sample is melted, without changing the base area) __When using the data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/slf-data-service/)__ __To download live data use our [API](https://measurement-api.slf.ch)__. __To download data older than 7 days use our [File Download](https://measurement-data.slf.ch)__. proprietary @@ -17399,6 +18339,7 @@ mawson_south_sat_1 Mawson Escarpment South Satellite Image Map 1:100000 AU_AADC mawsonbathy_gis_1 Bathymetry of Approaches to Mawson Station AU_AADC STAC Catalog 1987-02-03 1992-03-04 62, -68, 63, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214313619-AU_AADC.umm_json Bathymetric contours and height range polygons of approaches to Mawson Station, derived from RAN Fair sheet, Aurora Australis and GEBCO soundings. proprietary mbs_wilhelm_msa_hooh_1 15 year Wilhelm II Land MSA and HOOH shallow ice core record from Mount Brown South (MBS) AU_AADC STAC Catalog 1984-01-01 1998-12-31 86.082, -69.13, 86.084, -69.12 https://cmr.earthdata.nasa.gov/search/concepts/C1214313640-AU_AADC.umm_json This work presents results from a short firn core spanning 15 years collected from near Mount Brown, Wilhelm II Land, East Antarctica. Variations of methanesulphonic acid (MSA) at Mount Brown were positively correlated with sea-ice extent from the coastal region surrounding Mount Brown (60-1208 E) and from around the entire Antarctic coast (0-3608 E). Previous results from Law Dome identified this MSA-sea-ice relationship and proposed it as an Antarctic sea-ice proxy (Curran and others, 2003), with the strongest results found for the local Law Dome region. Our data provide supporting evidence for the Law Dome proxy (at another site in East Antarctica), but a deeper Mount Brown ice core is required to confirm the sea-ice decline suggested by Curran and others (2003). Results also indicate that this deeper record may also provide a more circum-Antarctic sea-ice proxy. This work was completed as part of ASAC project 757 (ASAC_757). proprietary mcdonald_dem_may2012_1 A Digital Elevation Model of McDonald Island derived from GeoEye-1 stereo imagery captured 19 May 2012 AU_AADC STAC Catalog 2012-05-19 2012-05-19 72.533, -53.067, 72.74, -53.003 https://cmr.earthdata.nasa.gov/search/concepts/C1214311211-AU_AADC.umm_json This dataset consists of: 1 GeoEye-1 stereo imagery of an area of approximately 100 square kilometres including McDonald Island, captured 19 May 2012 2 A Digital Elevation Model (DEM) derived from the GeoEye-1 stereo imagery; and 3 Image products derived from the most vertical dataset of the stereo imagery and orthorectified using the DEM. 4 Contours generated from the DEM. The DEM was produced at a 1 metre pixel size and is available in ESRI grid, ESRI ascii and BIL formats. The DEM and image products are stored in a Universal Transverse Mercator zone 43 south projection, based on the WGS84 datum. The image products are geotiffs as follows. McDonald_Island_BGRN.tif: GeoEye-1 4-band multispectral (vis blue, green, red and Near Infrared), 2 metre resolution. McDonald_Island_PAN.tif: GeoEye-1 panchromatic, 0.5 metre resolution. McDonald_Island_PS_BGRN.tif: GeoEye-1 pansharpened, 4-band multispectral (vis blue, green, red and Near Infrared), 0.5 metre resolution. McDonald_Island_RGB.tif: GeoEye-1 pansharpened, natural colour enhancement, 0.5 metre resolution. proprietary +mcm_seals Marine and Coastal Management (MCM) - Seal Surveys CEOS_EXTRA STAC Catalog 1974-04-08 2001-06-01 11.68, -34.98, 26.11, -17.47 https://cmr.earthdata.nasa.gov/search/concepts/C2232477678-CEOS_EXTRA.umm_json Marine and Coastal Management (MCM) is one of four branches of the Department of Environmental Affairs and Tourism. It is the regulatory authority responsible for managing all marine and coastal activities. The seal data set is a collection of seals shot at-sea cruises, and has been collected from cruises around the South African Coast, and currently contains 2440 records of 1 family (Otariidae). proprietary mean-insect-occupancy-1970-2020_1.0 Mean insect occupancy 1970–2020 ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226082591-ENVIDAT.umm_json This dataset contains all data, on which the following publication below is based. **Paper Citation**: Neff, F., Korner-Nievergelt, F., Rey, E., Albrecht, M., Bollmann, K., Cahenzli, F., Chittaro, Y., Gossner, M. M., Martínez-Núñez, C., Meier, E. S., Monnerat, C., Moretti, M., Roth, T., Herzog, F., Knop, E. 2022. Different roles of concurring climate and regional land-use changes in past 40 years' insect trends. Nature Communications, DOI: [10.1038/s41467-022-35223-3](https://doi.org/10.1038/s41467-022-35223-3) Please cite this paper together with the citation for the datafile. Please also refer to this publication for details on the methods. ## Summary Mean annual occupancy estimates for 390 insect species (215 butterflies [Papilionoidea, incl. Zygaenidae moths], 103 grasshoppers [Orthoptera], 72 dragonflies [Odonata]) for nine bioclimatic zones in Switzerland. Covers the years 1970-2020 (for butterflies) and 1980-2020 (for grasshoppers and dragonflies). Mean occupancy denotes the average number of 1 km x 1 km squares in a zone occupied by the focal species. Occupancy estimates stem from occupancy-detection models run with species records data hosted and curated by [info fauna](http://www.infofauna.ch). Data on the level of single MCMC iterations of model fitting are included (4000 sampling iterations). The nine bioclimatic zones were defined based on biogeographic regions and two elevation classes (square above or below 1000 m. asl) proprietary medical_bibliography_1 A bibliography of polar medicine related articles AU_AADC STAC Catalog 1947-01-01 2007-06-06 60, -90, 160, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214311212-AU_AADC.umm_json This bibliography contains a list of publications in medical sciences from Australian National Antarctic Research Expeditions (ANARE) and the Australian Antarctic Program (AAP) from 1947-2007. The bibliography also contains publications related to Australian involvement in the International Biomedical Expedition to the Antarctic (IBEA), 1980-1981. Currently (as at 2007-06-06) the bibliography stands at 285 references, but is updated annually. The publications are divided into the following areas: Clinical medicine Clinical medicine - case reports Telemedicine Dentistry Diving Epidemiology Polar human research - general Physiology Immunology Photobiology Microbiology Psychology and behavioural studies Nutrition Theses Popular articles Miscellaneous IBEA Posters The fields in this dataset are: Author Title Journal Year proprietary mega-plots_1.0 Towards comparable species richness estimates across plot-based inventories - data ENVIDAT STAC Catalog 2022-01-01 2022-01-01 -14.0625, 33.1375512, 42.1875, 72.1818036 https://cmr.earthdata.nasa.gov/search/concepts/C2789816317-ENVIDAT.umm_json "The data file refers to the data used in Portier et al. ""Plot size matters: towards comparable species richness estimates across plot-based inventories"" (2022) *Ecology and Evolution*. This paper describes a methodoligical framework developed to allow meaningful species richness comparisons across plot-based inventories using different plot sizes. To this end, National Forest Inventory data from Switzerland, Slovakia, Norway and Spain were used. NFI plots were aggregated into mega-plots of larger sizes to build rarefaction curves. The data stored here correspond to the mega-plot level data used in the analyses, including for each country the size of the mega-plots in square meters (A), the corresponding species richness (SR) as well as all enrionmental heterogeneity measures described in the corresponding paper. Mega-plots of country-specific downscaled datasets are also provided. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). Contact details for data requests from all NFIs can be found in the ENFIN website (http://enfin.info/)." proprietary @@ -17520,6 +18461,7 @@ newcomb_bay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Newcomb B nexeastimpacts_1 NEXRAD Mosaic East IMPACTS V1 GHRC_DAAC STAC Catalog 2019-12-31 2020-02-29 -85, 32.5, -67.525, 46.475 https://cmr.earthdata.nasa.gov/search/concepts/C1995866059-GHRC_DAAC.umm_json The NEXRAD Mosaic East IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) 3D mosaic files created from Level II surveillance data gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Mosaic East dataset is composed of Level II data from 19 NEXRAD sites in the eastern U.S.. These data files are available in netCDF-4 format and contain meteorological and dual-polarization base data quantities including radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio from January 1 through February 29, 2020. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary nexmidwstimpacts_1 NEXRAD Mosaic Midwest IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-01 2020-02-29 -93, 36, -79.025, 45.975 https://cmr.earthdata.nasa.gov/search/concepts/C1995866123-GHRC_DAAC.umm_json The NEXRAD Mosaic Midwest IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) 3D mosaic files created from Level II surveillance data gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Mosaic Midwest dataset is composed of Level II data from 11 NEXRAD sites in the midwestern U.S. These data files are available in netCDF-4 format and contain meteorological and dual-polarization base data quantities including radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio from January 1 through February 29, 2020. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary niche-partitioning-between-wild-bees-and-honeybees_1.0 Niche partitioning between wild bees and honeybees ENVIDAT STAC Catalog 2021-01-01 2021-01-01 8.4299469, 47.3172277, 8.6949921, 47.4130345 https://cmr.earthdata.nasa.gov/search/concepts/C2789816101-ENVIDAT.umm_json "Cities are socio-ecological systems that filter and select species, thus establishing unique species assemblages and biotic interactions. Urban ecosystems can host richer wild bee communities than highly intensified agricultural areas, specifically in resource-rich urban green spaces such as allotment and family gardens. At the same time, urban beekeeping has boomed in many European cities, raising concerns that the fast addition of a large number of managed bees could deplete the existing floral resources, triggering competition between wild bees and honeybees. The data has been used to investigated the interplay between resource availability and the number of honeybees at local and landscape scales and how this relationship influences wild bee diversity. This dataset contains the raw and processed data supporting the findings from the paper: ""Low resource availability drives feeding niche partitioning between wild bees and honeybees in a European city"". The data contains: 1. Bee trait measurements at the species and individual-level of five functional traits. 2. The values of the feeding niche partitioning (functional dissimilarity to honeybees) 3. The predictors of resource availability and beekeeping intensity at local and landscape scales used in the modelling of the paper for the 23 experimental sites." proprietary +nigeria_marine Nigerian Institute for Oceanography and Marine Research - Marine Species CEOS_EXTRA STAC Catalog 1970-01-01 -5, 4.27417, 5.89333, 6.39722 https://cmr.earthdata.nasa.gov/search/concepts/C2232477671-CEOS_EXTRA.umm_json The Nigerian Institute for Oceanography and Marine Research (NIOMR), was created from the Marine Research Division of the Federal Department of Fisheries. The Aquaculture department is mandated to research into the development of Aquaculture, including improvement of transportation devices for juveniles to reduce mortality. This collection was compiled from publications, and it currently consists of 556 records of 106 families. proprietary nitrogen_deposition_730_1 Nitrogen Deposition onto the United States and Western Europe ORNL_CLOUD STAC Catalog 1987-01-01 1994-12-31 -124, 25, 44.5, 70.5 https://cmr.earthdata.nasa.gov/search/concepts/C2776873201-ORNL_CLOUD.umm_json This data set contains data for wet and dry nitrogen-species deposition for the United States and Western Europe. Deposition data were acquired directly from monitoring programs in the United States and Europe for time periods from 1978-1994 for wet deposition and from 1989-1994 for dry deposition and evaluated using similar quality assurance criteria to ensure comparability. A standard geostatistical method (kriging) was used to interpolate data onto a 0.5 x 0.5 degree resolution map for wet and dry deposition. proprietary nlcd_1992 National Land Cover Data set 1992 (NLCD1992) USGS_LTA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567916-USGS_LTA.umm_json National Land Cover Dataset 1992 (NLCD1992) is a 21-class land cover classification scheme that has been applied consistently across the lower 48 United States at a spatial resolution of 30 meters. NLCD92 is based primarily on the unsupervised classification of Landsat Thematic Mapper (TM) circa 1990's satellite data. Other ancillary data sources used to generate these data included topography, census, and agricultural statistics, soil characteristics, and other types of land cover and wetland maps. NLCD1992 is the only NLCD dataset that can be downloaded by state and by user defined area from the MRLC Consortium Viewer. proprietary nlcd_1992_2001_retrofit NLCD 1992/2001 Retrofit Land Cover Change Product USGS_LTA STAC Catalog 1992-01-01 2001-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567922-USGS_LTA.umm_json "Developments in mapping methodology, new sources of input data, and changes in the mapping legend for the 2001 National Land Cover Database (NLCD2001) will confound any direct comparison between NLCD2001 and National Land Cover Dataset 1992 (NLCD1992). Users are cautioned that direct comparison of these two independently created land cover products is not recommended. This NLCD 1992/2001 Retrofit Land Cover Change Product was developed to offer users more accurate direct change analysis between the two products. The NLCD 1992/2001 Retrofit Land Cover Change Product uses a specially developed methodology to provide land cover change information at the Anderson Level I classification scale (Anderson et al., 1976*), relying on decision tree classification of Landsat satellite imagery from circa 1992 and 2001. Unchanged pixels between the two dates are coded with the NLCD01 Anderson Level I class code, while changed pixels are labeled with a ""from-to"" land cover change value. Additional details about this product are available in the metadata included in the multi-zone downloadable zip file. This product is designed for regional application only and is not recommended for local scales." proprietary @@ -17815,6 +18757,7 @@ seawifs_624_1 SAFARI 2000 SeaWiFS Images for Core Study Sites, 2000-2001 ORNL_CL seawifs_region_625_1 SAFARI 2000 SeaWiFS Images for the Southern African Region, 1999-2001 ORNL_CLOUD STAC Catalog 1999-01-01 2001-03-03 8.73, -35.26, 43.2, -7.49 https://cmr.earthdata.nasa.gov/search/concepts/C2804817543-ORNL_CLOUD.umm_json This data set contains Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery for the southern African region. These images are Level-1a swaths of the southern African region selected from global area coverage (GAC) at 4.5-km resolution. The data are provided in HDF format files. proprietary secondary-ice-production-processes-in-wintertime-alpine-mixed-phase-clouds_1.0 "Data for the publication ""Secondary ice production processes in wintertime alpine mixed-phase clouds""" ENVIDAT STAC Catalog 2022-01-01 2022-01-01 7.983151, 46.548308, 7.983151, 46.548308 https://cmr.earthdata.nasa.gov/search/concepts/C2789817759-ENVIDAT.umm_json This repository contains all WRF model outputs and observational data sets used for the paper: Georgakaki, P., Sotiropoulou, G., Vignon, É., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-760, in review, 2021. proprietary sediment-transport-observations-in-swiss-mountain-streams_1.0 Sediment transport observations in Swiss mountain streams ENVIDAT STAC Catalog 2020-01-01 2020-01-01 7.1095008, 46.1234183, 9.5849948, 47.0451026 https://cmr.earthdata.nasa.gov/search/concepts/C2789817859-ENVIDAT.umm_json The Swiss Federal Research Institute WSL has extensive experience with surrogate bedload transport measurements. The first measuring site was established in the Erlenbach stream, a small (first-order) catchment in the pre-alpine valley Alptal in central Switzerland. Continuous bedload transport measurements were started in 1986, using first piezoelectric sensors (1986 to 1999) and then geophone sensors (from 2002 onwards) underneath a steel plate and mounted flush with the streambed. In the meantime, the so-called Swiss plate geophone (SPG) system has been installed at more than 20 field sites, primarily in smaller and steeper streams in Switzerland, Austria, and Italy but also in a few larger rivers and in some other streams worldwide (Israel, USA, Japan). Sediment transport observations in Switzerland with the SPG system concern the following streams: Erlenbach near Brunni (Alptal valley), Albula at Tiefencastel, Navisence at Zinal, Avançon de Nant near Pont de Nant (see map). The data in this repository primarily refer to calibration measurements with the SPG system. The publications listed here discuss primarily the performance of the measuring system but also process-based aspects of bedload transport. proprietary +sediments_gom Gulf of Maine Contaminated Sediments Database CEOS_EXTRA STAC Catalog 1970-01-01 -71.4661, 40.6306, -67.2693, 44.6999 https://cmr.earthdata.nasa.gov/search/concepts/C2231553179-CEOS_EXTRA.umm_json The overall objective of this project is to create a database of existing data on contaminants in sediment for the Gulf of Maine region that will be useful to persons throughout the region for scientific and management purposes. This task involves identification of data sources, entry of data into the database format, validation or scientific editing of the database, some analysis and synthesis of the compiled data, and publication of the database and associated bibliographies. The tasks of locating and entering data are being shared among the principle investigators in this project because they require a thorough knowledge of the geographic regions under consideration, an understanding of the types of data identified, and familiarity with active research in these regions. This cooperative approach insures that a more thorough identification and collection of data occurs than could take place from one institution. It also insures that the compiled database will be used by all the participants and their colleagues in the future. Objectives of the work: 1) Develop a comprehensive inventory (database) of available information on sediment contaminants, both inorganic and organic, for the Gulf of Maine 2) Encourage the cooperation and active participation of multiple agencies and organizations in locating, incorporating, and utilizing the data. 3) Place these and ancillary data in interactive, user-friendly, and readily exchangeable forms (such as desktop computer, FTP, and CD-ROM). 4) Map and analyze sediment contaminant distributions in order to provide the best assemblage of information possible for use in determining contaminant baselines 5) Utilize the database to address specific scientific questions about transport and fate of contaminants in the GOM system. 6) Provide guidance for other agencies and organizations to further the usefulness of the data in research, resource management, and public policy decisions. 7) Provide guidance on where to sample and how to analyze samples in the future to make more effective use of limited resources proprietary seilaplan-herunterladen-des-dhm-mit-swissgeodownloader_1.0 Seilaplan Tutorial: DTM download with SwissGeoDownloader ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226083074-ENVIDAT.umm_json In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. The plugin ‘Swiss Geo Downloader’, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model ‘swissALTI3D’ with the help of the ‘Swiss Geo Downloader’ and how to use it for digital planning of a cable line with the plugin ‘Seilaplan’. Please note that the tutorial language is German! Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader Link to Seilaplan website: https://seilaplan.wsl.ch ********************* Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Das Plugin Swiss Geo Downloader, welches für das Open Source Geoinformationssystem QGIS zur Verfügung steht, ermöglicht frei verfügbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgenössischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue Höhenmodell swissALTI3D herunterladen und für die Seillinienplanung mit dem Plugin Seilaplan verwenden kann. Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader Link zur Seilaplan-Webseite: https://seilaplan.wsl.ch proprietary seilaplan-tutorial-dhm-kacheln-zusammenfugen_1.0 Seilaplan Tutorial: Merge DTM tiles ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226083081-ENVIDAT.umm_json In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. In this tutorial video, we show how to merge multiple DTM raster tiles into one file, using the QGIS tool ‘Virtual Raster’. This simplifies the digital planning of a cable line using the QGIS plugin ‘Seilaplan’. Please note that the tutorial language is German! Link to Seilaplan website: https://seilaplan.wsl.ch *************************** Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. In diesem Tutorialvideo zeigen wir, wie man mit dem QGIS-Plugin Virtuelles Raster mehrere DHM-Kacheln zu einem einzigen Rasterfile zusammenfügen und abspeichern kann. Für die Seillinienplanung mit Seilaplan muss nun nur noch eine Datei, mein neues virtuelles Raster, ausgewählt werden. Link zur Seilaplan-Website: https://seilaplan.wsl.ch proprietary seilaplan-tutorial-herunterladen-des-dhm-von-swisstopo-website_1.0 Seilaplan Tutorial: DTM download from swisstopo website ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226083089-ENVIDAT.umm_json In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. As an alternative to using the ‘Swiss Geo Downloader’ plugin, the DTM can be obtained directly from Swisstopo. In this tutorial we explain step by step how to download the necessary DTM from the Swisstopo Website, and how to use it in QGIS for the digital planning of a cable line using the plugin ‘Seilaplan’. Please note that the tutorial language is German! Link to the elevation model on the swisstopo website: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details Link to the rope map website: https://seilaplan.wsl.ch ******************** Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Als Alternative zum Swiss Geo Downloader erklären wir in diesem Tutorial Schritt für Schritt, wie man das nötige Höhenmodell von der Swisstopo Webseite herunterladen und in QGIS zur Seillinienplanung verwenden kann. Link zum Höhenmodell auf der swisstopo Webseite: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details Link zur Seilaplan-Website: https://seilaplan.wsl.ch proprietary @@ -18124,6 +19067,7 @@ uiucsndimpacts_1 Mobile UIUC Soundings IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01 uk_met_c-130_720_1 SAFARI 2000 C-130 Aerosol and Meteorological Data, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-09-05 2000-09-16 5, -35, 60, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2788398494-ORNL_CLOUD.umm_json The Met Office C-130 research aircraft was based at Windhoek, Namibia, between September 5-16, 2000, where it conducted a series of flights over Namibia as part of the SAFARI 2000 Dry Season Aircraft Campaign. The aims of the Met Office's research were as follows: (1) In-situ measurements of the physical, chemical and optical properties of the aerosol. The data set includes aerosol samples ranging from near source regions to aged plumes several hundreds of kilometres from source, some of which have been cloud processed. (2) Investigation of the direct radiative impact of aerosol over sea, land and low-level cloud. (3) In-situ measurements of aerosol properties in conjunction with ground-based sites to validate the ground-based retrievals of, for example, aerosol size distributions. (4) In-situ measurements of aerosol properties in conjunction with TERRA overpasses, in order to validate the satellite-based retrievals of aerosol properties. (5) In-situ measurements of stratus/stratocumulus cloud of Namibia/Angola in conjunction with TERRA overpasses, in order to validate satellite-based retrievals of cloud properties. proprietary umd_landcover_xdeg_969_1 ISLSCP II University of Maryland Global Land Cover Classifications, 1992-1993 ORNL_CLOUD STAC Catalog 1992-04-01 1993-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784890869-ORNL_CLOUD.umm_json The objective of the International Satellite Land Surface Climatology Project (ISLSCP II) study that produced this data set, ISLSCP II University of Maryland Global Land Cover Classifications 1992-1993, was to create a land cover map derived from 1 kilometer Advanced Very High Resolution Radiometer (AVHRR) data using all available bands, derived Normalized Difference Vegetation Index (NDVI), and a full year of data (April 1992-March 1993). This thematic map was resampled to 0.25, 0.5 and 1.0 degree grids. During this re-processing, the original University of Maryland (UMD) land cover type and fraction maps were adjusted to match the water/land fraction of the ISLSCP II land/water mask. These maps were generated for use by modelers of global biogeochemical cycles and others in need of an internally consistent, global depiction of land cover. This 1km map was also one of the Moderate resolution Imaging Spectroradiometer (MODIS) at-launch land cover maps. This product describes the geographic distributions of 13 classes of vegetation cover (plus water and unclassified classes) based on a modified International Geosphere-Biosphere Programme (IGBP) legend. The data set also provides the fraction of each of the 15 classes within the coarser resolution cells, at three spatial resolutions of 0.25, 0.5 and 1.0 degrees in latitude and longitude. proprietary und_refl_304_1 BOREAS RSS-19 1994 Seasonal Understory Reflectance Data ORNL_CLOUD STAC Catalog 1994-02-06 1994-09-16 -105.12, 53.8, -98.29, 55.93 https://cmr.earthdata.nasa.gov/search/concepts/C2807645574-ORNL_CLOUD.umm_json Average spectral reflectance measurements of the ground surface of BOREAS flux tower sites. Measurements made along a transect with the instrument held at approximately one meter above the ground. proprietary +unep_marineturtle Marine Turtle Nesting Database CEOS_EXTRA STAC Catalog 1970-01-01 20, -39, 165, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2232849059-CEOS_EXTRA.umm_json Distribution of marine turtles in the Indian Ocean. Information was obtained from published and unpublished literature, and through liaison with turtle fieldworkers. It was intended that the database would be of use to a wide audience, including biologists, coastal planners and those concerned with emergency response to oil spills. Assessing the level of demand for these data, and the feasibility of maintaining data to reflect best available information. proprietary urals-latitudinal-decline-in-treeline-biomass-and-productivity_1.0 Urals: latitudinal decline in treeline biomass and productivity ENVIDAT STAC Catalog 2020-01-01 2020-01-01 52.9101563, 46.5830301, 74.7070313, 71.42438 https://cmr.earthdata.nasa.gov/search/concepts/C2789817471-ENVIDAT.umm_json 1. Stand characteristics of treeline ecotone along 18 elevational gradients of the Ural mountains. 2. Extrapolated climate data at treeline using nearby meteo station (1976-2006). 3. Air and soil temperatures measured in situ at treeline in the South and Polar Urals. Soil temperature sensors were placed at 10 cm depth in open areas in between tree clusters but not under tree canopy. 4. Further plot specific information is available upon request. proprietary urn:eop:VITO:CGS_S1_GRD_L1_V001 Sentinel-1 Level-1 Ground Range Detected (GRD) products. FEDEO STAC Catalog 2015-07-06 2021-12-31 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2207478611-FEDEO.umm_json Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model such as WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range. Ground range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution. The Interferometric Wide (IW) swath mode is the main acquisition mode over land and satisfies the majority of service requirements. For the IW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarisation. proprietary urn:eop:VITO:CGS_S1_GRD_SIGMA0_L1_V001 Sentinel-1 Level-1 Ground Range Detected (GRD) SIGMA0 products. FEDEO STAC Catalog 2014-10-03 2029-10-03 -1.0589251, 47.6603055, 11.6780987, 53.6748736 https://cmr.earthdata.nasa.gov/search/concepts/C2207478517-FEDEO.umm_json The Sigma0 product describes how much of the radar signal that was sent out by Sentinel-1 is reflected back to the sensor, and depends on the characteristics of the surface. This product is derived from the L1-GRD product. Typical SAR data processing, which produces level 1 images such as L1-GRD product, does not include radiometric corrections and significant radiometric bias remains. Therefore, it is necessary to apply the radiometric correction to SAR images so that the pixel values of the SAR images truly represent the radar backscatter of the reflecting surface. The radiometric correction is also necessary for the comparison of SAR images acquired with different sensors, or acquired from the same sensor but at different times, in different modes, or processed by different processors. For this Sigma0 product, radiometric calibration was performed using a specific Look Up Table (LUT) that is provided with each original GRD product. This LUT applies a range-dependent gain including the absolute calibration constant, in addition to a constant offset. Next to calibration, also orbit correction, border noise removal, thermal noise removal, and range doppler terrain correction steps were applied during production of Sigma0. The terrain correction step is intended to compensate for distortions due to topographical variations of the scene and the tilt of the satellite sensor, so that the geometric representation of the image will be as close as possible to the real world. proprietary @@ -18240,20 +19184,38 @@ usgs_nps_agatefossilbeds Agate Fossil Beds National Monument, Field Plots Data B usgs_nps_agatefossilbedsspatial Agate Fossil Beds National Monument Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System CEOS_EXTRA STAC Catalog 1995-07-29 1995-07-29 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231550884-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (July, 1995). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to ""develop, test, refine, and finalize the standards and protocols"" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Agate Fossil Beds National Monument was designated as one of the prototype parks. The monument is located in the high Great Plains. It contains prairie, hill, and riverine environments, with vegetation types that include prairie grassland, riverine woodland, and wetlands. The vegetation units were photointerpreted from stereo-paired, natural color photography. Agate Fossil Beds National Monument was created by the National Park Service on June 5, 1965. The park occupies 4.5 square miles of land straddling the Niobrara River in the middle of the Nebraska Panhandle. The park is noted for its history, prehistoric fossils, and scenic quality. Historically, the park was a part of the Agate Springs Ranch, owned by Captain James H. Cook. The park has a collection of ranching and Native American artifacts and memorabilia as a result of its donation from the Ranch. Paleontologically, the park contains a number of Miocene fossil quarries that were excavated through the late 19th century and early 20th century. From a scenic aspect, the park has views of rolling hills, bluffs, and the Niobrara River floodplain. Ranching is also an active part of the landscape. The park is located in the grassy rolling hills of Western Nebraska. The park landscape consists of the east-west trending cap-rocked northern and southern hills, with the treeless Niobrara River floodplain running down the middle of the valley. The city of Harrison is located 23 miles to the north, Mitchell is 34 miles to the south. State Highway 29 runs north-south through the western part of the park. The Vegetation mapping was conducted in Agate Fossil Beds National Moument, Nebraska with a 400 meter buffer. A total of 39 plots were obtained from July 10 through August 15, 1995. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in August of 1997 to assess the initial mapping effort and to refine map class. Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfospatial.html"" and converted to the NASA Directory Interchange Format. Another site to obtain the data is located at Online_Resource: ""ftp://ftp.cbi.usgs.gov/pub/vegmapping/agfo/agfo.exe""." proprietary usgs_nps_congareeswamp Congaree Swamp National Monument Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1996-06-01 1996-09-01 -80.85, 33.75, -80.67083, 33.84167 https://cmr.earthdata.nasa.gov/search/concepts/C2231552960-CEOS_EXTRA.umm_json "Vegetation field plots at Congaree Swamp National Monument were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The vegetation plots were used to describe the vegetation in and around Congaree Swamp National Monument and to assist in developing a final mapping classification system. On June 30, 1983, Congaree Swamp National Monument became an International Biosphere Reserve. Congaree is noted for containing one of the last significant stands of old growth bottomland hardwood forest, over 11,000 acres in all. The Monument contains over 90 species of trees, 16 of which hold state records for size. Included in this list of records is a national record sweet gum with a basal circumference of nearly 20 feet. Congaree Swamp National Monument is located approximately 15 miles southeast of Columbia, the state capitol of South Carolina. Old Bluff Highway (old Highway 48) lies just north of the Monument boundary. The eastern boundary is located just northwest of the confluence of the Congaree and Wateree Rivers. The Monument extends west to where Cedar Creek and Myers Creek join. The methods used for the sampling and analysis of vegetation data and the development of the classification generally followed the standards Doutline in the Field Methods for Vegetation Mapping document ""http://biology.usgs.gov/npsveg/fieldmethods/index.html"" produced for the USGS-NPS Vegetation Mapping project. This process began with the development of a provisional list of twenty-five vegetation types from teh International Classification of Ecological Communities (ICEC) that were thought to have a high likelihood of being in the park based on an initial field visit on 13-14 June, 1996. One hundred twenty-eight plots were sampled by two two-person field teams in July, August, and September of 1996. In a devation from the methodology outlined in the Field Methods document, initial sample points were selected in order to have plots in each of the aerial photograph signature types. The gradsect approach was rejected because there appeared to be no potential for stratifying sampling of the park based on slope, aspect, elevation, soil or other natural features due to a lack of available information. Furthermore, because of isolation from roads and trails of many portions of the park, it was deemed not feasible to use a transect to establish plot locations. After sampling, plots were tentatively assigned to the ICEC at the alliance level and our goal was to have at least five plots in each of the twenty-five provisional vegetation types. TIme limitations precluded the ability of the field teams to install ten plots in each of the expected vegetation types as recommended in the Field Methods document. The information for the metadata came from ""http://biology.usgs.gov/npsveg/cosw/metacoswfield.html""" proprietary usgs_nps_congareeswampspatial Congaree Swamp National Monument Spatial Vegetation Data; Cover Type/Association Level of the National Vegetation Classification System CEOS_EXTRA STAC Catalog 1996-04-27 1996-04-27 -80.85, 33.75, -80.67083, 33.84167 https://cmr.earthdata.nasa.gov/search/concepts/C2231550252-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (April, 1996). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to ""develop, test, refine, and finalize the standards and protocols"" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Congaree Swamp National Monument was designated as one of the prototype parks. Congaree Swamp National Monument, established in 1976, was designated as one of the prototypes within the National Park System. The park contains approximately 22,200 acres (34 square miles). Congaree Swamp National Monument is located approximately 15 miles southeast of Columbia, the state capitol of South Carolina. The Congaree River, draining over 8,000 square miles of Piedmont land to the northwest, forms the southern border. On June 30, 1983, Congaree Swamp National Monument became an International Biosphere Reserve. Congaree is noted for containing one of the last significant stands of old growth bottomland hardwood forest, over 11,000 acres in all. The Monument contains over 90 species of trees, 16 of which hold state records for size. Included in this list of records is a national record sweet gum with a basal circumference of nearly 20 feet. Congaree Swamp National Monument is located approximately 15 miles southeast of Columbia, the state capitol of South Carolina. Old Bluff Highway (old Highway 48) lies just north of the Monument boundary. The eastern boundary is located just northwest of the confluence of the Congaree and Wateree Rivers. The Monument extends west to where Cedar Creek and Myers Creek join. The normal process in vegetation mapping is to conduct an initial field reconnaissance, map the vegetation units through photointerpretation, and then conduct a field verification. The field reconnaissance visit serves two major functions. First, the photointerpreter keys the signature on the aerial photos to the vegetation on the ground at each signature site. Second, the photointerpreter becomes familiar with the flora, vegetation communities and local ecology that occur in the study area. Park and/or TNC field biologists that are familiar with the local vegetation and ecology of the park are present to help the photointerpreter understand these elements and their relationship with the geography of the park. Upon completion of the field reconnaissance, photo interpreters delineate vegetation units on mylar that overlay the 9x9 aerial photos. This effort is conducted in accordance with the TNC vegetation classification and criteria for defining each community or alliance. The initial mapping is then followed by a field verification session, whose purpose is to verify that the vegetation units were mapped correctly. Any PI related questions are also addressed during the visit. The vegetation mapping at Congaree Swamp National Monument in general followed the normal mapping procedure as described in the above paragraph with two major exceptions: 1) Preliminary delineations for most of the park, including a set of Focused Transect overlays that were labeled with an initial PI signature commenced prior to the field reconnaissance visit. 2) A TNC classification did not exist at the time the initial delineations began. TNC ecologist and AIS photo interpreters worked together to develop an interim signature key which addressed what was known at the time. At that time, no comprehensive study containing plot data was available to create an interim classification. From the onset of the Vegetation Inventory and Mapping Program, a standardized program-wide mapping criteria has been used. The mapping criteria contains a set of documented working decision rules used to facilitate the maintenance of accuracy and consistency of the photointerpretation. This criteria assists the user in understanding the characteristics, definition and context for each vegetation community. The mapping criteria for Congaree Swamp National Monument was composed of four parts: The standardized program-wide general mapping criteria A park specific mapping criteria A working photo signature key The TNC classification, key and descriptions The following sections detail the mapping criteria used during the photointerpretation of Congaree Swamp. General Mapping Criteria The mapping criteria at Congaree Swamp are a modified version from previously mapped parks. The criteria differs primarily in that the height and density variables were not mapped at Congaree Swamp. Instead, two additional variables were addressed: pre-hurricane Hugo community types and areas of pine that have been logged since the time of the 1976 aerial photography. These two categories will be addressed in the Park Specific Mapping Criteria section of this report. Since forest densities within the Monument are nearly always greater than 60%, it served little or no purpose in addressing this element as a separate attribute in the database. In addition it was also determined that height categories are extremely difficult to map in the Monument due to variability of the tree emergent layer, and lack of any significant reference points that help in determining canopy heights. Alliance / Community Associations The assignment of alliance and community association to the vegetation is based on criteria formulated by the field effort and classification development. In the case of Congaree Swamp National Monument, TNC provided AIS with a tentative community classification in April 1998. A final vegetation classification, key, and descriptions of each alliance and community, was provided in October 1998. In addition, TNC provided AIS with detailed plot data showing how the communities were developed in the Monument. The information for the metadata came from ""http://biology.usgs.gov/npsveg/cosw/metacoswspatial.html"" and was converted to the NASA Directory Interchange Format." proprietary +usgs_nps_d_microbialcontam Microbial Contamination of Water Resources in the Chatahoochee River National Recreation Area, Georgia CEOS_EXTRA STAC Catalog 1999-03-01 2000-04-01 -86, 30, -81, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231549590-CEOS_EXTRA.umm_json The study area is the watershed for the Chattahoochee River from Buford Dam to just downstream of the mouth of Peachtree Creek. This study area includes the entire Chattahoochee River National Recreation Area, much of Metropolitan Atlanta, and extends downstream of two major wastewater treatment plant outfalls for the City of Atlanta and Cobb County. The 2-year study is for fiscal years 1999 and 2000. There are six months of microbial sampling in each fiscal year spanning from April 1, 1999 through March 30, 2000. This study measures fecal-indicator bacteria (fecal coliform, E. coli, and enterococci) every five days from April 1, 1999 to September 30, 1999 and every 8 days from October 1, 1999 to March 30, 2000 at three main stem Chattahoochee River sites. The five-day and eight-day sampling intervals will ensure mid week and weekend flow conditions are sampled. Indicator bacteria samples will also be collected during one 26-hour period to look at diel fluctuations. Another indicator bacteria (Clostridium perfringens), F-specific coliphages, somatic coliphages, and chemical sewage tracers will be measured as part of several synoptic surveys at 3 fixed sites and 9 synoptic sites. The 2-year project investigates the existence, severity, and extent of microbial contamination in the Chattahoochee River and 8 major tributaries within the Chattahoochee River National Recreation Area (CRNRA). High levels of fecal-indicator bacteria are the principal basis for impairment of streams in the CRNRA. Three data-collection activities include: 1.Fixed interval: Sample fecal-indicator bacteria and predictor variables (stream stage, stream flow, turbidity, and field water-quality parameters) every 5 days from April 1 to September 30, 1999 and every 8 days from October 1, 1999 to March 30, 2000 at 3 Chattahoochee River sites. (view map) 2.Synoptic surveys: Sample fecal-indicator bacteria, Clostridium perfringens, viruses, predictor variables, and chemical sewage tracers at 4 Chattahoochee River sites and 8 tributary sites during critical seasons and hydrologic conditions. 3.Diel samples: Sample fecal-indicator bacteria and predictor variables every 2 hours for one 26-hour period (August 4-5, 1999) at the Chattahoochee River at Atlanta, which is downstream of the CRNRA. Four proposed main stem sampling sites in downstream order on the Chattahoochee River include: 1.Chattahoochee River at Settles Bridge Road near Suwanee 2.Chattahoochee River at Johnsons Ferry Road near Atlanta 3.Chattahoochee River at Atlanta (Paces Ferry Road; downstream from Palisades Unit) 4.Chattahoochee River at State Highway 280, near Atlanta (Synoptic site only; downstream from all of the CRNRA, much of Metropolitan Atlanta, and 2 major wastewater treatment outfalls for the City of Atlanta and Cobb County; will provide microbial data for a Chattahoochee River site directly affected by point sources of wastewater effluent) Eight proposed tributary sampling sites within the CRNRA watershed in downstream order include: 1.James Creek near Cumming (James Burgess Road) 2.Suwanee Creek near Suwanee (at US Route 23, Buford Hwy) 3.Johns Creek near Warsaw (Buice Road) 4.Crooked Creek near Norcross (Spalding Road) 5.Big Creek near Roswell (below Water Works intake) 6.Willeo Creek near Roswell (State Route 120) 7.Sope Creek near Marietta (Lower Roswell Road) 8.Rottenwood Creek near Smyrna (Interstate Parkway North) In general, fecal-indicator bacteria are used to assess the public-health acceptability of water. The concentration of indicator bacteria is a measure of water safety for body-contact recreation or for consumption (Myers and Sylvester, 1997). Indicator bacteria do not typically cause diseases (pathogenic), but they indicate the possible presence of pathogenic organisms. Escherichia coli (E. coli) and enterococci are currently the preferred fecal indicators for recreational freshwaters because they are superior to fecal coliforms and fecal streptococci as predictors of swimming-associated gastroenteritis (Cabelli, 1977; Dufour, 1984); however fecal coliforms are still used by many states including Georgia to monitor recreational waters. Most historical indicator bacteria data for surface water within the CRNRA are fecal coliform counts collected once a month on a mid-weekday during normal working hours. This study proposes to measure fecal coliform using the membrane filter technique (preferred over the broth technique used by Georgia EPD),E. coli, and enterococci every five days during the recreation season at three main stem sites. The five-day cycle will ensure mid week and weekend flow conditions are sampled. All samples will be collected using USGS protocols for bacteria and equal width interval (EWI) sampling. Clostridium perfringens (C. perfringens) is another indicator bacteria that is present in large numbers in human and animal wastes, and its spores are more resistant to disinfection and environmental stresses than are most other bacteria. It is also a sensitive indicator of microorganisms that enter streams from point sources (Sorenson and others, 1989). It must be analyzed under anaerobic conditions in a laboratory and is best attempted by a biologist or highly trained technician. This study proposes to measure C. perfringens at 4 main stem and 8 tributary sites as part of synoptic surveys during critical seasons and hydrologic conditions. Because monitoring of enteric viruses is recognized as being difficult,time consuming, and expensive, some researchers advocate the use of coliphage for routine viral monitoring. Coliphages are bacteriophages that infect and replicate in coliform bacteria. Although somatic and Fecal-Specific coliphages are not consistently found in feces, they are found in high numbers in sewage and are thought to be reliable indicators of the sewage contamination of waters (International Association on Water Pollution Research and Control, 1991). Coliphage is also recognized to be representative of the survival transport of viruses in the environment. However, to date, they have not been found to correlate with the presence of pathogenic viruses. This study proposes to measure enteric viruses at 4 main stem and 8 tributary sites as part of synoptic surveys during critical seasons and hydrologic conditions. proprietary usgs_nps_devilstower Devils Tower National Monument Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1996-08-01 1996-08-01 -104.75, 44.5, -104.63, 44.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231553793-CEOS_EXTRA.umm_json "Vegetation field plots at Devils Tower NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The purpose of the data is to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. The geographic extent of the data set is Devils Tower National Monument and about a 2 mile environs around Monument Boundaries - Black Hills, Wyoming, USA Field sampling was conducted using releve plots. Information was obtained from ""http://biology.usgs.gov/npsveg/deto/metadetofield.html""" proprietary usgs_nps_devilstowerspatial Devils Tower National Monument Spatial Vegetation Data:Cover Type/Association Level of the National Vegetation Classification System CEOS_EXTRA STAC Catalog 1995-09-12 1995-09-12 -104.75, 44.5, -104.63, 44.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231548756-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation at Devils Tower National Monument was mapped using 1:16,000 scale U.S. Forest Service Color Aerial Photography acquired July 29, 1993. The mapping classification used two separate classification systems. All natural vegetation used the National Vegetation Classification System (NVCS) as a base. The vegetation classification was created after extensive on site sampling and numerical analysis. The vegetation map units were derived from the vegetation classification. Other non-natural or cultural mapping units used the Anderson Level II classification system. The mapped area includes a buffer around the Monument boundary. This mapping effort originates from a long-term vegetation monitoring program that is part of a larger Inventory and Monitoring (I&M) program started by the National Park Service (NPS). I&M goals are, among others, to map the vegetation of all national parks and monuments and provide a baseline inventory of vegetation. The I&M program currently works in close cooperation with the Biological Resources Division (BRD) of the United States Geological Survey (USGS). The USGS/BRD continues overall management and oversight of all ongoing mapping efforts in close cooperation with the NPS. The purposes of the mapping effort are varied and include the following: Provides support for NPS Resources Management. Promotes vegetation-related research for both NPS and USGS/BRD. Provides support for NPS Planning and Compliance. Adds to the information base for NPS Interpretation. Assists in NPS Operations. The geographic extent of the data set is Devils Tower National Monument and about a 2 mile environs around Monument Boundaries - Black Hills, Wyoming, USA. Information was obtained from ""http://biology.usgs.gov/npsveg/deto/metadetospatial.html"" and converted to NASA Directory Interchange Format." proprietary +usgs_nps_fortlaramie Fort Laramie National Historic Site, Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1997-06-21 1997-06-29 -104.34382, 42.11212, -104.13276, 42.13276 https://cmr.earthdata.nasa.gov/search/concepts/C2231552621-CEOS_EXTRA.umm_json "Vegetation field plots at Fort Laramie NHS were visited, described, and documented in a digital database. The database consists of two parts - (1) Physical Descriptive and Stratum Data, and (2) Species Listings. The purpose of the field plots is to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. The dataset is of the Fort Laramie National Historic Site and surroundings. Fort Laramie is located in Goshen County, Wyoming. Field sampling using releve plots. Information for this metadata was obtained from ""http://biology.usgs.gov/npsveg/fola/metafolafield.html"" and put into NASA Directory Interchange Format." proprietary +usgs_nps_fortlaramiespatial Fort Laramie National Historic Site, Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System CEOS_EXTRA STAC Catalog 1995-07-15 1995-07-15 -104.5729, 41.18889, -104.5269, 42.225 https://cmr.earthdata.nasa.gov/search/concepts/C2231548993-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (July, 1995). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to ""develop, test, refine, and finalize the standards and protocols"" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Fort Laramie National Historic Site was designated as one of the prototype parks. The monument is located in the high Great Plains. It contains prairie, hill, and riverine environments, with vegetation types that include upland woodland, prairie grassland, riverine woodland, and wetlands. Fort Laramie National Historic Site was created by the National Park Service on July 16, 1938. The park occupies 833 acres of land on the Laramie River, west of its confluence with the North Platte River in western Wyoming. Bureau of Land Management land south of the park (referred to as Plot 3) and northwest of the park (referred to as Plots 1 and 5) are also within the mapping study area. The park is primarily preserved as an historic site. The fort site was occupied first as a fur trading center, then subsequently as a military outpost. It further served as a way station for trappers, traders, and emigrants on the Oregon Trail. The old fort site, located in the western end of the park, contains a complex of restored buildings and ruins, dating from mid and late 19th century, surrounding a lawn quadrangle. The remainder of the park contains disturbed prairie and floodplains. The park itself lies mainly on the floodplain terrace of the Laramie River, with a portion on the North Platte River floodplain terrace just west of their confluence. A small portion of the northwest corner of the park lies above the terrace. Plot 3 lies directly south of the park, across the Fort Laramie Canal. It is an area of rolling hills. Plots 1 and 5 lie 1/4 mile northwest of the park, also in rolling hills. The park is surrounded by rolling hills that are used for grazing and some agricultural cultivation. The city of Fort Laramie is located 3 miles to the northeast of the park. The sampling approach used in this mapping effort was typical of small park sampling, where all polygons within the park boundary are sampled. Two levels of field data gathering were conducted in this park; plots and observations. Plots represented the most intensive sampling of the landscape and used TNC's 'Plot Form'. Observations consisted of brief descriptions and were designed to obtain a quick overview of the landscape without spending a large amount of time at each sample site. Observation points used the 'Observation Form' data sheet. Examples of both 'Plot' and 'Observation' forms are included in the companion report by TNC. Initially, plots were used to describe the vegetation of the park. A total of 49 plots were obtained from July through August 1996. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in July of 1997 to assess the initial mapping effort and to refine map class. The data can also be obtained from ""ftp://ftp.cbi.usgs.gov/pub/vegmapping/fola/fola.exe"". Information for this metadata was obtained from ""http://biology.usgs.gov/npsveg/fola/metafolaspatial.html"" and put into NASA Directory Interchange Format." proprietary +usgs_nps_isleroyale Isle Royale National Park, Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1997-06-01 1997-09-01 -89.125, 47.8, -88.4, 48.2 https://cmr.earthdata.nasa.gov/search/concepts/C2231553838-CEOS_EXTRA.umm_json Vegetation field plots at Isle Royale National Park were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data and (2) Species Listings. The vegetation plots were used to describe the vegetation in and around Wind Cave National Park and to assist in developing a final mapping classification system. The purpose of the vegetation plots was to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data are collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the Park's vegetation types. Isle Royale National Park was authorized on March 3, 1931; it was formally established in 1940, and officially dedicated in 1946. Most of the park's land area (98%) was designated as a Wilderness area in October 1976, and later additions increased the total Wilderness to 99% of the park. The park was designated an International Biosphere Reserve in 1980. Field sampling was performed using releve plots. Information for this metadata was obtained from the site http://biology.usgs.gov/npsveg/isro/metaisrofield.html and converted to NASA Directory Interchange Format. proprietary +usgs_nps_isleroyalespatial Isle Royale National Park Spatial Vegetation Data; Cover Type / Association level of the National Vegetation Classification System CEOS_EXTRA STAC Catalog 1994-08-26 1996-04-25 -89.125, 47.8, -88.4, 48.2 https://cmr.earthdata.nasa.gov/search/concepts/C2231550981-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (spring - 1996 and fall - 1994). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to wisely manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to ""develop, test, refine, and finalize the standards and protocols"" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Isle Royale National Park was initially identified as one of the prototypes within the National Park System for the USGS-NPS Vegetation Mapping Program. Isle Royale National Park was established March 3, 1931 and was also designated as an International Biosphere Reserve in 1980. The park contains approximately 571,790 acres of land and water (893 square miles) of which 133,782 acres is land and the rest is open water of Lake Superior as well as inland lakes and ponds. Isle Royale National Park is an archipelago of islands located in the northwestern region of Lake Superior close to the United States-Canada border. The main island, Isle Royale, consists of a series of ridges and valleys running the length of the island and is surrounded by approximately 200 smaller islands. The primary methods of transportation on the island are hiking and boating. Isle Royale National Park was authorized on March 3, 1931; it was formally established in 1940, and officially dedicated in 1946. Most of the park's land area (98%) was designated as a Wilderness area in October 1976, and later additions increased the total Wilderness to 99% of the park. The park was designated an International Biosphere Reserve in 1980. Isle Royale National Park is an archipelago of islands located in the northwestern region of Lake Superior close to the United States-Canada border. The park is located about 60 miles northwest of Michigan.s Keweenaw Peninsula, about 22 miles east of Grand Portage, Minnesota, and about 35 miles southeast of Thunder Bay, Ontario. Information for this metadata was obtained from the site ""http://biology.usgs.gov/npsveg/isro/metaisrospatial.html"" and converted to NASA Directory Interchange Format." proprietary +usgs_nps_jewelcave Jewel Cave National Monument, Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1996-07-01 1996-08-01 -103.87, 43.62, -103.75, 43.77 https://cmr.earthdata.nasa.gov/search/concepts/C2231553594-CEOS_EXTRA.umm_json "Vegetation field plots at Jewel Cave NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listing. The purpose is to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. Field sampling was conducted using releve plots. Information for this metadata was obtained from the site ""http://biology.usgs.gov/npsveg/jeca/metajecafield.html"" and put into NASA Directory Interchange Format." proprietary +usgs_nps_jewelcavespatial Jewel Cave National Monument Spatial Vegetation Data;Cover Type / Association level of the National Vegetation Classification System CEOS_EXTRA STAC Catalog 1995-09-12 1995-09-12 -103.87, 43.62, -103.75, 43.77 https://cmr.earthdata.nasa.gov/search/concepts/C2231548897-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation at Jewel Cave National Monument was mapped using 1:16,000 scale U.S. Forest Service Color Aerial Photography acquired August 22, 1993. The mapping classification used two separate classification systems. All natural vegetation used the National Vegetation Classification System (NVCS) as a base. The vegetation classification was created after extensive on site sampling and numerical analysis. The vegetation map units were derived from the vegetation classification. Other non-natural or cultural mapping units used the Anderson Level II classification system. The mapped area includes a buffer around the Monument boundary. This mapping effort originates from a long-term vegetation monitoring program that is part of a larger Inventory and Monitoring (I&M) program started by the National Park Service (NPS). I&M goals are, among others, to map the vegetation of all national parks and monuments and provide a baseline inventory of vegetation. The I&M program currently works in close cooperation with the Biological Resources Division (BRD) of the United States Geological Survey (USGS). The USGS/BRD continues overall management and oversight of all ongoing mapping efforts in close cooperation with the NPS. The purposes of the mapping effort are varied and include the following: Provides support for NPS Resources Management. Promotes vegetation-related research for both NPS and USGS/BRD. Provides support for NPS Planning and Compliance. Adds to the information base for NPS Interpretation. Assists in NPS Operations. The location of the mapping is Jewel Cave National Monument and about a 2 mile environs around Monument Boundaries - Black Hills, South Dakota. Jewel Cave National Monument was responsible for obtaining permission from adjacent land owners for property access for sampling purposes. Most of the private lands were under some form of grazing or farming. Consequently, sampling on these lands was not necessary. The remainder of the lands within the mapping area are U.S. Forest Service Lands so permission was not necessary. To reduce duplicating previous work and to help in our effort we collected existing vegetation reports and maps from the staff at Jewel Cave National Monument. These materials were referenced during the mapping process and the information contained in them was incorporated where it was deemed useful. Because soils also affect the distribution of vegetation, soil maps and soil descriptions were also obtained as reference. These were not converted to a digital file. Digital elevation models (DEM) were obtained to create slope and aspect maps that helped in determining vegetation community distribution. The sampling approach used in this mapping effort was typical of small park sampling, where all polygons within the park boundary are sampled. Two levels of field data gathering were conducted in this park; plots and observations. Plots represented the most intensive sampling of the landscape and used TNC's 'Plot Form'. Observations consisted of brief descriptions and were designed to obtain a quick overview of the landscape without spending a large amount of time at each sample site. Observation points used the 'Observation Form' data sheet. Examples of both 'Plot' and 'Observation' forms are included in the companion report by TNC. Initially, plots were used to describe the vegetation of the park. A total of 28 plots were obtained from July 29 through August 1, 1996. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in May of 1997 to assess the initial mapping effort and to refine map classes. Information for this metadata was obtained from the site ""http://biology.usgs.gov/npsveg/jeca/metajecaspatial.html"" and put into NASA Directory Interchange Format." proprietary +usgs_nps_mountrushmore Mount Rushmore National Monument, Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1997-06-01 1997-08-01 -103.5, 43.8, -103.4, 43.9 https://cmr.earthdata.nasa.gov/search/concepts/C2231549070-CEOS_EXTRA.umm_json "Vegetation field plots at Mount Rushmore NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The purpose of the data plots were to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. Field sampling was conducted using releve plots. Information for this metadata was obtained from the site ""http://biology.usgs.gov/npsveg/moru/metamorufield.html"" and put into NASA Directory Interchange Format." proprietary usgs_npwrc_acutetoxicity_Version 06JUL2000 Acute Toxicity of Fire-Control Chemicals, Nitrogenous Chemicals, and Surfactants to Rainbow Trout CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551569-CEOS_EXTRA.umm_json Laboratory studies were conducted to determine the acute toxicity of three ammonia-based fire retardants (Fire-Trol LCA-F, Fire-Trol LCM-R, and Phos-Chek 259F), five surfactant-based fire-suppressant foams (FireFoam 103B, FireFoam 104, Fire Quench, ForExpan S, and Pyrocap B-136), three nitrogenous chemicals (ammonia, nitrate, and nitrite) and two anionic surfactants (linear alkylbenzene sulfonate [LAS] and sodium dodecyl sulfate [SDS]) to juvenile rainbow trout Oncorhynchus mykiss in soft water. The descending rank order of toxicity (96-h concentration lethal to 50% of test organisms [96-h LC50]) for the fire retardants was as follows: Phos-Chek 259F (168 mg/L) > Fire-Trol LCA-F (942 mg/L) = Fire-Trol LCM-R (1,141 mg/L). The descending rank order of toxicity for the foams was as follows: FireFoam 103B (12.2 mg/L) = FireFoam 104 (13.0 mg/L) > ForExpan S (21.8 mg/L) > Fire Quench (39.0 mg/L) > Pyrocap B-136 (156 mg/L). Except for Pyrocap B-136, the foams were more toxic than the fire retardants. Un-ionized ammonia (NH3; 0.125 mg/L as N) was about six times more toxic than nitrite (0.79 mg/L NO2-N) and about 13,300 times more toxic than nitrate (1,658 mg/L NO3-N). Linear alkylbenzene sulfonate (5.0 mg/L) was about five times more toxic than SDS (24.9 mg/L). Estimated total ammonia and NH3 concentrations at the 96-h LC50s of the fire retardants indicated that ammonia was the primary toxic component in these formulations. Based on estimated anionic surfactant concentrations at the 96-h LC50s of the foams and reference surfactants, LAS was intermediate in toxicity and SDS was less toxic to rainbow trout when compared with the foams. Comparisons of recommended application concentrations to the test results indicate that accidental inputs of these chemicals into streams require substantial dilutions (100-1,750-fold) to reach concentrations nonlethal to rainbow trout. proprietary usgs_npwrc_alpha_Version 16MAY2000 Alpha Status, Dominance, and Division of Labor in Wolf Packs. CEOS_EXTRA STAC Catalog 1986-01-01 1998-12-31 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231552683-CEOS_EXTRA.umm_json "The prevailing view of a wolf (Canis lupus) pack is that of a group of individuals ever vying for dominance but held in check by the ""alpha"" pair, the alpha male and the alpha female. Most research on the social dynamics of wolf packs, however, has been conducted on non-natural assortments of captive wolves. Here I describe the wolf-pack social order as it occurs in nature, discuss the alpha concept and social dominance and submission, and present data on the precise relationships among members in free-living packs based on a literature review and 13 summers of observations of wolves on Ellesmere Island, Northwest Territories, Canada. I conclude that the typical wolf pack is a family, with the adult parents guiding the activities of the group in a division-of-labor system in which the female predominates primarily in such activities as pup care and defense and the male primarily during foraging and food-provisioning and the travels associated with them." proprietary +usgs_npwrc_canvasbacks_Version 13NOV2001 Influence of Age and Selected Environmental Factors on Reproductive Performance of Canvasbacks CEOS_EXTRA STAC Catalog 1974-01-01 1980-01-01 -102.5, 48, -95, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2231549601-CEOS_EXTRA.umm_json Age, productivity, and other factors affecting breeding performance of canvasbacks (Aythya valisineria) are poorly understood. Consequently, we tested whether reproductive performance of female canvasbacks varied with age and selected environmental factors in southwestern Manitoba from 1974 to 1980. Neither clutch size, nest parasitism, nest success, nor the number of ducklings/brood varied with age. Return rates, nest initiation dates, renesting, and hen success were age-related. Return rates averaged 21% for second-year (SY) and 69% for after-second-year (ASY) females (58% for third-year and 79% for after-third-year females). Additionally, water conditions and spring temperatures influenced chronology of arrival, timing of nesting, and reproductive success. Nest initiation by birds of all ages was affected by minimum April temperatures. Clutch size was higher in nests initiated earlier. Interspecific nest parasitism did not affect clutch size, nest success, hen success, or hatching success. Nest success was lower in dry years (17%) than in moderately wet (54%) or wet (60%) years. Nests per female were highest during wet years. No nests of SY females were found in dry years. In years of moderate to good wetland conditions, females of all ages nested. Predation was the primary factor influencing nest success. Hen success averaged 58% over all years. The number of ducklings surviving 20 days averaged 4.7/brood. Because SY females have lower return rates and hen success than ASY females, especially during drier years, management to increase canvasback populations might best be directed to increasing first year recruitment (no. of females returning to breed) and to increasing overall breeding success by reducing predation and enhancing local habitat conditions during nesting. proprietary usgs_npwrc_ducks_Version 07JAN98 Assessing Breeding Populations of Ducks by Ground Counts. CEOS_EXTRA STAC Catalog 1952-01-01 1959-12-31 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231554819-CEOS_EXTRA.umm_json Waterfowl inventories taken during the breeding season are recognized as a basic technique in assessing the number of ducks per unit area. That waterfowl censusing is still an inexact technology leading to divergent interpretations of results is also recognized. The inexactness stems from a wide spectrum of factors that include weather, breeding phenology, asynchronous nesting periods, vegetative growth, species present and their daily activity, previous field experience of personnel, plus others (Stewart et al., 1958; Diem and Lu, 1960; Crissey, 1963a). In spite of the possible errors, accurate estimates are necessary to our understanding of production rates of all North American breeding waterfowl. Statistically adequate censuses of breeding pairs and accurate predictions of young produced per pair still remain as two of the primary statistics in determining yearly recruitment rate of species breeding in particular units of pond habitats. Without precise breeding pair and production data, the problems involved in describing the reproductive potential of any species and its environmental or density-dependent limiting factors cannot be adequately resolved. The purposes of this paper are to (1) describe methods used to estimate yearly breeding pair abundance on two study areas, one in Manitoba and the other in Saskatchewan; (2) assess the relative consistency, precision, and accuracy of pair counts as related to the breeding biology of duck species; and (3) recommend census methods that can more closely approximate absolute populations breeding in parkland and grassland habitats. proprietary usgs_npwrc_graywolves_Version 30APR2001 Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear CEOS_EXTRA STAC Catalog 1970-01-01 -168, 43.5, -75, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2231553641-CEOS_EXTRA.umm_json We evaluated the accuracy and precision of tooth wear for aging gray wolves (Canis lupus) from Alaska, Minnesota, and Ontario based on 47 known-age or known-minimum-age skulls. Estimates of age using tooth wear and a commercial cementum annuli-aging service were useful for wolves up to 14 years old. The precision of estimates from cementum annuli was greater than estimates from tooth wear, but tooth wear estimates are more applicable in the field. We tended to overestimate age by 1-2 years and occasionally by 3 or 4 years. The commercial service aged young wolves with cementum annuli to within year of actual age, but under estimated ages of wolves 9 years old by 1-3 years. No differences were detected in tooth wear patterns for wild wolves from Alaska, Minnesota, and Ontario, nor between captive and wild wolves. Tooth wear was not appropriate for aging wolves with an underbite that prevented normal wear or severely broken and missing teeth. proprietary +usgs_npwrc_incidentalmarinecatc_Version 11APR2001 Incidental Catch of Marine Birds in the North Pacific High Seas Driftnet Fisheries in 1990. CEOS_EXTRA STAC Catalog 1990-01-01 1990-01-01 -140, 20, 140, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231553439-CEOS_EXTRA.umm_json "The incidental take of marine birds was estimated for the following North Pacific driftnet fisheries in 1990: Japanese squid, Japanese large-mesh, Korean squid, and Taiwanese squid and large-mesh combined. The take was estimated by assuming that the data represented a random sample from an unstratified population of all driftnet fisheries in the North Pacific. Estimates for 13 species or species groups are presented, along with some discussion of inadequacies of the design. About 416,000 marine birds were estimated to be taken incidentally during the 1990 season; 80 % of these were in the Japanese squid fishery. Sooty Shearwaters, Short-tailed Shearwaters, and Laysan Albatrosses were the most common species in the bycatch. Regression models were also developed to explore the relations between bycatch rate of three groups Black-footed Albatross, Laysan Albatross, and ""dark"" shearwatersand various explanatory variables, such as latitude, longitude, month, vessel, sea surface temperature, and net soak time (length of time nets were in the water). This was done for only the Japanese squid fishery, for which the most complete information was available. For modeling purposes, fishing operations for each vessel were grouped into 5-degree blocks of latitude and longitude. Results of model building indicated that vessel had a significant influence on bycatch rates of all three groups. This finding emphasizes the importance of the sample of vessels being representative of the entire fleet. In addition, bycatch rates of all three groups varied spatially and temporally. Bycatch rates for Laysan Albatrosses tended to decline during the fishing season, whereas those for Black-footed Albatrosses and dark shearwaters tended to increase as the season progressed. Bycatch rates were positively related to net soak time for Laysan Albatrosses and dark shearwaters. Bycatch rates of dark shearwaters were lower for higher sea surface temperatures." proprietary usgs_npwrc_manitobaspiders_Version 16JUL97 A Checklist of Manitoba Spiders (Araneae) with Notes on Geographic Relationships CEOS_EXTRA STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231553142-CEOS_EXTRA.umm_json An annotated list of spider species is compiled from museum collections and several personal collections. This list includes 483 species in 20 families; 139 species are new provincial records. The spider fauna of Manitoba is compared with that of British Columbia, Quebec, and Newfoundland. Manitoba's spider fauna is composed of northern elements (arctic or subarctic species), boreal elements (holarctic or nearctic), and eastern elements (mainly species of the eastern deciduous forest), and a few that are regarded as introductions from abroad. Forty-three species reach the limits of their ranges here. This relatively small province (6.5% of the total land mass of Canada) contains 59% of the Canadian spider families and 37% of the Canadian species. proprietary +usgs_npwrc_muskoxen_Version 31MAY2000 Lack of Reproduction in Muskoxen and Arctic Hares Caused by Early Winter CEOS_EXTRA STAC Catalog 1998-07-01 1998-07-11 -86.1, 79.5, -85.9, 80.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549051-CEOS_EXTRA.umm_json A lack of young muskoxen (Ovibos moschatus) and arctic hares (Lepus arcticus) in the Eureka area of Ellesmere Island, Northwest Territories (now Nunavut), Canada, was observed during summer 1998, in contrast to most other years since 1986. Evidence of malnourished muskoxen was also found. Early winter weather and a consequent 50% reduction of the 1997 summer replenishment period appeared to be the most likely cause, giving rise to a new hypothesis about conditions that might cause adverse demographic effects in arctic herbivores. The study area included a 150 km2 region of the Fosheim Peninsula in a 180o arc north of Eureka, Ellesmere Island, Nunavut, Canada (all within about 9 km of 80oN, 86oW). The area, extending from Eureka Sound to Remus Creek and from Slidre Fiord to Eastwind Lake, included shoreline, hills, lowlands, creek bottoms, and the west side of Blacktop Ridge. An associate, Layne Adams, and I spent 1-11 July 1998 in this area on all-terrain vehicles, following a pair of wolves Canis lupus (Mech, 1994). Adams and I also surveyed the surrounding area with binoculars for prey animals, in much the same manner that my assistants and I have practiced for one to six weeks each summer in the same area since 1986 (Mech, 1995, 1997). Because both muskoxen and arctic hares were common residents of the area during most years and were not the focus of our studies, no standardized counts were made. However, general field notes were sufficient to document that during most summers both species and their young were present. proprietary +usgs_npwrc_nestingsuccess_Version 26MAR2001 Importance of Individual Species of Predators on Nesting Success of Ducks in the Canadian Prairie Pothole Region CEOS_EXTRA STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231551032-CEOS_EXTRA.umm_json We followed 3094 upland nests of several species of ducks. Clutches in most nests were lost to predation. We related daily nest predation rates to indices of activity of eight egg-eating predators, precipitation during the nesting season, and measures of wetland conditions. Activity indices of red fox (Vulpes vulpes), striped skunk (Mephitis mephitis), and raccoon (Procyon lotor) activity were positively correlated, as were activity indices of coyote (Canis latrans), Franklin's ground squirrel (Spermophilus franklinii), and black-billed magpie (Pica pica). Indices of fox and coyote activity were strongly negatively correlated (r = early-season nests were lower in areas and years in which larger fractions of seasonal wetlands contained water. For late-season nests, a similar relationship held involving semipermanent wetlands. We suspect that the wetland measures, which reflect precipitation during some previous period, also indicate vegetation growth and the abundance of buffer prey, factors that may influence nest predation rates. proprietary usgs_npwrc_purpleloostrife_Version 04JUN99 Avian Use of Purple Loosestrife Dominated Habitat Relative to Other Vegetation Types in a Lake Huron Wetland Complex CEOS_EXTRA STAC Catalog 1994-01-01 1995-12-31 -84.2, 43.3, -82.5, 44.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231555362-CEOS_EXTRA.umm_json Purple loosestrife (Lythrum salicaria), a native of Eurasia, is an introduced perennial plant in North American wetlands that displaces other wetland plants. Although not well studied, purple loosestrife is widely believed to have little value as habitat for birds. To examine the value of purple loosestrife as avian breeding habitat, we conducted early, mid-, and late season bird surveys during two years (1994 and 1995) at 258 18-m (0.1 ha) fixed-radius plots in coastal wetlands of Saginaw Bay, Lake Huron. We found that loosestrife-dominated habitats had higher avian densities, but lower avian diversities than other vegetation types. The six most commonly observed bird species in all habitats combined were Sedge Wren (Cistothorus platensis), Marsh Wren (C. palustris), Yellow Warbler (Dendroica petechia), Common Yellowthroat (Geothylpis trichas), Swamp Sparrow (Melospiza georgiana), and Red-winged Blackbird (Agelaius phoeniceus). Swamp Sparrow densities were highest and Marsh Wren densities were lowest in loosestrife dominated habitats. We observed ten breeding species in loosestrife dominated habitats. We conclude that avian use of loosestrife warrants further quantitative investigation because avian use may be higher than is commonly believed. Received 27 May 1998, accepted 26 Aug. 1998. proprietary +usgs_npwrc_saltmam Mammal Checklists of the United States - Salton Sea National Wildlife Refuge CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231552573-CEOS_EXTRA.umm_json Wildlife species in this brochure have been grouped into four categories: Birds, Mammals, Reptiles and Amphibians, and Fish. All mammals listed are considered resident species with the exception of the bats which migrate on a seasonal basis like many of the birds. Families follow that of A Field Guide to the Mammals by Burt and Grossenheider. proprietary usgsbrdasc00000004 Air quality monitoring protocol - Denali National Park and Preserve SCIOPS STAC Catalog 1992-01-01 1998-01-01 -149, 63, -148, 64 https://cmr.earthdata.nasa.gov/search/concepts/C1214607513-SCIOPS.umm_json Ambient air quality monitoring is important in Denali, to document baseline conditions and to track long term trends. Denali National Park and Preserve is the only National Park in Alaska designated as class I under the Clean Air Act. Geographic Description: Specific coordinates in the Denali National Park and Preserve, Alaska. Denali National Park and Preserve is located in the central Alaska Range, approximately 210 km southwest of Fairbanks, Alaska. Methodology: Denali currently participates in three nationwide air quality monitoring networks: National Atmospheric Deposition Program (NADP), Interagency Monitoring of Protected Visual Environments (IMPROVE), National Park Service Gaseous Pollutant Monitoring Network (ozone monitoring). Air quality monitoring protocols have been written for each network, and approved by the respective network steering committees. Since there is no local control over methodology, the network manuals are the park's guiding documents. This is a compilation of network protocols. proprietary +usgsbrdfcsc_d_seagrass Mapping and Characterizing Seagrass Areas Important to Manatees in Puerto CEOS_EXTRA STAC Catalog 1994-10-01 1995-06-01 -65.75, 18.15, -65.5, 18.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231549769-CEOS_EXTRA.umm_json "The population of manatees in Puerto Rico is the only group of Antillean manatees (Trichechus manatus manatus) managed and protected by the United States. The Manatee Recovery Plan for the Puerto Rico Population of West Indian Manatees includes requirements to identify and manage habitats and develop criteria and biological information important to its recovery. To this end, the Sirenia Project initiated telemetry studies of manatees in Puerto Rico at the U.S. Naval Station Roosevelt Roads (RRNS) in 1992. Concurrently, the Project began gathering information on habitats critical to manatee in eastern Puerto Rico. Computer aided mapping based on the interpretation of aerial photographs and field groundtruthing was used in the current project to define these habitats and map their distribution in the area of high manatee use. Benthic habitats along approximately 32 miles (52 kilometers of RRNS shoreline were mapped. Field assessment and characterization of important seagrass habitats was conducted as a means of identifying seagrass and macroalgae communities, especially in areas with known manatee feeding sites. The purpose of this dataset is to identify and manage manatee habitats and to develop biological information important to the manatees' recovery. Data was obtained during ground truthing in October, 1994 and June, 1995. One hundred and twenty-five sites, many representing questions raised during preliminary habitat delineations were visited, along with sites with characteristic signatures useful for broader interpretations. Transects were made over several areas with rapidly changing benthic communities and confusing signatures. Data recorded at each site included depth (range 0.5-7.1 m), classification, dominant community, subdominant community, and pertinent comments. the locations of all groundtruth sites were plotted onto one Arc Cad layer of mapping information. Groundtruthing was used to field verify and correct the initial delineations made. Improvements were made to the draft classification scheme based on field observations. Sites of questionable draft delineations were located on the water and confirmed or corrected. Known manatee use of the area for resting or feeding was noted. These sites were accurately located on the overlay for inclusion on the maps. Site location (latitude and longitude) was determined with a Garmin 45 GPS and water depth (tape), temperature (hand-held mercury thermometer), and salinity (hand-held temperature compensated refractometer) recorded. In addition, salinity measurements were made at select nearshore locations to assess the influence of drainage creeks and ditches on nearshore water salinity. Underwater video photography and 35 mm photography were used to document observations. A review of vertical images of waters of RRNS was taken on December 17, 1993, for the United States Navy, along with other collateral information, was used to develop a benthic habitat classification system useful for mapping benthic communities in the area. The system developed for this project was similar to that developed for Geographic Information System (GIS) mapping of benthic communities in the Florida Keys National Marine Sanctuary. Clear acetate overlays were placed over the 9"" x 9"" aerial prints and the polygon method of delineation used to outline habitats on the overlays. Computer aided design methods (PC Arc Cad) were used to create a shoreline base map from navigational charts for this region of Puerto Rico. Habitat polygons extending as far from shore as allowed by the resolution of the images were digitized onto the base map. A minimum mapping unit of 0.5 acres was applied based on the scale and quality of the images. Once finalized, maps were printed in both color and black-line. The information for this metadata was partially taken from the document Mapping and Characterizing Seagrass Areas Important to Manatees in Puerto Rico - Benthic Communities Mapping and Assessment. Prepared for the U.S. Department of Interior, National Biological Service, Sirenia Project. Prepared by Curtis Kruer, Senior Biologist, Caribbean Fisheries Consultants, Inc." proprietary +usgsbrdfcsc_d_vieques Mapping and Characterization of Nearshore Benthic Habitats around Vieques Island, Puerto Rico CEOS_EXTRA STAC Catalog 1995-09-01 -65.75, 18.15, -65.5, 18.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231553026-CEOS_EXTRA.umm_json "The Vieques Island Mapping Project was initiated in September 1995 as a cooperative effort between NSRR and the Sirenia Project (Military Interdepartmental Purchase Request no. NOO38995MP00012). Caribbean Fisheries Consultants, Inc. was contracted by the Sirenia Project to help produce the desired information in conjunction with Sirenia Project biologists. Products include maps delineating Vieques' benthic habitat and coastal wetlands, an electronic georeferenced habitat map (UTM coordinate system) in a format compatible with ARC/INFO (Environmental Systems Research Institute, Inc.) and a report describing methods used, the classification scheme, and the relationship of these habitats to manatee use of Vieques Island. These map products complement the Navy's Vieques Land Use Management Plan by identifying marine resources targeted for protection in the plan. Objectives include producing maps of the coastal seagrass beds and other bottom habitat (including coral reefs) surrounding the island of Vieques and characterizing the species composition and density of seagrasses in areas frequented by manatees near Vieques. Ground truthing by boat around Vieques Island was conducted from May 14 to May 19 1996 and from October 4 through October 9 1996. The ground truthing was conducted to verify the interpretation of benthic habitat visible in the images, verify accuracy of the shoreline limits, and refine the habitat classification scheme used for the Vieques maps. Three hundred and thirty-two ground truth stations were established around Vieques Island, located on the aerial image overlays, and digitized. These sites are plotted as a layer on the habitat map. The listing of ground truth sites includes site identifier, latitude and longitude, community classification, depteh, dominant community elements, less dominant elements, and other pertinent information. Latitude and longitude were obtained for each station in the field using a Garmin 45 GPS unit. Water depth for each station was determined from a Hummingbird LCR - 400 Video Fathometer with transom mounted transducer. Underwater Hi-8 video and 35 mm photography were used to document observations at selected sites. The habitat classification scheme used is similar to that used by Kruer and others in southern Forida seagrass beds and other benthic habitats in the Florida Keys National Marine Sanctuary and Biscayne National Park. This scheme, also used for benthic habitat mapping at NSRR in 1994/1995 (Kruer 1995), was refined for the Vieques Island mapping project by adding the category ""sand bottom with rock"". Also, mangroves were mapped in interior areas. The information for this metadata was partially taken from the report - Mapping and characterization of Nearshore Benthic Habitats around Vieques Island, Puerto Rico." proprietary usgsbrdnpwrc_d_birds_checklists_Version 12MAY03 Birds Checklists of the United States CEOS_EXTRA STAC Catalog 1996-01-01 -125, 25, -67, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231550188-CEOS_EXTRA.umm_json This resource is known as Bird Checklists of the United States. Bird Checklists of the United States. For years, people and groups have developed listings or checklists of birds that occur in a particular region. Information on the distribution or seasonal occurrence of birds in an area, however, can change over time. Bird checklists often are outdated in only a few years after printing, but budget and time constraints prohibit regular updates. The Internet provides new opportunities for the compilation and dissemination of current information on bird distribution. Here we offer bird checklists developed by others that indicate the seasonal occurrence of birds in state, federal, and private management areas, nature preserves, and other areas of special interest in the United States. Bird checklists exist for Great Plains States: Colorado, Iowa, Kansas, Minnesota, Missouri, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Wyoming; East of Great Plains states: Alabama, Arkansas, Connecticut, Delaware, Florida, Georgia, Illinois, Indiana, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Mississippi, New Hampshire, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina, Tennessee, Vermont, Virginia, West Virginia, Wisconsin; and West of Great Plains: Arizona, California, Idaho, Nevada, Oregon, Utah, Washington. It is hoped that these checklists will serve several purposes. First, we hope the checklists will help bird enthusiasts decide where to visit. A visit to these unique areas can be a rewarding experience for both the amateur and expert birdwatcher. Second, we hope that these checklists will provide potential visitors with a guide to birds that might occur in a region during a particular season. The checklists were kept simple to facilitate printing so they can be easily carried into the field. And third, we hope that these checklists will stimulate and encourage visitors to these areas to help improve the accuracy and completeness of the checklists. The information in some checklists already has been updated; these checklists contain more current information than the printed versions. Sightings of birds and other wildlife are an important part of monitoring wildlife use. Visitors are encouraged to share their observations of rare, aberrant, or occasional birds with the staff at these areas. With each checklist, we have included an address for visitors to send information on rare birds so that checklists can be updated. To assist in establishing standards in observation and reporting, we also provide a Record Documentation Form to document supporting details of rare bird observations. The efforts and dedication of the many birders, birding groups, biologists, and resource managers who developed these checklists are acknowledged. The information for this metadata was partially taken from the Northern Prairie Wildlife Research Center website at http://www.npwrc.usgs.gov/resource/birds/chekbird/index.htm proprietary +usgsbrdnpwrc_d_ndfleas_Version 16JUL97 Fleas of North Dakota CEOS_EXTRA STAC Catalog 1970-01-01 -104, 46, -96.5, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231553614-CEOS_EXTRA.umm_json The dataset contains distribution maps for the following species of fleas: Aetheca wagneri, Amaradix euphorbi, Amphipsylla sibirica pollionis, Callistopsyllus terinus campestris, Cediopsylla inaequalis inaequalis, Ceratophyllus (Ceratophyllus) idius, Corrodopsylla curvata curvata, Chaetopsylla lotoris, Ctenocephalides canis, Epitedia faceta, Epitedia wenmanni, Euhoplopsyllus glacialis affinis, Eumolpianus eumolpi eumolpi, Foxella ignota albertensis, Hystrichopsylla dippiei dippiei, Megabothris (Megabothris) asio megacolpus, Megabothris (Amegabothris) lucifer, Meringis jamesoni, Myodopsylla insignis, Nearctopsylla genalis hygini, Neopsylla inopina, Nosopsyllus fasciatus, Oropsylla (Oropsylla) arctomys, Opisodasys (Opisodasys) pseudarctomys, Orchopeas caedens, Orchopeas howardi, Peromyscopsylla hamifer, Pleochaetis exilis, Pules irritans, Rhadinopsylla (Actenophthalmus) fraterna, Thrassis bacchi bacchi. The information for this metadata was partially taken from the Northern Prairie Wildlife Research Center website at http://www.npwrc.usgs.gov/resource/insects/ndfleas/ proprietary usgsbrdnpwrcb00000013_Version 30SEP2002 A Bibliography of Fisheries Biology in North and South Dakota CEOS_EXTRA STAC Catalog 1970-01-01 -104, 43, -96, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231550006-CEOS_EXTRA.umm_json This bibliography lists as many fisheries biology and related references as possible from North Dakota and South Dakota waters for use by fishery biologists. Selected references from contiguous states sharing river basins with the Dakotas are included. Studies in the Missouri River downstream from Gavins Point Dam are also included. In addition to published fishery and related aquatic studies, attempts were made to list all dissertations and Masters theses in these fields. proprietary usgsbrdnpwrcb00000016_Version 16JUL97 American Wildcelery (Vallisneria americana) Ecological Considerations for Restoration CEOS_EXTRA STAC Catalog 1970-01-01 -125, 25, -67, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231548602-CEOS_EXTRA.umm_json The success of vegetation management programs for waterfowl is dependent on knowing the physical and physiological requirements of the target species. Lakes and riverine impoundments that contain an abundance of the American wildcelery plant (Vallisneria americana) have traditionally been favored by canvasback ducks (Aythya valisineria) and other waterfowl species as feeding areas during migration. Information on the ecology of V. americana is summarized to serve as a guide for potential wetland restoration projects. Because of the geographic diversity and wetland conditions in which V. americana is found, we have avoided making hard-and-fast conclusions about the requirements of the plant. Rather, we present as much general information as possible and provide the sources of more specific information. Vallisneria americana is a submersed aquatic plant that has management potential. Techniques are described for transplanting winter buds from one location to another. Management programs that employ these techniques should define objectives clearly and evaluate the water regime carefully before initiating a major effort. proprietary +usgsbrdnpwrcd00000002_Version 02MAR98 Ecological Effects of Fire Retardant Chemicals and Fire Suppressant Foams CEOS_EXTRA STAC Catalog 1993-01-01 1998-01-01 -98, 47, -98, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231550534-CEOS_EXTRA.umm_json Laboratory studies with algae, aquatic invertebrates, and fish. Short-term toxicity tests showed that both fire-retardant and foam-suppressant chemicals were very toxic to aquatic organisms including algae, aquatic invertebrates, and fish. Foam-suppressant are more toxic than fire-retardant chemicals. The primary toxicant in fire-retardants is the ammonia component, whereas the nitrite and nitrate components do not seem to contribute much to the toxicity of the formulations. In foam suppressants the primary toxicant is the surfactant component. The most sensitive life-stage for fish is the swim-up stage. Accidental spills of fire-fighting chemicals in streams could cause substantial fish kills depending on the stream size and flow rate. For example, the retardant Fire-Trol GTS-R is prepared for field use by mixing 1.66 pounds per gallon of water to produce 1.1 gallons of slurry, which is equivalent to 198,930 mg/liter. Comparing the concentration of FT GTS-R field mixture to the acute toxicity values for the most sensitive life stage for rainbow trout gives a ratio of 853 in soft water and 1474 in hard water. Applying a safety factor of 100 to this ratio suggests a dilution of 85, 300 in soft water and 147,400 in hard water is needed to lower the chemical concentration in a receiving water to limit adverse effects, i.e., fish kill, in a stream. For rainbow trout, other dilution factors would be 52,100 for Fire-Trol LCG-R, 85,600 for Phos-Chek D75-F, 71,400 for Phos-Chek WD-881, and 50,000 for Silv-ex. Fire-fighting chemicals are very toxic in aquatic environments and fire control managers need to consider protection of aquatic resources, especially if endangered species are present. proprietary usgsbrdnpwrcd00000012_Version 31JUL97 Changes in Breeding Bird Populations in North Dakota: 1967 to 1992-93. CEOS_EXTRA STAC Catalog 1967-01-01 1993-01-01 -104, 46, -97, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231551668-CEOS_EXTRA.umm_json Breeding bird populations in North Dakota were compared using surveys conducted in 1967 and 1992-93. In decreasing order, the five most frequently occurring species were Horned Lark (Eremophia alpestris), Brown-headed Cowbird (Molothrus ater), Western Meadowlark (Sturnella neglecta), Red-winged Blackbird (Agelaius phoeniceus), and Eastern Kingbird (Tyrannus tyrannus). The five most abundant species - Horned Lark, Chestnut-collared Longspur (Calcarius ornatus), Red-winged Blackbird, Western Meadowlark, and Brown-headed Cowbird - accounted for 31-41% of the estimated statewide breeding bird population in the three years. Although species composition remained relatively similar among years, between-year patterns in abundance and frequency varied considerably among species. Data from this survey and the North American Breeding Bird Survey indicated that species exhibiting significant declines were primarily grassland- and wetland-breeding birds, whereas species exhibiting significant increases primarily were those associated with human structures and woody vegetation. Population declines and increases for species with similar habitat associations paralleled breeding habitat changes, providing evidence that factors on the breeding grounds are having a detectable effect on breeding birds in the northern Great Plains. proprietary usgsbrdnpwrcd0000001_Version 15DEC98 An Assessment of Exotic Plant Species of Rocky Mountain National Park CEOS_EXTRA STAC Catalog 1987-01-01 1991-01-01 -106, 40, -105, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231551467-CEOS_EXTRA.umm_json "The invasion of exotic plants is becoming a problem in many ecosystems including some areas in Rocky Mountain National Park (RMNP) (Rocky Mountain National Park Resource Management Reports #1 and #13). Some exotic species, such as leafy spurge and spotted knapweed, are capable of rapidly colonizing areas, altering community composition, and even displacing native species (Belcher and Wilson 1989, Tyser and Key 1988). In many cases, the processes of invasion are poorly documented, and little information is available on an area's past history. However, there is a large amount of information available in the literature which relates to the life history traits of exotic species and the distribution of exotic species. This information can be used to help predict the potential distribution and threat of exotic species to ecosystems. Exotic plants can be thought of as those plants which did not originally occur in the ecosystem, and have since been introduced to the area. The National Park Service (NPS) defines an exotic species as, ""those that occur in a given place as a result of direct or indirect, deliberate, or accidental actions by humans."" This somewhat conservative definition of exotic species is necessary to insure that natural resources in national parks are preserved. NPS policy generally prohibits the introduction of exotic species into natural areas of national parks. Exotic species which threaten park resources or public health are to be managed or eliminated if possible. In addition, the NPS recently signed a memorandum of understanding with 10 other federal and state agencies in the state of Colorado. This agreement states that all paid management agencies will work with private and county entities to manage exotic plants and, in particular, ""noxious weeds."" RMNP is currently working with Estes Park in exotic plant control as part of this agreement." proprietary +usgsbrdnpwrcd0000003_Version 16JUL97 Human Disturbances of Waterfowl: An Annotated Bibliography. CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231551896-CEOS_EXTRA.umm_json The expansion of outdoor recreational activities has increased greatly the interaction between the public and waterfowl and waterfowl habitat. The effects of these interactions on waterfowl habitats are more visible and obvious, whereas the effects of interactions which disrupt the normal behavior of waterfowl are more subtle and often overlooked, but perhaps no less of a problem than destruction of habitat. This bibliography contains excerpts or annotations from 211 articles that contained information about effects of human disturbances on waterfowl. Indices are provided for subject/keywords, geographic locations, species of waterfowl, and authors used in this bibliography. proprietary usgsbrdnpwrcs0000004_Version 12MAY03 Collecting and Analyzing Data from Duck Nesting Studies CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231554360-CEOS_EXTRA.umm_json Northern Prairie has a long history of studying nest success of upland nesting ducks. Over the years, we have developed standardized procedures for collecting and analyzing these types of data. Data forms and instruction manuals developed by the Center are used widely by biologists throughout the northern Great Plains and elsewhere. Extensive use of standardized procedures led to a cooperative effort among Federal, State, Private, and other Non-Government Organizations that has allowed us to compile the Nest File, a data base of more than 75,000 duck nests spanning 30+ years in the northern Great Plains. proprietary validation-of-the-critical-crack-length-in-snowpack_1.0 Validating and improving the critical crack length in SNOWPACK ENVIDAT STAC Catalog 2019-01-01 2019-01-01 9.78797, 46.80757, 9.809407, 46.8292944 https://cmr.earthdata.nasa.gov/search/concepts/C2789817607-ENVIDAT.umm_json To validate the critical crack length as implemented in the snow cover model SNOWPACK, PST experiments were conducted for three winter seasons (2015-2017) at two field site above Davos, Switzerland. This dataset contains manually observed snow profiles and stability tests. Furthermore, corresponding SNOWPACK simulations are included. These data were analyzed and results were published in Richter et al. (2019). Please refer to the Readme file for further details on the data. These data are the basis of the following publication: Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, 2019. proprietary vanderford_data_1983_85_1 Airborne Topographic and Ice Thickness Survey of the Vanderford Glacier, 1983-85 AU_AADC STAC Catalog 1983-01-01 1985-12-31 108, -67.5, 113, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311394-AU_AADC.umm_json A report outlining the work done on the Vanderford (and Adams) glaciers in 1983/84 and 1984/85, detailing the methods they used for determining ice thickness and velocity. Includes a copy of the program used to process the raw data, gravity observations, and velocity results. These documents have been archived at the Australian Antarctic Division. proprietary